CN113377086A - Vehicle power shortage early warning method - Google Patents
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- CN113377086A CN113377086A CN202110732111.1A CN202110732111A CN113377086A CN 113377086 A CN113377086 A CN 113377086A CN 202110732111 A CN202110732111 A CN 202110732111A CN 113377086 A CN113377086 A CN 113377086A
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- 239000000284 extract Substances 0.000 claims description 3
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- 238000012545 processing Methods 0.000 abstract description 5
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- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0267—Fault communication, e.g. human machine interface [HMI]
- G05B23/027—Alarm generation, e.g. communication protocol; Forms of alarm
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Abstract
The invention discloses a vehicle power shortage early warning method, which is characterized in that big data are used for extracting background data of networked vehicle types, the data are classified and summarized, and a wake-up source log system is used for positioning and judging vehicles with dormancy or abnormal wake-up problems, so that the vehicles are difficult to position and need to analyze the present matters; the method comprises the steps that a vehicle with dark current exceeding a normal value or in a low-voltage problem is contacted with a terminal colleague or a visiting user for processing; and all the problems form a power shortage problem tracking table for tracking. According to the method, the abnormal power-lack vehicles are actively discovered by utilizing the big data, the reasons of the abnormal power-lack vehicles are analyzed, the power-lack complaint rate of users is effectively reduced, and the product quality is improved.
Description
Technical Field
The invention belongs to the technical field of vehicle electrical systems.
Background
Along with the development of automobile electromotion and intellectualization, automobile functions are more and more complex, automobile electric control units and software logic strategies are increased, the coupling degree of the electric control units is higher and higher, and the probability of vehicle power shortage is deepened.
Due to the increase of electrification of the whole vehicle and the increase of software quantity, a small software bug of the whole vehicle, coupling error between systems, quality problems of parts and the like, the power shortage of the whole vehicle can be caused, so that extremely bad influence is brought to users, and the public praise of enterprises is extremely bad.
In the traditional power shortage processing mode, after a power shortage vehicle case is received, the battery voltage, the use of a user, the field data capture by using a bus device and the like are confirmed to locate the reason of the whole vehicle.
The traditional treatment method has the problems that:
1. as the types of power deficit increase and go beyond routine knowledge, technicians and maintenance personnel often spend long learning and handling times from analysis to the processing of a problem.
2. The problem of power shortage is treated from problem discovery to problem analysis to problem solution, technical personnel and maintenance personnel are always in a passive and afterknowledge status, and faults are usually not on line after power shortage occurs, so that great trouble is brought to the fundamental location of reasons, complaints of users are increased, great trouble is brought to customers, and great influence is brought to public praise of enterprises.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a vehicle power shortage early warning method, which actively discovers abnormal power shortage vehicles by utilizing big data and analyzes the reasons of the abnormal power shortage vehicles, so that the power shortage complaint rate of users is effectively reduced, and the product quality is improved.
The technical scheme of the invention is as follows:
the invention provides a vehicle power shortage early warning method which comprises the following two parts:
(1) real-time early warning: based on big data of networked vehicle types, a background system extracts data of the networked vehicle types in real time, classifies and summarizes the data, and the robot calculates the big data in real time according to the extracted background data, judges whether vehicles with voltage lower than a set value or with a continuous set time length and without sleep problems exist, and if yes, sends real-time early warning information to an early warning reporting group of the vehicle enterprise project robot and forms a power shortage problem tracking table for tracking the problems; the robot is the sum of background systems of the vehicle enterprises.
(2) Timing early warning: the background system utilizes big data to extract data of nearly 2 days of all networked vehicle types regularly every day, displays vehicles with the problems of continuous non-dormancy, intermittent awakening, dark current exceeding a set value or voltage lower than the set value on a problem handling webpage of a vehicle enterprise, and forms a power shortage problem tracking table for tracking the problems.
Preferably, in the real-time presetting, the voltage is lower than the set value by 10.5V, and the duration of the set time is 3 hours.
Further, the timing early warning method further comprises the steps that a background system distinguishes and displays the data of the networked vehicle models extracted at regular time every day on the problem handling webpage according to projects or different stage states of the projects, and the data are classified and displayed according to VIN, a large area where the vehicle is located, checking time, SOC range, non-dormancy, intermittent awakening, low power-off voltage, low power-on voltage, high dark current and dark current value.
Further, in the timing early warning, for a vehicle which does not sleep for more than 1 hour or has intermittent awakening problem for more than 6 hours continuously, the TUID of the vehicle is obtained through the frame number, and the condition of the vehicle which does not sleep and awakens is inquired through the TUID, so that a controller or a wire harness increasing or switching device and the like which have problems are accurately positioned.
