CN117325881A - Vehicle alarm prediction method and device, electronic equipment and storage medium - Google Patents
Vehicle alarm prediction method and device, electronic equipment and storage medium Download PDFInfo
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- CN117325881A CN117325881A CN202311469962.7A CN202311469962A CN117325881A CN 117325881 A CN117325881 A CN 117325881A CN 202311469962 A CN202311469962 A CN 202311469962A CN 117325881 A CN117325881 A CN 117325881A
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- 230000007613 environmental effect Effects 0.000 claims description 12
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
- B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
- B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
- B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
- B60R16/0231—Circuits relating to the driving or the functioning of the vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/143—Alarm means
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Abstract
The application discloses a vehicle alarm prediction method, a vehicle alarm prediction device, electronic equipment and a storage medium, and relates to the technical field of Internet of vehicles. The method comprises the following steps: when the current power system temperature of a target vehicle is monitored to be abnormal based on a power system alarm prediction model, vehicle state information reported by the target vehicle is input into the power system alarm prediction model, and whether the target vehicle belongs to a first type of abnormality cause is determined; if the target vehicle is determined to belong to a first type of abnormality cause, generating first warning information of the target vehicle according to the first type of abnormality cause; if the target vehicle is determined not to belong to the first type of abnormality reasons, predicting a second type of abnormality reasons of the target vehicle according to the power system temperature of the target vehicle and the power system alarm prediction model, and generating second alarm information of the target vehicle according to the second type of abnormality reasons.
Description
Technical Field
The application relates to the technical field of internet of vehicles, in particular to a vehicle alarm prediction method, a device, electronic equipment and a storage medium.
Background
With the development of the internet of vehicles technology, more and more vehicles are connected and interacted with the outside through the internet of vehicles technology. The Internet of vehicles is widely applied to the fields of intelligent transportation, vehicle management, driving assistance and the like, and has important social significance and economic value. For example, in a vehicle management scene, the vehicle networking technology can realize the omnibearing centralized management of the vehicle, the vehicle state, the position, the oil consumption and other data are monitored in real time through the vehicle-mounted sensor and are transmitted back to the cloud for processing, and a user or a manager can check the running condition of the vehicle at any time and any place and perform early warning and maintenance management. Further, in the above scenario, the power system is a critical part of the vehicle, directly determining the performance and driving force of the vehicle, and affecting the reliability and economy of the vehicle.
At present, for power system management of a vehicle, a corresponding alarm signal is sent after detecting that the oil temperature or the water temperature of the vehicle reaches a certain threshold value, so as to remind a user or a manager of carrying out exception handling. It can be seen that in this way, the cause of the abnormality of the vehicle cannot be located timely and accurately.
Disclosure of Invention
The application provides a vehicle alarm prediction method, a device, electronic equipment and a storage medium, which are used for timely reminding and accurately positioning the abnormality reason of a vehicle when the vehicle is abnormal.
In a first aspect, a vehicle alert prediction method is provided, including:
when the current power system temperature of a target vehicle is monitored to be abnormal based on a power system alarm prediction model, vehicle state information reported by the target vehicle is input into the power system alarm prediction model, and whether the target vehicle belongs to a first type of abnormality cause is determined;
if the target vehicle is determined to belong to a first type of abnormality cause, generating first warning information of the target vehicle according to the first type of abnormality cause;
if the target vehicle is determined not to belong to the first type of abnormality reasons, predicting a second type of abnormality reasons of the target vehicle according to the power system temperature of the target vehicle and the power system alarm prediction model, and generating second alarm information of the target vehicle according to the second type of abnormality reasons.
Optionally, the method further comprises:
acquiring a historical sample data set of a plurality of vehicles and maintenance record information of each of the plurality of vehicles; wherein the plurality of vehicles includes the target vehicle therein;
and constructing the power system warning prediction model according to the historical sample data set and the maintenance record information of each of the plurality of vehicles.
Optionally, the historical sample data set at least comprises a power system temperature, acceleration and/or speed, an ambient temperature, a gradient, a liquid level state, an oil pump state, a water pump state of each of the plurality of vehicles;
the maintenance record information of each of the plurality of vehicles at least comprises a vehicle identification code, a replacement part name and maintenance description content of each of the plurality of vehicles.
