CN114463876B - Method, apparatus, device, medium and program product for reminding of supplementing energy - Google Patents

Method, apparatus, device, medium and program product for reminding of supplementing energy Download PDF

Info

Publication number
CN114463876B
CN114463876B CN202210134101.2A CN202210134101A CN114463876B CN 114463876 B CN114463876 B CN 114463876B CN 202210134101 A CN202210134101 A CN 202210134101A CN 114463876 B CN114463876 B CN 114463876B
Authority
CN
China
Prior art keywords
energy
line
clustering
preset
distance
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210134101.2A
Other languages
Chinese (zh)
Other versions
CN114463876A (en
Inventor
罗峰
李宁
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ecarx Hubei Tech Co Ltd
Original Assignee
Ecarx Hubei Tech Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ecarx Hubei Tech Co Ltd filed Critical Ecarx Hubei Tech Co Ltd
Priority to CN202210134101.2A priority Critical patent/CN114463876B/en
Publication of CN114463876A publication Critical patent/CN114463876A/en
Application granted granted Critical
Publication of CN114463876B publication Critical patent/CN114463876B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/004—Indicating the operating range of the engine
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0808—Diagnosing performance data
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0816—Indicating performance data, e.g. occurrence of a malfunction
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0816—Indicating performance data, e.g. occurrence of a malfunction
    • G07C5/0825—Indicating performance data, e.g. occurrence of a malfunction using optical means
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0816—Indicating performance data, e.g. occurrence of a malfunction
    • G07C5/0833—Indicating performance data, e.g. occurrence of a malfunction using audio means
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Abstract

The application provides a method, a device, equipment, a medium and a program product for reminding of supplementing energy sources, wherein historical operation data and current operation data of a vehicle are obtained; determining at least one clustering line according to the preprocessed historical operation data by using a preset clustering model, wherein the clustering line is used for representing the driving habit and/or the energy supplementing habit of a user; determining a preset reminding range according to at least one clustering line and a preset distance threshold value, and judging whether the current operation data is in the preset reminding range or not; if yes, outputting prompt information to prompt a user to supplement energy for the vehicle. The technical problem of how to carry out intelligent early warning prompt according to the driving habit and/or the energy supplementing habit of the user is solved. The energy supplementing prompt is timely sent out according to the running state of the vehicle and the habit of the user, so that the effectiveness of prompt information to the user is improved, and the technical effect that the vehicle cannot be used due to insufficient energy is avoided.

