CN115122933A - Electric automobile standing abnormity identification method and device - Google Patents
Electric automobile standing abnormity identification method and device Download PDFInfo
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- CN115122933A CN115122933A CN202211009789.8A CN202211009789A CN115122933A CN 115122933 A CN115122933 A CN 115122933A CN 202211009789 A CN202211009789 A CN 202211009789A CN 115122933 A CN115122933 A CN 115122933A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
Abstract
The invention discloses a method and a device for identifying standing abnormity of an electric automobile. The electric automobile standing abnormity identification method comprises the following steps: acquiring data information in a preset time period, wherein the data information comprises an uploading time point and basic data information when the data is uploaded; acquiring vehicle data change information according to the data information of every two adjacent uploading time points; acquiring vehicle data change information meeting a condition to be judged as standing data to be judged; and judging whether the vehicle belongs to an abnormally used vehicle or not according to the standing data to be judged. By the electric automobile standing abnormity identification method, the use data uploaded to the cloud end by the vehicle can be identified on the basis of not increasing hardware, whether the vehicle has a standing abnormity behavior or not is judged, the user is reminded of the standing abnormity behavior of the electric automobile in real time, the service life of a battery is prolonged, and the driving range of the vehicle is increased.
Description
Technical Field
The application relates to the technical field of electric automobiles, in particular to an electric automobile standing abnormity identification method and an electric automobile standing abnormity identification device.
Background
At present, the preservation quantity of electric automobiles is increased year by year, the use and maintenance methods of the electric automobiles are different from those of traditional automobiles, a power battery is a core component of the electric automobiles, and the problems of battery overdischarge caused by self-discharge and abnormal discharge, battery calendar life attenuation caused by long-time standing and the like can cause the service life of the battery to be reduced and the driving range to be reduced; the traditional method mostly depends on an instruction manual to guide a client, but the thinking of driving the traditional vehicle by the client cannot be changed in a short time, and the problem of service life attenuation of the battery caused by standing of the electric vehicle is not understood and is not considered; most vehicle enterprises only detect and judge whether the battery system is abnormal or failed, but do not monitor the influence of the abnormal standing behavior of the electric vehicle on the battery system, so that the abnormal state of the battery system cannot be reminded, and a driver cannot be informed of possible failure problems in advance to take protective measures, thereby avoiding the occurrence of driving safety problems and failures.
Disclosure of Invention
The invention aims to provide a method for identifying the standing abnormity of the electric automobile, which solves at least one technical problem.
The invention provides the following scheme:
according to one aspect of the invention, an electric vehicle standing abnormity identification method is provided, and comprises the following steps:
acquiring data information in a preset time period, wherein the data information comprises an uploading time point and basic data information when the data is uploaded;
acquiring vehicle data change information according to the data information of every two adjacent uploading time points;
acquiring vehicle data change information meeting the conditions to be judged as standing data to be judged;
and judging whether the vehicle belongs to an abnormally used vehicle or not according to the standing data to be judged.
Optionally, the acquiring the vehicle data change information meeting the condition to be determined as the data to be determined to be left includes: the conditions to be judged comprise long-time standing judgment conditions and self-discharge judgment conditions;
acquiring vehicle data change information which meets the long-time standing judgment condition in the vehicle data change information as long-time information to be judged or acquiring vehicle data change information which meets the self-discharge judgment condition in the vehicle data change information as self-discharge information to be judged;
judging the information to be judged for long time and the information to be judged for self-discharge by a standing abnormity judging method, judging whether the information to be judged belongs to abnormal use data, if so, judging whether the information to be judged belongs to abnormal use data
And judging that the vehicle belongs to an abnormal use vehicle.
Optionally, the vehicle data change information includes the upload start time point, the upload end time point, the vehicle mileage data, and the vehicle basic information;
the acquiring of the vehicle data change information satisfying the long-time standing judgment condition in the vehicle data change information as the long-time information to be judged includes:
acquiring the time length of vehicle data change information according to the uploading starting time point and the uploading ending time point;
and judging whether vehicle mileage data of the vehicle data change information with the time length of the vehicle data change information being greater than or equal to a first time threshold value and greater than or equal to the first time threshold value is smaller than the first mileage threshold value in each piece of vehicle data change information, if so, acquiring the data as long-time information to be judged.