Optionally, the predicting the second type of abnormality cause of the target vehicle according to the power system temperature of the target vehicle and the power system warning prediction model includes:
generating a current power system temperature rise curve of the target vehicle according to the power system temperature of the target vehicle acquired in a set period;
comparing the power system temperature rising curve with a target temperature fitting curve in the power system warning prediction model to obtain a temperature rising deviation result of the target vehicle; the target temperature fitting curve is a temperature fitting curve fitted by the target vehicle based on historical sample data of the target vehicle under the condition of normal temperature change;
and predicting a second type of abnormality reason of the target vehicle according to the temperature rise deviation result and the target maintenance record information of the target vehicle, which is called from the power system warning prediction model.
Optionally, the determining that the target vehicle belongs to a first type of abnormality cause includes:
determining that the target vehicle belongs to the first type of abnormality cause if at least one of the following conditions is satisfied:
the liquid level state of the target vehicle in the vehicle state information is lower than a set low liquid level threshold value;
in the vehicle state information, a water pump and/or an oil pump of the target vehicle are/is in an unoperated state;
the acceleration of the target vehicle in the vehicle state information is larger than a set acceleration threshold value;
the gradient of the target vehicle in the vehicle state information is greater than a set gradient threshold value;
and the environmental temperature difference of the target vehicle in the vehicle state information is larger than a set environmental temperature difference temperature threshold value.
In a second aspect, there is provided a vehicle warning device including:
the first abnormality detection module is used for inputting vehicle state information reported by a target vehicle into the power system alarm prediction model when abnormality of the current power system temperature of the target vehicle is detected based on the power system alarm prediction model, and determining whether the target vehicle belongs to a first type of abnormality cause; if the target vehicle is determined to belong to a first type of abnormal reason, an indication alarm module generates first alarm information of the target vehicle according to the first type of abnormal reason;
and the second abnormality detection module is used for determining that the target vehicle does not belong to the first type of abnormality reasons, predicting the second type of abnormality reasons of the target vehicle according to the power system temperature of the target vehicle and the power system alarm prediction model, indicating the alarm module, and generating second alarm information of the target vehicle according to the second type of abnormality reasons.
Optionally, the apparatus further comprises a model building module; the model construction module is used for acquiring historical sample data sets of a plurality of vehicles and maintenance record information of each of the plurality of vehicles; wherein the plurality of vehicles includes the target vehicle therein;
and constructing the power system warning prediction model according to the historical sample data set and the maintenance record information of each of the plurality of vehicles.
Optionally, the historical sample data set at least comprises a power system temperature, acceleration and/or speed, an ambient temperature, a gradient, a liquid level state, an oil pump state, a water pump state of each of the plurality of vehicles;
the maintenance record information of each of the plurality of vehicles at least comprises a vehicle identification code, a replacement part name and maintenance description content of each of the plurality of vehicles.
Optionally, the second abnormality detection module is specifically configured to:
generating a current power system temperature rise curve of the target vehicle according to the power system temperature of the target vehicle acquired in a set period;
comparing the power system temperature rising curve with a target temperature fitting curve in the power system warning prediction model to obtain a temperature rising deviation result of the target vehicle; the target temperature fitting curve is a temperature fitting curve fitted on the basis of historical sample data of the target vehicle under the condition of normal temperature change of the target vehicle, or is a temperature fitting curve fitted on the basis of the historical sample data of the same type of vehicle under the condition of normal temperature change of the same type of vehicle;
and predicting a second type of abnormality reason of the target vehicle according to the temperature rise deviation result and the target maintenance record information of the target vehicle, which is called from the power system warning prediction model.
Optionally, the first abnormality detection module is specifically configured to:
determining that the target vehicle belongs to the first type of abnormality cause if at least one of the following conditions is satisfied:
the liquid level state of the target vehicle in the vehicle state information is lower than a set low liquid level threshold value;
in the vehicle state information, a water pump and/or an oil pump of the target vehicle are/is in an unoperated state;
the acceleration of the target vehicle in the vehicle state information is larger than a set acceleration threshold value;
the gradient of the target vehicle in the vehicle state information is greater than a set gradient threshold value;
and the environmental temperature difference of the target vehicle in the vehicle state information is larger than a set environmental temperature difference threshold value.
In the embodiment of the application, as the power system temperature of the target vehicle can be monitored in real time, when the power system temperature of the target vehicle is determined to be abnormal through the constructed power system alarm prediction model, the power system alarm prediction model can be used for carrying out first-dimension abnormality analysis on the vehicle state information of the target vehicle; under the condition that the target vehicle is determined not to belong to the first type of abnormality reasons, further performing second-dimension abnormality analysis, and predicting the second type of abnormality reasons of the target vehicle according to the power system temperature of the target vehicle and the power system alarm prediction model, so that the abnormality reasons of the target vehicle can be predicted timely and accurately, corresponding alarm information is output to prompt a user, and user experience is met.
