CN118464052A - Path planning method, device, equipment, storage medium and program product - Google Patents
Path planning method, device, equipment, storage medium and program product Download PDFInfo
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Abstract
The application relates to a path planning method, a path planning device, a path planning equipment, a path planning storage medium and a path planning program product, and relates to the technical field of big data. The method comprises the following steps: in the process of planning a path of a target vehicle, acquiring current road condition data and driving behavior recommendation data corresponding to the target vehicle; according to the driving behavior recommendation data and the road condition data, determining the predicted carbon emission amount of the target vehicle corresponding to a plurality of candidate paths; and determining a target path from the candidate paths according to the predicted carbon emission and the road condition data. By adopting the method, the path planning flexibility can be improved.
Description
Technical Field
The present application relates to the field of big data technologies, and in particular, to a path planning method, apparatus, device, storage medium, and program product.
Background
With the rapid development of global economy, the number of motor vehicles is continuously increased, a large amount of carbon emission is generated by the use of the motor vehicles, and in order to realize energy conservation and emission reduction, the carbon emission of the motor vehicles can be effectively reduced by reasonably planning the running path of the motor vehicles, for example, the running path of the bank working vehicle is reasonably planned, and the carbon emission of the bank working vehicle in the working process is reduced.
In the related art, a general carbon emission prediction model is used to plan a travel route with low carbon emissions to control the carbon emissions during the travel of a motor vehicle.
However, the above-described technique has a problem of poor path planning flexibility.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a path planning method, apparatus, device, storage medium, and program product that can improve the flexibility of path planning.
In a first aspect, the present application provides a path planning method, the method comprising:
In the process of planning a path of a target vehicle, acquiring current road condition data and driving behavior recommendation data corresponding to the target vehicle;
According to the driving behavior recommendation data and the road condition data, determining the predicted carbon emission amount of the target vehicle corresponding to the candidate paths;
and determining a target path from the candidate paths according to the predicted carbon emission and the road condition data.
In one embodiment, the determining the target path from the candidate paths according to the predicted carbon emission and the road condition data includes:
acquiring initial recommendation degree of each segmented path in each candidate path;
And determining a target path according to the initial recommendation degrees, the predicted carbon emission amounts and the road condition data.
In one embodiment, determining the target path according to each initial recommendation, each predicted carbon emission, and the road condition data includes:
Determining the congestion degree of each segmented path according to the road condition data, and adjusting each initial recommendation degree according to each congestion degree to obtain a target recommendation degree corresponding to each segmented path;
and determining a target path according to each target recommendation degree and each predicted carbon emission amount.
In one embodiment, the determining the target path according to the target recommendation degree and the predicted carbon emission amount includes:
determining a plurality of candidate segment paths according to the recommendation degree of each target;
obtaining the expected carbon emission of the segments corresponding to each candidate segment path;
And inputting the predicted carbon emission amounts of the segments and the predicted carbon emission amounts into a carbon emission route prediction model to obtain a target path, wherein the target carbon emission amount corresponding to the target path meets the preset requirement.
In one embodiment, the method further comprises:
and sending at least the driving behavior recommendation data and the target path to the terminal.
In one embodiment, the method further comprises:
Acquiring user feedback data and actual carbon emission sent by a terminal, wherein the actual carbon emission is recommended data of a target vehicle based on driving behaviors and carbon emission generated after a target path runs;
and determining difference data according to the target carbon emission and the actual carbon emission, and performing model parameter adjustment processing on the carbon emission route prediction model according to the difference data.
In one embodiment, the obtaining driving behavior recommendation data corresponding to the target vehicle includes:
Acquiring vehicle unit fuel consumption of a target vehicle, an emission system equipment state of the target vehicle, historical driving behavior data of the target vehicle and historical carbon emission information of the target vehicle;
At least driving behavior recommendation data of the target vehicle is determined based on the vehicle unit fuel consumption, the emission system device state, the historical driving behavior data, and the historical carbon emission information.
In a second aspect, the present application also provides a path planning apparatus, including:
the acquisition module is used for acquiring current road condition data and driving behavior recommendation data corresponding to the target vehicle in the process of planning the path of the target vehicle;
The determining module is used for determining the predicted carbon emission amount corresponding to the target vehicle and the candidate paths according to the driving behavior recommendation data and the road condition data;
And the planning module is used for determining a target path from the candidate paths according to the predicted carbon emission and the road condition data.
In a third aspect, the application also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method according to the first aspect described above when the computer program is executed by the processor.
In a fourth aspect, the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method as described in the first aspect above.
In a fifth aspect, the application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method as described in the first aspect above.