Optionally, the vehicle data change information further includes charge state information;
the acquiring of the vehicle data change information satisfying the self-discharge judgment condition in the vehicle data change information as the self-discharge information to be judged includes:
judging whether the charging state information of the vehicle data change information is in a non-charging state or whether the time length of the vehicle data change information exceeds a second time threshold value in each piece of vehicle data change information, if so, judging that the charging state information of the vehicle data change information is in a non-charging state or the time length of the vehicle data change information exceeds the second time threshold value
Judging whether the vehicle data change information simultaneously meets the following conditions:
the vehicle mileage data of the vehicle data change information is greater than or equal to zero and smaller than a second preset mileage threshold, and the charge state information of the vehicle mileage data judges that the residual electric quantity is not increased;
and if the vehicle data change information simultaneously meets the condition, acquiring the vehicle data change information as self-discharge information to be judged.
Optionally, the acquiring, as the self-discharge to-be-determined information, vehicle data change information that satisfies the self-discharge determination condition in the vehicle data change information further includes:
and acquiring data meeting a third preset condition in the self-discharge information to be judged as self-discharge sub-window data to be judged.
Optionally, the determining, by the stationary abnormal determining method, the information to be determined for a long time, and determining whether the information to be determined belongs to the abnormal usage data includes:
extracting total data characteristics according to basic data information in the data information within a preset time period;
extracting long-time standing characteristics according to an uploading starting time point, an uploading ending time point, vehicle mileage data and vehicle basic information of the long-time information to be judged;
obtaining a trained long-time standing abnormity judgment classifier;
inputting the total data characteristics and the long-time standing characteristics into the long-time standing abnormity judging classifier so as to obtain long-time standing judging classification labels, wherein the long-time standing judging classification labels comprise long-time standing abnormity labels, and if the classification labels are the long-time standing abnormity labels, the classification labels are classified
And judging that the vehicle belongs to an abnormal use vehicle.
Optionally, the determining, by the static anomaly determination method, the to-be-determined-self-discharge information, and determining whether the to-be-determined information belongs to the abnormal usage data includes:
extracting self-discharge characteristics according to an uploading starting time point, an uploading ending time point, vehicle mileage data and vehicle basic information of self-discharge information to be judged;
extracting the data characteristics of the sub-window according to the basic information of the vehicle in the sub-window data to be judged by self-discharge;
acquiring a trained self-discharge abnormity judgment classifier;
inputting the self-discharge characteristics and the sub-window data characteristics into the self-discharge abnormity judgment classifier so as to obtain a self-discharge judgment classification label, wherein the self-discharge judgment classification label comprises a self-discharge abnormity label, and if the classification label is the self-discharge abnormity label, the self-discharge abnormity label is obtained
And judging that the vehicle belongs to an abnormal use vehicle.
Optionally, the electric vehicle standing abnormality identification method further includes:
and when the vehicle is judged to be abnormally used, generating an alarm signal and transmitting the alarm signal to the vehicle for alarming.
Optionally, the electric vehicle standing abnormality identification method further includes:
acquiring a disposal database, wherein the disposal database comprises long-time standing abnormal disposal suggestion information and self-discharge abnormal disposal suggestion information;
when the long-time standing abnormal label is judged, transmitting the long-time standing abnormal handling suggestion information to a vehicle;
and when the self-discharge abnormal label is judged, transmitting the self-discharge abnormal handling suggestion information to the vehicle.
The application also provides an electric automobile unusual recognition device that stews, includes:
the data information acquisition module is used for acquiring data information in a preset time period, and the data information comprises an uploading time point and basic data information when the data is uploaded;
the vehicle data change information acquisition module is used for acquiring vehicle data change information according to the data information of every two adjacent uploading time points;
the judging module is used for acquiring vehicle data change information meeting the conditions to be judged as standing data to be judged;
and the vehicle abnormity judgment module is used for judging whether the vehicle belongs to an abnormally used vehicle according to the standing data to be judged.
Compared with the prior art, the method has the following advantages:
by the electric vehicle standing abnormity identification method, on the basis of not increasing hardware, whether the vehicle has the problems of self-discharge, battery overdischarge caused by abnormal discharge, battery calendar life attenuation caused by long-time standing and the like can be identified according to the use data uploaded to the cloud end by the vehicle, so that the user is reminded of the electric vehicle standing abnormity behavior, the speed of performance reduction of the battery along with the influence of the calendar life is reduced, the service life of the battery is prolonged, and the driving range of the vehicle is increased; in addition, the method can also prejudge the possible hidden danger of the vehicle by identifying the static abnormal behavior of the electric vehicle, and inform the driver of the possible hidden danger in advance for maintenance, thereby preventing accidents caused by the failure of the battery system in the driving process of the vehicle.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of an electric vehicle standing anomaly identification method according to an embodiment of the present application;
fig. 2 is a schematic structural view of an electric vehicle static abnormality recognition apparatus according to another embodiment of the present application;
FIG. 3 is a system architecture diagram of an electronic device;
fig. 4 is a schematic diagram of an electric vehicle static anomaly identification method according to an embodiment of the present application.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of an electric vehicle standing abnormality identification method according to an embodiment of the present application.