In a third aspect, there is provided an electronic device comprising:
a memory for storing a computer program; a processor for implementing the method steps of any one of the first aspects when executing a computer program stored on the memory.
In a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method steps of any of the first aspects.
The technical effects of each of the second to fourth aspects and the technical effects that may be achieved by each aspect are referred to above for the technical effects that may be achieved by the first aspect or each possible aspect in the first aspect, and the detailed description is not repeated here.
Drawings
Fig. 1 is a schematic diagram of an application scenario applicable to an embodiment of the present application;
FIG. 2 is a flowchart for constructing a power system warning prediction model according to an embodiment of the present application;
FIG. 3 is a flowchart of a vehicle alert prediction method according to an embodiment of the present application;
FIG. 4 is a flowchart of a complete vehicle alert prediction method according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a vehicle alarm prediction apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings. The specific method of operation in the method embodiment may also be applied to the device embodiment or the system embodiment. It should be noted that "a plurality of" is understood as "at least two" in the description of the present application. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. A is connected with B, and can be represented as follows: both cases of direct connection of A and B and connection of A and B through C. In addition, in the description of the present application, the words "first," "second," and the like are used merely for distinguishing between the descriptions and not be construed as indicating or implying a relative importance or order.
In order to better understand the embodiments of the present application, technical terms related to the embodiments of the present application will be first described below.
(1) The vehicle identification code (Vehicle Identification Number, VIN) is used to identify the identity of the vehicle, and contains information about the manufacturer, age, model, body type and code, engine code, and assembly location of the vehicle.
(2) The powertrain (Powertrain System) typically includes a majority of an engine, transmission, driveline, fuel system, cooling system, exhaust system, and the like.
The following description is made for some simple descriptions of application scenarios applicable to the technical solutions of the embodiments of the present application, and it should be noted that the application scenarios described below are only used for illustrating the embodiments of the present application and are not limiting. In specific implementation, the technical scheme provided by the embodiment of the application can be flexibly applied according to actual needs.
Fig. 1 is a schematic diagram of an application scenario applicable to the embodiment of the present application. As shown in fig. 1, the scene includes a vehicle 101, a vehicle alert prediction device 102; the information interaction is performed between the vehicle 101 and the vehicle alarm prediction device 102 through the internet of vehicles technology. The number of vehicles may be greater and fig. 1 is depicted with only one vehicle.
The vehicle 101 is mounted with various sensors (e.g., a temperature sensor, a liquid level sensor, a vehicle speed sensor, an acceleration sensor, an ambient temperature sensor, etc., shown in fig. 1), and a vehicle body controller; the temperature sensor can be used for acquiring the temperature of the power system of the vehicle 101 in real time and transmitting the acquired temperature of the power system to the vehicle body controller in real time; the liquid level sensor can be used for collecting the liquid level state of the vehicle 101 in real time and transmitting the collected liquid level state to a vehicle body controller in real time; the vehicle speed sensor can be used for acquiring the vehicle speed of the vehicle 101 in real time and transmitting the acquired vehicle speed to a vehicle body controller in real time; the acceleration sensor can be used for acquiring the acceleration of the vehicle 101 in real time and transmitting the acquired acceleration to the vehicle body controller in real time, and the ambient temperature sensor can be used for acquiring the ambient temperature of the vehicle 101 in real time and transmitting the acquired ambient temperature to the vehicle body controller in real time; the body controller may be configured to receive information collected by each of the sensors, to generalize the collected information into vehicle status information, and to send the vehicle status information to the vehicle alert prediction device 102.
The vehicle warning prediction device 102 is equipped with a power system warning prediction model, and when the vehicle state information is acquired from the vehicle body controller, processes such as monitoring of power system temperature abnormality, analysis of cause of power system temperature abnormality, warning processing, and the like can be performed on the vehicle 101 in real time during traveling based on the power system warning prediction model.
Optionally, the power system alarm prediction model may be constructed based on a historical sample data set of a plurality of vehicles and maintenance record information of each of the plurality of vehicles, so as to facilitate subsequent retrieval and use from the power system alarm prediction model. As shown in FIG. 2, a flowchart for constructing a power system warning prediction model is schematically shown. The method comprises the following steps:
201: a historical sample data set of a plurality of vehicles is obtained, and maintenance record information of each of the plurality of vehicles is obtained.
