CN115164922A - Path planning method, system, equipment and storage medium - Google Patents
Path planning method, system, equipment and storage medium Download PDFInfo
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- CN115164922A CN115164922A CN202210690835.9A CN202210690835A CN115164922A CN 115164922 A CN115164922 A CN 115164922A CN 202210690835 A CN202210690835 A CN 202210690835A CN 115164922 A CN115164922 A CN 115164922A
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- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3469—Fuel consumption; Energy use; Emission aspects
Abstract
The invention provides a path planning method, a system, a device and a storage medium, comprising the following steps: dividing the target map into a plurality of road sections according to a preset rule; acquiring historical driving data of the electric vehicle, and calculating historical average energy consumption of each road section according to the historical driving data; and calculating a minimum energy consumption route between two addresses in the target map according to the historical average energy consumption to obtain a path planning result. The energy consumption of a single road section is calculated through big data, a most power-saving travel route is planned for an electric vehicle user, and the user can enjoy extra electric vehicle cruising ability brought by the path planning with least energy consumption.
Description
Technical Field
The present invention relates to the field of electric vehicle technologies, and in particular, to a path planning method, system, device, and storage medium.
Background
Electric vehicles are being accepted by more and more users due to their low pollution level and low noise in use. However, the problems of difficult charging and insufficient endurance still plague many users of electric vehicles, and for the rental industry, the problem of endurance of the electric vehicles causes poor use feeling of the users, so that the development of electric vehicle rental is greatly limited.
For this reason, much research has been focused on improving the endurance of electric vehicles. The existing endurance improvement methods are some optimization means after a user plans a route from a departure place to a destination, for example, research is carried out on the control of a motor from the perspective of energy consumption conversion, and the ineffective consumption is reduced; from the perspective of energy recycling, the research is carried out, and redundant energy released in braking and freewheeling is converted into electric energy through a motor and stored; there is the research from the angle of intelligent accuse car, acquires real-time road conditions information through real-time sensor, thing networking etc. for the adjustment speed of a motor vehicle, reduce the ineffective discharge, if perceive that the place ahead 20 meters is the red light, control vehicle this moment and do not need accelerate etc..
Although the power consumption is reduced to a certain extent by the scheme, the running state of the electric vehicle needs to be detected and calculated in real time, the calculated amount of real-time calculation is large, the difficulty is high, and the operation cost and the labor cost are increased to a certain extent.
Therefore, those skilled in the art have been devoted to develop a method for improving the cruising ability of an electric vehicle by optimizing the driving path of the electric vehicle.
Disclosure of Invention
In view of the foregoing defects in the prior art, an object of the present invention is to provide a method, a system, a device and a storage medium for path planning, which are used to solve the problems in the prior art that the running state of an electric vehicle needs to be detected and calculated in real time, and the operation cost and the labor cost are increased to a certain extent.
To achieve the above object, a first aspect of the present invention provides a path planning method, including:
dividing the target map into a plurality of road sections according to a preset rule;
acquiring historical driving data of the electric vehicle, and calculating historical average energy consumption of each road section according to the historical driving data;
and calculating a minimum energy consumption route between two addresses in the target map according to the historical average energy consumption to obtain a path planning result.
In an embodiment of the present invention, the historical driving data includes positioning information, electric quantity information, and wheel axle number;
the calculating the historical average energy consumption of each road section according to the historical driving data comprises the following steps:
acquiring each driving path of the electric vehicle according to the positioning information;
dividing each driving path into road sections according to a target map, and calculating unit energy consumption of each divided road section in the corresponding driving path according to the electric quantity information and the wheel axle turns;
and (3) counting the unit energy consumption of each road section in different driving paths, and taking the median of all the unit energy consumption as the historical average energy consumption of each road section.
In an embodiment of the present invention, the unit energy consumption of each segmented road segment in the corresponding driving path calculated according to the electric quantity information and the number of turns of the wheel axle is:
the ith driving path is divided into n road sections; k is the kth road section in the driving path;
c is the circumference of the electric vehicle tire.
In an embodiment of the present invention, the method further includes:
sequencing the unit energy consumption of each road section in a descending order to obtain a first sequencing result, wherein in all driving paths, if the times that the unit energy consumption of one road section is larger than the unit energy consumption of the other road section is larger than the preset times, the sequencing of one road section is considered to be before the other road section, and vice versa;
sorting the historical average energy consumption of each section in a descending order to obtain a second sorting result;
and if the first sequencing result and the second sequencing result of the two road sections are inconsistent, calculating the average value of the unit energy consumption and the historical average energy consumption of the two road sections in all corresponding driving paths, and taking the average value as the new historical average energy consumption of the two road sections.
In an embodiment of the invention, the calculating the minimum energy consumption route between two addresses in the target map according to the historical average energy consumption includes:
and calculating a minimum energy consumption route between two addresses in the target map based on a shortest path algorithm by using the historical average energy consumption as a weight of each road section.
In an embodiment of the present invention, the method further includes:
selecting a model entering characteristic based on the historical average energy consumption of each road section, and constructing an abnormal road section prediction model based on the model entering characteristic; the mode entering characteristics comprise an average value and a variance of historical average energy consumption in preset first time, the number of unit energy consumption of the road section which is contained in all the recent driving paths and is higher than the unit energy consumption of other road sections in the driving paths, and the proportion of the average energy consumption of each road section in preset second time in adjacent road sections which is higher than the historical average energy consumption;
and if the energy consumption of a certain road section is detected to be abnormal, predicting the real-time energy consumption of the next road section by adopting the abnormal road section prediction model, and recalculating the minimum energy consumption route based on the real-time energy consumption to obtain a new path planning result.