Further, in the timing early warning, the vehicle with the problem that the dark current exceeds 100mA for more than 6 hours is determined to be the condition that the dark current is too large.
Further, in the timing early warning, the vehicle with the problem that the voltage is lower than 10.5V under the condition that the upper electric power system is not started or the voltage is lower than 10.5V under the condition that the power supply gear is OFF is determined to be too low power-on voltage or too low power-OFF voltage.
Further, the method also comprises the steps of synchronizing the vehicle information which is subjected to the power shortage and causes no fire to be fired to a terminal service for disposal, disposing the vehicle information which is about to be subjected to the power shortage through a remote diagnosis system or contacting a terminal colleague for disposal or visiting a user.
Further, the problem needs to be manually extracted and analyzed before it is formed into a power shortage problem tracking table.
The invention has the following advantages:
1. by adopting the method, the background system can analyze the extracted batch vehicle data in real time or periodically, and can directly display the early warning result to the WeChat work group or the webpage, thereby being convenient for related personnel to inquire and use, and effectively fully exposing and closing the problems in the early stage of the project.
2. By adopting the method, the power shortage complaint rate of the user can be effectively reduced, the service quality of the terminal is improved, and the vehicle using satisfaction of the user can be improved.
3. By adopting the method, all networked vehicles can be effectively covered, the problems can be actively found and exposed, the problems can be efficiently positioned, and the problems of adding the automobile data recorder can be effectively judged.
Drawings
FIG. 1 is a process flow diagram of the present invention;
FIG. 2 is an example of a robot warning map as utilized in the present invention;
FIG. 3 is an example of a daily timing extraction of an abnormal web page in accordance with the present invention;
FIG. 4 is an example of a wake source log query graph as utilized by the present invention;
FIG. 5 is an example of a problem tracking table obtained by analyzing data according to the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the drawings.
The general idea of the invention is as follows: the method comprises the steps of extracting networking vehicle type background data by utilizing big data, wherein the data mainly comprises data reflecting the working state of a vehicle, such as voltage, electric quantity, accumulated discharge capacity, power supply state, door lock and the like, and classifying and summarizing the data by utilizing algorithms of preset voltage low, no sleep, abnormal awakening, abnormal accumulated consumed electric quantity and the like. Positioning and judging the vehicles with the problems of dormancy or abnormal awakening by utilizing an awakening source log system, and analyzing the in-situ object which is difficult to position and needs to be positioned; the method comprises the steps that a vehicle with dark current exceeding a normal value or in a low-voltage problem is contacted with a terminal colleague or a visiting user for processing; and all the problems form a power shortage problem tracking table for tracking.
As shown in fig. 1, the vehicle power shortage warning method in the embodiment includes the following two parts:
the first part is real-time early warning: the robot carries out big data calculation according to the extracted background data in real time, judges that the voltage is lower than 10.5V or does not sleep for 3 hours, carries out real-time early warning on the vehicle, is convenient to find and deal with problems in time, and reduces complaints of users.
The method comprises the following steps:
based on the big data of the networked vehicle type, the background system extracts the networked vehicle type data in real time, classifies and summarizes the data, and the robot calculates the big data according to the extracted background data in real time to judge whether the big data exists
If the voltage is lower than the set value by 10.5V or the vehicle does not sleep for 3 hours, the vehicle enterprise XX project robot early warning reporting group sends real-time early warning information to the vehicle enterprise XX project robot early warning reporting group, and the robot can be used for real-time synchronous display in a work group. And meanwhile, the above problems are tracked by forming a power shortage problem tracking table. Fig. 2 is an example of a robot warning map.
The second part is timing early warning, which is to extract all the data of the networked vehicle models in the last two days by utilizing big data every day in a timing manner, and show the data on a webpage, wherein the data do not sleep for more than 1 hour, or intermittently wake up for more than 6 hours, or dark current exceeds 100mA for more than 6 hours, or the voltage is lower than 10.5V under the condition that a power-on power system is not started, or the voltage is lower than 10.5V under the condition that a power gear is OFF. As shown in fig. 3, that is, at 8 am every day, the background actively pulls data of the background for nearly two days, differentiates and displays the vehicle data according to the project or different stage states of the project, and performs classification and display according to VIN, the large area where the vehicle is located, the checking time, the SOC range, the no-sleep state, the intermittent wake-up state, the low power-off voltage, the low power-on voltage, the high dark current and the dark current value.
Further, as shown in fig. 4, in the timing early warning, for a vehicle which does not sleep for more than 1 hour or has intermittent wake-up problem for more than 6 hours, the vehicle machine TUID can be obtained through the frame number, and the non-sleep and wake-up conditions of the vehicle can be inquired through the TUID, so that which controller or wire harness is the problem can be accurately located.