Optionally, the historical sample data set includes at least powertrain temperature, acceleration and/or speed, ambient temperature, grade, fluid level status, oil pump status, water pump status, etc. of the plurality of vehicles. The plurality of vehicles may be different types of vehicles in different areas, and the number of each type of vehicles may be more than one, and the plurality of vehicles include the following target vehicles therein.
Optionally, the maintenance record information of each of the plurality of vehicles at least includes a vehicle identification code, a replacement part name, a maintenance description content of each of the plurality of vehicles, and may further include maintenance time, a maintenance place, and the like. As shown in report 1, an example table of the dimension protection record information is exemplarily shown.
Table 1: dimension-keeping record information example table
202: and constructing the power system warning prediction model according to the historical sample data set and the maintenance record information of each of the plurality of vehicles.
Alternatively, the power system alarm prediction model may be constructed by using a neural network structure, or other similar machine learning algorithms, which is not limited in this embodiment of the present application.
In the embodiment of the application, the constructed power system warning prediction model has good applicability, can provide functions of trend analysis, abnormality detection and the like of the power system temperature for different types of vehicles and vehicles in different areas, helps a user to know the abnormal state of the vehicle in real time, and reminds the user to take corresponding measures in time.
In order to further explain the technical solutions provided in the embodiments of the present application, the following details are described with reference to the accompanying drawings and the detailed description. Although the embodiments of the present application provide the method operational steps as shown in the following embodiments or figures, more or fewer operational steps may be included in the method, either on a routine or non-inventive basis. In steps where there is logically no necessary causal relationship, the execution order of the steps is not limited to the execution order provided by the embodiments of the present application. The method may be performed sequentially or and in accordance with the method shown in the embodiments or drawings when the actual process or apparatus is performed.
Fig. 3 is a flowchart of a vehicle alarm prediction method provided in an embodiment of the present application. The flow may be performed by a vehicle alert prediction device (such as the vehicle alert prediction device 102 shown in fig. 1), which may be implemented in software and/or hardware. As shown in fig. 3, the process includes the steps of:
301: when the current power system temperature of the target vehicle is monitored to be abnormal based on the power system warning prediction model, vehicle state information reported by the target vehicle is input into the power system warning prediction model.
Optionally, the power system temperature of the target vehicle (such as the vehicle 101 shown in fig. 1) may be obtained according to a set period (for example, every 5 s), the power system temperature obtained in real time is input into the power system alarm prediction model (such as the power system alarm prediction model constructed in fig. 2), if the current power system temperature exceeds the power system temperature threshold set in the model, it indicates that the current power system temperature of the target vehicle is abnormal, and the vehicle state information reported by the target vehicle is input into the power system alarm prediction model, so as to further analyze the cause of the abnormality; if the current power system temperature does not exceed the power system temperature threshold set in the model, the current power system temperature of the target vehicle is indicated to be in a normal state, and the power system temperature of the target vehicle can be continuously obtained according to a set period, and the power system temperature of the target vehicle is continuously monitored.
302: it is determined whether the target vehicle belongs to a first type of abnormality cause.
Optionally, if at least one of the following conditions is met, determining that the target vehicle belongs to the first type of abnormality cause and going to 303, otherwise going to 304:
the liquid level state of the target vehicle in the vehicle state information is lower than a set low liquid level threshold value;
in the vehicle state information, a water pump and/or an oil pump of a target vehicle are/is in an unoperated state;
the acceleration of the target vehicle in the vehicle state information is larger than a set acceleration threshold value;
the gradient of the target vehicle in the vehicle state information is greater than a set gradient threshold value;
the environmental temperature difference (Deltat DEG C) of the vehicle state information in which the target vehicle is located is greater than a set environmental temperature difference threshold (Deltat DEG C).
303: and generating first warning information of the target vehicle according to the first type of abnormality reasons.
Specifically, different first alarm information may be generated according to the above different first type of abnormality reasons.
For example, when it is determined that the liquid level state of the target vehicle is lower than the set low liquid level threshold, first warning information for prompting that the liquid level of the target vehicle is low may be generated, and further, the first warning information is sent to the user, so that the user is reminded to take corresponding measures in time.
For another example, when it is determined that the water pump of the target vehicle is in an unoperated state, first warning information for prompting that the water pump of the target vehicle is unoperated may be generated, and further, the first warning information is sent to the user, so as to prompt the user to take corresponding measures in time.