The method for identifying the standing abnormity of the electric automobile shown in the figure 1 comprises the following steps:
step 1: acquiring data information in a preset time period, wherein the data information comprises an uploading time point and basic data information when the data is uploaded;
step 2: acquiring vehicle data change information according to the data information of every two adjacent uploading time points;
and step 3: acquiring vehicle data change information meeting the conditions to be judged as standing data to be judged;
and 4, step 4: and judging whether the vehicle belongs to an abnormal use vehicle or not according to the standing data to be judged. Compared with the prior art, the method has the following advantages:
by the electric vehicle standing abnormity identification method, on the basis of not increasing hardware, whether the vehicle has the problems of self-discharge, battery overdischarge caused by abnormal discharge, battery calendar life attenuation caused by long-time standing and the like can be identified according to the use data uploaded to the cloud end by the vehicle, so that the user is reminded of the electric vehicle standing abnormity behavior, the speed of performance reduction of the battery along with the influence of the calendar life is reduced, the service life of the battery is prolonged, and the driving range of the vehicle is increased; the method can also pre-judge the potential hazards of the vehicle through the identification of the static abnormal behavior of the electric vehicle, and inform a driver of the potential hazards in advance for maintenance, thereby preventing accidents caused by the failure of a battery system in the driving process of the vehicle.
In this embodiment, acquiring the vehicle data change information satisfying the condition to be determined as the data to be determined to be stationary includes:
the conditions to be judged comprise long-time standing judgment conditions and self-discharge judgment conditions;
acquiring vehicle data change information meeting a long-time standing judgment condition in the vehicle data change information as long-time information to be judged or acquiring vehicle data change information meeting a self-discharge judgment condition in the vehicle data change information as self-discharge information to be judged;
judging the information to be judged for a long time and the information to be judged for self-discharge through a standing abnormity judging method, judging whether the information to be judged belongs to abnormal use data, if so, judging whether the information to be judged belongs to abnormal use data
And judging that the vehicle belongs to the abnormal use vehicle.
In this embodiment, the vehicle data change information includes the upload start time point, upload end time point, vehicle mileage data, and vehicle basic information;
the step of acquiring the vehicle data change information satisfying the long-time standing judgment condition in the vehicle data change information as the long-time information to be judged includes:
acquiring the time length of the vehicle data change information according to the uploading starting time point and the uploading ending time point;
and judging whether vehicle mileage data of the vehicle data change information with the time length of the vehicle data change information being greater than or equal to a first time threshold value and greater than or equal to the first time threshold value is smaller than the first mileage threshold value in each piece of vehicle data change information, if so, acquiring the data as long-time information to be judged.
In the present embodiment, the vehicle data change information further includes charge state information;
the step of obtaining the vehicle data change information meeting the self-discharge judgment condition in the vehicle data change information as the self-discharge information to be judged comprises the following steps:
judging whether the charging state information of the vehicle data change information is in a non-charging state or whether the time length of the vehicle data change information exceeds a second time threshold value in each piece of vehicle data change information, if so, judging that the charging state information of the vehicle data change information is in a non-charging state or the time length of the vehicle data change information exceeds the second time threshold value
Judging whether the vehicle data change information simultaneously meets the following conditions:
the vehicle mileage data of the vehicle data change information is greater than or equal to zero and smaller than a second preset mileage threshold, and the charge state information of the vehicle mileage data judges that the residual electric quantity is not increased;
and if the vehicle data change information simultaneously meets the conditions, acquiring the vehicle data change information as self-discharge information to be judged.
In this embodiment, the acquiring the vehicle data change information satisfying the self-discharge determination condition among the vehicle data change information as the self-discharge to-be-determined information further includes:
and acquiring data meeting a third preset condition in the self-discharge information to be judged as self-discharge sub-window data to be judged.
In this embodiment, the determining information to be determined for a long time by a standing anomaly determination method, and determining whether the information to be determined belongs to anomalous usage data includes:
extracting total data characteristics according to basic data information in the data information within a preset time period;
extracting long-time standing characteristics according to an uploading starting time point, an uploading ending time point, vehicle mileage data and vehicle basic information of the long-time information to be judged;
acquiring a trained long-time standing abnormity judgment classifier;
inputting the total data characteristics and the long-time standing characteristics into a long-time standing abnormity judgment classifier so as to obtain a long-time standing judgment classification label, wherein the long-time standing judgment classification label comprises a long-time standing abnormity label, and if the classification label is the long-time standing abnormity label, the long-time standing judgment classification label is obtained
And judging that the vehicle belongs to the abnormal use vehicle.