Description

Method, apparatus, device, medium and program product for reminding of supplementing energy
Technical Field
The present disclosure relates to the field of intelligent electronic products, and in particular, to a method, apparatus, device, medium, and program product for reminding of energy replenishment.
Background
With the development of technology and the improvement of living standard of people, vehicles have become an indispensable tool for riding instead of walking in work and daily life. The driving energy such as gasoline, diesel oil, liquefied hydrogen, liquefied natural gas, electric energy and the like is indispensable when the vehicle is used.
At present, the prior art generally sends out the early warning prompt when the residual amount of the driving energy is smaller than or equal to a preset threshold value. However, since the current traffic infrastructure is not very complete, if there is no corresponding driving energy supplementary station in the area where the vehicle is traveling, or if the remaining amount of driving energy cannot support the vehicle to travel to the driving energy supplementary station closest to the vehicle, the use of the vehicle will be seriously affected, and the early warning prompt at this time cannot play the intended role of prompt.
Therefore, how to perform intelligent early warning prompt according to the driving habit and/or the energy supplementing habit of the user becomes a technical problem to be solved.
Disclosure of Invention
The application provides a method, a device, equipment, a medium and a program product for reminding of supplementing energy, which are used for solving the technical problem of how to carry out intelligent early warning reminding according to the driving habit and/or the energy supplementing habit of a user.
In a first aspect, the present application provides a method of alerting a supplemental energy source comprising:
acquiring historical operation data and current operation data of a vehicle;
determining at least one clustering line according to the preprocessed historical operation data by using a preset clustering model, wherein the clustering line is used for representing the driving habit and/or the energy supplementing habit of a user;
determining a preset reminding range according to at least one clustering line and a preset distance threshold value, and judging whether the current operation data is in the preset reminding range or not;
if yes, outputting prompt information to prompt a user to supplement energy for the vehicle.
Optionally, the clustering line includes: at least one of a straight line, a curved line, and a broken line composed of a plurality of straight line segments and/or curved line segments.
In one possible design, the historical operating data includes: historical values of the energy surplus at each historical moment, and current operation data comprise: the energy surplus is the current value at the current moment.
In one possible design, determining at least one cluster line from the preprocessed historical operating data using a preset cluster model includes:
preprocessing historical operation data to determine a clustering sample set;
Circularly calculating the distance between each sample data in the clustering sample set and each clustering line to be selected in a clustering space, wherein the clustering space corresponds to a plurality of preset clustering dimensions;
and determining at least one clustering line from the to-be-selected clustering lines according to the distances and preset screening requirements.
In one possible design, preprocessing historical operating data to determine a set of clustered samples includes:
calculating the variable quantity of any two adjacent recorded values in the historical operation data;
judging whether the variation meets the preset variation requirement or not;
when the variation meets the preset variation requirement, the previous recorded value in the two adjacent recorded values is added into the clustering sample set.
In one possible design, the historical operating data includes a record of the energy remaining of the vehicle at each detection instant;
the variation comprises a difference value between a first energy residual record at the previous moment and a second energy residual record at the later moment;
the preset change requirements include: the difference value is a negative value, and the absolute value of the difference value is larger than or equal to a preset charging threshold value.
In one possible design, the plurality of preset cluster dimensions includes: the energy charging time and the energy surplus, and the cluster line to be selected is determined by the energy charging time, the energy surplus and the dimension weight.
In one possible design, the plurality of candidate cluster lines includes: a plurality of time lines corresponding to the charging time and a plurality of energy source quantity lines corresponding to the energy source surplus, wherein the time lines and the energy source quantity lines are respectively parallel to coordinate axes of the clustering space;
correspondingly, the cluster line includes: the energy charging time cluster center line and the energy remaining quantity cluster center line.
In one possible design, when the to-be-selected cluster line is a time line, circularly calculating distances between each sample data in the clustered sample set and each to-be-selected cluster line in the cluster space includes:
determining a first distance to be selected according to the time coordinate values of the sample data and the time corresponding to the time line;
determining a second distance to be selected according to the first distance to be selected and a preset time period;
and selecting the smaller value of the first distance to be selected and the second distance to be selected as the distance.
In a second aspect, the present application provides an apparatus for alerting a supplemental energy source comprising:
the acquisition module is used for acquiring historical operation data and current operation data of the vehicle;
a processing module for:
determining at least one clustering line according to the preprocessed historical operation data by using a preset clustering model, wherein the clustering line is used for representing the driving habit and/or the energy supplementing habit of a user;
Judging whether the current operation data is in a preset reminding range or not according to the current operation data, at least one clustering line and a preset distance threshold value;
if yes, outputting prompt information to prompt a user to supplement energy for the vehicle.
Optionally, the clustering line includes: at least one of a straight line, a curved line, and a broken line composed of a plurality of straight line segments and/or curved line segments.
In one possible design, the historical operating data includes: historical values of the energy surplus at each historical moment, and current operation data comprise: the energy surplus is the current value at the current moment.
In one possible design, the processing module is configured to:
preprocessing historical operation data to determine a clustering sample set;
circularly calculating the distance between each sample data in the clustering sample set and each clustering line to be selected in a clustering space, wherein the clustering space corresponds to a plurality of preset clustering dimensions;
and determining at least one clustering line from the to-be-selected clustering lines according to the distances and preset screening requirements.
In one possible design, the processing module is configured to:
calculating the variable quantity of any two adjacent recorded values in the historical operation data;
judging whether the variation meets the preset variation requirement or not;
When the variation meets the preset variation requirement, the previous recorded value in the two adjacent recorded values is added into the clustering sample set.
In one possible design, the historical operating data includes a record of the energy remaining of the vehicle at each detection instant; the variation comprises a difference value between a first energy residual record at the previous moment and a second energy residual record at the later moment; the preset change requirements include: the difference value is a negative value, and the absolute value of the difference value is larger than or equal to a preset charging threshold value.
In one possible design, the plurality of preset cluster dimensions includes: the energy charging time and the energy surplus, and the cluster line to be selected is determined by the energy charging time, the energy surplus and the dimension weight.
In one possible design, the plurality of candidate cluster lines includes: a plurality of time lines corresponding to the charging time and a plurality of energy source quantity lines corresponding to the energy source surplus, wherein the time lines and the energy source quantity lines are respectively parallel to coordinate axes of the clustering space;
correspondingly, the cluster line includes: the energy charging time cluster center line and the energy remaining quantity cluster center line.
In one possible design, the processing module is configured to:
Determining a first distance to be selected according to the time coordinate values of the sample data and the time corresponding to the time line;
determining a second distance to be selected according to the first distance to be selected and a preset time period;
and selecting the smaller value of the first distance to be selected and the second distance to be selected as the distance.
In a third aspect, the present application provides an electronic device, comprising:
a memory for storing program instructions;
and the processor is used for calling and executing the program instructions in the memory and executing any one of the possible energy source supplementing reminding methods provided in the first aspect.
In a fourth aspect, the present application provides a vehicle comprising any one of the possible electronic devices provided in the third aspect.
In a fifth aspect, the present application provides a storage medium having stored therein a computer program for performing any one of the possible methods of reminding of replenishing energy provided in the first aspect.
In a sixth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements any one of the possible system methods of reminding of replenishing energy provided in the first aspect.
The application provides a method, a device, equipment, a medium and a program product for reminding of supplementing energy sources, wherein historical operation data and current operation data of a vehicle are obtained; determining at least one clustering line according to the preprocessed historical operation data by using a preset clustering model, wherein the clustering line is used for representing the driving habit and/or the energy supplementing habit of a user; determining a preset reminding range according to at least one clustering line and a preset distance threshold value, and judging whether the current operation data is in the preset reminding range or not; if yes, outputting prompt information to prompt a user to supplement energy for the vehicle. The technical problem of how to carry out intelligent early warning prompt according to the driving habit and/or the energy supplementing habit of the user is solved. The energy supplementing prompt is timely sent out according to the running state of the vehicle and the habit of the user, so that the effectiveness of prompt information to the user is improved, and the technical effect that the vehicle cannot be used due to insufficient energy is avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is an application scenario schematic diagram of a method for reminding a user of supplementing energy provided by the application;
FIG. 2 is a flow chart of a method for reminding of energy replenishment according to an embodiment of the present application;
FIG. 3 is a flow chart of another method for reminding a user of supplementing energy according to the embodiment of the present application;
FIG. 4 is a schematic diagram of a cluster line obtained by analyzing a cluster sample set according to the present application
Fig. 5 is a schematic structural diagram of a device for reminding of supplementing energy according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device provided in the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, including but not limited to combinations of embodiments, which can be made by one of ordinary skill in the art without inventive faculty, are intended to be within the scope of the present application, based on the embodiments herein.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be capable of operation in sequences other than those illustrated or described herein, for example. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Energy is the fundamental source of vehicle running power, but the energy can be supplemented everywhere because the construction degree of the current traffic infrastructure can not be realized. Therefore, when a user drives a vehicle, the user needs to pay attention to the remaining amount of energy, such as the remaining amount of gasoline, diesel oil, liquefied natural gas, and liquefied petroleum gas, for a conventional automobile, and the remaining amount of electric energy and liquefied hydrogen for a new energy automobile. The conventional prompting mechanism is to send out a prompting message, such as a fuel or electric quantity warning lamp on the instrument panel when the residual quantity is smaller than or equal to a preset threshold value.
However, if the vehicle is located at a position far from the energy supply stations such as the gas station, the charging station, the docking station, and the like, the remaining energy of the vehicle may not support the form of the energy supply station, so that the vehicle cannot run, the user can only call for rescue, and the use experience of the user is seriously affected.
In order to solve the technical problems, the invention concept of the application is as follows:
the inventor of the application finds that the situations of energy source supplement of users are divided into two types by combining experience with a large amount of user real data analysis: 1. the energy surplus is reduced to a threshold value which is considered to be insufficient by the psychology of the user; 2. users are habitually supplied with periodic energy (e.g., adding fuel or hydrogen or charging once a week or during the course of work) for a certain period of time.
Therefore, based on the historical change data of the energy surplus of the user at the vehicle-mounted terminal, the application performs preliminary characteristic engineering treatment (such as mutation analysis of the energy surplus) at the vehicle-mounted terminal, and analyzes the energy supplementing behavior (such as supplementing time and energy surplus during supplementing) of the user; then, carrying out cluster analysis to divide the sample into psychological anxiety data and/or periodic behavior data; after the far-away group points are removed, detecting whether the period sample data really has a period or not, and setting a period reminder if the period sample data really has the period; and reasonably reminding the psychological anxiety samples through different characters and different driving habits of the user.
The following describes in detail how the present application enables the transfer of information by tactile sensation.
Fig. 1 is an application scenario schematic diagram of a method for reminding of energy supplement provided in the present application. As shown in fig. 1, the vehicle 102 performs energy replenishment at the same or different energy replenishment sites 101 at different time points, and each time the replenishment is performed, the vehicle terminal records the time of replenishing the energy and the remaining amount of the energy before the replenishment. According to the method and the device, the reminding mode of energy source supplementation is adjusted through the cluster analysis of the energy source supplementation behavior of the user.
Fig. 2 is a flow chart of a method for reminding of supplementing energy according to an embodiment of the present application. As shown in fig. 2, the specific steps of the method for reminding the supplement of energy include:
s201, acquiring historical operation data and current operation data of the vehicle.
In the step, when the vehicle runs, the vehicle machine side acquires running data of the vehicle once at preset time intervals, for example, 1-5 minutes, and stores the running data to form historical running data. The current operation data is the operation data which is acquired up to date.
In the present embodiment, the history operation data includes: historical values of the energy surplus at each historical moment, and current operation data comprise: the energy surplus is the current value at the current moment.
Specifically, when the historical value of the energy remaining amount is recorded, the historical value of the energy remaining amount needs to be compared with the last energy remaining amount every time a new energy remaining amount is detected, and when the current energy remaining amount is larger than the last energy remaining amount and larger than a certain preset threshold value, the user is considered to be in progress or has completed energy replenishment, such as oiling, charging and the like, during the current detection. And the historical operation data can correspondingly record the value of the energy surplus and the corresponding time.
It will be appreciated that other data may be included in the historical operating data and the current operating data, such as current vehicle position information, current vehicle operating state information, vehicle travel state data, such as engine operating data, gearbox operating data, drive motor operating data, brake system operating data, communication system operating data, body stability system operating data, and the like.
S202, determining at least one clustering line according to the preprocessed historical operation data by using a preset clustering model.
