CN113808402A - New energy automobile operation risk classification disposal method based on big data intelligence - Google Patents
New energy automobile operation risk classification disposal method based on big data intelligence Download PDFInfo
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Abstract
According to the new energy automobile operation risk classification processing method based on big data intelligence, warning situations of different levels are respectively processed by classifying warning data to be processed, so that traffic accident processing of the new energy automobile is guaranteed, passive handling is converted into active prevention and control, and the possibility of operation risk of the new energy automobile is greatly reduced; meanwhile, the weight of the low-timeliness four-level alarm is adjusted to be low through the weight adjusting time threshold, the condition with higher priority is prevented from being delayed by a large number of four-level alarms, the technical scheme of the invention is ensured, and the invention has higher practicability.
Description
Technical Field
The invention relates to the technical field of intelligent traffic control, in particular to a new energy automobile operation risk classification disposal method based on big data intelligence.
Background
With the development of new energy automobiles, the accident rate related to the new energy automobiles also increases year by year, wherein the number of new energy automobile accidents reported in 2018, 2019 and 2020 is 1201, 3593 and 4986 respectively. But the traffic accident is not defended by the influence of people, vehicles, roads and environment. According to the national standard requirement of the technical specification (GB/T32960) of the electric vehicle remote service and management system, the new energy vehicle reports the running basic data of the vehicle for no more than 30 seconds at most. As shown in fig. 1, the platform is a big data intelligence-based new energy vehicle operation risk research and judgment platform, each new energy vehicle transmits the operation basic data of the vehicle to a new energy vehicle manufacturing enterprise platform within 30 seconds at most through a vehicle-mounted unit, and the enterprise platform transmits all the new energy vehicle data to the new energy vehicle operation risk research and judgment platform in real time; in the new energy automobile operation risk studying and judging platform, data are received by a data acquisition module and transmitted to a risk pre-judging module for operation risk studying and judging, and are stored in a storage module; the risk judgment module transmits the early warning data to an early warning processing module to determine a processing strategy; how to classify and dispose the risk judgment module and convert the traffic accident correspondence from the existing passive correspondence to the active prevention and control is a problem to be solved urgently.
Disclosure of Invention
In order to convert the correspondence of the traffic accident from passive correspondence to active prevention and control, the invention provides a new energy automobile operation risk classification disposal method based on big data intelligence.
The technical scheme of the invention is as follows: new energy automobile operation risk classification processing method based on big data intelligence is characterized by comprising the following steps:
s1: receiving early warning data to be processed transmitted by a risk judgment module in real time, and storing the early warning data to be processed into an early warning queue;
setting the state corresponding to the early warning data to be processed as open in the early warning queue;
the pre-warning data to be processed comprises: the method comprises the following steps of (1) numbering new energy vehicles, classifying alarm conditions and numbering new energy vehicle operation basic data;
s2: judging the warning situation level of the warning data according to the warning situation classification from the warning queue;
the alert level includes: the first-level alarm condition, the second-level alarm condition, the third-level alarm condition and the fourth-level alarm condition from high to low;
s3: taking out the early warning data to be processed one by one according to a storage time sequence from the early warning queue, and editing early warning information corresponding to the early warning data to be processed:
the early warning information includes: the method comprises the steps of early warning information numbering, warning condition classification, new energy vehicle number plate and warning condition grade;
when the alarm condition level of the taken out early warning data to be processed is a four-level alarm condition, carrying out weight adjustment on the early warning data to be processed; otherwise, real-time step S4;
s4: acquiring positioning data corresponding to the early warning data to be processed based on the new energy vehicle operation basic data number in the early warning data;
s5: according to the warning situation level of the to-be-processed warning data, the processing of the warning information comprises the following steps:
when the alert level is a first-level alert: positioning data corresponding to the early warning data to be processed is used for retrieving accident handlers at the positions of the new energy vehicles, and the early warning information is sent to mobile police terminals of the accident handlers;
when the warning situation level is a secondary warning situation: the positioning data corresponding to the early warning data to be processed is used for retrieving a gate of the new energy automobile, and the early warning information is sent to a police terminal of the gate;
when the warning situation level is a third-level warning situation: positioning data corresponding to the early warning data to be processed, retrieving an audible and visual alarm and a warning screen of the new energy automobile, sending the early warning information to servers of the audible and visual alarm and the warning screen, and warning in an audible and visual mode;
when the alert level is a fourth alert: the method comprises the steps of locating data corresponding to the to-be-processed early warning data, retrieving a traffic guidance screen of the position of the new energy automobile, sending the early warning information to the traffic guidance screen, sending the early warning information to a broadcasting station for broadcasting the traffic information, and notifying the new energy automobile through the traffic guidance screen and the broadcasting station.