As further shown in fig. 5, closed loop processing of the problem is achieved by tracking the power shortage problem and periodically rechecking to ensure that the problem is actually closed.
The method also comprises the following steps:
and the network management ID which is not dormant is transmitted to a background storage for later reference by the vehicle machine in the 5 th minute after the power is turned OFF to the OFF gear, and the awakened network management ID is uploaded to the background storage for later reference by the vehicle machine.
All nodes participating in direct network management of the whole vehicle need to record the reason of each awakening of the vehicle and follow the reason in the network management ID for future reference, and the vehicle needs to transparently transmit the information to a background for future reference after each awakening.
The current consumption of a period of time is calculated by utilizing the accumulated discharge parameter value of the storage battery sensor, so that the severity and the reason of electric leakage can be analyzed.
Aiming at data phenomena of non-dormancy or abnormal intermittent awakening, an awakening source log system is utilized to inquire the reason of the non-dormancy or the reason of the awakening.
And synchronizing the vehicles which have the power shortage and cause no fire to the terminal service colleagues for handling, and handling or visiting the users for the vehicles which are about to have the power shortage through a remote diagnosis system or contacting the terminal colleagues.
Claims (8)
1. The vehicle power shortage early warning method is characterized by comprising the following two parts:
(1) real-time early warning: based on big data of networked vehicle types, a background system extracts networked vehicle type data in real time, classifies and summarizes the data, a robot calculates in real time according to the extracted background data, judges whether vehicles with the voltage lower than a set value or the continuous set duration and without sleeping are available, if yes, sends real-time early warning information to an early warning reporting group of vehicle enterprise project robots, and forms a power shortage problem tracking table for tracking the problems; the robot is the sum of the background systems of the vehicle enterprises;
(2) timing early warning: the background system utilizes big data to extract data of all networked vehicle types in a timing mode in nearly x days every day, displays vehicles with the problems of continuous non-dormancy, intermittent awakening, dark current exceeding a set value or voltage lower than the set value on a problem handling webpage of a vehicle enterprise, and tracks a power shortage problem tracking table formed by the problems.
2. The vehicle power shortage early warning method according to claim 1, wherein in the real-time early warning, the voltage is lower than the set value and is 10.5V, and the continuous non-sleep time is set to be 3 hours.
3. The vehicle power shortage early warning method according to claim 1, wherein in the timing early warning, a background system displays the data of the networked vehicle types extracted at regular time every day on the problem handling webpage according to different stage states of projects or projects in a distinguishing manner, and performs classified display according to VIN, a large area where the vehicle is located, check time, an SOC range, no dormancy, intermittent awakening, low power-down voltage, low power-up voltage, high dark current and low dark current value.
4. The vehicle power shortage early warning method according to claim 1, wherein in the timing early warning, for a vehicle which does not sleep for more than 1 hour or has intermittent wake-up problem for more than 6 hours continuously, a vehicle TUID is obtained through a frame number, and the vehicle does not sleep and wake-up condition is inquired through the TUID, so that a controller or a wire harness is increased or switched on and off, and the like, which have problems, are accurately positioned.
5. The vehicle power shortage early warning method according to claim 1, wherein in the timing early warning, a vehicle with a problem that the dark current exceeds 100mA for more than 6 hours is determined to be the excessive dark current.
6. The vehicle power shortage warning method according to claim 1, wherein in the timing warning, a vehicle with a problem that the voltage is lower than 10.5V in the case of no start of the upper electric power system or the voltage is lower than 10.5V in the case of power OFF gear is determined as the power-on voltage is too low or the power-OFF voltage is too low.
7. The vehicle power shortage early warning method according to claim 1, further comprising synchronizing vehicle information that a power shortage has occurred to cause a misfire to a terminal service, handling vehicle information of an impending power shortage through a remote diagnosis system or contacting a terminal colleague to handle or return to a user.
8. The vehicle power shortage warning method of claim 1, wherein the problem needs to be manually extracted and analyzed before forming the problem into a power shortage problem tracking table.
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| CN202110732111.1A CN113377086A (en) | 2021-06-29 | 2021-06-29 | Vehicle power shortage early warning method |
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| CN115277746A (en) * | 2022-06-24 | 2022-11-01 | 重庆长安汽车股份有限公司 | Vehicle battery abnormal state real-time monitoring method and system and readable storage medium |
| CN115016429B (en) * | 2022-05-27 | 2024-05-03 | 重庆长安汽车股份有限公司 | Automatic reset method and system for whole vehicle controller network not to sleep |
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Application publication date: 20210910 |