In this step, the historical operation data may be understood as current operation data corresponding to each historical moment, where the historical operation data needs to be preprocessed to screen out data related to the behavior of the user for supplementing energy, and the data is added into a cluster sample set, and then the cluster sample set is subjected to cluster recognition through a preset cluster model, so as to obtain at least one cluster line. The preprocessing can greatly reduce the data volume for cluster recognition, eliminate the interference of noise data, reduce the data processing volume and improve the clustering efficiency.
It should be noted that, if the current operation data in S201 can also show the behavior of the user for supplementing energy, the current operation data is also added to the cluster sample set.
The cluster lines are used to characterize the driving habits and/or energy replenishment habits of the user. The clustering line includes: at least one of a straight line, a curved line, and a broken line composed of a plurality of straight line segments and/or curved line segments.
Specifically, historical operation data is subjected to preliminary screening according to a plurality of preset clustering dimensions, and the screened data is placed into a clustering sample set. In the embodiment, firstly, calculating the variation of any two adjacent recorded values in the historical operation data; then judging whether the variation meets the preset variation requirement or not; when the variation meets the preset variation requirement, the previous recorded value in the two adjacent recorded values is added into the clustering sample set.
For example, when the history operation data includes the energy remaining amount records of the energy remaining amount of the vehicle at the respective detection timings, the variation includes the difference between the first energy remaining amount record at the previous timing and the second energy remaining amount record at the subsequent timing; the preset change requirements include: the difference value is a negative value, and the absolute value of the difference value is larger than or equal to a preset charging threshold value.
It should be noted that, in order to enable the preset clustering model to adapt to the user habit changes and reduce the calculation time, the clustering sample set may set a certain capacity limit, such as a fixed value of 100, according to the LRU (Least Recently Used ) caching rule, or a capacity value that can be dynamically changed and is set according to the occurrence frequency of the energy source supplementing behavior of the user. The first-in first-out principle is then followed, and the earliest sample is rejected when the number of samples exceeds a threshold.
Next, it is necessary to perform cluster division on each sample in the clustered sample set, which is a main function of the preset cluster model. In a preset clustering model, an initial cluster line to be selected is firstly determined according to one or more preset clustering dimensions, and f is used i (x 1 ,x 2 ,……,x n ) To represent the cluster line to be selected, i represents the number of the cluster line to be selected, x n Representing each preset clustering dimension, including: time, amount of energy remaining before each replenishment, etc. Each preset clustering dimension forms a clustering space, and the essence of the clustering analysis is to find the distribution condition of each sample point in the clustering space.
The method comprises the steps of firstly randomly selecting a straight line or a curve passing through a certain sample or a plurality of sample points as an initial clustering line to be selected, then circularly calculating the Euclidean distance between each sample and the clustering line to be selected by using an Euclidean distance formula, and then solving the average distance between all samples and the clustering line to be selected. And translating the to-be-selected cluster line to the position of the sample with larger Euclidean distance, recalculating the Euclidean distance and average distance between each sample and the to-be-selected cluster line, and finally selecting the to-be-selected cluster line meeting the requirement as a cluster line, namely a cluster center line according to the preset screening requirement.
It should be noted that, in the multi-dimensional clustering space, the number of the clustering lines may be set to be equal to the number of preset clustering dimensions, and the direct distance of each clustering line may be the maximum distance as far as possible, so that the clustered multiple sample clusters may be distinguished.
It is noted that, unlike the prior art in which the points are used as the clustering centers, the clustering center of the present application is a line, so that each sample value can be maximally utilized, and the behavior habit of the user sometimes indicates a clustering rule not around the center point but around a certain straight line or curve, so that the behavior habit of the user can be fully reflected. Therefore, the preset clustering model of the embodiment of the application can be obtained by modifying the clustering center point in the Kmeans++ model into the clustering center line.
S203, determining a preset reminding range according to at least one clustering line and a preset distance threshold value, and judging whether the current operation data is in the preset reminding range.
In the step, the preset distance threshold is obtained by screening out samples with the distance greater than the preset deviation threshold from other samples in the clustered sample set, fitting the samples through a Gaussian model, obtaining the mean value mu and the variance sigma of each sample point, and then calculating the preset distance threshold according to the mean value mu and the variance sigma.
For example, the preset distance threshold d may be expressed by the formula (1):
d＝Aμ+Bσ (1)
wherein a and B are dynamic parameters, which can be set according to specific application scenarios, and optionally, the value ranges of a and B can be set to [0,2], for example, a=1, b=1.5.
And calculating the distance between the current operation data and one or more clustering lines, judging whether each distance is smaller than or equal to a preset distance threshold value, and if so, considering that the current operation data is in a preset reminding range.
S204, outputting prompt information to prompt a user to supplement energy for the vehicle.
In the step, prompt information is sent to a user through a vehicle-mounted terminal, such as a vehicle-mounted display, a loudspeaker, a buzzer, an LED lamp and the like, so as to prompt the user to perform actions of supplementing energy sources, such as refueling, charging and the like, for the vehicle.
It should be noted that the clustering lines can be divided into two types, one is a time line with time as a clustering dimension, and the other is an energy line with energy remaining as a clustering dimension.
The time line is mainly used for identifying the periodic energy supplementing behavior of the user, and can also be used for carrying out auxiliary verification by adding geographic position information, and the energy line is mainly used for identifying psychological thresholds of different users, so that anxiety caused when the user forgets to check and suddenly finds that the energy remaining amount is lower than the psychological threshold is reduced.
The embodiment provides a system method for reminding of supplementing energy sources, which comprises the steps of obtaining historical operation data and current operation data of a vehicle; determining at least one clustering line according to the preprocessed historical operation data by using a preset clustering model, wherein the clustering line is used for representing the driving habit and/or the energy supplementing habit of a user; determining a preset reminding range according to at least one clustering line and a preset distance threshold value, and judging whether the current operation data is in the preset reminding range or not; if yes, outputting prompt information to prompt a user to supplement energy for the vehicle. The technical problem of how to carry out intelligent early warning prompt according to the driving habit and/or the energy supplementing habit of the user is solved. The energy supplementing prompt is timely sent out according to the running state of the vehicle and the habit of the user, so that the effectiveness of prompt information to the user is improved, and the technical effect that the vehicle cannot be used due to insufficient energy is avoided.
Fig. 3 is a flow chart of another method for reminding of supplementing energy provided in the implementation of the present application. As shown in fig. 3, the specific steps of the method for reminding the user of supplementing energy source include:
s301, acquiring historical operation data and current operation data of the vehicle.
In this step, the historical operating data includes: historical values of the energy surplus at each historical moment, and current operation data comprise: the energy surplus is the current value at the current moment.
S302, preprocessing historical operation data to determine a clustering sample set.