In an embodiment of the invention, the step of detecting the abnormal energy consumption of the certain road segment includes:
acquiring driving data of the electric vehicle in real time, and calculating the average energy consumption of each road section according to the driving data;
and if the average energy consumption of the continuous preset number of road sections is greater than the historical average energy consumption, or the difference between the average energy consumption of a certain road section and the historical average energy consumption of the road section is greater than a preset value, marking the corresponding road section as abnormal energy consumption.
The second aspect of the present invention also provides a path planning system, which includes.
The dividing module is used for dividing the target map into a plurality of road sections according to a preset rule;
the energy consumption calculation module is used for acquiring historical driving data of the electric vehicle and calculating historical average energy consumption of each road section according to the historical driving data;
and the path planning module is used for calculating a minimum energy consumption route between two addresses in the target map according to the historical average energy consumption to obtain a path planning result.
The third aspect of the present invention also provides a computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method steps as described in the method for path planning of an electric vehicle of the first aspect of the present invention when executing the computer program.
The fourth aspect of the present invention also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method steps as set forth in the path planning method for an electric vehicle of the first aspect of the present invention.
The path planning method, the system, the equipment and the storage medium provided by the invention have the following technical effects:
the method comprises the steps of dividing a target map into a plurality of road sections in advance, calculating historical average energy consumption of each road section according to historical driving data of the electric vehicle, and calculating a minimum energy consumption route between two addresses in the target map according to the historical average energy consumption to obtain a path planning result. The energy consumption of a single road section is calculated through big data, a most power-saving travel route is planned for an electric vehicle user, and the user can enjoy extra electric vehicle cruising ability brought by the path planning with least energy consumption.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a diagram of an exemplary embodiment of a path planning method for an electric vehicle;
FIG. 2 is a schematic flow chart diagram of a method for path planning for an electric vehicle in one embodiment;
FIG. 3 is a flow chart illustrating the calculation of historical average energy consumption according to another embodiment;
FIG. 4 is a schematic diagram illustrating a location relationship between a positioning reporting point and a road segment in another embodiment;
FIG. 5 is a block diagram of a path planning system for an electric vehicle in accordance with one embodiment;
FIG. 6 is a diagram of a computer device in one embodiment.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
Some exemplary embodiments of the invention have been described for illustrative purposes, and it is to be understood that the invention may be practiced otherwise than as specifically described.
Referring to fig. 1, the method for planning a path of an electric vehicle according to the embodiment of the present application may be applied to the application environment shown in fig. 1. The user terminal 101 and the electric vehicle 102 are respectively connected to the server 103 through a network.
For example, the path planning method for the electric vehicle is applied to the server 103, and the server 103 receives the path information sent by the user terminal 101, and pushes the minimum energy consumption route corresponding to the path information to the user terminal 101 as a path planning result; wherein the minimum energy consumption route is obtained based on historical driving data of the electric vehicle pre-stored on the server 103. It should be understood that the user terminal 101 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart car-mounted devices, and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, and the like. The server 103 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers. The user terminal 101 and the server 103 may be directly or indirectly connected through wired or wireless communication, and the electric vehicle 102 and the server 103 may be indirectly connected through wireless communication.
In one possible embodiment, the present invention provides a path planning method.
Referring to fig. 2, fig. 2 is a schematic flow chart of the present embodiment, which specifically includes:
step S201, dividing the target map into a plurality of road segments according to a preset rule.
Specifically, the target map may be formed in various ways, for example, a map based on network disclosure, in which a suitable area is selected to be formed; or the staff can carry out reconnaissance formation on the residential road or the complex road section by adopting a manual mode based on planning requirements; or a combination of the above two methods. After the target map is obtained, the target map is divided into a plurality of road sections according to a preset rule, it should be understood that the road section in the embodiment may be an existing road, or a road which is automatically surveyed and marked by a user, and if the road is long, several intersections can be spanned, and the road can be further subdivided according to needs.
Step S202, historical driving data of the electric vehicle are acquired, and historical average energy consumption of each road section is calculated according to the historical driving data.
Specifically, the electric vehicle in this embodiment may be a household electric vehicle, an electric bicycle, an electric tricycle, an electric vehicle for rental, a shared electric bicycle, or another vehicle using a battery as a power source. The electric vehicles have a communication function, and can establish communication connection with the server 103 in real time. It should be understood that, when the target map is divided into several road segments in step S201, the mileage of the road segment corresponding to the electric vehicle with the higher traveling speed may be set to be greater than the mileage of the road segment corresponding to the electric vehicle with the lower traveling speed, for example, the mileage of the road segment corresponding to the electric vehicle may be greater than the mileage of the road segment corresponding to the electric bicycle.
Continuing to describe, in the driving process of the electric vehicle, a heartbeat is reported to the server 103 at a certain interval, in this embodiment, the interval may be set to five seconds; the heartbeat includes positioning information and electric quantity information, and the server 103 stores the information to form historical driving data of the electric vehicle; or the electric vehicle reports the information to the manufacturer, and the server 103 imports the information from the manufacturer according to the protocol.
Based on the positioning information of the electric vehicle, a driving path of the electric vehicle can be acquired; based on the electric quantity information of the electric vehicle, the energy consumption of the electric vehicle in each road section can be obtained; the historical average energy consumption of each road section can be obtained by counting historical driving data of a plurality of electric vehicles.