For another example, when it is determined that the gradient of the target vehicle is greater than the set gradient threshold, first warning information for prompting that the climbing gradient of the target vehicle is excessive and the temperature of the power system is abnormally increased may be generated, and further, the first warning information is sent to the user, so that the user is prompted to take corresponding measures in time.
Optionally, the first alarm information may be displayed on a display page on the target vehicle, and may be prompted on a user terminal associated with the target vehicle.
304: and predicting a second type of abnormality reason of the target vehicle according to the power system temperature of the target vehicle and the power system alarm prediction model, and generating second alarm information of the target vehicle according to the second type of abnormality reason.
Optionally, predicting the second type of abnormality cause of the target vehicle according to the power system temperature of the target vehicle and the power system warning prediction model may include the following procedures: generating a current power system temperature rise curve of the target vehicle according to the power system temperature of the target vehicle acquired in a set period; comparing the power system temperature rising curve with a target temperature fitting curve in a power system warning prediction model to obtain a temperature rising deviation result of a target vehicle; and predicting a second type of abnormality cause of the target vehicle according to the temperature rise deviation result and the target maintenance record information of the target vehicle, which is called from the power system warning prediction model.
In some implementations, the target temperature fitting curve is a temperature fitting curve fitted to the target vehicle under normal temperature change conditions based on historical sample data of the target vehicle, for example, a temperature fitting curve fitted to the target vehicle based on power system temperatures generated by the target vehicle at different speeds, accelerations, etc. In other embodiments, the target temperature fitting curve is a temperature fitting curve fitted based on historical sample data of the same type of vehicle as the same type of vehicle under normal temperature changing conditions, for example, a temperature fitting curve fitted based on power system temperatures generated by the same type of vehicle at different speeds, accelerations and the like.
When the second type of abnormality cause of the target vehicle is predicted, the power system temperature of the target vehicle can be automatically and real-timely obtained, and the generated power system temperature rising curve is compared with a target temperature fitting curve in a power system alarm prediction model, so that the power system temperature change trend and the abnormality of the current target vehicle can be accurately compared; the maintenance record information of the target vehicle is combined, so that the problem point of the target vehicle can be further and rapidly positioned, and the problem processing efficiency is improved; the predicted second type of abnormal reasons can be notified to the user in advance, so that the user can conveniently take corresponding treatment measures, the safe operation of the power system is protected, and the vehicle using experience of the user is improved.
Optionally, the second warning information may be displayed on a display page on the target vehicle, and may also be prompted on a user terminal associated with the target vehicle.
Alternatively, the second different alert information may be generated based on the predicted second different type of cause of the anomaly. For example, as a result of the temperature rising deviation of the target vehicle, the current temperature of the power system (for example, abnormal temperature of the cooling system of the gearbox) continuously rises, and the target vehicle is determined to be changed by the cooling liquid of the gearbox by combining with maintenance record information of the target vehicle, and the generated second warning information can be obtained by predicting that the cooling capacity of the cooling system of the gearbox is insufficient due to improper maintenance operation.
In the embodiment of the application, as the power system temperature of the target vehicle can be monitored in real time, when the power system temperature of the target vehicle is determined to be abnormal through the constructed power system alarm prediction model, the power system alarm prediction model can be used for carrying out first-dimension abnormality analysis on the vehicle state information of the target vehicle; under the condition that the target vehicle is determined not to belong to the first type of abnormality reasons, further performing second-dimension abnormality analysis, and predicting the second type of abnormality reasons of the target vehicle according to the power system temperature of the target vehicle and the power system alarm prediction model obtained in real time, so that the abnormality reasons of the target vehicle can be predicted timely and accurately, and corresponding alarm information is output to prompt a user, thereby meeting user experience.
Based on the flow shown in fig. 3, fig. 4 illustrates a flowchart of a complete vehicle alarm prediction method provided in an embodiment of the present application. The process comprises the following steps:
401: and based on the power system warning prediction model, monitoring the temperature of the power system of the target vehicle in real time according to a set period.
402: whether the current power system temperature of the target vehicle is abnormal or not is judged, if yes, the process proceeds to 403, and if not, the process proceeds to 401.
403: the vehicle state information reported by the target vehicle is input into a power system warning prediction model.
404: and determining whether the target vehicle belongs to a first type of abnormality reason according to the vehicle state information, if so, turning to 405, and if not, turning to 406.
405: and generating first warning information of the target vehicle according to the first type of abnormality reasons.