In this step, specifically, the method includes: calculating the variable quantity of any two adjacent recorded values in the historical operation data; judging whether the variation meets the preset variation requirement or not; when the variation meets the preset variation requirement, the previous recorded value in the two adjacent recorded values is added into the clustering sample set.
In the present embodiment, the history operation data includes the energy remaining amount records of the energy remaining amount of the vehicle at each detection timing; the variation comprises a difference value between a first energy residual record at the previous moment and a second energy residual record at the later moment; the preset change requirements include: the difference value is a negative value, and the absolute value of the difference value is larger than or equal to a preset charging threshold value.
When the historical value of the energy surplus is recorded, the energy surplus needs to be compared with the last energy surplus each time when a new energy surplus is detected, and when the current energy surplus is larger than the last energy surplus and larger than a certain preset threshold value, the user is considered to be in progress or has completed energy replenishment, such as oiling, charging and the like, during the current detection. And the historical operation data can correspondingly record the value of the energy surplus and the corresponding time. In order to adapt the preset clustering model to the habit changes of the user and reduce the calculation time, the clustering sample set may set a certain capacity limit, such as a fixed value of 100, according to the LRU (Least Recently Used ) caching rule, or a capacity value that can be dynamically changed according to the occurrence frequency of the energy supplementing behavior of the user. The first-in first-out principle is then followed, and the earliest sample is rejected when the number of samples exceeds a threshold.
S303, circularly calculating the distance between each sample data in the clustering sample set and each candidate clustering line in the clustering space.
In this step, the cluster space corresponds to a plurality of preset cluster dimensions. A plurality of preset cluster dimensions, comprising: charging time and energy surplus.
The cluster line to be selected is determined by the charging time, the energy surplus and the dimension weight. The dimension weight can adjust the charging time and the specific gravity preference when classifying the behavior habit of the user according to different clustering demands, and even can set the curve to be clustered as a dimension clustering center line only with the charging time or the energy surplus before energy supplementation.
In one possible design, the plurality of candidate cluster lines includes: a plurality of time lines corresponding to the charging time and a plurality of energy source quantity lines corresponding to the energy source surplus, wherein the time lines and the energy source quantity lines are respectively parallel to coordinate axes of the clustering space; correspondingly, the cluster line includes: the energy charging time cluster center line and the energy remaining quantity cluster center line.
In one possible design, when the cluster line to be selected is a timeline, the steps specifically include:
determining a first distance to be selected according to the time coordinate values of the sample data and the time corresponding to the time line;
Determining a second distance to be selected according to the first distance to be selected and a preset time period;
and selecting the smaller value of the first distance to be selected and the second distance to be selected as the distance.
For ease of understanding, assume that the time corresponding to the timeline is t 0 The time coordinate value of the sample data is t, then the first distance D is selected 1 Can be expressed as: d (D) 1 ＝|t 0 -t|, second candidate distance D 2 Can be expressed as D 2 ＝|t+T-t 0 I, T is a time period, for example, when t=24 represents using 24 hours as one time cycle period, so the distance D can be expressed by the formula (2):
D＝min{D 1 ,D 2 } (2)
it should be noted that, since the time has its cycle special, for example, 24 is similar to 1,2 except that it is similar to 23, 22, so we consider the cycle characteristic when calculating the distance on the time axis, for example, when calculating the distance of 22 and 3, we get min (|22-3|, |3+24-22|) =5.
S304, determining at least one clustering line from the to-be-selected clustering lines according to the distances and the preset screening requirements.
Specifically, a straight line or a curve passing through a certain sample or a plurality of sample points is selected randomly as an initial clustering line to be selected, or one or more initial clustering lines to be selected are selected in a random mode, then the Euclidean distance between each sample and the clustering line to be selected is calculated circularly by using the Euclidean distance formula, and then the average distance between all samples and the clustering line to be selected is calculated. And calculating the barycenter position of each sample point, translating the to-be-selected cluster line to the barycenter position, re-calculating the Euclidean distance and the average distance between each sample and the to-be-selected cluster line, judging whether the translated to-be-selected cluster line meets the requirement according to the preset screening requirement, and if not, continuously repeating the process until the to-be-selected cluster line meeting the requirement is selected as a cluster line, namely a cluster center line.
In one possible implementation, for the two-dimensional case, i.e. two clustering dimensions exist, two clustering lines are corresponding at this time, so-called clustering is to assign all sample points to the periphery of the clustering line close to the clustering line as far as possible, and to group the sample points assigned to the same line into the same cluster; since the position of the sample point is not movable, the cluster line to be selected can be moved so that the cluster line to be selected moves towards the gravity center direction of the cluster, which is essentially to minimize the average distance of the cluster; and repeating the process, recalculating the distance between the two to-be-selected cluster lines for each sample, reassigning the cluster to which the sample points belong (because the cluster lines are moved, some sample points are possibly closer to other cluster lines), and continuously repeating the process until the to-be-selected cluster lines are balanced, namely the to-be-selected cluster lines do not need to be moved any more, or all to-be-selected cluster lines reach the center of gravity of the cluster, and the distance between all the sample points and the to-be-selected cluster lines of the cluster is smaller than or equal to the distance between the to-be-selected cluster lines of the cluster points and other cluster lines, so that the to-be-selected cluster lines can be determined to be final cluster lines.
It should be noted that, in the multi-dimensional clustering space, the number of the clustering lines may be set to be equal to the number of preset clustering dimensions, and the direct distance of each clustering line may be the maximum distance as far as possible, so that the clustered multiple sample clusters may be distinguished.
It is noted that, unlike the prior art in which the points are used as the clustering centers, the clustering center of the present application is a line, so that each sample value can be maximally utilized, and the behavior habit of the user sometimes indicates a clustering rule not around the center point but around a certain straight line or curve, so that the behavior habit of the user can be fully reflected.
Fig. 4 is a schematic diagram of a cluster line obtained by analyzing a cluster sample set provided in the present application. As shown in fig. 4, a sample point is a first type of sample point, i.e. a black sample point 401, when the abscissa of the sample point is farther from the timeline cluster centerline 42 than the ordinate of the sample point is from the cluster centerline 41, otherwise a gray sample point 402.
In one possible design, in this step, it is also necessary to further analyze the periodicity and calculate the period interval for a class of samples concentrated at a certain time, further analyze whether there is periodicity, such as daily, weekly, every two weeks, etc., if a higher probability is satisfied (so-called higher probability, say, 70% or more accords with periodicity, no strict periodicity is considered in practice, a person always has various reasons that cannot completely accord with regularity, temporary matters or oil amount is not enough to be added once in advance), we determine that there is a fueling habit of the periodicity time, so we can make fueling reminders at the periodicity time point.
S305, determining a preset reminding range according to at least one clustering line and a preset distance threshold value, and judging whether the current operation data is in the preset reminding range.