And step S203, calculating a minimum energy consumption route between two addresses in the target map according to the historical average energy consumption to obtain a path planning result.
Specifically, for any two addresses in the target map, for example, an address a and an address B, where the address a is a starting point and the address B is an ending point, multiple paths from the address a to the address B are selectable, each of the paths includes multiple road segments, energy consumption of each path is calculated according to historical average energy consumption of each road segment, and then a minimum energy consumption route from the address a to the address B, that is, a final route planning result, is obtained by comparing energy consumption of each path, and is stored in the server 103.
In the path planning method in the above embodiment, the target map is divided into a plurality of road sections, and then the historical average energy consumption of each road section is calculated according to the historical driving data of the electric vehicle, so that the energy consumption of the whole travel is divided into the energy consumption of a single road section, the single calculation amount is reduced, and the data processing capacity is improved; meanwhile, the energy consumption of a single road section also reduces the error of the overall operation, thereby improving the accuracy of the overall operation. When a route between any two addresses in the target map needs to be planned, a route with minimum energy consumption can be quickly obtained by comparing the energy consumption of all possible routes between the two addresses.
In a possible embodiment, the path planning method of the present invention further includes: and receiving path information selected by a user, and pushing the minimum energy consumption route corresponding to the path information to an electric vehicle user side as a path planning result.
Specifically, before the electric vehicle user goes out, the start address and the end address of the trip may be input on the user terminal 101, for example, the address C is selected as the start address, the address D is selected as the end address, the server 103 receives the path information selected by the user, queries the minimum energy consumption route corresponding to the path information, and pushes the minimum energy consumption route as a path planning result to the user terminal 101, so that the user may view the path planning result on the user terminal 101. It should be understood that the user side 101 is installed with an APP navigation system operable by the user, and the user can use the APP navigation system normally after networking registration, that is, establishing a communication connection with the server 103 and performing data interaction.
In the path planning method in the foregoing embodiment, the electric vehicle user establishes a communication connection with the server 103, and sends the start address and the end address of the trip to the server 103, so that the path planning result corresponding to the two addresses can be obtained from the server 103, and thus, the electric vehicle user can enjoy the extra cruising ability caused by the path planning with least energy consumption.
In one possible embodiment, the historical driving data of the electric vehicle further includes wheel axle turns.
Referring to fig. 3, the step S202 of calculating the historical average energy consumption of each road segment according to the historical driving data includes:
step S301, each driving path of the electric vehicle is obtained according to the positioning information.
Specifically, from the positioning information, the specific position of the electric vehicle in the target map, that is, on which road section the electric vehicle is located, can be determined. The positioning process can be obtained by inquiring a map through the longitude and latitude of the positioning information by means of the conventional navigation system. In order to improve the accuracy, the present embodiment further determines the position of the electric vehicle.
Referring to fig. 4, for example, for a road segment x, the longitude and latitude of the starting point is (lat 1, ng 1), the longitude and latitude of the ending point is (lat 2, ng 2), the longitude and latitude of the positioning report point of the electric vehicle is (lat, ng), the distance of the road segment is a, the distances between the positioning report point and the starting point and the ending point of the road segment are b and c, respectively, if the positioning report point satisfies the following conditions, that is, the positioning report point is considered to be located on the road segment:
a 2 +b 2 ＞c 2 (1)
a 2 +c 2 ＞b 2 (2)
Wherein S is the area of a triangle formed by positioning a reporting point, a road section starting point and a road section terminal point; d is the width of the road section and is a preset value, for example, the road section has a width of 10 m. When the distance between the positioning reporting point of the electric vehicle at the moment and the road section is smaller than the width d of the road section, and the positioning reporting point forms an acute triangle or an obtuse triangle with an obtuse angle on the positioning reporting point with the starting point and the end point of the road section, the positioning reporting point is considered to be on the road section x. In the positioning process, if one positioning report point belongs to a plurality of road sections, deleting the data.
By summarizing the above positioning process, each driving path of the electric vehicle can be obtained, and it should be understood that the driving path is composed of a plurality of road segments.
And step S302, dividing each driving path into road sections according to the target map, and calculating unit energy consumption of each divided road section in the corresponding driving path according to the electric quantity information and the wheel axle turns.
Specifically, each driving path is divided according to the target map to obtain a plurality of road segments, for example, a driving path i is divided into n road segments, which are marked as i 1 、i 2 、…i n (ii) a Calculating the unit energy consumption of each segmented road section in the corresponding driving path according to the electric quantity information and the number of turns of the wheel shaft, and obtaining the unit energy consumption by the following formula:
wherein k is the kth road section in the driving path i;
are respectively the ith k The first and last axle turns of the electric vehicle on the road segment;
are respectively the ith k First and last electric quantity information of the electric vehicle on the road section; c is the circumference of the electric vehicle tire.
Through calculation, the unit energy consumption of each road section in each driving path can be obtained. Further, in order to know the relative energy consumption of each road segment, the embodiment sorts the unit energy consumption of each road segment in a descending order to obtain a first sorting result. And in all the driving paths, if the times that the unit energy consumption of one road section is greater than that of the other road section is greater than the preset times, the sequence of the one road section is considered to be before the sequence of the other road section, and vice versa. It should be understood that, in one driving path, the ordering result of the unit energy consumption of each road segment is unique, but one battery car has multiple driving paths, and in this embodiment, to improve the planning accuracy, a large amount of data is used, and therefore, different driving paths exist inevitably, and the unit energy consumption of one road segment is different, for example, the road segment x and the road segment y exist in 10 driving paths at the same time, where, in 8 driving paths, the unit energy consumption of the road segment x is greater than that of the road segment y, and in 2 driving paths, the unit energy consumption of the road segment x is less than that of the road segment y, and assuming that the preset number of times is 5 times, the unit energy consumption of the road segment x is considered to be greater than that of the road segment y, and the ordering of the road segment x is before the road segment y, and vice versa.
