CN116767089B - Method and device for identifying abnormal water temperature of automobile and monitoring alarm - Google Patents
Method and device for identifying abnormal water temperature of automobile and monitoring alarm Download PDFInfo
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Abstract
The application provides a method and a device for identifying and alarming abnormal water temperature of an automobile, comprising the following steps: s1, acquiring all vehicle data transmitted by a vehicle-mounted terminal, and processing the vehicle data to obtain a flash lamp, a cooling liquid temperature, an engine warning lamp state and an engine cover state of an automobile; by monitoring and processing the water temperature data in real time, abnormal conditions can be found in time and an alarm can be provided. And the water temperature data is analyzed and judged by adopting an artificial intelligence algorithm, so that the possibility of misjudgment is reduced. By utilizing the internet of vehicles technology, remote monitoring and data transmission are realized, and convenient remote management and analysis capability is provided. The optimization algorithm module is utilized to carry out threshold iterative adjustment, so that the accuracy and the instantaneity of water temperature monitoring are improved, the water temperature is judged more accurately and comprehensively, abnormal alarms can be timely sent to vehicle owners and maintenance personnel, the timely processing efficiency of faults is improved, and meanwhile, more reliable and efficient judgment rules are provided for a host factory.
Description
Technical Field
The application relates to the technical field of big data processing, in particular to a method for identifying abnormal water temperature of an automobile and monitoring alarm.
Background
In the automotive industry, it is very important to accurately judge and monitor the water temperature of an automobile, which is directly related to the normal operation of an engine and the safety of the automobile. Traditional water temperature warning methods rely primarily on simple temperature sensors to monitor the water temperature and trigger a warning when a set threshold is exceeded. However, this method has certain limitations and cannot provide comprehensive and accurate water temperature judgment and monitoring.
Disclosure of Invention
The technical problem to be solved by the application is to provide the automobile water temperature abnormality identification and alarm monitoring method and device which can provide more reliable and efficient judgment rules for a host factory aiming at the defects of the technical scheme.
In a first aspect, the present application provides a method for identifying and monitoring abnormal water temperature of an automobile, the method comprising the steps of:
s1, acquiring all vehicle data transmitted by a vehicle-mounted terminal, and processing the vehicle data to obtain flash lamp, cooling liquid temperature, engine warning lamp state, engine cover state, safety belt state, left front door state, vehicle speed, accelerator pedal and engine speed data of an automobile;
s2, comparing the coolant temperature of all vehicles with a set coolant temperature alarm threshold value to screen out vehicles with possibly abnormal water temperature, if the water temperature abnormality continuously occurs in the observation period of j minutes, taking the vehicle owner into observation, and marking out the vehicle owner based on the taking observationWindow, and according to->Judging whether the water temperature abnormality exists at the moment of the vehicle owner again through the driving behavior of the vehicle owner in the driving scene of the vehicle in the window;
s3, carrying out threshold iterative adjustment based on a multi-scene linear programming model with a time window to obtain an optimal threshold, carrying out real-time identification of water temperature abnormality based on the adjusted optimal threshold, and carrying out repeated iterative update;
and S4, triggering an alarm mechanism and sending alarm information to the vehicle owner and maintenance personnel based on the real-time identification result in the step S3.
The application relates to a method for identifying and alarming abnormal water temperature of an automobile; in the step S2, the driving scene of the vehicle with abnormal water temperature includes a scene with excessive water temperature during driving and a scene with excessive water temperature during the rest of the vehicle, when the vehicle is in the scene with excessive water temperature during driving, the driving behavior of the vehicle owner is that the vehicle is stopped at a right speed or is turned on for double flashing, or is stopped at an idle speed or is turned on for right or is turned on for double flashing, when the vehicle is in the scene with excessive water temperature during the rest of the vehicle, the driving behavior of the vehicle owner is that an engine warning lamp is turned on, an engine is flameout, an engine cover is turned on, a safety belt is opened, a left front door is opened, and whether the vehicle is idling.
The application relates to a method for identifying and alarming abnormal water temperature of an automobile; in the step S2, the determination step based on the water temperature abnormality of the water temperature excessive scene during traveling and the water temperature excessive scene when the vehicle is stationary is as follows:
s21, performing S21; judgingWhether the temperature of the cooling liquid is continuously abnormal in the time interval or not, if so, the cooling liquid is ordered according to the time interval conditions of j minutes and T minutes in an ascending order according to the time sequence:；
s22, performing S22; acquisition ofThe cooling liquid temperature, the speed of the vehicle, the condition of a flash lamp, whether an engine warning lamp is opened, whether an engine is abnormally flameout, whether an engine cover is opened, whether a safety belt is unfastened and whether a left front door is opened or not are data at corresponding moments;
s23, performing S23; scribing and takingWindow +.>The engine warning lamp is lighted in the time interval, and the number of continuous lighting times of the engine warning lamp is maximum>/>And the number of times the engine warning lamp is on>/>Then it is determined that the water temperature of the vehicle owner is abnormal at the moment, wherein +.>Is->Threshold value of maximum number of continuous lighting of engine warning lamp set in time interval, +.>Is->The number of times the engine warning lamp is on in the time interval; if scratch outWindow +.>When the engine cover is opened in the time interval, the water temperature of the vehicle owner is abnormal at the moment.
The application relates to a method for identifying and alarming abnormal water temperature of an automobile; the step S2 of determining that the water temperature abnormality occurs based on the water temperature scene that is too high during traveling and the water temperature scene that is too high when the vehicle is stationary further includes:
s24, performing S24; if the car owner is atSpeed of vehicle in time interval>Duration of 0->Judging that the vehicle owner is in driving, if the vehicle owner is +.>Speed of vehicle in time interval>Duration of 0->In a resting state, in which +.>Is->Speed set in time intervalA threshold value greater than 0 time period;
s25, performing S25; when the vehicle owner is driving, judging the vehicle ownerAt time intervals, whether the double flash opening and the speed reduction occur or not, and the right parking action is relied on, if the double-flash opening and the deceleration occur and the right parking action is followed, the water temperature abnormality of the car owner at the moment is judged.
S26, performing S26; when the vehicle owner is in a static state, judging the vehicle ownerThe time interval is whether idling occurs, a safety belt is opened, and a car door is opened, if the idling occurs, the safety belt is opened, and the car door is opened, the water temperature of the car owner at the moment is judged to be abnormal, wherein the idling is judged according to the speed, an accelerator pedal and the rotation speed of an engine; based on the generated idling speed, opening the safety belt, and opening the car door, sending alarm information to car owners and maintenance personnel to remind the car owners and maintenance personnel of the abnormal water temperature.
The application relates to a method for identifying and alarming abnormal water temperature of an automobile; in the step S3, a linear programming model with a time window is built to maximize the prediction accuracy in the time window, and consider multiple scene discussions, where the threshold conditions related to each scene are different, specifically, the number of scenes is recorded asThe threshold condition of each feature of each scene is recorded as +.>The time window size is +.>By the variable->Is indicated at +.>Inner reach scene->The number of people, the variable->Is indicated at +.>Inner reach scene->Corresponding car owner->Treated features of->,/>Is indicated at +.>Total number of people in the interior->Is indicated at +.>The prediction accuracy rate in the model; />Is indicated at +.>Inner reach scene->Whether the number of people in the scene satisfies a threshold condition of the scene, wherein +.>Indicates satisfaction of (I)>Indicating that it is not satisfied; />To +.>Whether the interior satisfies the scene->Threshold condition of (2), wherein->Indicates satisfaction of (I)>Indicates that it is not satisfied and that it is required to satisfy the scenario +.>Is conditional on the threshold value of->Requirement in scene->。
The application relates to a method for identifying and alarming abnormal water temperature of an automobile; the following objective function is established according to the variables in the step S3 wherein ,/>Expressed as +.>The sum of the individual scene prediction accuracy reaches a maximum, < >>Indicating total time, at time +.>In-frame, the prediction accuracy is up toTo scene->The ratio of the number of people to the total number of people, wherein the calculation formula of the prediction accuracy is +.>； wherein ,/>Representing the number of scenes.
The application relates to a method for identifying and alarming abnormal water temperature of an automobile; reaching a scene within a time window in said step S3Whether the number of people in the scene satisfies the threshold condition of +.>； wherein ,/>Representation scene->Is set in the above-described state.
The application relates to a method for identifying and alarming abnormal water temperature of an automobile; within a time window in said step S3Whether the sum of the individual scene prediction accuracy reaches the maximum value satisfies the following seven constraint conditions:
wherein the first constraint is that； wherein ,/>；
Wherein the second constraint is that； wherein ,/>；
Wherein the third constraint is that； wherein ,/>；
Wherein the fourth constraint is； wherein ,/>；
Wherein the fifth constraint is thatThe method comprises the steps of carrying out a first treatment on the surface of the Then->The method comprises the steps of carrying out a first treatment on the surface of the Otherwise it isThe method comprises the steps of carrying out a first treatment on the surface of the I.e. < ->The method comprises the steps of carrying out a first treatment on the surface of the Where t=1, 2, … …, T, s=1, 2, S, i=1, 2, … … I, j=1, 2,3,yt；
wherein the sixth constraint is whenThen->Otherwise, it is->，； wherein ,/>；
When (when),/>Then->The method comprises the steps of carrying out a first treatment on the surface of the Otherwise it is 0 and the number of the cells is,； wherein ,/>；
Wherein the seventh constraint is；
Wherein the first constraint isThe method is used for ensuring that the predicted abnormal number is not more than the total number; second constraint->Third constraint->And fourth constraint->For ensuring variable-> and />Is not negative of (2); fifth constraint->For ensuring each timeEvery scene +.>Is not lower than the threshold +.>Not higher than threshold->And introduces the ternary variable +.>To indicate whether a threshold condition is met; sixth constraint->Binary variable +.>To indicate at time +.>Whether the corresponding scenes are met or not is judged, wherein according to the priority, if the scene 1 is met, other scenes are not considered any more, and the number of people in each scene is acquired according to the rule>The mutual exclusion of the people in each scene is achieved; seventh constraint conditionSolving an optimal critical value +.f in a certain time window range by using the seven constraint conditions with the time window for predicting the abnormal number after the weight removal> and />。
In a second aspect, an apparatus for identifying and alarming and monitoring abnormal water temperature of an automobile based on big data of internet of vehicles is provided, which is characterized by comprising data transmission and processingThe system comprises a module, a water temperature abnormality identification module, a threshold iteration optimization module and an alarm module, wherein the data transmission and processing module is used for acquiring all vehicle data transmitted by a vehicle-mounted terminal and processing the vehicle data to obtain data of a flash lamp, a cooling liquid temperature, an engine warning lamp state, an engine cover state, a safety belt state, a left front door state, a vehicle speed, an accelerator pedal and an engine speed of an automobile; the water temperature abnormality identification module is used for comparing the coolant temperature of all vehicles with a set coolant temperature alarm threshold value to screen out vehicles possibly with water temperature abnormality, if water temperature abnormality continuously occurs in an observation period of j minutes, the vehicle owner is taken into observation, and the vehicle owner based on the taken-in observation is drawn outWindow, and according to->Judging whether the water temperature abnormality exists at the moment of the vehicle owner again through the driving behavior of the vehicle owner in the driving scene of the vehicle in the window; the threshold iterative optimization module is used for carrying out threshold iterative adjustment based on a multi-scene linear programming model with a time window to obtain an optimal threshold value, carrying out real-time identification of water temperature abnormality based on the adjusted optimal threshold value, and carrying out repeated iterative update; the alarm module is used for triggering an alarm mechanism and sending alarm information to an owner and maintenance personnel based on the result identified by the threshold iteration optimization module in real time.
The method for identifying and alarming the abnormal water temperature of the automobile can timely discover abnormal conditions and provide alarming by monitoring and processing the water temperature data in real time. And the water temperature data is analyzed and judged by adopting an artificial intelligence algorithm, so that the possibility of misjudgment is reduced. By utilizing the internet of vehicles technology, remote monitoring and data transmission are realized, and convenient remote management and analysis capability is provided. The optimization algorithm module is utilized to carry out threshold iterative adjustment, so that the accuracy and the instantaneity of water temperature monitoring are improved, the water temperature is judged more accurately and comprehensively, abnormal alarms can be timely sent to vehicle owners and maintenance personnel, the timely processing efficiency of faults is improved, and meanwhile, more reliable and efficient judgment rules are provided for a host factory.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of a method for identifying and monitoring abnormal water temperature of an automobile according to the present application;
FIG. 2 is a block diagram illustrating operation of an embodiment of the device for identifying and warning the abnormal water temperature of an automobile according to the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
FIG. 1 is a schematic flow chart of an embodiment of the method for identifying and monitoring abnormal water temperature of an automobile according to the present application. The method for identifying and alarming and monitoring the abnormal water temperature of the automobile comprises the following steps:
in step S1, acquiring all vehicle data transmitted by a vehicle-mounted terminal, and processing the vehicle data to obtain flash lamp, coolant temperature, engine warning lamp state, engine cover state, safety belt state, left front door state, speed, accelerator pedal and engine speed data of an automobile;
in step S2, the coolant temperature of all vehicles is compared with a set coolant temperature alarm threshold to screen out vehicles with possibly abnormal water temperature, if water temperature abnormality occurs continuously in the observation period of j minutes, the vehicle owner is taken into observation, and based on the vehicle owner taken into observation, the vehicle owner is drawn outWindow, and according to->Judging whether the water temperature abnormality exists at the moment of the vehicle owner again through the driving behavior of the vehicle owner in the driving scene of the vehicle in the window;
in step S3, performing iterative adjustment of a threshold value based on a multi-scenario linear programming model with a time window to obtain an optimal threshold value, performing real-time identification of water temperature abnormality based on the adjusted optimal threshold value, and performing iterative update;
in step S4, based on the result of the real-time identification in step S3, an alarm mechanism is triggered and alarm information is sent to the vehicle owner and the maintenance personnel.
In an embodiment, in the step S2, the driving scenario of the vehicle with abnormal water temperature includes a scenario where the water temperature is too high during driving and a scenario where the water temperature is too high during the vehicle is stationary, the driving behavior of the vehicle owner is to stop at a right speed or to turn on a double flashing, or to stop at an idle speed or to turn on a right speed or to turn on a double flashing, and the driving behavior of the vehicle owner is to turn on an engine warning lamp, whether the engine is abnormally turned off, whether the engine cover is opened, whether the seat belt is unfastened, whether the left front door is opened, and whether the vehicle is idling when the water temperature is too high during the vehicle is stationary.
In one embodiment, the step S2 of determining the water temperature abnormality based on the water temperature excessive scene during running and the water temperature excessive scene when the vehicle is stationary is as follows:
in step S21; judgingWhether the temperature of the cooling liquid is continuously abnormal in the time interval or not, if so, the cooling liquid is ordered according to the time interval conditions of j minutes and T minutes in an ascending order according to the time sequence:；
in step S22; acquisition ofThe cooling liquid temperature, the speed of the vehicle, the condition of a flash lamp, whether an engine warning lamp is opened, whether an engine is abnormally flameout, whether an engine cover is opened, whether a safety belt is unfastened and whether a left front door is opened or not are data at corresponding moments;
in step S23; scribing and takingWindow +.>The engine warning lamp is lighted in the time interval, and the number of continuous lighting times of the engine warning lamp is maximum>/>And the number of times the engine warning lamp is on>/>Then it is determined that the water temperature of the vehicle owner is abnormal at the moment, wherein +.>Is->Threshold value of maximum number of continuous lighting of engine warning lamp set in time interval, +.>Is->The number of times the engine warning lamp is on in the time interval; if scratch outWindow +.>When the engine cover is opened in the time interval, the water temperature of the vehicle owner is abnormal at the moment.
In an embodiment, the step of determining in the step S2 that the water temperature abnormality occurs based on the water temperature scene that is too high during running and the water temperature scene that occurs when the vehicle is stationary further includes:
in step S24; if the car owner is atSpeed of vehicle in time interval>Duration of 0->Judging that the vehicle owner is in driving, if the vehicle owner is +.>Speed of vehicle in time interval>Duration of 0->In a resting state, in which +.>Is->A speed set in the time interval is greater than a threshold value of 0 time duration;
in step S25; when the vehicle owner is driving, judging the vehicle ownerAt time intervals, whether the double flash opening and the speed reduction occur or not, and the right parking action is relied on, if the double-flash opening and the deceleration occur and the right parking action is followed, the water temperature abnormality of the car owner at the moment is judged.
In step S26; when the vehicle owner is in a static state, judging the vehicle ownerThe time interval is whether idling occurs, a safety belt is opened, and a car door is opened, if the idling occurs, the safety belt is opened, and the car door is opened, the water temperature of the car owner at the moment is judged to be abnormal, wherein the idling is judged according to the speed, an accelerator pedal and the rotation speed of an engine; based on the generated idling speed, opening the safety belt, and opening the car door, sending alarm information to car owners and maintenance personnel to remind the car owners and maintenance personnel of the abnormal water temperature.
In one embodiment, the method for identifying and monitoring abnormal water temperature of an automobile is provided by the application; in the step S3, a linear programming model with a time window is built to maximize the prediction accuracy in the time window, and consider multiple scene discussions, where the threshold conditions related to each scene are different, specifically, the number of scenes is recorded asThe threshold condition of each feature of each scene is recorded as +.>The time window size is +.>By the variable->Is indicated at +.>Inner reach scene->The number of people, the variable->Is indicated at +.>Inner reach scene->Corresponding car owner->Treated features of->,/>Is indicated at +.>Total number of people in the interior->Is indicated at +.>The prediction accuracy rate in the model; />Is indicated at +.>Inner reach scene->Whether the number of people in the scene satisfies a threshold condition of the scene, wherein +.>Indicates satisfaction of (I)>Indicating that it is not satisfied; />To +.>Whether the interior satisfies the scene->Threshold condition of (2), wherein->Indicates satisfaction of (I)>Indicates that it is not satisfied and that it is required to satisfy the scenario +.>Is conditional on the threshold value of->Requirement in scene->。
In one embodiment, the following objective function is established based on the variables in the step S3 wherein ,/>Expressed as +.>The sum of the individual scene prediction accuracy reaches a maximum, < >>Indicating total time, at time +.>In, the prediction accuracy rate reaches the scene +.>The ratio of the number of people to the total number of people, wherein the calculation formula of the prediction accuracy is +.>； wherein ,/>Representing the number of scenes.
In one embodiment, the scene is reached within a time window in said step S3Whether the number of people in the scene satisfies the threshold condition of +.>； wherein ,/>Representation scene->Is set in the above-described state.
In one embodiment, in said step S3, within a time windowWhether the sum of the individual scene prediction accuracy reaches the maximum value satisfies the following seven constraint conditions:
wherein the first constraint is that； wherein ,/>；
Wherein the second constraint is that； wherein ,/>；
Wherein the third constraint is that； wherein ,/>；
Wherein the fourth constraint is； wherein ,/>；
Wherein the fifth constraint is thatThe method comprises the steps of carrying out a first treatment on the surface of the Then->The method comprises the steps of carrying out a first treatment on the surface of the Otherwise it isThe method comprises the steps of carrying out a first treatment on the surface of the I.e. < ->The method comprises the steps of carrying out a first treatment on the surface of the Where t=1, 2, … …, T, s=1, 2, S, i=1, 2, … … I, j=1, 2,3,yt；
wherein the sixth constraint is whenThen->Otherwise, it is->，； wherein ,/>；
When (when),/>Then->The method comprises the steps of carrying out a first treatment on the surface of the Otherwise it is 0 and the number of the cells is,； wherein ,/>；
Wherein the seventh constraint is；
Wherein the first constraint isThe method is used for ensuring that the predicted abnormal number is not more than the total number; second constraint->Third constraint->And fourth constraint->For ensuring variable-> and />Is not negative of (2); fifth constraint->For ensuring +/per scene per time window>Is not lower than the threshold +.>Not higher than threshold->And introduces the ternary variable +.>To indicate whether a threshold condition is met; sixth constraint->Binary variable +.>To indicate at time +.>Whether the corresponding scenes are met or not is judged, wherein according to the priority, if the scene 1 is met, other scenes are not considered any more, and the number of people in each scene is acquired according to the rule>The mutual exclusion of the people in each scene is achieved; seventh constraint conditionSolving an optimal critical value +.f in a certain time window range by using the seven constraint conditions with the time window for predicting the abnormal number after the weight removal> and />。
The second aspect provides an automobile water temperature abnormality identification and alarm monitoring device based on big data of the Internet of vehicles, which is characterized by comprising a data transmission and processing module, a water temperature abnormality identification module, a threshold iteration optimization module and an alarm module, wherein the data transmission and processing module is used for acquiring all vehicle data transmitted by an automobile terminal and processing the vehicle data to obtain flash lamps, cooling liquid temperature, engine alarm lamp state, engine cover state, safety belt state, left front door state, speed, accelerator pedal and engine speed data of the automobile; the water temperature abnormality identification module is used for comparing the cooling liquid temperatures of all vehicles with a set cooling liquid temperature alarm threshold value, screening out vehicles possibly having water temperature abnormality, if the water temperature abnormality continuously occurs in the observation period of j minutes, taking the vehicle owner into observation, drawing a window based on the vehicle owner taken into observation, and judging whether the vehicle owner has water temperature abnormality at the moment again according to the driving behavior of the vehicle owner in the driving scene of the vehicle in the window; the threshold iterative optimization module is used for carrying out threshold iterative adjustment based on a multi-scene linear programming model with a time window to obtain an optimal threshold value, carrying out real-time identification of water temperature abnormality based on the adjusted optimal threshold value, and carrying out repeated iterative update; the alarm module is used for triggering an alarm mechanism and sending alarm information to an owner and maintenance personnel based on the result identified by the threshold iteration optimization module in real time.
Specifically, the information such as the double flash opening time, the water temperature, the engine warning lamp, the safety belt state, whether the left front door is opened or not, the idling and the like. These data can be collected in real time by means of in-vehicle sensors and internet of vehicles technology and transmitted to a central processing unit. And by utilizing an intelligent optimization algorithm and combining analysis and processing of the big data of the Internet of vehicles, the judgment rule is further optimized, so that whether the water temperature is abnormal or not is more accurately judged, and a corresponding alarm is triggered. Specifically, the application uses the double-flash open time length as an important parameter for indicating the state of the vehicle, and can infer whether the vehicle encounters an emergency situation by monitoring the open time and the duration of the double-flash. The water temperature is a key indicator for evaluating the working state of the engine, and the high temperature may indicate that the engine has a fault or overheat condition. The status of the engine warning light may provide additional fault information. The belt state and the left front door state can be used to determine whether the vehicle is in a stopped state to distinguish between an abnormality in water temperature during running and an increase in water temperature during stopping. Idle speed refers to the time that the vehicle is operating in an idle state, and long idle speeds may cause the engine to overheat. The idle behavior is determined according to the speed, the accelerator pedal and the engine speed. Based on the behavior, alarm information is sent to the vehicle owner and the maintenance personnel to remind the existence of abnormal water temperature.
According to the water temperature abnormality recognition and alarm monitoring device integrated in the automobile as shown in fig. 2, the device can acquire water temperature data and various state data of the automobile in real time. And according to the complicated rule definition, carrying out cross judgment and analysis on the real-time data, and if the conditions of a plurality of rules are met, judging that the water temperature is abnormal and triggering an alarm. The warning signal can be notified to the driver by means of a vehicle-mounted display screen, a vehicle-mounted sound system and the like, and simultaneously sent to a background monitoring system for further processing and recording.
Through the technical scheme based on the complicating rule, the method and the device can accurately judge the abnormal condition of the water temperature of the automobile, trigger an alarm in time and provide a more comprehensive and accurate water temperature judging rule for a host factory.
In step S1, data related to water temperature warning is collected in real time by using a vehicle-mounted sensor and a vehicle networking system, and all collected vehicle data are preprocessed, wherein preprocessing includes data cleaning, outlier processing, data standardization and the like, so as to ensure accuracy and consistency of the data.
Specifically, the data samples related to the water temperature abnormality judgment of the original data samples are, for example, as follows:
TABLE 1
In step S2, the coolant temperature W is compared with the minimum value of the set coolant temperature alarm thresholdMaximum->For comparison, if the temperature of the cooling liquid is +>Exceeding the set threshold +.>Or below the set threshold +.>Then consider whenThe water temperature is abnormal at the previous moment.
If the water temperature is too high during running, the actions of stopping at the right side, starting double flashing, stopping at the right side at idle speed, starting double flashing and the like exist, and after the actions, the problems related to unlocking a safety belt for checking, opening a vehicle door for checking, contacting a maintenance shop and the like can exist; when the water temperature is too high during the vehicle is stationary, the water temperature is found to be too high during the vehicle is stationary, the vehicle is idling to wait for the water temperature to drop, and the behaviors such as opening the door of the safety belt are generated.
Wherein the idle speed scene is identified as collecting the driving data of the vehicle at first, and judgingJudging whether the speed of the vehicle is zero at the moment or not, if so, judging +.>Whether the engine speed is lower than a preset threshold at the moment, if yes, judging +.>And (5) judging whether the value of the acceleration pedal is zero at the moment, and recording time and position information of idle behavior and the like.
The water temperature anomaly identification totally relates to 4 scenes）：/>The time window size is 10 minutes (++>=10), total time 9 (++>For opening 9 windows), wherein the total number of people per window is +.>The number of threshold conditions i for each scene is as follows:
TABLE 2
Corresponding to the threshold:
scene 1:
TABLE 3 Table 3
Scene 2:
TABLE 4 Table 4
Setting a maximum and minimum water temperature threshold, a maximum and minimum continuous double-flashing time length threshold, a maximum and minimum vehicle speed threshold which is greater than 0 time length, a maximum and minimum right parking time length threshold and the like in a scene 3; setting a maximum and minimum water temperature threshold, a maximum and minimum continuous double-flashing time length threshold, a maximum vehicle speed threshold which is greater than 0 time length threshold, whether the safety belt is unfastened, idling, a vehicle door threshold opening and the like in a scene 3;
according to the historical data, predicting the number of people and the total number of people reaching the scene s under the corresponding window, and the accuracy is as follows，/>，/>，/>）：
TABLE 5
Therefore, a multidimensional linear programming model is applied to solve the problem. MouldThe goal of the model is to maximize the 9 times 10 minute intra-window prediction accuracy while meeting the threshold condition for each scene. And (3) based on a threshold iterative optimization setting step of the linear programming model in multiple scenes with time windows, the threshold iterative optimization can enable the model to more accurately judge whether the water temperature is abnormal or not. The method is characterized in that the threshold iterative optimization can dynamically adjust the threshold according to the prediction result of the model and the deviation of the actual condition from the actual condition, so that the problems of misjudgment and missed judgment caused by fixed threshold are avoided. Based on a plurality of threshold ranges preliminarily set by experience, taking influence caused by time factors into consideration, establishing a linear programming model with a time window to solve with optimal prediction accuracy as a target, acquiring an optimal threshold, carrying out real-time identification of water temperature abnormality by using the optimal threshold, and carrying out repeated iterative updating. Expressed using the following variables:the representation is shown at time +.>Inner scene->The number of predicted persons; />Is indicated at +.>Inner scene->The number of predicted persons; />Is indicated at +.>Inner scene->The number of predicted persons; />Is indicated at +.>Inner scene->The number of predicted persons; />Is indicated at +.>The total number of people in the house; />Is indicated at +.>The prediction accuracy rate in the model; />Is indicated at +.>Whether the threshold condition of any one scene is satisfied.
From the variables above, we can build the following integer programming model:
wherein the constraint conditions are as follows:
the first constraint is that； wherein ,/>；
The second constraint is that； wherein ,/>；
The third constraint is that wherein ,/>；
The fourth constraint is that； wherein ,/>；
The fifth constraint is thatThen->The method comprises the steps of carrying out a first treatment on the surface of the Otherwise it isThe method comprises the steps of carrying out a first treatment on the surface of the I.e. < ->Where t=1, 2, … …, T, s=1, 2, S, i=1, 2, … … I, j=1, 2,3,yt；
the sixth constraint is whenThen->Otherwise, it is->，； wherein ,/>；
When (when),/>Then->The method comprises the steps of carrying out a first treatment on the surface of the Otherwise it is 0 and the number of the cells is,； wherein ,/>；
The seventh constraint is that；
The model may be solved using an integer programming solver to obtain an optimal solution of:
,/>,/>,/>,/>,/>,/>；
，/>，/>，/>，/>，/>，/>，；
,/>,/>,/>,/>,/>,/>；
，/>，/>，/>，/>，/>，/>；
，/>，/>，/>，/>，/>，/>；
，/>，/>，/>，/>，/>，/>；
，/>，/>，/>，/>，/>，；
，/>，/>，/>，/>，/>，/>；/>
，/>，/>，/>，/>，/>，/>；
according to the solving result, the total visit amount and the prediction accuracy rate of each day in a 9-time window can be known, each threshold value in 4 scenes is further adjusted according to the prediction accuracy rate, and the water temperature abnormality recognition accuracy rate is improved.
The optimal critical value in a certain time window range can be solved by the multi-dimensional linear programming model with the time window and />Thereby realizing iterative optimization adjustment according to a given range and considering the influence of a time window and prediction accuracy. Meanwhile, in practical application, different optimization targets and strategies can be achieved by adjusting weights, sensitivity coefficients and constraint conditions.
Based on the threshold value of iterative optimization in the step S3, the abnormal condition of the water temperature is identified in real time, and alarm information is sent to the car owners and the private shops.
According to the application, by utilizing a plurality of sensor data of the vehicle and big data of the Internet of vehicles, whether the water temperature is abnormal or not is comprehensively judged by analyzing parameters such as double-flash opening time, water temperature, engine alarm fault lamps, whether the engine is abnormally flameout, whether the engine cover is opened or not, whether the safety belt is unfastened or not, whether the left front door is opened or not, idle speed and the like. Among other things, artificial intelligence techniques are applied to the processing and analysis of data to improve the accuracy and reliability of the determination.
Through hardware devices and corresponding software algorithms connected with vehicle sensors and the internet of vehicles system. And collecting and transmitting sensor data of the vehicle in real time, processing and analyzing the data through a software algorithm, judging whether the water temperature is abnormal or not, and triggering corresponding alarm when required.
Compared with the traditional method, the water temperature alarm judging method and the monitoring device can provide more accurate and comprehensive judging rules and a reliable water temperature abnormality monitoring scheme for a host factory. By accurately judging whether the water temperature is abnormal, the problems of engine failure, potential safety hazard, vehicle damage and the like caused by overhigh water temperature can be avoided.
In summary, the automobile water temperature alarm judging method and the monitoring device based on the Internet of vehicles big data and the intelligent optimization algorithm have the advantages of accurate judgment and comprehensive information, provide a reliable water temperature abnormality monitoring solution for a host factory, and have wide application prospect and economic value.
The method for identifying and alarming the abnormal water temperature of the automobile has the advantages that:
1. the water temperature abnormality judgment is carried out by utilizing big data and the information of the Internet of vehicles, so that the water temperature abnormality condition can be more accurately identified, and the occurrence of misjudgment and missed judgment is reduced.
2. The dynamic rule generation and updating and the self-adaptive threshold adjustment can flexibly adjust the judgment rule according to the data and the vehicle state information acquired in real time, adapt to different driving conditions and vehicle states, and improve the adaptability and the sensitivity of water temperature abnormality judgment.
3. Based on the rules provided by multiple intersections, the information of multiple aspects such as double-flash opening time, water temperature, engine alarm fault lamps, whether an engine is abnormally flameout, whether an engine cover is opened, whether a safety belt is unfastened, whether a left front door is opened, idling and the like is comprehensively considered, the possibility of water temperature abnormality can be comprehensively evaluated, and more comprehensive water temperature alarm judgment is provided.
4. The rule and the adjustment threshold value can be updated in real time by utilizing the data collected in real time and the feedback information of the monitoring device, so that the judging system has higher instantaneity and flexibility, can respond to the abnormal condition of the water temperature in time, and provides timely alarming and monitoring functions.
5. The regular evaluation and optimization are carried out on the rules and the models through the model evaluation and optimization process, so that the reliability and stability of the system under different driving scenes are ensured, and the working efficiency and performance of the system are improved.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present application.
Therefore, the above description is only a preferred embodiment of the present application, and the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application, which is defined by the claims.
Claims (6)
1. The method for identifying and alarming abnormal water temperature of the automobile is characterized by comprising the following steps of:
s1, acquiring all vehicle data transmitted by a vehicle-mounted terminal, and processing the vehicle data to obtain flash lamp, cooling liquid temperature, engine warning lamp state, engine cover state, safety belt state, left front door state, vehicle speed, accelerator pedal and engine speed data of an automobile;
s2, comparing the coolant temperature of all vehicles with a set coolant temperature alarm threshold value to screen out vehicles with possibly abnormal water temperature, if the water temperature abnormality continuously occurs in the observation period of j minutes, taking the vehicle owner into observation, and marking out the vehicle owner based on the taking observationWindow, and according to->Judging whether the water temperature is abnormal or not in the observation period of the vehicle owner for j minutes again through the driving behavior of the vehicle owner in the running scene of the vehicle in the window;
s3, carrying out threshold iterative adjustment based on a multi-scene linear programming model with a time window to obtain an optimal threshold, carrying out real-time identification of water temperature abnormality based on the adjusted optimal threshold, and carrying out repeated iterative update;
s4, triggering an alarm mechanism and sending alarm information to the vehicle owner and maintenance personnel based on the real-time identification result in the step S3;
in the step S2, the determination step based on the water temperature abnormality of the water temperature excessive scene during traveling and the water temperature excessive scene when the vehicle is stationary is as follows:
s21, performing S21; judgingWhether the temperature of the cooling liquid is continuously abnormal in the time interval or not, if so, the cooling liquid is ordered according to the time interval conditions of j minutes and T minutes in an ascending order according to the time sequence:；
s22, performing S22; acquisition ofThe cooling liquid temperature, the speed of the vehicle, the condition of a flash lamp, whether an engine warning lamp is opened, whether an engine is abnormally flameout, whether an engine cover is opened, whether a safety belt is unfastened and whether a left front door is opened or not are data at corresponding moments;
s23, performing S23; scribing and takingWindow +.>The engine warning lamp is lighted in the time interval, and the number of continuous lighting times of the engine warning lamp is maximum>/>And the number of times the engine warning lamp is on>/>Then it is determined that the water temperature of the vehicle owner is abnormal at the moment, wherein +.>Is->Threshold value of maximum number of continuous lighting of engine warning lamp set in time interval, +.>Is->The number of times the engine warning lamp is on in the time interval; if scratch outWindow +.>When the engine cover is opened in the time interval, the water temperature of the vehicle owner is abnormal at the moment;
in the step S3, a linear programming model with a time window is built to maximize the prediction accuracy in the time window, and consider multiple scene discussions, where the threshold conditions related to each scene are different, specifically, the number of scenes is recorded asThe threshold condition of each feature of each scene is recorded as +.>The time window size is +.>By the variable->Is indicated at +.>Inner reach scene->The number of people, the variable->Is indicated at +.>Inner reach scene->Corresponding car owner->Treated features of->,/>Is indicated at +.>Total number of people in the interior->Is indicated at +.>The prediction accuracy rate in the model; />Is indicated at +.>Inner reach scene->Whether the number of people in the scene satisfies a threshold condition of the scene, wherein +.>Representation ofSatisfy (S)>Indicating that it is not satisfied; />To +.>Whether the interior satisfies the scene->Threshold condition of (2), wherein->Indicates satisfaction of (I)>Indicates that it is not satisfied and that it is required to satisfy the scenario +.>Is conditional on the threshold value of->Requirement in scene->；
The following objective function is established according to the variables in the step S3； wherein ,/>Expressed as +.>The sum of the individual scene prediction accuracy reaches a maximum, < >>Indicating total time, at time +.>In, the prediction accuracy rate reaches the scene +.>The ratio of the number of people to the total number of people, wherein the calculation formula of the prediction accuracy is that； wherein ,/>Representing the number of scenes.
2. The method according to claim 1, wherein the driving scene of the vehicle having the abnormal water temperature in the step S2 includes a water temperature excessive scene during driving and a water temperature excessive scene during the vehicle is stationary, the driving behavior of the vehicle owner is a deceleration right stop or a double flashing, or an idling right stop or a double flashing, and the driving behavior of the vehicle owner is an engine warning lamp on, an engine flameout, an engine cover on, a seat belt on, a left front door on, and an idling when the water temperature excessive scene occurs during the vehicle is stationary.
3. The method for identifying and monitoring the abnormal water temperature of the automobile according to claim 1, wherein the step of determining in the step S2 that the abnormal water temperature is based on the water temperature in the running water temperature scene and the water temperature scene in which the water temperature is too high when the vehicle is stationary further comprises:
s24, performing S24; if the car owner is atVehicle speed in time intervalSpeed of speed>Duration of 0->Judging that the vehicle owner is in driving, if the vehicle owner is +.>Speed of vehicle in time interval>Duration of 0->In a resting state, in which +.>Is->A speed set in the time interval is greater than a threshold value of 0 time duration;
s25, performing S25; when the vehicle owner is driving, judging the vehicle ownerAt time intervals, judging whether the opening double flashing and decelerating are generated and the right parking behavior is relied on, and if the opening double flashing and decelerating are generated and the right parking behavior is relied on, judging that the water temperature of the vehicle owner is abnormal at the moment;
s26, performing S26; when the vehicle owner is in a static state, judging the vehicle ownerThe time interval is whether idling occurs, a safety belt is opened, and a car door is opened, if the idling occurs, the safety belt is opened, and the car door is opened, the water temperature of the car owner at the moment is judged to be abnormal, wherein the idling is judged according to the speed, an accelerator pedal and the rotation speed of an engine; based on the generated idling speed, opening the safety belt, and opening the car door, sending alarm information to car owners and maintenance personnel to remind the car owners and maintenance personnel of the abnormal water temperature.
4. The method for identifying and monitoring abnormal water temperature of automobile according to claim 1, wherein,reaching a scene within a time window in said step S3Whether the number of people in the scene satisfies the threshold condition of +.>； wherein ,representation scene->Is set in the above-described state.
5. The method for identifying and monitoring the abnormality of the water temperature of a vehicle according to claim 4, wherein in said step S3, within a time windowWhether the sum of the individual scene prediction accuracy reaches the maximum value satisfies the following seven constraint conditions:
wherein the first constraint is that； wherein ,/>；
Wherein the second constraint is that； wherein ,/>；
Wherein the third constraint is that； wherein ,/>；
Wherein the fourth constraint is； wherein ,/>；
Wherein the fifth constraint is thatThe method comprises the steps of carrying out a first treatment on the surface of the Then->The method comprises the steps of carrying out a first treatment on the surface of the Otherwise it isThe method comprises the steps of carrying out a first treatment on the surface of the I.e. < ->； wherein ,；
wherein the sixth constraint is whenThen->Otherwise, it is->，； wherein ,/>；
When (when),/>Then->The method comprises the steps of carrying out a first treatment on the surface of the Otherwise it is 0 and the number of the cells is,； wherein ,/>；
Wherein the seventh constraint is ；
Wherein the first constraint isThe method is used for ensuring that the predicted abnormal number is not more than the total number; second constraint->Third constraint->And fourth constraint->For ensuring variables and />Is not negative of (2); fifth constraint->For ensuring +/per scene per time window>Is not lower than the threshold +.>Not higher than threshold->And introduces the ternary variable +.>To indicate whether a threshold condition is met; sixth constraint->Binary variable +.>To indicate at time +.>Whether the corresponding scenes are met or not is judged, wherein according to the priority, if the scene 1 is met, other scenes are not considered any more, and the number of people in each scene is acquired according to the rule>The mutual exclusion of the people in each scene is achieved; seventh constraint conditionSolving an optimal critical value +.f in a certain time window range by using the seven constraint conditions with the time window for predicting the abnormal number after the weight removal> and />。
6. The device is characterized by comprising a data transmission and processing module, a water temperature abnormality identification module, a threshold iteration optimization module and an alarm module, wherein the data transmission and processing module is used for acquiring all vehicle data transmitted by a vehicle-mounted terminal, processing vehicle data to obtain data of a flash lamp, a cooling liquid temperature, an engine warning lamp state, an engine cover state, a safety belt state, a left front door state, a speed of a vehicle, an accelerator pedal and an engine speed; the water temperature abnormality identification module is used for comparing the coolant temperature of all vehicles with a set coolant temperature alarm threshold value to screen out vehicles possibly with water temperature abnormality, if water temperature abnormality continuously occurs in an observation period of j minutes, the vehicle owner is taken into observation, and the vehicle owner based on the taken-in observation is drawn outWindow, and according to->Judging whether the water temperature is abnormal or not in the observation period of the vehicle owner for j minutes again through the driving behavior of the vehicle owner in the running scene of the vehicle in the window; the threshold iterative optimization module is used for carrying out threshold iterative adjustment based on a multi-scene linear programming model with a time window to obtain an optimal threshold value, carrying out real-time identification of water temperature abnormality based on the adjusted optimal threshold value, and carrying out repeated iterative update; the alarm module is used for triggering an alarm mechanism and sending alarm information to a vehicle owner and maintenance personnel based on the result identified by the threshold iteration optimization module in real time;
the water temperature abnormality identification module judges based on the water temperature abnormality of the water temperature excessive scene in running and the water temperature excessive scene in the vehicle stationaryWhether the temperature of the cooling liquid is continuously abnormal in the time interval or not, if so, the cooling liquid is ordered according to the time interval conditions of j minutes and T minutes in an ascending order according to the time sequence:；
acquisition ofThe cooling liquid temperature, the speed of the vehicle, the condition of a flash lamp, whether an engine warning lamp is opened, whether an engine is abnormally flameout, whether an engine cover is opened, whether a safety belt is unfastened and whether a left front door is opened or not are data at corresponding moments; marking->Window +.>The engine warning lamp is lighted in the time interval, and the number of continuous lighting times of the engine warning lamp is maximum>/>And the number of times the engine warning lamp is on>/>Then it is determined that the water temperature of the vehicle owner is abnormal at the moment, wherein +.>Is->Threshold value of maximum number of continuous lighting of engine warning lamp set in time interval, +.>Is->Time intervalThe number of times the engine warning lamp is on in the interval; if the mark is marked->Window +.>When the engine cover is opened in the time interval, the water temperature of the vehicle owner is abnormal at the moment;
the threshold iterative optimization module aims at maximizing the prediction accuracy in a time window by establishing a linear programming model with the time window, simultaneously considers various scene discussions, and specifically marks the number of scenes asThe threshold condition of each feature of each scene is recorded as +.>The time window size is +.>By the variable->Is indicated at +.>Inner reach scene->The number of people, the variable->Is indicated at +.>Inner reach scene->Corresponding car owner->Treated features of->,/>Is indicated at +.>Total number of people in the interior->Is indicated at +.>The prediction accuracy rate in the model; />Is indicated at +.>Inner reach scene->Whether the number of people in the scene satisfies a threshold condition of the scene, wherein +.>Indicates satisfaction of (I)>Indicating that it is not satisfied; />To +.>Whether or not the interior meetsScene->Threshold condition of (2), wherein->Indicates satisfaction of (I)>Indicates that it is not satisfied and that it is required to satisfy the scenario +.>Is conditional on the threshold value of->Requirement in scene->；
Establishing the following objective function according to the variable in the threshold iterative optimization module； wherein ,expressed as +.>The sum of the individual scene prediction accuracy reaches a maximum, < >>Indicating total time, at time +.>In, the prediction accuracy rate reaches the scene +.>The number of people in (2) is the total number of peopleWherein the calculation formula of the prediction accuracy is +.>； wherein ,/>Representing the number of scenes.
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