In the step, the preset distance threshold is obtained by screening out samples with the distance greater than the preset deviation threshold from other samples in the clustered sample set, fitting the samples through a Gaussian model, obtaining the mean value mu and the variance sigma of each sample point, and then calculating the preset distance threshold according to the mean value mu and the variance sigma.
In this embodiment, assuming that a cluster of samples is in a gaussian distribution, taking fueling as an example, when the remaining amount of fuel fluctuates around the user's psychological anxiety value, the user begins to feel anxiety to the situation of actual fueling, and also needs to consider the distance of the vehicle from the fueling station, and whether it is convenient to refuel at that time. According to the Gaussian distribution property, the area under the Gaussian distribution curve is 68.27% of the total area in the interval of (mu-sigma, mu+sigma); within plus or minus 2 standard deviations, i.e., within the (μ -2σ, μ+2σ) interval, the area was 95.44%, and we set the anxiety reminding value to μ+1.5σ, approximately covering 90% of anxiety cases.
If the current energy remaining amount is within the preset threshold, i.e., μ+1.5σ, it is determined that the reminding is required, and S306 is executed.
If the current time is within the range corresponding to the time line or within the periodic interval of the periodic behavior obtained by further analysis of the time lines, it is determined that the reminding is required, and S306 is executed.
S306, outputting prompt information to prompt a user to supplement energy for the vehicle.
The embodiment provides a system method for reminding of supplementing energy sources, which comprises the steps of obtaining historical operation data and current operation data of a vehicle; determining at least one clustering line according to the preprocessed historical operation data by using a preset clustering model, wherein the clustering line is used for representing the driving habit and/or the energy supplementing habit of a user; determining a preset reminding range according to at least one clustering line and a preset distance threshold value, and judging whether the current operation data is in the preset reminding range or not; if yes, outputting prompt information to prompt a user to supplement energy for the vehicle. The technical problem of how to carry out intelligent early warning prompt according to the driving habit and/or the energy supplementing habit of the user is solved. The energy supplementing prompt is timely sent out according to the running state of the vehicle and the habit of the user, so that the effectiveness of prompt information to the user is improved, and the technical effect that the vehicle cannot be used due to insufficient energy is avoided.
Fig. 5 is a schematic structural diagram of a device for reminding of supplementing energy according to an embodiment of the present application. The means 500 for reminding the replenishment of energy can be implemented by software, hardware or a combination of both.
As shown in fig. 5, the apparatus 500 for reminding a user of supplementing energy includes:
an obtaining module 501, configured to obtain historical operation data and current operation data of a vehicle;
a processing module 502, configured to:
determining at least one clustering line according to the preprocessed historical operation data by using a preset clustering model, wherein the clustering line is used for representing the driving habit and/or the energy supplementing habit of a user;
determining a preset reminding range according to at least one clustering line and a preset distance threshold value, and judging whether the current operation data is in the preset reminding range or not;
if yes, outputting prompt information to prompt a user to supplement energy for the vehicle.
Optionally, the clustering line includes: at least one of a straight line, a curved line, and a broken line composed of a plurality of straight line segments and/or curved line segments.
In one possible design, the historical operating data includes: historical values of the energy surplus at each historical moment, and current operation data comprise: the energy surplus is the current value at the current moment.
In one possible design, the processing module 502 is configured to:
preprocessing historical operation data to determine a clustering sample set;
circularly calculating the distance between each sample data in the clustering sample set and each clustering line to be selected in a clustering space, wherein the clustering space corresponds to a plurality of preset clustering dimensions;
and determining at least one clustering line from the to-be-selected clustering lines according to the distances and preset screening requirements.
In one possible design, the processing module 502 is configured to:
calculating the variable quantity of any two adjacent recorded values in the historical operation data;
judging whether the variation meets the preset variation requirement or not;
when the variation meets the preset variation requirement, the previous recorded value in the two adjacent recorded values is added into the clustering sample set.
In one possible design, the historical operating data includes a record of the energy remaining of the vehicle at each detection instant; the variation comprises a difference value between a first energy residual record at the previous moment and a second energy residual record at the later moment; the preset change requirements include: the difference value is a negative value, and the absolute value of the difference value is larger than or equal to a preset charging threshold value.
In one possible design, the plurality of preset cluster dimensions includes: the energy charging time and the energy surplus, and the cluster line to be selected is determined by the energy charging time, the energy surplus and the dimension weight.
In one possible design, the plurality of candidate cluster lines includes: a plurality of time lines corresponding to the charging time and a plurality of energy source quantity lines corresponding to the energy source surplus, wherein the time lines and the energy source quantity lines are respectively parallel to coordinate axes of the clustering space;
correspondingly, the cluster line includes: the energy charging time cluster center line and the energy remaining quantity cluster center line.
In one possible design, the processing module 502 is configured to:
determining a first distance to be selected according to the time coordinate values of the sample data and the time corresponding to the time line;
determining a second distance to be selected according to the first distance to be selected and a preset time period;
and selecting the smaller value of the first distance to be selected and the second distance to be selected as the distance.
It should be noted that, the apparatus provided in the embodiment shown in fig. 5 may perform the method provided in any of the above method embodiments, and the specific implementation principles, technical features, explanation of terms, and technical effects are similar, and are not repeated herein.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 6, the electronic device 600 may include: at least one processor 601 and a memory 602. Fig. 6 shows an electronic device, for example a processor.
A memory 602 for storing programs. In particular, the program may include program code including computer-operating instructions.
The memory 602 may include high-speed RAM memory or may further include non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor 601 is configured to execute computer-executable instructions stored in the memory 602 to implement the methods described in the method embodiments above.
The processor 601 may be a central processing unit (central processing unit, abbreviated as CPU), or an application specific integrated circuit (application specific integrated circuit, abbreviated as ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
Alternatively, the memory 602 may be separate or integrated with the processor 601. When the memory 602 is a device separate from the processor 601, the electronic device 600 may further include:
A bus 603 for connecting the processor 601 and the memory 602. The bus may be an industry standard architecture (industry standard architecture, abbreviated ISA) bus, an external device interconnect (peripheral component, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. Buses may be divided into address buses, data buses, control buses, etc., but do not represent only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 602 and the processor 601 are integrated on a chip, the memory 602 and the processor 601 may complete communication through an internal interface.
The embodiment of the application also provides a vehicle, which comprises any one possible electronic device shown in fig. 6.
Embodiments of the present application also provide a computer-readable storage medium, which may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, and specifically, the computer readable storage medium stores program instructions for the methods in the above method embodiments.
The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the above-described method embodiments.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims (10)