In this embodiment, the determining the self-discharge information to be determined by the static anomaly determination method, and determining whether the information to be determined belongs to the abnormal usage data includes:
extracting self-discharge characteristics according to an uploading starting time point, an uploading ending time point, vehicle mileage data and vehicle basic information of the self-discharge information to be judged;
extracting the data characteristics of the sub-window according to the basic information of the vehicle in the sub-window data to be judged by self-discharge;
acquiring a trained self-discharge abnormity judgment classifier;
inputting the self-discharge characteristics and the data characteristics of the sub-window into a self-discharge abnormity judgment classifier so as to obtain a self-discharge judgment classification label, wherein the self-discharge judgment classification label comprises a self-discharge abnormity label, and if the classification label is the self-discharge abnormity label, the self-discharge abnormity label is obtained
And judging that the vehicle belongs to the abnormal use vehicle.
In this embodiment, the electric vehicle standing abnormality identification method further includes:
and when the vehicle is judged to be abnormally used, generating an alarm signal and transmitting the alarm signal to the vehicle for alarming.
In this embodiment, the electric vehicle standing abnormality identification method further includes:
acquiring a disposal database, wherein the disposal database comprises long-time standing abnormal disposal suggestion information and self-discharge abnormal disposal suggestion information;
when the long-time standing abnormal label is judged, transmitting the long-time standing abnormal handling suggestion information to the vehicle;
and when the self-discharge abnormal label is judged, transmitting self-discharge abnormal handling suggestion information to the vehicle.
The embodiment also provides an electric automobile unusual recognition device that stews, specifically includes:
the data information acquisition module is used for acquiring data information in a preset time period, wherein the data information comprises an uploading time point and basic data information when the data is uploaded;
the vehicle data change information acquisition module is used for acquiring vehicle data change information according to the data information of every two adjacent uploading time points;
the judging module is used for acquiring vehicle data change information meeting the conditions to be judged as standing data to be judged;
the vehicle abnormity judging module is used for judging whether the vehicle belongs to an abnormally used vehicle according to the standing data to be judged.
It should be noted that, although the present system only discloses the basic function modules such as the data information obtaining module, the vehicle data change information obtaining module, the determining module and the vehicle abnormality determining module, the present device is not limited to the basic function modules, and the present invention is to be expressed in terms of that, on the basis of the basic function modules, one skilled in the art can arbitrarily add one or more function modules in combination with the prior art to form an infinite number of embodiments or technical solutions, that is, the present system is open rather than closed, and the protection scope of the present invention claims is considered to be limited to the basic function modules disclosed above because the present embodiment only discloses individual basic function modules.
In this embodiment, the cloud is configured to perform steps 1 to 4 described above, and transmit the alarm information to the vehicle.
The present application is described in further detail below by way of examples, it being understood that the examples do not constitute any limitation to the present application.
The method for recognizing the standing abnormity of the electric automobile in one embodiment of the application specifically comprises the following steps:
step 1: acquiring data information in a preset time period, wherein the data information is selected in the two-month time period from 2022-1-1 to 2022 year 3-1, and the data information comprises an uploading time point and basic data information when the data is uploaded; for example, in the data information in the two-month period of 3-1 in 2022-1-1 to 2022, the user uses the one-time vehicle No. 2022-1-4, in which the one-time data information (hereinafter, data information a) is uploaded at the time of driving, the one-time data information (hereinafter, data information B) is uploaded at the time of power-off, the one-time vehicle is used at the time of 2022-1-22, the one-time data information (hereinafter, data information C) is uploaded at the time of driving, the one-time data information (hereinafter, data information D) is uploaded at the time of power-off, the one-time data information (hereinafter, data information E) is uploaded at the time of driving, and the one-time data information (hereinafter, data information F) is uploaded at the time of power-off.
And 2, step: acquiring vehicle data change information according to the data information of every two adjacent uploading time points; for example, if the data information a and the data information B are two adjacent uploading time points, the first vehicle data change information is formed according to the information of the data information a and the data information B;
similarly, if the data information B and the data information C are two adjacent uploading time points, second vehicle data change information is formed according to the information of the data information B and the data information C;
if the data information C and the data information D are two adjacent uploading time points, third vehicle data change information is formed according to the information of the data information C and the data information D;
if the data information D and the data information E are two adjacent uploading time points, fourth vehicle data change information is formed according to the information of the data information D and the data information E;
and if the data information E and the data information F are two adjacent uploading time points, forming fifth vehicle data change information according to the information of the data information E and the data information F.