It is further characterized in that:
the weight adjusting step includes:
a 1: setting a weight adjustment time threshold;
a 2: comparing that the time for storing the early warning data to be processed in the early warning queue does not exceed the weight adjustment time threshold;
when the time for storing the early warning data to be processed in the early warning queue does not exceed the weight adjustment time threshold, setting the early warning data to be adjustable;
a 3: starting from the adjustable early warning data, searching the subsequent to-be-processed early warning data with the alarm level not being the fourth-level alarm one by one, exchanging the processing sequence of the adjustable early warning data with the subsequently entered to-be-processed early warning data, and executing step S4;
when the step a3 is carried out, a blockage early warning confirmation is carried out;
the blockage early warning confirmation comprises the following steps:
b 1: a blockage warning value N is set,
b 2: when the adjustable early warning data appears and the following conditions are met, counting the blockage early warning value;
the time for searching the subsequent non-four-level alarm condition of the adjustable early warning data exceeds the timing time;
N= N+1；
b3, when non-adjustable early warning data appear, clearing N;
b 4: comparing N with a preset blockage early warning threshold value;
when N is equal to the blockage early warning threshold value, the system generates a blockage early warning alarm and informs manual processing of current data;
b 2-b 4 are executed in a circulating mode;
it also includes the following steps:
s6: after the early warning information is sent out, waiting for early warning feedback information; after the early warning feedback information is received, setting the state corresponding to the early warning data to be processed in the early warning queue as off;
in step S6, a feedback information waiting time threshold is set, after the warning information is sent out, a waiting timer is set for each warning information, and after the waiting timer is greater than the feedback information waiting time threshold, the warning information is sent again; after the early warning feedback information is continuously sent for three times and is not received, marking the early warning information as abnormal data and submitting the abnormal data to a technician for manual confirmation;
the classification of the warning situations in the to-be-processed warning data comprises the following steps: the method comprises the following steps of (1) power loss, insufficient power, vehicle fire, vehicle out of control, rear-end collision accident, unilateral accident, suspected illegal driving behavior, driving in a congestion driving state, driving in a complex landform and blacklisted vehicles;
wherein the primary alert comprises: rear-end accidents, collision accidents, unilateral accidents;
the secondary warning condition comprises: power loss, insufficient power, vehicle fire, vehicle out of control and the suspicion of illegal driving behaviors;
the tertiary alarm condition includes: a blacklisted vehicle appears;
the four-level warning situation comprises: driving in a congested driving state and driving in a complex landform;
the weight adjustment time threshold is set to 5 min.
According to the new energy automobile operation risk classification processing method based on big data intelligence, warning situations of different levels are respectively processed by classifying warning data to be processed, so that traffic accident processing of the new energy automobile is guaranteed, passive handling is converted into active prevention and control, and the possibility of operation risk of the new energy automobile is greatly reduced; meanwhile, the weight of the low-timeliness four-level alarm is adjusted to be low through the weight adjusting time threshold, the condition with higher priority is prevented from being delayed by a large number of four-level alarms, the technical scheme of the invention is ensured, and the invention has higher practicability.
Drawings
FIG. 1 is a schematic diagram of an overall module structure of a new energy automobile operation risk research and judgment platform;
FIG. 2 is a schematic structural diagram of an early warning handling sub-module;
fig. 3 is a flowchart of a new energy vehicle operation risk classification processing method.
Detailed Description
According to the national standard requirement of technical specification of electric vehicle remote service and management system (GBT-32960), the operation basic data uploaded by the new energy vehicle comprises the following steps: vehicle data, positioning data, alarm data and vehicle alarm level.
The alarm system with the vehicle alarm grade as the vehicle is based on vehicle operation data, such as: according to the comprehensive fault judgment results of the faults of the parts such as the motor, the battery and the DC, the CAN network fault of the whole vehicle and the fault of the VCU; the whole vehicle alarm level comprises: and indicating a 0-level fault, a 1-level fault, a 2-level fault and a 3-level fault with gradually-increased fault levels.
Wherein the vehicle state comprises starting, flameout, abnormity and invalidation; the running state comprises charging, running, stopping state, abnormity and invalidation; the operation modes comprise pure electric, hybrid, fuel, abnormal and invalid; the range of the effective value of the vehicle speed is 0-2200 (representing 0-220 km/h), and the minimum metering unit is 0.1 km/h; the range of the effective mileage value is 0-9999999 (representing 0-999999.9 km), and the minimum metering unit is 0.1 km; the effective value range of the total voltage is 0-10000 (representing 0-1000V), the minimum metering unit: 0.1V; the effective value range of the total current is 0-20000 (offset 1000A, representing-1000A- + 1000A), the minimum metering unit: 0.1A; the SOC effective value range is 0-100 (representing 0% -100%), and the minimum metering unit is 1%; the DC-DC state comprises working, disconnecting, abnormal and invalid.
The positioning data includes positioning status, longitude, latitude, speed, direction, etc. Wherein, the positioning state comprises effective positioning, ineffective positioning, north latitude, south latitude, east longitude and west longitude; latitude values whose longitude is in degrees are multiplied by 10 to the power of 6 to the nearest millionth; latitude values in degrees of latitude are multiplied by the power of 6 of 10 to the nearest millionth; the effective speed value range is 0-2200 (representing 0-220 km/h), the minimum metering unit: 0.1 km/h; range of directional effective values: 0 to 359, true north is 0, clockwise.
The alarm data includes: faults of a direct current converter, faults of a power battery, faults of an electric control and faults of a driving motor; the direct current converter faults comprise a DC-DC temperature alarm, a DC-DC state alarm and a high-voltage interlocking state alarm.
The power battery faults include: the method comprises the following steps of single battery overvoltage, single battery undervoltage, battery high temperature, SOC overhigh, SOC overlow, SOC jump, unmatched rechargeable energy storage system, vehicle-mounted energy storage device type undervoltage, vehicle-mounted energy storage device type overcharge, vehicle-mounted energy storage device type overvoltage and poor consistency of the power storage battery.
The electric control fault comprises the following steps: there are insulation alarm and brake system alarm.
The drive motor failure includes: the temperature alarm of the driving motor and the temperature alarm of the driving motor controller are provided.
As shown in fig. 1, the basic data is sent to the new energy vehicle operation risk research and judgment platform via the enterprise platform and stored in the storage module. The risk pre-judging module is used for studying and judging risks which may occur in the running process of the new energy automobile based on running basic data of the new energy automobile and by combining a digital map of a city, traffic management data of a traffic management platform and real-time weather data to obtain to-be-processed early warning data of the new energy automobile with running risks; as shown in fig. 2, the risk pre-judging module sends the pre-warning data to be processed to an alarm notification generation module in the alarm notification generation module, and performs a subsequent new energy vehicle operation risk classification processing method based on big data intelligence, as shown in fig. 3, specifically including the following steps.
S1: the warning condition notification generation module receives the to-be-processed early warning data transmitted by the risk judgment module in real time and stores the to-be-processed early warning data into an early warning queue;
setting the state corresponding to the early warning data to be processed as open in the early warning queue;
the to-be-processed early warning data comprises: the new energy vehicle number plate, the warning condition classification and the new energy vehicle operation basic data serial number.
S2: judging the warning situation level of the warning data according to the warning situation classification from the warning queue;
the alert level includes: the first-level alarm condition, the second-level alarm condition, the third-level alarm condition and the fourth-level alarm condition from high to low;
in this embodiment, after risk studying and judging in the risk pre-judging module, the classification of the alert condition in the to-be-processed alert data sent to the alert handling module includes: the method comprises the following steps of (1) power loss, insufficient power, vehicle fire, vehicle out of control, rear-end collision accident, unilateral accident, suspected illegal driving behavior, driving in a congestion driving state, driving in a complex landform and blacklisted vehicles;
wherein, first-order alert condition includes: rear-end accidents, collision accidents, unilateral accidents;
the second-level warning situation comprises: power loss, insufficient power, vehicle fire, vehicle out of control and the suspicion of illegal driving behaviors;
the tertiary warning situation includes: a blacklisted vehicle appears;
the four-level warning situation comprises: driving in a congested driving state and driving in a complex landform.
S3: processing the early warning data to be processed in an early warning queue in a first-in first-out principle, and ensuring that the problem of delayed warning situation processing is not delayed;
taking out the early warning data to be processed one by one according to the storage time sequence, and editing early warning information corresponding to the early warning data to be processed:
the early warning information includes: the method comprises the steps of early warning information numbering, warning condition classification, new energy vehicle number plate and warning condition grade;
when the warning condition level of the taken out to-be-processed warning data is a fourth warning condition, carrying out weight adjustment on the to-be-processed warning data; otherwise, real-time step S4;
the weight adjusting step comprises:
a 1: setting a weight adjustment time threshold;
a 2: comparing that the time for storing the early warning data to be processed in the early warning queue does not exceed the weight adjustment time threshold;
when the time for storing the early warning data to be processed in the early warning queue does not exceed the weight adjustment time threshold, setting the early warning data to be adjustable;
a 3: starting from the adjustable early warning data, searching the subsequent to-be-processed early warning data with the alarm level not being the fourth-level alarm one by one, and exchanging the processing sequence of the adjustable early warning data with the subsequently entered to-be-processed early warning data;
step S4 is executed.
If 100 pieces of data are stored in the early warning queue according to the entering time sequence, when the 10 th piece of data is processed according to the first-in first-out principle, the 10 th piece of early warning data to be processed is in a four-stage warning condition, the storage time of the 10 th piece of data in the early warning queue is set to be 3min, and the weight adjusting time threshold is set to be 5min, and then the weight adjusting time threshold is set to be adjustable early warning data; starting to search from the 11 th data, setting the 20 th data as a non-four-level alarm condition, setting the serial number of the adjustable early warning data as 20, setting the original 20 th data as the 10 th data, and executing the step S4 on the 10 th data after replacement;
in order to avoid system loop death caused by urban heavy traffic congestion, in the implementation process of a3, a timer is set, the timing time is set as a weight adjustment time threshold, when the time for searching for subsequent non-fourth-level warning situations by the adjustable early warning data exceeds the timing time, the implementation of step a3 is stopped, and the implementation of step S4 is directly performed.
Meanwhile, a blockage early warning value N and a blockage early warning threshold value are set, wherein in the embodiment, the blockage early warning threshold value is set to be 3;
when non-adjustable early warning data appear, resetting N;
when adjustable early warning data appear and the following conditions are met, counting the blockage early warning value;
the time for searching the subsequent non-four-level warning condition of the adjustable early warning data exceeds the timing time.
Namely: when 3 pieces of adjustable early warning data continuously exist and the time for searching the subsequent non-four-stage warning condition of the 3 pieces of adjustable early warning data exceeds the timing time, generating an alarm for blocking early warning by the system and informing the system of manually processing the current data; in the technical scheme of the invention, the processing weight of the four-level warning situations such as congested driving state driving, complex landform driving and the like with less urgent processing time is reduced by adjusting the weight of the time threshold value, so that the situation that the processing of other warning situations is obstructed by a large number of four-level warning situations is avoided, and the technical scheme of the invention is ensured to have higher practicability.
S4: and acquiring positioning data corresponding to the early warning data to be processed based on the new energy vehicle operation basic data number in the early warning data.
S5: according to the warning situation level of the warning data to be processed, the processing of the warning information is as follows. When the system is specifically implemented, the early warning information is sent to the accident handling terminal based on the traffic management command platform on the basis of the warning condition transmission module in a network mode and the like.
In this embodiment, the accident handling terminal includes: the mobile police terminal, the police car vehicle-mounted terminal, the audible and visual alarm, the warning screen, the traffic guidance screen and the radio station are realized on the basis of the prior art.
When the alert level is a first-level alert: positioning data corresponding to the early warning data to be processed, searching accident handlers at the positions of the new energy vehicles, and sending early warning information to mobile police terminals of the accident handlers; when the new energy automobile rear-end collision accident, the single-side accident and other traffic accidents are found or received, the first-level response of the alarm is started immediately, and the accident handling personnel are dispatched to the accident site for rapid handling.
When the warning situation level is a second-level warning situation: the method comprises the steps that positioning data corresponding to early warning data to be processed are searched for a gate where a new energy automobile is located, and early warning information is sent to a police terminal of the gate; when running risks such as power loss, insufficient power, vehicle runaway and the like of a new energy automobile on an expressway or an urban expressway are found, starting an alarm condition primary response in the first time, dispatching a road patrol policeman to rush to the driving front of a vehicle, and guiding the vehicle to rapidly get off the expressway or the urban expressway; or when potential hidden dangers of the vehicle, such as power loss, insufficient power, fire out-of-control and the like, caused by abnormal vehicle state, abnormal running state, disconnection or abnormality of a DC-DC state and power battery failure are found, starting an alarm secondary response, and dispatching a bayonet guard duty policeman in front of the vehicle to intercept the vehicle; and starting an alert secondary response to the new energy automobile with illegal behaviors such as 'behaviors obstructing driving, giving way according to regulations, violating traffic signals, speeding, improper control, misoperation, drunk driving or drunk driving', and dispatching the vehicle to go ahead to a checkpoint guard police and police investigation vehicle.
When the warning situation level is a third-level warning situation: positioning data corresponding to the early warning data to be processed, retrieving an audible and visual alarm and a warning screen of the new energy automobile, sending early warning information to a server of the audible and visual alarm and the warning screen, and warning in an audible and visual mode; after receiving the notice of the blacklist of the new energy automobile with the potential safety hazard or entering the blacklist of the new energy automobile with illegal behaviors such as driving obstructing behaviors, giving way according to regulations, violating traffic signals, speeding, improper control, misoperation, drunk driving or drunk driving, the warning three-level response is started, and warning informing services are carried out on the blacklist of the new energy automobile in a mode of sound, light, electricity, vision and graph at the necessary place of the blacklist of the new energy automobile.
When the alert level is a fourth alert level: the method comprises the steps that positioning data corresponding to early warning data to be processed are searched, a traffic guidance screen of the position of a new energy automobile is searched, early warning information is sent to the traffic guidance screen and is sent to a broadcasting station for broadcasting traffic information, and notification is carried out through the traffic guidance screen and the broadcasting station; the method comprises the following steps that (1) for new energy automobiles driven in congested driving states such as peak hours and midday hours on duty, an alert four-stage response is started, and safety prompt is conducted on operating new energy automobiles such as renting, public transportation, freight transportation, network contract and leasing in modes such as a traffic guidance screen and traffic broadcasting; or for the new energy automobile in the complex landform driving states such as an extreme natural environment, a complex road condition, an abnormal traffic state and the like, because of the three-electricity performance vulnerability, the four-stage warning response is started in the place where the new energy automobile is present, and the driver of the new energy automobile is reminded to pay attention to the safety of the automobile and the driving safety.
S6: sending out early warning information based on the existing traffic management command platform through an alarm condition transmission module, and then waiting for early warning feedback information; after the early warning feedback information is received through the feedback receiving module, setting the state corresponding to the early warning data to be processed as off in an early warning queue;
meanwhile, in order to avoid errors in data transmission, in the technical scheme of the invention, a feedback information waiting time threshold is set in a feedback receiving module, when the early warning information is sent out, waiting timing is set for each early warning information, and when the waiting timing is greater than the feedback information waiting time threshold, the early warning information is sent again; after the early warning feedback information is continuously sent for three times and is not received, the early warning information is marked as abnormal data and submitted to a technician for manual confirmation, so that the phenomenon that the warning data is lost due to the unexpected conditions such as line problems is avoided.
Claims (7)
1. New energy automobile operation risk classification processing method based on big data intelligence is characterized by comprising the following steps:
s1: receiving early warning data to be processed transmitted by a risk judgment module in real time, and storing the early warning data to be processed into an early warning queue;
setting the state corresponding to the early warning data to be processed as open in the early warning queue;
the pre-warning data to be processed comprises: the method comprises the following steps of (1) numbering new energy vehicles, classifying alarm conditions and numbering new energy vehicle operation basic data;
s2: judging the warning situation level of the warning data according to the warning situation classification from the warning queue;
the alert level includes: the first-level alarm condition, the second-level alarm condition, the third-level alarm condition and the fourth-level alarm condition from high to low;
s3: taking out the early warning data to be processed one by one according to a storage time sequence from the early warning queue, and editing early warning information corresponding to the early warning data to be processed:
the early warning information includes: the method comprises the steps of early warning information numbering, warning condition classification, new energy vehicle number plate and warning condition grade;
when the alarm condition level of the taken out early warning data to be processed is a four-level alarm condition, carrying out weight adjustment on the early warning data to be processed; otherwise, real-time step S4;
s4: acquiring positioning data corresponding to the early warning data to be processed based on the new energy vehicle operation basic data number in the early warning data;
s5: according to the warning situation level of the to-be-processed warning data, the processing of the warning information comprises the following steps:
when the alert level is a first-level alert: positioning data corresponding to the early warning data to be processed is used for retrieving accident handlers at the positions of the new energy vehicles, and the early warning information is sent to mobile police terminals of the accident handlers;
when the warning situation level is a secondary warning situation: the positioning data corresponding to the early warning data to be processed is used for retrieving a gate of the new energy automobile, and the early warning information is sent to a police terminal of the gate;
when the warning situation level is a third-level warning situation: positioning data corresponding to the early warning data to be processed, retrieving an audible and visual alarm and a warning screen of the new energy automobile, sending the early warning information to servers of the audible and visual alarm and the warning screen, and warning in an audible and visual mode;
when the alert level is a fourth alert: the method comprises the steps of locating data corresponding to the to-be-processed early warning data, retrieving a traffic guidance screen of the position of the new energy automobile, sending the early warning information to the traffic guidance screen, sending the early warning information to a broadcasting station for broadcasting the traffic information, and notifying the new energy automobile through the traffic guidance screen and the broadcasting station.