1. A method of reminding a supplemental energy source, comprising:
acquiring historical operation data and current operation data of a vehicle;
preprocessing the historical operation data to determine a clustering sample set;
Circularly calculating the distance between each sample data in the clustered sample set and each cluster line to be selected in a clustered space, wherein the clustered space corresponds to a plurality of preset clustered dimensions; the plurality of preset cluster dimensions includes: charging time and energy surplus; the cluster line to be selected is determined by the charging time, the energy surplus and the dimension weight;
determining at least one clustering line from the to-be-selected clustering lines according to the distances and preset screening requirements, wherein the clustering line is used for representing the driving habit and/or the energy supplementing habit of a user;
determining a preset reminding range according to at least one clustering line and a preset distance threshold value, and judging whether the current operation data is in the preset reminding range or not;
if yes, outputting prompt information to prompt a user to supplement energy for the vehicle;
when the to-be-selected cluster line is a time line corresponding to the charging time, the circularly calculating the distance between each sample data in the clustered sample set and each to-be-selected cluster line in a cluster space includes:
determining a first distance to be selected according to the time coordinate value of each sample data and the moment corresponding to the time line;
Determining a second distance to be selected according to the first distance to be selected and a preset time period;
and selecting the smaller value of the first distance to be selected and the second distance to be selected as the distance.
2. The method of reminding a supplemental energy source according to claim 1, wherein the cluster line comprises: at least one of a straight line, a curved line, and a broken line composed of a plurality of straight line segments and/or curved line segments.
3. The method of reminding a supplemental energy source according to claim 1, wherein the historical operating data comprises: historical values of the energy surplus at each historical moment, wherein the current operation data comprise: and the current value of the energy surplus at the current moment.
4. The method of claim 1, wherein preprocessing the historical operating data to determine a clustered sample set comprises:
calculating the variation of any two adjacent recorded values in the historical operation data;
judging whether the variation meets a preset variation requirement or not;
and when the variation meets the preset variation requirement, adding the previous recorded value in the adjacent twice recorded values into the clustering sample set.
5. The method of reminding supplemental energy according to claim 4, wherein the historical operating data includes a record of a remaining amount of energy of the vehicle at each detection time;
the variation comprises a difference value between a first energy residual record at the previous moment and a second energy residual record at the later moment;
the preset change requirement includes: the difference value is a negative value, and the absolute value of the difference value is larger than or equal to a preset charging threshold value.
6. The method of reminding a user of energy replenishment according to claim 1, wherein a plurality of the candidate cluster lines comprise: a plurality of time lines corresponding to the charging time and a plurality of energy lines corresponding to the energy remaining amount, wherein the time lines and the energy lines are respectively parallel to coordinate axes of the clustering space;
correspondingly, the cluster line includes: the energy charging time cluster center line and the energy remaining quantity cluster center line.
7. A device for alerting a supplemental energy source, comprising:
the acquisition module is used for acquiring historical operation data and current operation data of the vehicle;
a processing module for:
preprocessing the historical operation data by using a preset clustering model to determine a clustering sample set;
Circularly calculating the distance between each sample data in the clustered sample set and each cluster line to be selected in a clustered space, wherein the clustered space corresponds to a plurality of preset clustered dimensions; the plurality of preset cluster dimensions includes: charging time and energy surplus; the cluster line to be selected is determined by the charging time, the energy surplus and the dimension weight;
determining at least one clustering line from the to-be-selected clustering lines according to the distances and preset screening requirements, wherein the clustering line is used for representing the driving habit and/or the energy supplementing habit of a user;
judging whether the current operation data is in a preset reminding range or not according to the current operation data, at least one clustering line and a preset distance threshold;
if yes, outputting prompt information to prompt a user to supplement energy for the vehicle;
when the to-be-selected clustering line is a time line corresponding to the energy charging time, the processing module is specifically configured to determine a first to-be-selected distance according to a time coordinate value of each sample data and a time corresponding to the time line when circularly calculating a distance between each sample data in the clustering sample set and each to-be-selected clustering line in a clustering space; determining a second distance to be selected according to the first distance to be selected and a preset time period; and selecting the smaller value of the first distance to be selected and the second distance to be selected as the distance.
8. An electronic device, comprising:
a processor; the method comprises the steps of,
a memory for storing a computer program of the processor;
wherein the processor is configured to perform the method of reminding of replenishing energy of any one of claims 1 to 6 via execution of the computer program.
9. A vehicle comprising the electronic device of claim 8.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the method of reminding a supplemental energy source according to any of claims 1 to 6.
CN202210134101.2A 2022-02-14 2022-02-14 Method, apparatus, device, medium and program product for reminding of supplementing energy Active CN114463876B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210134101.2A CN114463876B (en) 2022-02-14 2022-02-14 Method, apparatus, device, medium and program product for reminding of supplementing energy