And 3, step 3: specifically, in this embodiment, acquiring the standing data to be determined according to the vehicle data change information includes the following steps:
step 31: obtaining a long-time standing judgment condition and a self-discharge judgment condition;
step 32: acquiring vehicle data change information meeting a long-time standing judgment condition in the vehicle data change information as long-time information to be judged or acquiring vehicle data change information meeting a self-discharge judgment condition in the vehicle data change information as self-discharge information to be judged;
for example, there is a time length of the vehicle data change information greater than or equal to the first time threshold and a mileage change of the vehicle data change information greater than or equal to the first time threshold is less than the first mileage threshold.
In this embodiment, the first time threshold is 7 days, and the first mileage threshold is 0.2 km.
For example, in the above example, it is found that the second vehicle data change information has a time length exceeding 7 days (2022-1-4-2022-1-22, 18 days have elapsed), and it is found from the vehicle mileage data in the second vehicle data change information that the mileage is changed to 0km within the 10 days (for example, the mileage of the data information B is 2236, and the mileage of the data information C is 2236), it is considered that the second vehicle data change information is the vehicle data change information satisfying the long-time standing determination condition, and the vehicle data change information is acquired as the long-time waiting determination information.
By analyzing the above method, it may be found that a plurality of pieces of vehicle data change information satisfy the above-mentioned long-time-standing determination condition, for example, in the present embodiment, there are at least two pieces of vehicle data change information P 1 ，P 2 The information is to be judged for a long time.
Extracting total data characteristics of data information of two months and long-time standing characteristics of information to be judged after long-time standing, specifically, respectively extracting information P to be judged after long-time standing 1 ，P 2 The characteristics of (A) are as follows:
p1 is characterized by:
| attribute name | Attribute interpretation |
| Length of |
10 days |
| Residual |
10％ |
| |
2%/month |
P2 is characterized by:
| attribute name | Attribute interpretation |
| Length of parking | 7 days |
| Residual nuclear power state | 50％ |
| |
2%/month |
The overall data set is characterized by:
inputting the above features into a long-time standing abnormity judgment classifier (which may be generated based on machine learning algorithms classified by Logistic classification algorithm, random forest classification algorithm, Adaboost classification algorithm, and the like), so as to obtain a classification label, for example, if the obtained classification label is a long-time standing abnormity label, the vehicle is considered to belong to an abnormally-used vehicle, at this time, disposal advice information is generated and transmitted to the vehicle, in this embodiment, if the obtained classification label belongs to the long-time standing abnormity label, the disposal policy in this application is: the instrument prompts the vehicle owner to maintain the vehicle or to use the vehicle normally as soon as possible according to the instructions to activate the battery equalization state.
In another embodiment, if the acquired vehicle data change information does not meet the long-time standing judgment condition, it may also be judged whether the vehicle data change information meets the self-discharge judgment condition, and the specific judgment method is as follows:
judging whether the charging state information of the vehicle data change information is in a non-charging state or whether the time length of the vehicle data change information exceeds a second time threshold value in each piece of vehicle data change information, if so, judging that the charging state information of the vehicle data change information is in a non-charging state or the time length of the vehicle data change information exceeds the second time threshold value
Judging whether the vehicle data change information simultaneously meets the following conditions:
the vehicle mileage data of the vehicle data change information is greater than or equal to zero and smaller than a second preset mileage threshold, and the charge state information of the vehicle mileage data judges that the residual electric quantity is not increased;
and if the vehicle data change information simultaneously meets the conditions, acquiring the vehicle data change information as the self-discharge information to be judged.
Acquiring data meeting a third preset condition in the self-discharge information to be judged as self-discharge data to be judged of the sub-window, specifically, acquiring the data of the sub-window by adopting the following method:
screening is carried out in the self-discharge information to be judged, sub-window data are obtained according to entering and ending conditions, and the sub-window data are summarized, in the embodiment, the sub-window is defined as a Space (Space), and the entering and ending conditions of the Space are as follows:
space start: the vehicle speed V is 0;
space end: the vehicle speed V is not 0 or the data ends;
the Space legal condition is as follows: the current I and the power P are in constant states and the mileage is unchanged;
if the above state exists, extracting the lattice and recording the lattice as S 1 ，S 2 ，S 3 ，S 4 ......S n ；
The vehicle state of the sub-window is obtained through the power utilization condition of the sub-window, for example, the standby current, power and DC state of the vehicle, the current, power and DC state of a high-voltage air conditioner, the electric appliance A, the electric appliance B and the like of the vehicle, the power utilization state of the vehicle in the non-driving state is obtained through the permutation and combination, and whether the vehicle belongs to the standby state or not is determined according to the power utilization states.
Referring to fig. 4, fig. 4 is a schematic diagram of a sub-window S1 presented from a self-discharge information to be determined. As can be seen from fig. 4, the sub-window satisfies the following condition:
the sub-window entering condition is that the vehicle speed is 0, the sub-window ending condition is that data is terminated, the power is in a constant state, and the mileage is unchanged.
The method for extracting the self-discharge characteristics of the self-discharge information to be judged and the sub-window data characteristics of the sub-window data specifically comprises the following steps:
the features contained in S1 are as follows:
| attribute name | Attribute interpretation |
| Grid length | The duration of the grid is 48 hours |
| State of electrical equipment | Vehicle standby state |
| Starting electrical quantity | 19％ |
| Complete the |
0％ |
| Variation of electric quantity | 19％ |
The self-discharge characteristics were as follows:
inputting the above features into a self-discharge anomaly judgment classifier (which may be generated based on machine learning algorithms classified by Logistic classification algorithm, random forest classification algorithm, Adaboost classification algorithm, and the like), so as to obtain a classification label, for example, if the obtained self-discharge anomaly label is obtained, the vehicle is considered to belong to an anomalous vehicle, at this time, disposal suggestion information is generated and transmitted to the vehicle, in this embodiment, if the obtained self-discharge anomaly label is the self-discharge anomaly label, the disposal policy in this application is: the instrument prompts the owner of the vehicle that the vehicle needs to be overhauled and please go to the 4S store for inspection as soon as possible.
Fig. 2 is a schematic structural diagram of an electric vehicle standing abnormality recognition apparatus according to an embodiment of the present application;
the electric vehicle stationary abnormality recognition apparatus shown in fig. 2 includes:
the data information acquisition module is used for acquiring data information in a preset time period, and the data information comprises an uploading time point and basic data information when the data is uploaded;
the vehicle data change information acquisition module is used for acquiring vehicle data change information according to the data information of every two adjacent uploading time points;
the judging module is used for acquiring vehicle data change information meeting the conditions to be judged as standing data to be judged;
the vehicle abnormity judging module is used for judging whether the vehicle belongs to an abnormally used vehicle according to the standing data to be judged.
Referring to fig. 3, the present application also provides an electronic device including: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory has stored therein a computer program that, when executed by the processor, causes the processor to execute the steps of the electric vehicle standing abnormality identification method.
The present application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the electric vehicle standing abnormality identification method.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The electronic device includes a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on top of the operating system. The hardware layer includes hardware such as a Central Processing Unit (CPU), a Memory Management Unit (MMU), and a Memory. The operating system may be any one or more computer operating systems that implement control of the electronic device through a Process (Process), such as a Linux operating system, a Unix operating system, an Android operating system, an iOS operating system, or a windows operating system. In the embodiment of the present invention, the electronic device may be a handheld device such as a smart phone and a tablet computer, or an electronic device such as a desktop computer and a portable computer, which is not particularly limited in the embodiment of the present invention.
The execution main body of the electronic device control in the embodiment of the present invention may be an electronic device, or a functional module capable of calling a program and executing the program in the electronic device. The electronic device may obtain the firmware corresponding to the storage medium, the firmware corresponding to the storage medium is provided by a vendor, and the firmware corresponding to different storage media may be the same or different, which is not limited herein. After the electronic device acquires the firmware corresponding to the storage medium, the firmware corresponding to the storage medium may be written into the storage medium, specifically, the firmware corresponding to the storage medium is burned into the storage medium. The process of burning the firmware into the storage medium can be realized by adopting the prior art, and details are not described in the embodiment of the present invention.
The electronic device may further acquire a reset command corresponding to the storage medium, where the reset command corresponding to the storage medium is provided by a vendor, and the reset commands corresponding to different storage media may be the same or different, which is not limited herein.
At this time, the storage medium of the electronic device is a storage medium in which the corresponding firmware is written, and the electronic device may respond to the reset command corresponding to the storage medium in which the corresponding firmware is written, so that the electronic device resets the storage medium in which the corresponding firmware is written according to the reset command corresponding to the storage medium. The process of resetting the storage medium according to the reset command may be implemented in the prior art, and is not described in detail in the embodiment of the present invention.
For convenience of description, the above devices are described as being divided into various units and modules by functions, respectively. Of course, the functions of the units and modules may be implemented in one or more software and/or hardware when the present application is implemented.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For simplicity of explanation, the method embodiments are described as a series of acts or combinations, but those skilled in the art will appreciate that the embodiments are not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments.