According to the path planning method, the device, the equipment, the storage medium and the program product, the current road condition data and the driving behavior recommendation data corresponding to the target vehicle are obtained in the process of path planning of the target vehicle, the estimated carbon emission amounts corresponding to the target vehicle and the candidate paths are determined according to the driving behavior recommendation data and the road condition data, and then the target path is determined from the candidate paths according to the estimated carbon emission amounts and the road condition data. According to the method, through data of two dimensions of driving behavior recommendation data and road condition data, the predicted carbon emission corresponding to a plurality of candidate paths can be determined according to the influence of the driving behavior and road condition on the carbon emission, so that the influence of a target vehicle on the carbon emission when the target vehicle runs along the target path is comprehensively considered according to the predicted carbon emission and the target path determined by the predicted carbon emission and the road condition data, the flexibility of path planning can be improved, and compared with the mode of path planning through a universal carbon emission prediction model in the prior art, the influence of the driving behavior of a user and the individual difference of the vehicle on the carbon emission can be fully considered, and the flexibility of path planning of the target vehicle is improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
FIG. 1 is an application environment diagram of a path planning method in one embodiment;
FIG. 2 is a flow diagram of a method of path planning in one embodiment;
FIG. 3 is a flow chart of a method of path planning in another embodiment;
FIG. 4 is a flow chart of a path planning method according to another embodiment;
FIG. 5 is a flow chart of a path planning method according to another embodiment;
FIG. 6 is a flow chart of a method of path planning in another embodiment;
FIG. 7 is a flow chart of a method of path planning in another embodiment;
FIG. 8 is a block diagram of a path planning apparatus in one embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
With the rapid development of global economy, the number of vehicles is increasing, and the use of vehicles generates a large amount of carbon emissions. Carbon emissions are one of the main causes of global climate warming, and therefore, planning of carbon emissions is critical for making emission reduction policies, advancing green finances, and advancing sustainable development. In order to achieve energy conservation and emission reduction, the carbon emission of the motor vehicle can be effectively reduced by reasonably planning the running path of the motor vehicle, for example, the carbon emission of the bank working vehicle in the working process is reduced by reasonably planning the running path of the bank working vehicle.
In the related art, a general carbon emission prediction model is used to plan a travel route with low carbon emissions to control the carbon emissions during the travel of a motor vehicle. A generic carbon emission prediction model such as Kaya identities. The Kaya identity further researches the influence of each factor on the carbon emission by decomposing the energy carbon emission into four factors of human energy consumption, human GDP, energy intensity and carbon emission intensity.
However, the above models are all generalized carbon emission predictions, and for situations of driving habits, vehicle equipment states and the like with large individual differences, carbon emission influencing factors decomposed by simple Kaya identities cannot completely meet carbon emission planning requirements of analysis users and vehicle individual differences, so that the problem of poor path planning flexibility exists.
Therefore, embodiments of the present application provide a path planning method, apparatus, device, storage medium, and program product, which can solve the above technical problems.
The path planning method provided by the embodiment of the application can be applied to an implementation environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 obtains current road condition data and driving behavior recommendation data corresponding to the target vehicle, determines predicted carbon emission amounts corresponding to the target vehicle and a plurality of candidate paths according to the driving behavior recommendation data and the road condition data, determines target paths from the candidate paths according to the predicted carbon emission amounts and the road condition data, and sends the target paths and the driving behavior recommendation data to the terminal 102, and the terminal 102 displays the target paths and the driving behavior recommendation data so that a user can drive according to the driving behavior recommendation data and the target paths to realize route planning. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, and tablet computers, and the server 104 may be implemented as a stand-alone server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a path planning method is provided, which is used for the server 104 for illustration, and the method includes the following steps:
S202, in the process of planning a path of a target vehicle, acquiring current road condition data and driving behavior recommendation data corresponding to the target vehicle.
The target vehicle may be a work vehicle of a bank, for example, a bank note transport vehicle. In the embodiment of the present application, a target vehicle is described as an example of an armor-paper-transport vehicle. In the process that the target vehicle starts from the origin and goes to the destination, in order to reduce carbon emission of the target vehicle in the driving process, the server can plan a path of the target vehicle between the origin and the destination so as to reduce the carbon emission as a target to plan a driving path.
In the embodiment of the application, the server can acquire the current road condition data so as to carry out path planning according to the road condition data. The road condition data is a road condition, including road congestion and road characteristics, which may be road type, road gradient, etc. The server can acquire current road condition data through the traffic application program data.
After the current road condition data is obtained, the server can also obtain driving behavior recommendation data corresponding to the target vehicle. The driving behavior recommendation data is driving operation data of a driver under the condition that the carbon emission of the target vehicle meets the preset condition, and the driving behavior recommendation data can comprise lamplight operation recommendation and average vehicle speed recommendation.
In one possible embodiment, the server may acquire historical driving behavior data of the driver and historical carbon emission data of the driver driving the target vehicle, and determine the driving behavior recommendation data according to the historical driving behavior data and the historical carbon emission data.
In another possible embodiment, the server may acquire vehicle parameters of the target vehicle, historical driving behavior data of the driver, and historical carbon emission data of the driver driving the target vehicle, and determine the driving behavior recommendation data according to the vehicle parameters of the target vehicle, the historical driving behavior data, and the historical carbon emission data.
S204, according to the driving behavior recommendation data and the road condition data, determining the predicted carbon emission amounts of the target vehicle corresponding to the candidate paths.
In the embodiment of the application, a target vehicle can travel from an origin to a destination, a plurality of candidate paths can exist, partial path coincidence can exist among the plurality of candidate paths, and the target vehicle can travel from the origin to the destination through any one of the candidate paths. For each candidate path, the server can estimate the predicted carbon emission corresponding to each candidate path according to the driving behavior recommendation data and the road condition data, namely, the carbon emission generated by the target vehicle after the driver goes from the origin to the destination along the candidate path according to the driving behavior recommendation data and the road condition data.
Wherein, the carbon emission amount produced by the target vehicle is simply identified as: carbon emission = mileage x unit fuel consumption x emission factor, the mileage identifies the mileage of the target vehicle in kilometers, the calculation of the mileage can be obtained through an odometer on the target vehicle, or the total mileage is calculated through a travel track recorded by using a GPS or Beidou positioning system; the unit fuel consumption means the fuel consumption per kilometer of the target vehicle in liters/kilometer; the emission factor is determined according to the type of fuel used, for example, carbon dioxide emissions of about 2.3-2.7 kg carbon dioxide per liter of gasoline or diesel are produced. In current practice, carbon footprint tools are typically used to calculate carbon emissions based on activities of different organizations or individuals, energy consumption, carbon emission factors, and the like.
In one possible implementation manner, the server may obtain historical driving behavior data of the driver and historical carbon emission data of the driver driving the target vehicle, determine a first mapping relationship between the historical driving behavior data and the historical carbon emission data, determine a carbon emission average value corresponding to different driving behavior data, and determine the carbon emission average value corresponding to the driving behavior recommendation data according to the driving behavior recommendation data and the first mapping relationship. On the basis of determining the average value of the carbon emission, the server can acquire road condition data and historical carbon emission data, determine a second mapping relation between the road condition data and the historical carbon emission data, and the second mapping relation can represent an influence relation between road condition congestion degree and carbon emission variation, for example, the influence relation corresponds to the carbon emission increase amount with higher congestion degree. And the server can determine the carbon emission variation corresponding to the current road condition data according to the second mapping relation, and sum the carbon emission mean value and the carbon emission variation to obtain the predicted carbon emission corresponding to the candidate path. In this way, the server can acquire the predicted carbon emission amount corresponding to each candidate route.
S206, determining a target path from the candidate paths according to the predicted carbon emission and the road condition data.
The target route is a candidate route in which the carbon emission amount of the target vehicle from the starting place to the destination meets the preset emission amount requirement.
In one possible implementation manner, the server may determine, from among the candidate paths, candidate paths having an estimated carbon emission lower than a preset emission threshold according to the estimated carbon emission of each candidate path, and determine, from among the candidate paths having an estimated carbon emission lower than the preset emission threshold, candidate paths having good road conditions, which may be the most unobstructed or least time-consuming road congestion, according to the road condition data, and determine the candidate paths having good road conditions as the target paths.
In another possible implementation manner, the server may input the predicted carbon emission amount and the road condition data corresponding to each candidate path into a preset carbon emission route prediction model, and the carbon emission prediction model may output the target path under the condition of comprehensively considering the predicted carbon emission amount and the road condition according to the predicted carbon emission amount and the weight corresponding to the road condition data. The carbon emission route prediction model may be obtained by training based on historical carbon emission data and historical driving route as training tags, historical driving behavior data, map data and road condition data as input data, and the carbon emission route prediction model may be a transform graph convolution deep learning algorithm model, and training is performed by using the input data and the training tags until the model accuracy reaches a threshold, and the carbon emission route prediction model may include: a feature extraction module; the path planning module is used for modeling a driving behavior planning and regional traffic static space structure; a dynamic segment path module modeling the regional traffic static space structure and the road condition; and the fusion module is used for fusion prediction and finally outputting a trained carbon emission route prediction model.
In the path planning method, in the process of path planning of the target vehicle, current road condition data and driving behavior recommendation data corresponding to the target vehicle are obtained, the predicted carbon emission amounts corresponding to the target vehicle and a plurality of candidate paths are determined according to the driving behavior recommendation data and the road condition data, and then the target path is determined from the candidate paths according to the predicted carbon emission amounts and the road condition data. According to the method, through data of two dimensions of driving behavior recommendation data and road condition data, the predicted carbon emission corresponding to a plurality of candidate paths can be determined according to the influence of the driving behavior and road condition on the carbon emission, so that the influence of a target vehicle on the carbon emission when the target vehicle runs along the target path is comprehensively considered according to the predicted carbon emission and the target path determined by the predicted carbon emission and the road condition data, the flexibility of path planning can be improved, and compared with the mode of path planning through a universal carbon emission prediction model in the prior art, the influence of the driving behavior of a user and the individual difference of the vehicle on the carbon emission can be fully considered, and the flexibility of path planning of the target vehicle is improved.
The above embodiments have mentioned that the target path is determined from among the candidate paths based on each predicted carbon emission amount and the road condition data. Next, an embodiment in which the server 104 determines a target route from among the candidate routes based on each predicted carbon emission amount and the road condition data will be described.
Based on the embodiment shown in fig. 2, referring to fig. 3, the step S206 may include the following steps:
s302, obtaining initial recommendation degree of each segmented path in each candidate path.
Each segmented path in the candidate paths may be obtained by dividing the candidate paths according to the intersection, and there may be a coincident segmented path in each candidate path.
In the embodiment of the application, the server can determine the initial recommendation degree corresponding to each segmented path in each candidate path according to the predicted carbon emission amount corresponding to each candidate path, and the initial recommendation degree can be determined according to the predicted carbon emission amount of the candidate path where each segmented path is located, and the higher the predicted carbon emission amount corresponding to the candidate path is, the lower the initial recommendation degree corresponding to each segmented path in the candidate path is. For example, the server may obtain the initial recommendation degree corresponding to each segment path in each candidate path from a mapping relationship table of carbon emission amount and initial recommendation degree. For each candidate path, the initial recommendation degree corresponding to the plurality of segmented paths in each candidate path is the same.
S304, determining a target path according to the initial recommendation degrees, the predicted carbon emission amounts and the road condition data.
In the embodiment of the application, the server can determine the target path according to the initial recommendation degree corresponding to each segmented path, the predicted carbon emission corresponding to each candidate path and the road condition data.
In one possible implementation manner, the server may determine a recommended value quantization value and a carbon emission quantization value corresponding to the initial recommended value and the predicted carbon emission amount according to the initial recommended value and the predicted carbon emission amount corresponding to each segment path, determine an integrated quantization value corresponding to each candidate path according to the recommended value quantization value, the carbon emission quantization value, the recommended value weight and the carbon emission weight, determine three candidate paths with the highest integrated quantization value from the candidate paths, and determine the target path from the three candidate paths according to the road condition data.
In another possible implementation manner, the server may adjust an initial recommendation degree of each segment path in each candidate path based on the road condition data, for example, for a segment path with serious road congestion, reduce its recommendation degree, obtain a target recommendation degree corresponding to each segment path, and determine a target path based on the target recommendation degree corresponding to each segment path and each predicted carbon emission.
Therefore, each segmented path corresponding to each candidate path can be determined through the method, so that path planning can be more accurate according to each segmented path, a target path is determined based on the initial recommendation degree of each segmented path, each predicted carbon emission amount and road condition data, the target path can be determined from three different dimensions, and the flexibility of path planning can be effectively improved.
The above embodiments refer to determining the target path based on each initial recommendation, each predicted carbon emission, and the road condition data. Next, an embodiment of the server 104 determining the target route according to each initial recommendation, each predicted carbon emission, and the road condition data will be described.
Based on the embodiment shown in fig. 3, referring to fig. 4, the step S304 may include the following steps:
s402, determining the congestion degree of each segmented path according to the road condition data, and adjusting each initial recommendation degree according to each congestion degree to obtain a target recommendation degree corresponding to each segmented path.
In the embodiment of the application, according to the real-time road conditions, the situation that the small-section route is jammed and the like can exist, the server can determine the jam degree of each section path according to the road condition data, wherein the road condition data comprises information such as vehicle density, speed, flow and the like, the jam degree of each section path is calculated by utilizing the collected information such as the vehicle density, the speed, the flow and the like, and the jam degree can be classified by adopting a rule-based method or a machine learning algorithm, for example, road sections are classified into clear, slightly jammed, moderately jammed, severely jammed and the like.
After determining the congestion degree of each segmented path, the server may adjust each initial recommendation degree according to each congestion degree, for the segmented paths with medium congestion degree and severe congestion degree, reduce the initial recommendation degree of the segmented paths, for the segmented paths with smooth congestion degree, promote the initial recommendation degree of the segmented paths, and for the segmented paths with mild congestion degree, maintain the initial recommendation degree of the segmented paths unchanged, so as to obtain the target recommendation degree after the initial recommendation degree adjustment of each segmented path.
S404, determining a target path according to each target recommendation degree and each predicted carbon emission amount.
In one possible implementation manner, the server may determine a comprehensive recommendation degree corresponding to each candidate path based on the target recommendation degree corresponding to each segment path, for example, sum the target recommendation degrees corresponding to each segment path in the same candidate path to obtain the comprehensive recommendation degree, and determine the target path based on the comprehensive recommendation degree corresponding to each candidate path and the predicted carbon emission amount corresponding to each candidate path.
In another possible implementation manner, as an optional example, a plurality of candidate segment paths are determined according to each target recommendation degree; obtaining the expected carbon emission of the segments corresponding to each candidate segment path; and inputting the predicted carbon emission amounts of the segments and the predicted carbon emission amounts into a carbon emission route prediction model to obtain a target path, wherein the target carbon emission amount corresponding to the target path meets the preset requirement.
In the embodiment of the application, the server can determine a plurality of candidate segment paths with highest target recommendation degree from the segment paths on the basis of mutual communication of the segment paths according to the target recommendation degree corresponding to the segment paths, and the head and tail positions of the candidate segment paths can be directly communicated, for example, the road section end of the candidate segment path 1 is communicated with the road section starting point of the candidate segment path, and the candidate segment paths can be directly spliced to obtain a complete driving route from the starting point to the destination. Then, the server may obtain the estimated carbon emission amounts of the segments corresponding to the candidate segment paths, and the estimated carbon emission amounts of the segments corresponding to the candidate segment paths may be calculated in the above manner in S202, which will not be described herein.
For each of the segmented predicted carbon emissions and each of the predicted carbon emissions, the server may input each of the segmented predicted carbon emissions and each of the predicted carbon emissions into a carbon emission route prediction model, and output a corresponding target route from the carbon emission route prediction model. The carbon emission route prediction model may also be trained based on the carbon emissions of the segmented path and the predicted carbon emissions of the candidate path.
In the embodiment of the present application, the target carbon emission amount corresponding to the target path is a sum of the estimated carbon emission amounts of the segments corresponding to the segment paths in the target path, and the target carbon emission amount meets a preset requirement, for example, the preset requirement may be lower than a carbon emission amount threshold, that is, the target carbon emission amount is lower than the carbon emission amount threshold.
After determining the target path, as an optional embodiment, the server sends at least the driving behavior recommendation data and the target path to the terminal, and the terminal performs visual display on the driving behavior recommendation data and the target path, for example, displays a route and a driving behavior recommendation list, and the user may obtain the driving behavior recommendation data and the target path through the terminal, and drive the target vehicle from the starting place to the destination based on the driving behavior recommendation data and the target path. Optionally, in the process of obtaining the driving behavior recommendation data, the server may further obtain fuel consumption recommendation data, where the fuel consumption recommendation data is fuel consumption data required for going from the starting place to the destination. The server may send the driving behavior recommendation data, the fuel consumption recommendation data, and the target path to the terminal, so that the user drives the target vehicle from the origin to the destination based on the driving behavior recommendation data, the fuel consumption recommendation data, and the target path.
In this way, by the above-described method, the initial recommendation degree of each segment path can be adjusted according to the congestion degree of each segment path, and the carbon emission amount is increased when the path is congested, so that the flexibility of path planning can be improved based on the target path determined by the target recommendation degree. In addition, a plurality of candidate segment paths and the segment predicted carbon emission amounts corresponding to the candidate segment paths are determined based on the target recommendation degree, the target paths can be determined according to the segment predicted carbon emission amounts and the predicted carbon emission amounts, and the flexibility of path planning can be effectively improved.
The above embodiment refers to acquiring driving behavior recommendation data and a target path. Next, an embodiment in which the server 104 processes the driving behavior recommendation data and the target route will be described.
Based on the embodiment shown in fig. 4, the path planning method in the embodiment of the present application may further include the following steps:
S502, acquiring user feedback data sent by a terminal and the actual carbon emission.
The user feedback data may include, among other things, user acceptance/rejection behavior, text, or other ratings and feedback behavior with respect to each path planning recommendation service.
In the embodiment of the application, the terminal can acquire the user feedback data and upload the user feedback data to the server, and the server can acquire the user feedback data sent by the terminal and process the user feedback data to obtain the user satisfaction. Based on the user satisfaction, the server may adjust model parameters of the carbon emission route prediction model according to the user satisfaction, so that a target path output by the carbon emission route prediction model accords with personal preference of the user as much as possible.
In addition, the server can also obtain the actual carbon emission of the target vehicle uploaded by the vehicle sensor through the vehicle-mounted terminal, wherein the actual carbon emission is the carbon emission generated after the target vehicle runs based on the driving behavior recommended data and the target path.
S504, determining difference data according to the target carbon emission amount and the actual carbon emission amount, and performing model parameter adjustment processing on the carbon emission route prediction model according to the difference data.
In the embodiment of the application, the server can calculate and compare the actual carbon emission amount with the target carbon emission amount to obtain the difference data, and the difference data can be a carbon emission amount difference value. For example, the difference data may be obtained by subtracting the target carbon emission amount from the actual carbon emission amount. The server may then perform model parameter adjustment processing on the carbon emission route prediction model based on the difference data and/or the user feedback data. Illustratively, the difference data and the user feedback data are added as regularization terms to the carbon emission route prediction model, which is retrained to make optimal adjustments to the carbon emission route prediction model.
Therefore, through the mode, the model parameter adjustment processing can be carried out on the carbon emission route prediction model according to the user feedback data and the difference data, the optimization model is continuously trained, the user activity is improved, and more satisfactory and reasonable path planning is provided for the user.
The above embodiment refers to acquiring driving behavior recommendation data corresponding to the target vehicle. Next, an embodiment in which the server 104 acquires driving behavior recommendation data corresponding to the target vehicle will be described.
Based on the embodiment shown in fig. 2, referring to fig. 6, the step S202 may include the following steps:
S602, acquiring a vehicle unit fuel consumption amount of the target vehicle, an exhaust system device state of the target vehicle, historical driving behavior data of the target vehicle, and historical carbon emission information of the target vehicle.
The vehicle unit fuel consumption amount can be determined according to the type of the target vehicle, and the vehicle unit fuel consumption amounts corresponding to different types of vehicles are different. The exhaust system device state refers to whether the canister control valve is normal, whether the tank cap is screwed down, and the like, and the server may acquire the exhaust system device state of the target vehicle through the sensor of the vehicle-mounted terminal. The historical driving behavior data refers to driving habit historical data of a driver, including historical light operation duration, average speed per hour and the like, and the server can acquire the historical driving behavior data through behavior operation data recorded by the vehicle-mounted terminal. The historical carbon emission information refers to the carbon emission amount when the driver drives the target vehicle.
S604, determining at least driving behavior recommendation data of the target vehicle based on the vehicle unit fuel consumption, the emission system device state, the historical driving behavior data, and the historical carbon emission information.
In the embodiment of the application, the server can convert the data format of the unit fuel consumption of the vehicle, the equipment state of the emission system and the historical driving behavior data into the target data format, and the target data format can be the data format required by the model input data. Then, the server may input the vehicle unit fuel consumption, the emission system device state, the historical driving behavior data, and the historical carbon emission information into the graph convolutional neural network by using a transducer graph convolutional neural network of the encoder-decoder in combination with the historical carbon emission information, determine the historical driving behavior data corresponding to the lowest carbon emission amount in the historical carbon emission information, and determine the historical driving behavior data as the driving behavior recommendation data of the target vehicle.
In addition, based on the vehicle unit fuel consumption and the historical carbon emission information, the influence degree of the vehicle unit fuel consumption on the historical carbon emission is determined, and corresponding fuel consumption recommended data is determined when the carbon emission is low according to the influence degree and the distance between the starting place and the destination. The server may determine the target path based on the driving behavior recommendation data and the fuel consumption recommendation data. Optionally, the server may perform data integration processing on the driving behavior recommendation data and the fuel consumption recommendation data to obtain driving planning data. And carrying out feature extraction on the driving planning data, the road condition data and the map data to obtain driving behavior features, road condition features and map features, modeling the map features and the driving behavior features to obtain dynamic features of a plurality of candidate paths, wherein the dynamic features can comprise time speed control, light control, predicted driving time, predicted energy consumption and the like of each candidate path. Modeling road condition features and map features of each candidate path to obtain segmented path dynamic features of each candidate path, wherein the segmented path dynamic features can be predicted carbon emission of each segmented path, the predicted carbon emission of each segmented path is fused with the dynamic features to obtain fusion features, and the fusion features are input into a path planning model to obtain a target path.
In this way, in the above manner, at least the driving behavior recommendation data of the target vehicle can be determined based on the vehicle unit fuel consumption amount, the exhaust system device state, the historical driving behavior data, and the historical carbon emission information, and the driving behavior of the driving target vehicle can be determined from the dimension of the reduced carbon emission amount, providing a data base for the determination of the target path.
In one embodiment, a path planning method is provided for the server 104, see fig. 7, the method comprising the steps of:
s701, acquiring current road condition data in the process of planning a path of a target vehicle;
S702, acquiring the unit fuel consumption of the target vehicle, the state of the emission system equipment of the target vehicle, the historical driving behavior data of the target vehicle and the historical carbon emission information of the target vehicle;
s703, determining at least driving behavior recommendation data of the target vehicle according to the unit fuel consumption of the vehicle, the state of equipment of the emission system, the historical driving behavior data and the historical carbon emission information;
S704, determining the predicted carbon emission amount corresponding to the target vehicle and the candidate paths according to the driving behavior recommendation data and the road condition data;
S705, obtaining the initial recommendation degree of each segmented path in each candidate path;
s706, determining the congestion degree of each segmented path according to the road condition data, and adjusting each initial recommendation degree according to each congestion degree to obtain a target recommendation degree corresponding to each segmented path;
S707, determining a plurality of candidate segment paths according to the recommendation degree of each target;
S708, obtaining the expected carbon emission of the segments corresponding to each candidate segment path;
s709, inputting the predicted carbon emission of each segment and each predicted carbon emission into a carbon emission route prediction model to obtain a target path;
S710, at least sending driving behavior recommendation data and a target path to the terminal;
S711, acquiring user feedback data sent by a terminal and the actual carbon emission;
s712, determining difference data according to the target carbon emission amount and the actual carbon emission amount, and performing model parameter adjustment processing on the carbon emission route prediction model according to the difference data.
Hereinafter, implementation of the path planning method of the above embodiment will be exemplarily described.
As shown in fig. 7, in the process of planning a path of a target vehicle, current road condition data is obtained, vehicle unit fuel consumption of the target vehicle, emission system equipment state of the target vehicle, historical driving behavior data of the target vehicle and historical carbon emission information of the target vehicle are obtained, at least driving behavior recommendation data of the target vehicle is determined according to the vehicle unit fuel consumption, the emission system equipment state, the historical driving behavior data and the historical carbon emission information, estimated carbon emission amounts corresponding to a plurality of candidate paths of the target vehicle are determined according to the driving behavior recommendation data and the road condition data, initial recommendation degree of each of the candidate paths is obtained, congestion degree of each of the candidate paths is determined according to the road condition data, target recommendation degree of each of the candidate paths is obtained by adjusting each of the initial recommendation degrees, a plurality of candidate segment paths are determined according to each of the target recommendation degree, segmented estimated carbon emission amounts corresponding to each of the candidate segment paths are obtained, the segmented estimated carbon emission amounts and the estimated carbon emission amounts are input into a carbon emission route prediction model to obtain the target paths, and at least the driving behavior data and the target paths are transmitted to a terminal. User feedback data and actual carbon emission amount sent by a terminal are obtained, difference data are determined according to the target carbon emission amount and the actual carbon emission amount, and model parameter adjustment processing is carried out on a carbon emission route prediction model according to the difference data.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a path planning device for realizing the path planning method. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in one or more embodiments of the path planning apparatus provided below may refer to the limitation of the path planning method hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 8, there is provided a path planning apparatus including: an acquisition module 801, a determination module 802, a planning module 803, wherein:
the obtaining module 801 is configured to obtain current road condition data and driving behavior recommendation data corresponding to a target vehicle in a process of planning a path of the target vehicle;
a determining module 802, configured to determine, according to the driving behavior recommendation data and the road condition data, an expected carbon emission amount of the target vehicle corresponding to the plurality of candidate paths;
and the planning module 803 is used for determining a target path from the candidate paths according to the predicted carbon emission and the road condition data.
In another embodiment, another path planning apparatus is provided, where, based on the above embodiment, the planning module 803 includes a recommendation degree obtaining unit and a determining unit, where:
The recommendation degree acquisition unit is used for acquiring the initial recommendation degree of each segmented path in each candidate path;
and the determining unit is used for determining a target path according to the initial recommendation degrees, the predicted carbon emission amounts and the road condition data.
Optionally, the determining unit is specifically configured to: determining the congestion degree of each segmented path according to the road condition data, and adjusting each initial recommendation degree according to each congestion degree to obtain a target recommendation degree corresponding to each segmented path; and determining a target path according to each target recommendation degree and each predicted carbon emission amount.
Optionally, the determining unit is further specifically configured to: determining a plurality of candidate segment paths according to the recommendation degree of each target; obtaining the expected carbon emission of the segments corresponding to each candidate segment path; and inputting the predicted carbon emission amounts of the segments and the predicted carbon emission amounts into a carbon emission route prediction model to obtain a target path, wherein the target carbon emission amount corresponding to the target path meets the preset requirement.
In another embodiment, another path planning apparatus is provided, where, on the basis of the foregoing embodiment, the apparatus further includes a sending module, where:
and the sending module is used for sending at least the driving behavior recommendation data and the target path to the terminal.
In another embodiment, another path planning apparatus is provided, where on the basis of the foregoing embodiment, the apparatus further includes a feedback acquisition module and an adjustment module, where:
The feedback acquisition module is used for acquiring user feedback data and actual carbon emission sent by the terminal, wherein the actual carbon emission is the recommended data of the target vehicle based on driving behaviors and the carbon emission generated after the target vehicle runs along a target path;
And the adjustment module is used for determining difference data according to the target carbon emission and the actual carbon emission, and carrying out model parameter adjustment processing on the carbon emission route prediction model according to the difference data.
In another embodiment, another path planning apparatus is provided, where, based on the above embodiment, the obtaining module 801 includes an information obtaining unit and a behavior determining unit, where:
An information acquisition unit configured to acquire a vehicle unit fuel consumption amount of a target vehicle, an exhaust system device state of the target vehicle, historical driving behavior data of the target vehicle, and historical carbon emission information of the target vehicle;
and a behavior determination unit configured to determine at least driving behavior recommendation data of the target vehicle based on the vehicle unit fuel consumption, the emission system device state, the historical driving behavior data, and the historical carbon emission information.
The various modules in the path planning apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In an exemplary embodiment, a computer device is provided, which may be a terminal or a server, for example, and the internal structure thereof may be as shown in fig. 9. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing historical driving behavior data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a path planning method.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
In the process of planning a path of a target vehicle, acquiring current road condition data and driving behavior recommendation data corresponding to the target vehicle; according to the driving behavior recommendation data and the road condition data, determining the predicted carbon emission amount of the target vehicle corresponding to the candidate paths; and determining a target path from the candidate paths according to the predicted carbon emission and the road condition data.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring initial recommendation degree of each segmented path in each candidate path; and determining a target path according to the initial recommendation degrees, the predicted carbon emission amounts and the road condition data.
In one embodiment, the processor when executing the computer program further performs the steps of:
determining the congestion degree of each segmented path according to the road condition data, and adjusting each initial recommendation degree according to each congestion degree to obtain a target recommendation degree corresponding to each segmented path; and determining a target path according to each target recommendation degree and each predicted carbon emission amount.
In one embodiment, the processor when executing the computer program further performs the steps of:
Determining a plurality of candidate segment paths according to the recommendation degree of each target; obtaining the expected carbon emission of the segments corresponding to each candidate segment path; and inputting the predicted carbon emission amounts of the segments and the predicted carbon emission amounts into a carbon emission route prediction model to obtain a target path, wherein the target carbon emission amount corresponding to the target path meets the preset requirement.
In one embodiment, the processor when executing the computer program further performs the steps of:
and sending at least the driving behavior recommendation data and the target path to the terminal.
In one embodiment, the processor when executing the computer program further performs the steps of:
Acquiring user feedback data and actual carbon emission sent by a terminal, wherein the actual carbon emission is recommended data of a target vehicle based on driving behaviors and carbon emission generated after a target path runs; and determining difference data according to the target carbon emission and the actual carbon emission, and performing model parameter adjustment processing on the carbon emission route prediction model according to the difference data.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring vehicle unit fuel consumption of a target vehicle, an emission system equipment state of the target vehicle, historical driving behavior data of the target vehicle and historical carbon emission information of the target vehicle; at least driving behavior recommendation data of the target vehicle is determined based on the vehicle unit fuel consumption, the emission system device state, the historical driving behavior data, and the historical carbon emission information.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
In the process of planning a path of a target vehicle, acquiring current road condition data and driving behavior recommendation data corresponding to the target vehicle; according to the driving behavior recommendation data and the road condition data, determining the predicted carbon emission amount of the target vehicle corresponding to the candidate paths; and determining a target path from the candidate paths according to the predicted carbon emission and the road condition data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring initial recommendation degree of each segmented path in each candidate path; and determining a target path according to the initial recommendation degrees, the predicted carbon emission amounts and the road condition data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining the congestion degree of each segmented path according to the road condition data, and adjusting each initial recommendation degree according to each congestion degree to obtain a target recommendation degree corresponding to each segmented path; and determining a target path according to each target recommendation degree and each predicted carbon emission amount.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Determining a plurality of candidate segment paths according to the recommendation degree of each target; obtaining the expected carbon emission of the segments corresponding to each candidate segment path; and inputting the predicted carbon emission amounts of the segments and the predicted carbon emission amounts into a carbon emission route prediction model to obtain a target path, wherein the target carbon emission amount corresponding to the target path meets the preset requirement.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and sending at least the driving behavior recommendation data and the target path to the terminal.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Acquiring user feedback data and actual carbon emission sent by a terminal, wherein the actual carbon emission is recommended data of a target vehicle based on driving behaviors and carbon emission generated after a target path runs; and determining difference data according to the target carbon emission and the actual carbon emission, and performing model parameter adjustment processing on the carbon emission route prediction model according to the difference data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring vehicle unit fuel consumption of a target vehicle, an emission system equipment state of the target vehicle, historical driving behavior data of the target vehicle and historical carbon emission information of the target vehicle; at least driving behavior recommendation data of the target vehicle is determined based on the vehicle unit fuel consumption, the emission system device state, the historical driving behavior data, and the historical carbon emission information.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
In the process of planning a path of a target vehicle, acquiring current road condition data and driving behavior recommendation data corresponding to the target vehicle; according to the driving behavior recommendation data and the road condition data, determining the predicted carbon emission amount of the target vehicle corresponding to the candidate paths; and determining a target path from the candidate paths according to the predicted carbon emission and the road condition data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring initial recommendation degree of each segmented path in each candidate path; and determining a target path according to the initial recommendation degrees, the predicted carbon emission amounts and the road condition data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining the congestion degree of each segmented path according to the road condition data, and adjusting each initial recommendation degree according to each congestion degree to obtain a target recommendation degree corresponding to each segmented path; and determining a target path according to each target recommendation degree and each predicted carbon emission amount.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Determining a plurality of candidate segment paths according to the recommendation degree of each target; obtaining the expected carbon emission of the segments corresponding to each candidate segment path; and inputting the predicted carbon emission amounts of the segments and the predicted carbon emission amounts into a carbon emission route prediction model to obtain a target path, wherein the target carbon emission amount corresponding to the target path meets the preset requirement.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and sending at least the driving behavior recommendation data and the target path to the terminal.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Acquiring user feedback data and actual carbon emission sent by a terminal, wherein the actual carbon emission is recommended data of a target vehicle based on driving behaviors and carbon emission generated after a target path runs; and determining difference data according to the target carbon emission and the actual carbon emission, and performing model parameter adjustment processing on the carbon emission route prediction model according to the difference data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring vehicle unit fuel consumption of a target vehicle, an emission system equipment state of the target vehicle, historical driving behavior data of the target vehicle and historical carbon emission information of the target vehicle; at least driving behavior recommendation data of the target vehicle is determined based on the vehicle unit fuel consumption, the emission system device state, the historical driving behavior data, and the historical carbon emission information.
It should be noted that, the data related to the present application (including, but not limited to, data for analysis, stored data, displayed data, etc.) are all data that are fully authorized by each party, and the collection, use and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile memory and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (RESISTIVE RANDOM ACCESS MEMORY, reRAM), magneto-resistive Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computation, an artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) processor, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the present application.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (11)
1. A method of path planning, the method comprising:
In the process of planning a path of a target vehicle, acquiring current road condition data and driving behavior recommendation data corresponding to the target vehicle;
according to the driving behavior recommendation data and the road condition data, determining the predicted carbon emission amount of the target vehicle corresponding to a plurality of candidate paths;
And determining a target path from the candidate paths according to the predicted carbon emission and the road condition data.
2. The method of claim 1, wherein determining a target path from among the candidate paths based on each of the predicted carbon emissions and the road condition data comprises:
acquiring initial recommendation degree of each segmented path in each candidate path;
and determining the target path according to the initial recommendation degree, the predicted carbon emission amount and the road condition data.
3. The method of claim 2, wherein the determining the target path based on each of the initial recommendation, each of the predicted carbon emissions, and the road condition data comprises:
Determining the congestion degree of each segmented path according to the road condition data, and adjusting each initial recommendation degree according to each congestion degree to obtain a target recommendation degree corresponding to each segmented path;
And determining the target path according to each target recommendation degree and each predicted carbon emission amount.
4. The method of claim 3, wherein the determining the target path based on each of the target recommended degrees and each of the predicted carbon emissions amounts comprises:
Determining a plurality of candidate segment paths according to the target recommendation degree;
obtaining the expected carbon emission of the segments corresponding to the candidate segment paths;
and inputting the segmented predicted carbon emission and the predicted carbon emission into a carbon emission route prediction model to obtain the target path, wherein the target carbon emission corresponding to the target path meets the preset requirement.
5. The method according to claim 4, wherein the method further comprises:
And sending at least the driving behavior recommendation data and the target path to a terminal.
6. The method of claim 5, wherein the method further comprises:
Acquiring user feedback data and actual carbon emission sent by the terminal, wherein the actual carbon emission is generated by the target vehicle after traveling based on the driving behavior recommendation data and the target path;
And determining difference data according to the target carbon emission and the actual carbon emission, and performing model parameter adjustment processing on the carbon emission route prediction model according to the difference data.
7. The method according to claim 1, wherein the obtaining driving behavior recommendation data corresponding to the target vehicle includes:
acquiring vehicle unit fuel consumption of the target vehicle, an exhaust system equipment state of the target vehicle, historical driving behavior data of the target vehicle and historical carbon emission information of the target vehicle;
and determining at least driving behavior recommendation data of the target vehicle according to the vehicle unit fuel consumption, the emission system equipment state, the historical driving behavior data and the historical carbon emission information.
8. A path planning apparatus, the apparatus comprising:
the acquisition module is used for acquiring current road condition data and driving behavior recommendation data corresponding to the target vehicle in the process of planning a path of the target vehicle;
the determining module is used for determining the predicted carbon emission amount corresponding to the target vehicle and the candidate paths according to the driving behavior recommendation data and the road condition data;
And the planning module is used for determining a target path from the candidate paths according to the predicted carbon emission and the road condition data.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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