And step S303, counting the unit energy consumption of each road section in different driving paths, and taking the median of all the unit energy consumption as the historical average energy consumption of the road section.
Specifically, the unit energy consumption of each road section in different driving paths is different, the unit energy consumption of all the driving paths appearing in the road section is counted, and the median is taken as the historical average energy consumption of the road section.
Further, in consideration of different traveling times of the electric vehicle and health degrees of the vehicle, particularly, the electric vehicle is also influenced by weights and riding habits of different users, in order to more objectively reflect energy consumption levels among road sections, the embodiment also ranks historical average energy consumption of each road section in a descending order to obtain a second ranking result. If the second ranking result of the two road segments is not consistent with the first ranking result in the step S302, calculating an average value of the unit energy consumption and the historical average energy consumption of the two road segments in all driving paths, and taking the average value as a new historical average energy consumption of the two road segments.
In the route planning method in the above embodiment, the road segment corresponding to the positioning information is determined according to the positioning information during the driving process of the electric vehicle, and whether the road segment is located is determined again according to the position relationship among the positioning report point, the starting point and the ending point, so that the positioning accuracy is higher, and the accurate driving route of the electric vehicle is obtained. By comparing the sequence of unit energy consumption of each road section in different driving paths and comparing the sequence of historical average energy consumption of each road section, the obtained historical average energy consumption is suitable for various driving conditions, and a reasonable numerical value is finally obtained.
In one possible embodiment, the calculating the minimum energy consumption route between two addresses in the target map according to the historical average energy consumption in step 203 includes: and calculating a minimum energy consumption route between two addresses in the target map based on a shortest path algorithm by adopting historical average energy consumption as a weight of each road section.
Specifically, the shortest path algorithm in this embodiment may use the existing Dijkstra algorithm or a star algorithm (a star algorithm); taking Dijkstra algorithm as an example, the algorithm can calculate the shortest path from one vertex to other vertexes in the graph theory, and solves the problem of the shortest path in the weighted graph by the idea of a greedy algorithm. The algorithm is expanded layer by layer to the outside by taking the starting point as the center until all nodes are traversed. In the embodiment, energy consumption of each road section is used as a weight, so that a travel route with the lowest energy consumption between any two addresses in a target map is obtained and stored in the server 103.
In the path planning method in the above embodiment, the energy consumption of each road segment is used as a weight, and the shortest path algorithm is used to calculate the travel route with the lowest energy consumption between any two addresses in the target map, so that a quick and accurate calculation result can be obtained.
In a possible embodiment, the path planning method of the present invention further includes: constructing an abnormal road section prediction model based on the historical average energy consumption of each road section obtained in the embodiment; in the running process of the electric vehicle, if the energy consumption of a certain road section is detected to be abnormal, the real-time energy consumption of the next road section is predicted by adopting an abnormal road section prediction model, the minimum energy consumption route is recalculated based on the real-time energy consumption, and the new minimum energy consumption route is used as a new route planning result and pushed to an electric vehicle user side. It should be understood that, in the above calculation process, due to large calculation amount, high complexity and high cost, the embodiment is provided with a trigger condition, and only when the energy consumption is abnormal during the driving process, the abnormal road section prediction model is adopted for prediction. In addition, after storing the new path planning result, the server 103 detects the road segment with abnormal energy consumption again at intervals, and updates the historical average energy consumption of the road segment if the re-detection result matches the historical average energy consumption of the road segment.
Specifically, the step of detecting the energy consumption abnormality of a certain road section includes:
acquiring running data of the electric vehicle in real time, and calculating the average energy consumption of each road section according to the running data; the method for calculating the average energy consumption of each road segment is the same as that in steps S301 to S303, and is not described herein again for brevity.
And if the average energy consumption of the continuous preset number of road sections is greater than the historical average energy consumption, or the difference between the average energy consumption of a certain road section and the historical average energy consumption of the road section is greater than a preset value, determining that the road section is abnormal, and marking the road section as abnormal energy consumption.
Continuing to explain, the step of constructing the abnormal road section prediction model based on the historical average energy consumption of each road section comprises the following steps:
selecting a mold entering characteristic based on the historical average energy consumption of each road section; the mode entering characteristics comprise an average value and a variance of historical average energy consumption in preset first time, the number of unit energy consumption of the road section which is contained in all the recent driving paths and is higher than the unit energy consumption of other road sections in the driving paths, and the proportion of the average energy consumption of each road section in preset second time in adjacent road sections which is higher than the historical average energy consumption;
and iteratively training the abnormal road section prediction model under the constraint of the loss function based on the model entering characteristics to obtain the trained abnormal road section prediction model.
It should be noted that the abnormal road segment prediction model in this embodiment may adopt machine learning models such as lightGBM and XGBoost, use historical average energy consumption of each road segment as a sample set, obtain a model entry feature based on the historical average energy consumption, mark part of data in the sample set, and randomly divide the marked sample set into a training set and a test set according to a preset proportion. Inputting the training set into the abnormal road section prediction model to obtain a predicted value, calculating the predicted value and a loss value of a label value by adopting a cross entropy loss function, correcting network parameters of the abnormal road section prediction model according to the loss value, stopping a correction process when the loss value reaches a preset condition, and selecting a parameter with the minimum loss value as a parameter of the abnormal road section prediction model after training; and inputting the test set into the trained abnormal road section prediction model, and verifying the prediction precision.
In the path planning method in the above embodiment, the path planning result stored in the server 103 is obtained by pre-calculating according to the historical average energy consumption of each road segment, and can meet the conventional needs of the electric vehicle user; however, when an abnormal condition occurs in the driving process of the electric vehicle, so that the travel energy consumption obviously exceeds the historical average energy consumption, the real-time energy consumption of the next road section is predicted by adopting the abnormal road section prediction model, a new route is planned for a user again, the time of the user is saved, and the energy consumption is reduced.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
Therefore, the target map is divided into the single road sections in advance, the historical average energy consumption of each road section is obtained through calculation according to the historical driving data of the electric vehicle, the historical average energy consumption is used as the weight of each road section, the minimum energy consumption route between two addresses in the target map is calculated based on the shortest path algorithm, and the minimum energy consumption route is pushed to the electric vehicle user.
Based on the same inventive concept, the embodiment of the present application further provides a path planning system for implementing the above related path planning method, and the implementation scheme provided by the system for solving the problem is similar to the implementation scheme recorded in the path planning method. The method specifically comprises the following steps:
referring to fig. 5, in one possible embodiment, the path planning system of the present invention includes:
and the dividing module is used for dividing the target map into a plurality of road sections according to a preset rule.
Specifically, the target map may be formed in a variety of ways, for example, a map based on network disclosure, in which a suitable area is selected to be formed; or the workers adopt a manual mode to carry out reconnaissance formation on the residential road or the complex road section based on the planning requirement; or a combination of the above two methods. After the target map is obtained, the target map is divided into a plurality of road sections according to a preset rule, it should be understood that the road section in the embodiment may be an existing road, or a road which is automatically surveyed and marked by a user, and if the road is long, several intersections can be spanned, and the road can be further subdivided according to needs.
And the energy consumption calculation module is used for acquiring historical driving data of the electric vehicle and calculating historical average energy consumption of each road section according to the historical driving data.
Specifically, the electric vehicle in this embodiment may be a household electric vehicle, an electric bicycle, an electric tricycle, an electric vehicle for rental, a shared electric bicycle, or another vehicle using a battery as a power source. The electric vehicles have a communication function, and can establish communication connection with the server 103 in real time. It should be understood that, when the dividing module divides the target map into a plurality of road segments, the mileage of the road segment corresponding to the electric vehicle with the higher driving speed may be set to be greater than the mileage of the road segment corresponding to the electric vehicle with the lower driving speed, for example, the mileage of the road segment corresponding to the electric vehicle may be greater than the mileage of the road segment corresponding to the electric bicycle.
Continuing to describe, in the driving process of the electric vehicle, a heartbeat is reported to the server 103 at a certain interval, in this embodiment, the interval may be set to five seconds; the heartbeat comprises positioning information and electric quantity information, and the server 103 stores the information to form historical driving data of the electric vehicle; or the electric vehicle reports the information to the manufacturer, and the server 103 imports the information from the manufacturer according to the protocol.
Based on the positioning information of the electric vehicle, a driving path of the electric vehicle can be obtained; based on the electric quantity information of the electric vehicle, the energy consumption of the electric vehicle on each road section can be obtained; the historical average energy consumption of each road section can be obtained by counting the historical driving data of a plurality of electric vehicles.
And the path planning module is used for calculating a minimum energy consumption route between two addresses in the target map according to the historical average energy consumption to obtain a path planning result.
Specifically, for any two addresses in the target map, for example, an address a and an address B, where the address a is a starting point and the address B is an ending point, multiple paths from the address a to the address B are selectable, each of the paths includes multiple road segments, energy consumption of each path is calculated according to historical average energy consumption of each road segment, and then a minimum energy consumption route from the address a to the address B, that is, a final route planning result, is obtained by comparing energy consumption of each path, and is stored in the server 103.
In the path planning method in the above embodiment, the target map is divided into a plurality of road sections, and then the historical average energy consumption of each road section is calculated according to the historical driving data of the electric vehicle, so that the energy consumption of the whole travel is divided into the energy consumption of a single road section, the single calculation amount is reduced, and the data processing capacity is improved; meanwhile, the energy consumption of a single road section also reduces the error of the whole operation, thereby improving the accuracy of the whole operation. When a route between any two addresses in the target map needs to be planned, a minimum energy consumption route can be quickly obtained by comparing the energy consumption of all possible paths between the two addresses.
In a possible embodiment, the path planning system of the present invention further includes: and the pushing module is used for receiving the path information selected by the user, and pushing the minimum energy consumption route corresponding to the path information to the electric vehicle user side as a path planning result.
Specifically, before the user of the electric vehicle goes out, the user terminal 101 may input a start address and an end address of the trip, for example, the address C is selected as the start address, the address D is selected as the end address, the server 103 receives the path information selected by the user, queries the minimum energy consumption route corresponding to the path information, and pushes the minimum energy consumption route as a path planning result to the user terminal 101, so that the user may view the path planning result on the user terminal 101. It should be understood that the user side 101 is installed with an APP navigation system operable by the user, and the user can use the system normally after networking and registering.
In the path planning method in the above embodiment, the electric vehicle user establishes a communication connection with the server 103, and sends the starting address and the ending address of the trip to the server 103, so that the path planning result corresponding to the two addresses can be obtained from the server 103, and thus, the electric vehicle user can enjoy the extra cruising ability of the electric vehicle caused by the path planning with the least energy consumption.
In one possible embodiment, the historical driving data of the electric vehicle further includes wheel axle turns. The energy consumption calculation module calculates the historical average energy consumption of each road section according to the historical driving data, and comprises the following steps:
step one, acquiring each driving path of the electric vehicle according to the positioning information.
Specifically, from the positioning information, the specific position of the electric vehicle in the target map, that is, on which road section the electric vehicle is located, can be determined. The positioning process can be obtained by inquiring a map through the longitude and latitude of the positioning information by means of the conventional navigation system. In order to improve the accuracy, the present embodiment further determines the position of the electric vehicle.
Referring to fig. 4, for example, for a road segment x, the longitude and latitude of the starting point is (lat 1, ng 1), the longitude and latitude of the ending point is (lat 2, ng 2), the longitude and latitude of the location reporting point of the electric vehicle is (lat, ng), the distance of the road segment is a, and the distances between the location reporting point and the starting point and the ending point of the road segment are b and c, respectively, if the location reporting point meets the following conditions, that is, the location reporting point is considered to be located on the road segment:
a 2 +b 2 ＞c 2 (1)
a 2 +c 2 ＞b 2 (2)
S is the area of a triangle formed by positioning the reporting point, the road section starting point and the road section terminal point; d is the width of the road section and is a preset value, for example, the road section has a width of 10 meters. When the distance between the positioning reporting point of the electric vehicle and the road section at the moment is less than the width d of the road section, and the positioning reporting point forms an acute triangle or an obtuse triangle with an obtuse angle on the positioning reporting point with the starting point and the end point of the road section, the positioning reporting point is considered to be on the road section x. In the positioning process, if one positioning report point belongs to a plurality of road sections, deleting the data.
By summarizing the above positioning process, each driving path of the electric vehicle can be obtained, and it should be understood that the driving path is composed of a plurality of road segments.
And secondly, dividing each driving path according to a target map, and calculating unit energy consumption of each divided road section in each driving path according to the electric quantity information and the wheel axle turns.
Specifically, each driving path is divided according to the target map to obtain a plurality of road segments, for example, a driving path i is divided into n road segments, which are marked as i 1 、i 2 、…i n ；
Are respectively the ith k The first and last axle turns of the electric vehicle on the road segment;
are respectively the ith k First and last electric quantity information of the electric vehicle on the road section; c is the circumference of the electric vehicle tire.
Continuing to explain, calculating the unit energy consumption of each road section in each driving path after division according to the electric quantity information and the number of turns of the wheel axle as follows:
where k is the kth link in the driving route i.
Through calculation, the unit energy consumption of each road section in each driving path can be obtained. Further, in order to know the relative energy consumption of each road segment, the embodiment sorts the unit energy consumption of each road segment in a descending order to obtain a first sorting result. And in all the driving paths, if the number of times that the unit energy consumption of one road section is greater than that of another road section is greater than the preset number of times, the road section is considered to be sequenced before the other road section, and vice versa. It should be understood that, in one driving route, the result of ordering the energy consumption per unit for each road segment is unique, but one electric vehicle has multiple driving routes, and in the present embodiment, in order to improve the planning accuracy, a large amount of data is used, and therefore, different driving routes exist inevitably, and the energy consumption per unit for one road segment is different, for example, the road segment x and the road segment y exist in 10 driving routes at the same time, wherein, in 8 driving routes, the energy consumption per unit for the road segment x is greater than that of the road segment y, and in 2 driving routes, the energy consumption per unit for the road segment x is less than that of the road segment y, and assuming that the preset number is 5 times, the energy consumption per unit for the road segment x is considered to be greater than that of the road segment y, and the ordering for the road segment x precedes the road segment y, and vice versa.
And thirdly, counting the unit energy consumption of each road section in different driving paths, and taking the median of all the unit energy consumption as the historical average energy consumption of the road section.
Specifically, the unit energy consumption of each road section in different driving paths is different, the unit energy consumption of all the driving paths appearing in the road section is counted, and the median is taken as the historical average energy consumption of the road section.
Further, in consideration of different traveling times of the electric vehicle and health degrees of the vehicle, particularly, the electric vehicle is also influenced by weights and riding habits of different users, in order to more objectively reflect energy consumption levels among road sections, the embodiment also ranks historical average energy consumption of each road section in a descending order to obtain a second ranking result. If the second ranking result of the two road segments is not consistent with the first ranking result in the step S302, calculating an average value of the unit energy consumption and the historical average energy consumption of the two road segments in all driving paths, and taking the average value as a new historical average energy consumption of the two road segments.
In the route planning method in the above embodiment, the road segment corresponding to the positioning information is determined according to the positioning information during the driving process of the electric vehicle, and whether the road segment is located is determined again according to the position relationship among the positioning report point, the starting point and the ending point, so that the positioning accuracy is higher, and the accurate driving route of the electric vehicle is obtained. By comparing the sequence of the unit energy consumption of each road section in different driving paths and comparing the sequence of the historical average energy consumption of each road section, the obtained historical average energy consumption is suitable for various driving conditions, and a reasonable numerical value is finally obtained.
In one possible embodiment, the calculating, in the path planning module, the minimum energy consumption route between two addresses in the target map according to the historical average energy consumption includes: and calculating a minimum energy consumption route between two addresses in the target map based on a shortest path algorithm by adopting historical average energy consumption as a weight of each road section.
Specifically, the shortest path algorithm in this embodiment may use the existing Dijkstra algorithm or a star algorithm (a star algorithm); taking Dijkstra algorithm as an example, the algorithm can calculate the shortest path from one vertex to other vertexes in the graph theory, and solves the problem of the shortest path in the weighted graph by the idea of a greedy algorithm. The algorithm is expanded layer by layer to the outside by taking the starting point as the center until all nodes are traversed. In the embodiment, energy consumption of each road section is taken as a weight, so that a travel route with the lowest energy consumption between any two addresses in a target map is obtained and stored in the server 103.
In the path planning method in the above embodiment, the energy consumption of each road segment is used as a weight, and the travel route with the lowest energy consumption between any two addresses in the target map is calculated through the shortest path algorithm, so that a quick and accurate calculation result can be obtained.
In one possible embodiment, the path planning module further comprises an anomaly identification unit and a model training unit.
And the abnormity identification unit is used for detecting whether the energy consumption of a certain road section is abnormal or not in the driving process of the electric vehicle.
And the model training unit is used for constructing an abnormal road section prediction model based on the historical average energy consumption of each road section obtained in the embodiment.
If the abnormal recognition unit detects that the energy consumption of a certain road section is abnormal, the route calculation module predicts the real-time energy consumption of the next road section by adopting an abnormal road section prediction model and recalculates the minimum energy consumption route based on the real-time energy consumption. When the server 103 receives the path information selected by the user, the pushing module pushes the new minimum energy consumption route to the electric vehicle user side as a path planning result.
Specifically, the step of detecting whether the energy consumption of a certain road section is abnormal by the abnormality identification unit includes:
acquiring running data of the electric vehicle in real time, and calculating the average energy consumption of each road section according to the running data; the method for calculating the average energy consumption of each road section is the same as the step one-step three, and is not described herein again for the sake of brevity.
And if the average energy consumption of the continuous preset number of road sections is greater than the historical average energy consumption, or the difference between the average energy consumption of a certain road section and the historical average energy consumption of the road section is greater than a preset value, determining that the road section is abnormal, and marking the road section as abnormal energy consumption.
Continuing to explain, the step of constructing the abnormal road section prediction model by the model training unit based on the historical average energy consumption of each road section comprises the following steps:
selecting a mold entering characteristic based on the historical average energy consumption of each road section; the mode entering characteristics comprise an average value and a variance of historical average energy consumption in preset first time, the number of unit energy consumption of the road section which is contained in all the recent driving paths and is higher than the unit energy consumption of other road sections in the driving paths, and the proportion of the average energy consumption of each road section in preset second time in adjacent road sections which is higher than the historical average energy consumption;
and iteratively training the abnormal road section prediction model under the constraint of the loss function based on the model entering characteristics to obtain the trained abnormal road section prediction model.
It should be noted that the abnormal road segment prediction model in this embodiment may adopt machine learning models such as lightGBM and XGBoost, use historical average energy consumption of each road segment as a sample set, obtain a model entry feature based on the historical average energy consumption, mark part of data in the sample set, and randomly divide the marked sample set into a training set and a test set according to a preset proportion. Inputting the training set into the abnormal road section prediction model to obtain a predicted value, calculating the loss value of the predicted value and the label value by adopting a cross entropy loss function, correcting the network parameter of the abnormal road section prediction model according to the loss value, stopping the correction process when the loss value reaches a preset condition, and selecting the parameter with the minimum loss value as the parameter of the abnormal road section prediction model after training; and inputting the test set into the trained abnormal road section prediction model, and verifying the prediction precision.
In the path planning method in the above embodiment, the path planning result stored in the server 103 is obtained by pre-calculating according to the historical average energy consumption of each road segment, and can meet the conventional needs of the electric vehicle user; however, when an abnormal condition occurs in the driving process of the electric vehicle, so that the travel energy consumption obviously exceeds the historical average energy consumption, the real-time energy consumption of the next road section is predicted by adopting the abnormal road section prediction model, a new route is planned for a user again, the time of the user is saved, and the energy consumption is reduced. It should be noted that the division of each module of the above apparatus is only a logical division, and all or part of the actual implementation may be integrated into one physical entity or may be physically separated. And these modules can all be implemented in the form of software invoked by a processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the x module may be a processing element separately set up, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the function of the x module. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when one of the above modules is implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
It can be seen that, in the above embodiments of the present invention, the target map is divided into the individual road segments in advance, the historical average energy consumption of each road segment is calculated according to the historical driving data of the electric vehicle, the historical average energy consumption is used as the weight of each road segment, the minimum energy consumption route between two addresses in the target map is calculated based on the shortest path algorithm, and the minimum energy consumption route is pushed to the electric vehicle user.
Referring to fig. 6, in one possible embodiment, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor executes the computer program to implement the following steps:
dividing the target map into a plurality of road sections according to a preset rule;
acquiring historical driving data of the electric vehicle, and calculating historical average energy consumption of each road section according to the historical driving data;
and calculating a minimum energy consumption route between two addresses in the target map according to the historical average energy consumption to obtain a path planning result.
Where the memory and processor are connected by a bus, the bus may comprise any number of interconnected buses and bridges, the bus connecting together various circuits of the memory and the processor or processors. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor is transmitted over a wireless medium through an antenna, which further receives the data and transmits the data to the processor.