The invention discloses a vehicle of an electric vehicle standing abnormity identification device on the basis of electronic equipment and a storage medium corresponding to the electric vehicle standing abnormity identification method and device, and the vehicle specifically comprises the following components:
the electronic equipment is used for realizing the method for identifying the standing abnormity of the electric automobile;
a processor for executing a program, the processor executing the steps of the electric vehicle standing abnormality recognition method executed from data output from the electronic device when the program is executed;
and a storage medium for storing a program that executes the steps of the electric vehicle standing abnormality recognition method for data output from the electronic device when running.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications or substitutions do not depart from the spirit of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. The electric automobile standing abnormity identification method is characterized by comprising the following steps:
acquiring data information in a preset time period, wherein the data information comprises an uploading time point and basic data information when the data is uploaded;
acquiring vehicle data change information according to the data information of every two adjacent uploading time points;
acquiring vehicle data change information meeting a condition to be judged as standing data to be judged;
and judging whether the vehicle belongs to an abnormally used vehicle or not according to the standing data to be judged.
2. The electric vehicle standing abnormality identification method according to claim 1, wherein the acquiring of the vehicle data change information satisfying the condition to be judged as the standing data to be judged includes: the conditions to be judged comprise long-time standing judgment conditions and self-discharge judgment conditions;
acquiring vehicle data change information which meets the long-time standing judgment condition in the vehicle data change information as long-time information to be judged or acquiring vehicle data change information which meets the self-discharge judgment condition in the vehicle data change information as self-discharge information to be judged;
judging the information to be judged for long time and the information to be judged for self-discharge by a standing abnormity judging method, judging whether the information to be judged belongs to abnormal use data, if so, judging whether the information to be judged belongs to abnormal use data
And judging that the vehicle belongs to an abnormal use vehicle.
3. The electric vehicle standing abnormality identification method according to claim 2, wherein the vehicle data change information includes the upload start time point, upload end time point, vehicle mileage data, and vehicle basic information;
the acquiring of the vehicle data change information satisfying the long-time standing judgment condition in the vehicle data change information as the long-time information to be judged includes:
acquiring the time length of vehicle data change information according to the uploading starting time point and the uploading ending time point;
and judging whether vehicle mileage data of the vehicle data change information with the time length of the vehicle data change information being greater than or equal to a first time threshold value and greater than or equal to the first time threshold value is smaller than the first mileage threshold value in each piece of vehicle data change information, if so, acquiring the data as long-time information to be judged.
4. The electric vehicle standing abnormality identification method according to claim 2, wherein the vehicle data change information further includes charge state information;
the acquiring of the vehicle data change information satisfying the self-discharge judgment condition in the vehicle data change information as the self-discharge information to be judged includes:
judging whether the charging state information of the vehicle data change information is in a non-charging state or whether the time length of the vehicle data change information exceeds a second time threshold value in each piece of vehicle data change information, if so, judging that the charging state information of the vehicle data change information is in a non-charging state or the time length of the vehicle data change information exceeds the second time threshold value
Judging whether the vehicle data change information simultaneously meets the following conditions:
the vehicle mileage data of the vehicle data change information is greater than or equal to zero and smaller than a second preset mileage threshold, and the charge state information of the vehicle mileage data judges that the residual electric quantity is not increased;
and if the vehicle data change information simultaneously meets the condition, acquiring the vehicle data change information as self-discharge information to be judged.
5. The electric vehicle standing abnormality identification method according to claim 4, wherein the acquiring of the vehicle data change information satisfying the self-discharge determination condition among the vehicle data change information as the self-discharge information-to-be-determined further includes:
and acquiring data meeting a third preset condition in the self-discharge information to be judged as self-discharge sub-window data to be judged.
6. The electric vehicle standing abnormality identification method according to claim 5, wherein the judging of the long time information to be judged by the standing abnormality judgment method, and the judging whether the information to be judged belongs to the abnormal use data includes:
extracting total data characteristics according to basic data information in the data information within a preset time period;
extracting long-time standing characteristics according to an uploading starting time point, an uploading ending time point, vehicle mileage data and vehicle basic information of the long-time information to be judged;
acquiring a trained long-time standing abnormity judgment classifier;
inputting the total data features and the long-time standing features into the long-time standing abnormity judgment classifier so as to obtain a long-time standing judgment classification label, wherein the long-time standing judgment classification label comprises a long-time standing abnormity label, and if the classification label is the long-time standing abnormity label, the classification label is a long-time standing abnormity label
And judging that the vehicle belongs to an abnormal use vehicle.
7. The electric vehicle standing abnormality identification method according to claim 5, wherein the judging of the self-discharge information to be judged by the standing abnormality judgment method, and the judging whether the information to be judged belongs to the abnormal use data includes:
extracting self-discharge characteristics according to an uploading starting time point, an uploading ending time point, vehicle mileage data and vehicle basic information of the self-discharge information to be judged;
extracting the data characteristics of the sub-window according to the basic information of the vehicle in the sub-window data to be judged by self-discharge;
acquiring a trained self-discharge abnormity judgment classifier;
inputting the self-discharge characteristics and the sub-window data characteristics into the self-discharge abnormity judgment classifier so as to obtain a self-discharge judgment classification label, wherein the self-discharge judgment classification label comprises a self-discharge abnormity label, and if the classification label is the self-discharge abnormity label, the self-discharge abnormity label is obtained
And judging that the vehicle belongs to an abnormal use vehicle.
8. The electric vehicle standing abnormality recognition method according to any one of claims 1 to 7, characterized by further comprising:
and when the vehicle is judged to be abnormally used, generating an alarm signal and transmitting the alarm signal to the vehicle for alarming.
9. The electric vehicle standing abnormality recognition method according to claim 8, further comprising:
acquiring a disposal database, wherein the disposal database comprises long-time standing abnormal disposal suggestion information and self-discharge abnormal disposal suggestion information;
when the long-time standing abnormal label is judged, transmitting the long-time standing abnormal handling suggestion information to a vehicle;
and when the self-discharge abnormal label is judged, transmitting the self-discharge abnormal handling suggestion information to the vehicle.
10. The utility model provides an electric automobile unusual recognition device that stews which characterized in that, electric automobile unusual recognition device that stews includes:
the data information acquisition module is used for acquiring data information in a preset time period, and the data information comprises an uploading time point and basic data information when the data is uploaded;
the vehicle data change information acquisition module is used for acquiring vehicle data change information according to the data information of every two adjacent uploading time points;
the judging module is used for acquiring vehicle data change information meeting the conditions to be judged as standing data to be judged;
and the vehicle abnormity judgment module is used for judging whether the vehicle belongs to an abnormally used vehicle according to the standing data to be judged.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211009789.8A CN115122933A (en) | 2022-08-22 | 2022-08-22 | Electric automobile standing abnormity identification method and device |
| PCT/CN2023/089798 WO2024041003A1 (en) | 2022-08-22 | 2023-04-21 | Electric vehicle parking anomaly identification method and apparatus |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211009789.8A CN115122933A (en) | 2022-08-22 | 2022-08-22 | Electric automobile standing abnormity identification method and device |
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| Publication Number | Publication Date |
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| CN115122933A true CN115122933A (en) | 2022-09-30 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211009789.8A Pending CN115122933A (en) | 2022-08-22 | 2022-08-22 | Electric automobile standing abnormity identification method and device |
Country Status (2)
| Country | Link |
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| CN (1) | CN115122933A (en) |
| WO (1) | WO2024041003A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024041003A1 (en) * | 2022-08-22 | 2024-02-29 | 中国第一汽车股份有限公司 | Electric vehicle parking anomaly identification method and apparatus |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102015200492A1 (en) * | 2015-01-14 | 2016-07-14 | Robert Bosch Gmbh | Method for selecting a parking position for a vehicle |
| CN115691121A (en) * | 2020-10-20 | 2023-02-03 | 支付宝(杭州)信息技术有限公司 | Vehicle stop detection method and device |
| CN214874330U (en) * | 2021-02-03 | 2021-11-26 | 东莞市华锦技术有限公司 | Control system for preventing electric vehicle lead-acid storage battery from being lack of power |
| CN114148216B (en) * | 2021-12-31 | 2023-11-28 | 中国第一汽车股份有限公司 | Method, system, equipment and storage medium for detecting battery self-discharge rate abnormality |
| CN115122933A (en) * | 2022-08-22 | 2022-09-30 | 中国第一汽车股份有限公司 | Electric automobile standing abnormity identification method and device |
-
2022
- 2022-08-22 CN CN202211009789.8A patent/CN115122933A/en active Pending
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2023
- 2023-04-21 WO PCT/CN2023/089798 patent/WO2024041003A1/en unknown
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024041003A1 (en) * | 2022-08-22 | 2024-02-29 | 中国第一汽车股份有限公司 | Electric vehicle parking anomaly identification method and apparatus |
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| WO2024041003A1 (en) | 2024-02-29 |
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