This step is similar to 303 in fig. 3 and will not be repeated here.
406: and predicting a second type of abnormality reason of the target vehicle according to the power system temperature of the target vehicle and the power system warning prediction model, and generating second warning information of the target vehicle according to the second type of abnormality reason.
In this step, similar to 304 in fig. 3, a description is not repeated here.
Based on the same technical concept, the embodiment of the application also provides a vehicle alarm prediction device, which can realize the vehicle alarm prediction method flow in the embodiment of the application.
Fig. 5 is a schematic structural diagram of a vehicle alarm prediction apparatus according to an embodiment of the present application, where the apparatus includes a first abnormality detection module 501, a second abnormality detection module 502, an alarm module 503, and further includes a model building module 504.
The first abnormality detection module 501 is configured to, when an abnormality exists in a current power system temperature of a target vehicle based on a power system alarm prediction model, input vehicle state information reported by the target vehicle into the power system alarm prediction model, and determine whether the target vehicle belongs to a first type of cause of the abnormality; if it is determined that the target vehicle belongs to a first type of abnormality cause, the indication alarm module 503 generates first alarm information of the target vehicle according to the first type of abnormality cause.
And the second abnormality detection module 502 is configured to predict a second type of abnormality cause of the target vehicle according to the power system temperature of the target vehicle and the power system alarm prediction model, and instruct the alarm module to generate second alarm information of the target vehicle according to the second type of abnormality cause, if the target vehicle is determined not to belong to the first type of abnormality cause.
A model building module 504, configured to obtain a historical sample data set of a plurality of vehicles, and maintenance record information of each of the plurality of vehicles; wherein the plurality of vehicles includes the target vehicle therein; and constructing the power system warning prediction model according to the historical sample data set and the maintenance record information of each of the plurality of vehicles.
Optionally, the second abnormality detection module 502 is specifically configured to:
generating a current power system temperature rise curve of the target vehicle according to the power system temperature of the target vehicle acquired in a set period; comparing the power system temperature rising curve with a target temperature fitting curve in the power system warning prediction model to obtain a temperature rising deviation result of the target vehicle; the target temperature fitting curve is a temperature fitting curve fitted on the basis of historical sample data of the target vehicle under the condition of normal temperature change of the target vehicle, or is a temperature fitting curve fitted on the basis of the historical sample data of the same type of vehicle under the condition of normal temperature change of the same type of vehicle; and predicting a second type of abnormality reason of the target vehicle according to the temperature rise deviation result and the target maintenance record information of the target vehicle, which is called from the power system warning prediction model.
Optionally, the first abnormality detection module is specifically configured to:
determining that the target vehicle belongs to the first type of abnormality cause if at least one of the following conditions is satisfied:
the liquid level state of the target vehicle in the vehicle state information is lower than a set low liquid level threshold value; in the vehicle state information, a water pump and/or an oil pump of the target vehicle are/is in an unoperated state; the acceleration of the target vehicle in the vehicle state information is larger than a set acceleration threshold value; the gradient of the target vehicle in the vehicle state information is greater than a set gradient threshold value; and the environmental temperature difference of the target vehicle in the vehicle state information is larger than a set environmental temperature difference threshold value.
It should be noted that, the above device provided in the embodiment of the present application can implement all the method steps in the embodiment of the method and achieve the same technical effects, and the details of the same parts and the advantages as those of the embodiment of the method in the embodiment are not described here.
Based on the same technical concept, the embodiment of the application also provides electronic equipment, which can realize the function of the vehicle alarm prediction device.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
At least one processor 601, and a memory 602 connected to the at least one processor 601, a specific connection medium between the processor 601 and the memory 602 is not limited in the embodiment of the present application, and in fig. 6, the processor 601 and the memory 602 are connected by a bus 600 as an example. Bus 600 is shown in bold lines in fig. 6, and the manner in which the other components are connected is illustrated schematically and not by way of limitation. The bus 600 may be divided into an address bus, a data bus, a control bus, etc., and is represented by only one thick line in fig. 6 for convenience of representation, but does not represent only one bus or one type of bus. Alternatively, the processor 601 may be referred to as a controller, and the names are not limited.
In the embodiment of the present application, the memory 602 stores instructions executable by the at least one processor 601, and the at least one processor 601 may perform a vehicle warning prediction method as previously discussed by executing the instructions stored in the memory 602. The processor 601 may implement the functions of the respective modules in the apparatus shown in fig. 5.