2. The new energy automobile operation risk classification disposal method based on big data intelligence is characterized by comprising the following steps of: the weight adjusting step includes:
a 1: setting a weight adjustment time threshold;
a 2: comparing that the time for storing the early warning data to be processed in the early warning queue does not exceed the weight adjustment time threshold;
when the time for storing the early warning data to be processed in the early warning queue does not exceed the weight adjustment time threshold, setting the early warning data to be adjustable;
a 3: and starting from the adjustable early warning data, searching the subsequent to-be-processed early warning data with the alarm level not being the fourth-level alarm one by one, exchanging the processing sequence of the adjustable early warning data with the subsequently entered to-be-processed early warning data, and executing the step S4.
3. The new energy automobile operation risk classification disposal method based on big data intelligence is characterized by comprising the following steps of: when the step a3 is carried out, a blockage early warning confirmation is carried out;
the blockage early warning confirmation comprises the following steps:
b 1: a blockage warning value N is set,
b 2: when the adjustable early warning data appears and the following conditions are met, counting the blockage early warning value;
the time for searching the subsequent non-four-level alarm condition of the adjustable early warning data exceeds the timing time;
N= N+1；
b3, when non-adjustable early warning data appear, clearing N;
b 4: comparing N with a preset blockage early warning threshold value;
when N is equal to the blockage early warning threshold value, the system generates a blockage early warning alarm and informs manual processing of current data;
b 2-b 4 are executed in a loop.
4. The new energy automobile operation risk classification disposal method based on big data intelligence is characterized by comprising the following steps of: it also includes the following steps:
s6: after the early warning information is sent out, waiting for early warning feedback information; and after the early warning feedback information is received, setting the state corresponding to the early warning data to be processed in the early warning queue as off.
5. The new energy automobile operation risk classification disposal method based on big data intelligence is characterized by comprising the following steps of: in step S6, a feedback information waiting time threshold is set, after the warning information is sent out, a waiting timer is set for each warning information, and after the waiting timer is greater than the feedback information waiting time threshold, the warning information is sent again; after the early warning feedback information is continuously sent for three times and is not received, the early warning information is marked as abnormal data and submitted to a technician for manual confirmation.
6. The new energy automobile operation risk classification disposal method based on big data intelligence is characterized by comprising the following steps of: the classification of the warning situations in the to-be-processed warning data comprises the following steps: the method comprises the following steps of (1) power loss, insufficient power, vehicle fire, vehicle out of control, rear-end collision accident, unilateral accident, suspected illegal driving behavior, driving in a congestion driving state, driving in a complex landform and blacklisted vehicles;
wherein the primary alert comprises: rear-end accidents, collision accidents, unilateral accidents;
the secondary warning condition comprises: power loss, insufficient power, vehicle fire, vehicle out of control and the suspicion of illegal driving behaviors;
the tertiary alarm condition includes: a blacklisted vehicle appears;
the four-level warning situation comprises: driving in a congested driving state and driving in a complex landform.
7. The new energy automobile operation risk classification disposal method based on big data intelligence is characterized by comprising the following steps of: the weight adjustment time threshold is set to 5 min.
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| CN113808402B (en) | 2022-05-03 |
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