The processor is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And the memory may be used to store data used by the processor in performing operations.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one possible embodiment, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
dividing the target map into a plurality of road sections according to a preset rule;
and calculating a minimum energy consumption route between two addresses in the target map according to the historical average energy consumption to obtain a path planning result.
Preferably, the storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic disk, U-disk, memory card, or optical disk.
Any combination of one or more storage media may be employed. The storage medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The present invention is described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
In summary, according to the route planning method, system, device and storage medium of the present invention, the target map is divided into the individual road segments in advance, the historical average energy consumption of each road segment is calculated according to the historical driving data of the electric vehicle, the historical average energy consumption is used as the weight of each road segment, the minimum energy consumption route between two addresses in the target map is calculated based on the shortest path algorithm, and the minimum energy consumption route is pushed to the electric vehicle user. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Those skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which may be made by those skilled in the art without departing from the spirit and scope of the present invention as defined in the appended claims.
Claims (10)
1. A method of path planning, comprising:
dividing the target map into a plurality of road sections according to a preset rule;
acquiring historical driving data of the electric vehicle, and calculating historical average energy consumption of each road section according to the historical driving data;
and calculating a minimum energy consumption route between two addresses in the target map according to the historical average energy consumption to obtain a path planning result.
2. The path planning method according to claim 1, wherein the historical driving data includes positioning information, power information, and wheel axle turns;
the calculating the historical average energy consumption of each road section according to the historical driving data comprises the following steps:
acquiring a running path of the electric vehicle according to the positioning information;
dividing each driving path into road sections according to a target map, and calculating unit energy consumption of each divided road section in the corresponding driving path according to the electric quantity information and the wheel axle turns;
and (4) counting the unit energy consumption of each road section in different driving paths, and taking the median of all the unit energy consumption as the historical average energy consumption of each road section.
3. The path planning method according to claim 2, wherein the unit energy consumption of each segmented road segment in the corresponding driving path is calculated according to the electric quantity information and the number of turns of the wheel axle as follows:
the ith driving path is divided into n road sections; k is the kth road section in the driving path;
c is the circumference of the electric vehicle tire.
4. The path planning method according to claim 2, further comprising:
sequencing the unit energy consumption of each road section in a descending order to obtain a first sequencing result, wherein in all driving paths, if the times that the unit energy consumption of one road section is greater than that of the other road section is greater than a preset time, the sequencing of one road section is considered to be before that of the other road section, and vice versa;
sorting the historical average energy consumption of each road section in a descending order to obtain a second sorting result;
and if the first sequencing result and the second sequencing result of the two road sections are inconsistent, calculating the average value of the unit energy consumption and the historical average energy consumption of the two road sections in all corresponding driving paths, and taking the average value as the new historical average energy consumption of the two road sections.
5. The path planning method according to claim 1, wherein the calculating a minimum energy consumption route between two addresses in a target map according to the historical average energy consumption comprises:
and calculating a minimum energy consumption route between two addresses in the target map based on a shortest path algorithm by using the historical average energy consumption as a weight of each road section.
6. The path planning method according to claim 1, further comprising:
selecting a model entering characteristic based on the historical average energy consumption of each road section, and constructing an abnormal road section prediction model based on the model entering characteristic; the mode entering characteristics comprise an average value and a variance of historical average energy consumption of each road section within preset first time, the number of unit energy consumption of the road section which is contained in all recent driving paths and is higher than that of other road sections in the driving paths, and the proportion of the average energy consumption of each road section within preset second time in adjacent road sections which is higher than that of the historical average energy consumption of each road section;
and if the energy consumption of a certain road section is detected to be abnormal, predicting the real-time energy consumption of the next road section by adopting the abnormal road section prediction model, and recalculating the minimum energy consumption route based on the real-time energy consumption to obtain a new path planning result.
7. The path planning method according to claim 6, wherein the detecting of the energy consumption abnormality of the certain road segment includes:
acquiring running data of the electric vehicle in real time, and calculating the average energy consumption of each road section according to the running data;
and if the average energy consumption of the continuous preset number of road sections is greater than the historical average energy consumption, or the difference between the average energy consumption of a certain road section and the historical average energy consumption of the road section is greater than a preset value, marking the corresponding road section as abnormal energy consumption.
8. A path planning system, comprising:
the dividing module is used for dividing the target map into a plurality of road sections according to a preset rule;
the energy consumption calculation module is used for acquiring historical driving data of the electric vehicle and calculating historical average energy consumption of each road section according to the historical driving data;
and the path planning module is used for calculating a minimum energy consumption route between two addresses in the target map according to the historical average energy consumption to obtain a path planning result.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein: the processor, when executing the program, implements a path planning method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the path planning method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115871450A (en) * | 2023-02-16 | 2023-03-31 | 日照职业技术学院 | New energy automobile intelligent control method and system based on Internet of things |
| CN116572799A (en) * | 2023-07-13 | 2023-08-11 | 四川轻化工大学 | Power battery charge duration prediction method, system and terminal based on deep learning |
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| CN115871450A (en) * | 2023-02-16 | 2023-03-31 | 日照职业技术学院 | New energy automobile intelligent control method and system based on Internet of things |
| CN116572799A (en) * | 2023-07-13 | 2023-08-11 | 四川轻化工大学 | Power battery charge duration prediction method, system and terminal based on deep learning |
| CN116572799B (en) * | 2023-07-13 | 2023-09-05 | 四川轻化工大学 | Power battery charge duration prediction method, system and terminal based on deep learning |
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