The processor 601 is a control center of the device, and various interfaces and lines can be used to connect various parts of the whole control device, and through running or executing instructions stored in the memory 602 and calling data stored in the memory 602, various functions of the device and processing data can be performed, so that the device can be monitored as a whole.
In one possible design, processor 601 may include one or more processing units, and processor 601 may integrate an application processor and a modem processor, wherein the application processor primarily processes operating systems, user interfaces, application programs, and the like, and the modem processor primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 601. In some embodiments, processor 601 and memory 602 may be implemented on the same chip, or they may be implemented separately on separate chips in some embodiments.
The processor 601 may be a general purpose processor such as a Central Processing Unit (CPU), digital signal processor, application specific integrated circuit, field programmable gate array or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, which may implement or perform the methods, steps and logic blocks disclosed in embodiments of the present application. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of a vehicle alarm prediction method disclosed in connection with the embodiments of the present application may be directly embodied in a hardware processor for execution, or may be executed by a combination of hardware and software modules in the processor.
The memory 602 is a non-volatile computer readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs, and modules. The Memory 602 may include at least one type of storage medium, which may include, for example, flash Memory, hard disk, multimedia card, card Memory, random access Memory (Random Access Memory, RAM), static random access Memory (Static Random Access Memory, SRAM), programmable Read-Only Memory (Programmable Read Only Memory, PROM), read-Only Memory (ROM), charged erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory), magnetic Memory, magnetic disk, optical disk, and the like. Memory 602 is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 602 in the present embodiment may also be circuitry or any other device capable of implementing a memory function for storing program instructions and/or data.
By programming the processor 601, codes corresponding to a vehicle warning prediction method described in the foregoing embodiments can be cured into a chip, so that the chip can execute a vehicle warning prediction method of the embodiment shown in fig. 3 at run-time. How to design and program the processor 601 is a well-known technique for those skilled in the art, and will not be described in detail herein.
It should be noted that, the above power-on electronic device provided in the embodiment of the present application can implement all the method steps implemented in the embodiment of the method, and can achieve the same technical effects, and specific details of the same parts and beneficial effects as those of the embodiment of the method in the embodiment are not described herein.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer executable instructions for causing a computer to execute the vehicle warning prediction method in the embodiment.
Embodiments of the present application also provide a computer program product, which when invoked by a computer, causes the computer to perform one of the vehicle warning prediction methods of the above embodiments.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (12)
1. A vehicle warning prediction method, characterized by comprising:
when the current power system temperature of a target vehicle is monitored to be abnormal based on a power system alarm prediction model, vehicle state information reported by the target vehicle is input into the power system alarm prediction model, and whether the target vehicle belongs to a first type of abnormality cause is determined;
if the target vehicle is determined to belong to a first type of abnormality cause, generating first warning information of the target vehicle according to the first type of abnormality cause;
if the target vehicle is determined not to belong to the first type of abnormality reasons, predicting a second type of abnormality reasons of the target vehicle according to the power system temperature of the target vehicle and the power system alarm prediction model, and generating second alarm information of the target vehicle according to the second type of abnormality reasons.
2. The method of claim 1, wherein the method further comprises:
acquiring a historical sample data set of a plurality of vehicles and maintenance record information of each of the plurality of vehicles; wherein the plurality of vehicles includes the target vehicle therein;
and constructing the power system warning prediction model according to the historical sample data set and the maintenance record information of each of the plurality of vehicles.
3. The method of claim 2, wherein the historical sample dataset includes at least powertrain temperatures, accelerations and/or velocities, ambient temperatures, gradients, fluid level conditions, oil pump conditions, water pump conditions of each of the plurality of vehicles;
the maintenance record information of each of the plurality of vehicles at least comprises a vehicle identification code, a replacement part name and maintenance description content of each of the plurality of vehicles.
4. The method of claim 1, wherein predicting a second type of cause of the anomaly of the target vehicle based on the powertrain temperature of the target vehicle and the powertrain alert prediction model comprises:
generating a current power system temperature rise curve of the target vehicle according to the power system temperature of the target vehicle acquired in a set period;
comparing the power system temperature rising curve with a target temperature fitting curve in the power system warning prediction model to obtain a temperature rising deviation result of the target vehicle; the target temperature fitting curve is a temperature fitting curve fitted on the basis of historical sample data of the target vehicle under the condition of normal temperature change of the target vehicle, or is a temperature fitting curve fitted on the basis of the historical sample data of the same type of vehicle under the condition of normal temperature change of the same type of vehicle;
and predicting a second type of abnormality reason of the target vehicle according to the temperature rise deviation result and the target maintenance record information of the target vehicle, which is called from the power system warning prediction model.
5. The method of claim 1, wherein the determining that the target vehicle belongs to a first type of cause of the anomaly comprises:
determining that the target vehicle belongs to the first type of abnormality cause if at least one of the following conditions is satisfied:
the liquid level state of the target vehicle in the vehicle state information is lower than a set low liquid level threshold value;
in the vehicle state information, a water pump and/or an oil pump of the target vehicle are/is in an unoperated state;
the acceleration of the target vehicle in the vehicle state information is larger than a set acceleration threshold value;
the gradient of the target vehicle in the vehicle state information is greater than a set gradient threshold value;
and the environmental temperature difference of the target vehicle in the vehicle state information is larger than a set environmental temperature difference threshold value.
6. A vehicle warning prediction apparatus, characterized by comprising:
the first abnormality detection module is used for inputting vehicle state information reported by a target vehicle into the power system alarm prediction model when abnormality of the current power system temperature of the target vehicle is detected based on the power system alarm prediction model, and determining whether the target vehicle belongs to a first type of abnormality cause; if the target vehicle is determined to belong to a first type of abnormal reason, an indication alarm module generates first alarm information of the target vehicle according to the first type of abnormal reason;
and the second abnormality detection module is used for determining that the target vehicle does not belong to the first type of abnormality reasons, predicting the second type of abnormality reasons of the target vehicle according to the power system temperature of the target vehicle and the power system alarm prediction model, indicating the alarm module, and generating second alarm information of the target vehicle according to the second type of abnormality reasons.
7. The apparatus of claim 6, wherein the apparatus further comprises a model building module;
the model construction module is used for acquiring historical sample data sets of a plurality of vehicles and maintenance record information of each of the plurality of vehicles; wherein the plurality of vehicles includes the target vehicle therein;
and constructing the power system warning prediction model according to the historical sample data set and the maintenance record information of each of the plurality of vehicles.
8. The apparatus of claim 7, wherein the historical sample data set includes at least a powertrain temperature, acceleration and/or speed, an ambient temperature, a grade, a liquid level status, an oil pump status, a water pump status of each of the plurality of vehicles;
the maintenance record information of each of the plurality of vehicles at least comprises a vehicle identification code, a replacement part name and maintenance description content of each of the plurality of vehicles.
9. The apparatus of claim 6, wherein the second anomaly detection module is specifically configured to:
generating a current power system temperature rise curve of the target vehicle according to the power system temperature of the target vehicle acquired in a set period;
comparing the power system temperature rising curve with a target temperature fitting curve in the power system warning prediction model to obtain a temperature rising deviation result of the target vehicle; the target temperature fitting curve is a temperature fitting curve fitted on the basis of historical sample data of the target vehicle under the condition of normal temperature change of the target vehicle, or is a temperature fitting curve fitted on the basis of the historical sample data of the same type of vehicle under the condition of normal temperature change of the same type of vehicle;
and predicting a second type of abnormality reason of the target vehicle according to the temperature rise deviation result and the target maintenance record information of the target vehicle, which is called from the power system warning prediction model.
10. The apparatus of claim 6, wherein the first anomaly detection module is specifically configured to:
determining that the target vehicle belongs to the first type of abnormality cause if at least one of the following conditions is satisfied:
the liquid level state of the target vehicle in the vehicle state information is lower than a set low liquid level threshold value;
in the vehicle state information, a water pump and/or an oil pump of the target vehicle are/is in an unoperated state;
the acceleration of the target vehicle in the vehicle state information is larger than a set acceleration threshold value;
the gradient of the target vehicle in the vehicle state information is greater than a set gradient threshold value;
and the environmental temperature difference of the target vehicle in the vehicle state information is larger than a set environmental temperature difference threshold value.
11. An electronic device, comprising:
a memory for storing a computer program;
a processor for carrying out the method steps of any one of claims 1-5 when executing a computer program stored on said memory.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements the method steps of any of claims 1-5.
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| CN118152966A (en) * | 2024-05-09 | 2024-06-07 | 北京百度网讯科技有限公司 | Method, device, equipment and storage medium for processing vehicle information |
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| CN118152966A (en) * | 2024-05-09 | 2024-06-07 | 北京百度网讯科技有限公司 | Method, device, equipment and storage medium for processing vehicle information |
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