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210134101.2A CN114463876B (en) 2022-02-14 2022-02-14 Method, apparatus, device, medium and program product for reminding of supplementing energy

Publications (2)

Publication Number Publication Date
CN114463876A CN114463876A (en) 2022-05-10
CN114463876B true CN114463876B (en) 2023-12-19

Family

ID=81413153

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210134101.2A Active CN114463876B (en) 2022-02-14 2022-02-14 Method, apparatus, device, medium and program product for reminding of supplementing energy

Country Status (1)

Country Link
CN (1) CN114463876B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2458704A1 (en) * 2010-11-30 2012-05-30 Restore N.V. Method and system for charging a fleet of batteries
DE102016121768A1 (en) * 2015-11-19 2017-05-24 GM Global Technology Operations LLC METHOD AND DEVICE FOR DISTINCTING DRIVERS BASED ON DRIVING BEHAVIOR
WO2018145633A1 (en) * 2017-02-10 2018-08-16 上海蔚来汽车有限公司 Mobile internet based automobile integrated energy supplement system and method and storage medium
CN108447144A (en) * 2017-01-22 2018-08-24 北京嘀嘀无限科技发展有限公司 Oiling reminding method and oiling suggestion device
CN110239559A (en) * 2019-07-02 2019-09-17 绍兴数鸿科技有限公司 Dangerous driving vehicle checking method and device based on new energy car data
CN110375766A (en) * 2019-08-05 2019-10-25 清华大学 Vehicle electric drive Method for Calculate Mileage and vehicle electric drive mileage ratio evaluation method
CN111497856A (en) * 2020-01-14 2020-08-07 北京理工大学 Driving habit recognition and charging management method for electric vehicle user
CN112561548A (en) * 2019-09-25 2021-03-26 上海博泰悦臻电子设备制造有限公司 Method, apparatus and computer-readable storage medium for generating gas station information

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2458704A1 (en) * 2010-11-30 2012-05-30 Restore N.V. Method and system for charging a fleet of batteries
DE102016121768A1 (en) * 2015-11-19 2017-05-24 GM Global Technology Operations LLC METHOD AND DEVICE FOR DISTINCTING DRIVERS BASED ON DRIVING BEHAVIOR
CN108447144A (en) * 2017-01-22 2018-08-24 北京嘀嘀无限科技发展有限公司 Oiling reminding method and oiling suggestion device
WO2018145633A1 (en) * 2017-02-10 2018-08-16 上海蔚来汽车有限公司 Mobile internet based automobile integrated energy supplement system and method and storage medium
CN110239559A (en) * 2019-07-02 2019-09-17 绍兴数鸿科技有限公司 Dangerous driving vehicle checking method and device based on new energy car data
CN110375766A (en) * 2019-08-05 2019-10-25 清华大学 Vehicle electric drive Method for Calculate Mileage and vehicle electric drive mileage ratio evaluation method
CN112561548A (en) * 2019-09-25 2021-03-26 上海博泰悦臻电子设备制造有限公司 Method, apparatus and computer-readable storage medium for generating gas station information
CN111497856A (en) * 2020-01-14 2020-08-07 北京理工大学 Driving habit recognition and charging management method for electric vehicle user

Also Published As

Publication number Publication date
CN114463876A (en) 2022-05-10

Similar Documents

Publication Publication Date Title
WO2021135653A1 (en) Method and system for identifying abnormal stay of vehicle
Gawron et al. Deep decarbonization from electrified autonomous taxi fleets: Life cycle assessment and case study in Austin, TX
CN110969844A (en) Method for calculating driving behavior similarity based on driving data and application
CN112766578A (en) Vehicle use identification method and system based on vehicle network and storage medium
CN115496275A (en) Urban traffic carbon emission prediction method, device, electronic equipment and storage medium
CN113611104A (en) Risk identification method and device for freight vehicle, storage medium and terminal
CN114463876B (en) Method, apparatus, device, medium and program product for reminding of supplementing energy
Suarez et al. Benchmarking the driver acceleration impact on vehicle energy consumption and CO2 emissions
Kim et al. An empirical study on real-time data analytics for connected cars: Sensor-based applications for smart cars
CN115129594A (en) Test scene processing method and device, electronic equipment and storage medium
CN114612011A (en) Risk prevention and control decision method and device
CN113918718A (en) Vehicle insurance user classification method, device, equipment and medium based on artificial intelligence
CN111831630B (en) Data processing method, device, electronic equipment and computer readable storage medium
CN113570004B (en) Riding hot spot area prediction method, device, equipment and readable storage medium
CN107150603A (en) A kind of method and apparatus for the matching degree for improving charging pile and electric car
CN111598275B (en) Electric vehicle credit score evaluation method, device, equipment and medium
CN115099339A (en) Fraud behavior identification method and device, electronic equipment and storage medium
CN114493256A (en) Range extender maintenance method, server and storage medium
CN110070345B (en) Memorandum reminding method based on cloud platform
CN111025043B (en) Method for identifying charging behavior and terminal equipment
Wu et al. Optimization method to improve ecodriving acceptance and effectiveness based on driver type classification
CN113723795B (en) Information delivery strategy testing method and device, storage medium and electronic equipment
Gawron Evaluating the Potential Environmental Impacts of Connected and Automated Vehicles
Malik et al. Enhancing transportation safety: An integrated approach using FLFS and OSNCA for advanced driving behavior analysis
CN115860339A (en) Big data analysis-based rail transit energy consumption management method and related components

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant