CN117208003A - Vehicle abnormality detection method and device and electronic equipment - Google Patents
Vehicle abnormality detection method and device and electronic equipment Download PDFInfo
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Abstract
The application provides a method and a device for detecting vehicle abnormality and electronic equipment, comprising the following steps: acquiring preset original characteristic data in the current journey of the target vehicle, and further acquiring characteristic data to be detected; performing abnormality detection on the feature data to be detected by adopting a first vehicle abnormality detection model, a second vehicle abnormality detection model and a third vehicle abnormality detection model to obtain a first vehicle abnormality detection result, a second vehicle abnormality detection result and a third vehicle abnormality detection result, and determining a target vehicle detection result; and determining an alarm level based on the detection result of the target vehicle, and further determining maintenance advice according to the alarm level so as to prompt a user. According to the vehicle anomaly detection method, the vehicle anomaly detection is realized according to the original characteristic data of the target vehicle and the vehicle anomaly detection model, namely, the vehicle anomaly detection is driven by data, the obtained detection result is more accurate, and the user can be prompted timely.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for detecting a vehicle abnormality, and an electronic device.
Background
The abnormality of the new energy vehicle threatens the safety of the vehicle owner, timely discovers the abnormality of the vehicle and informs the vehicle owner of the vital importance to the personal safety of the vehicle owner.
At present, detection of abnormal vehicles is generally realized based on signals of the vehicles, and detection of abnormal vehicles based on signals of the vehicles (such as a fault code displayed in the vehicles) belongs to detection driven by experience, and the situation of false alarm missing (such as false alarm missing caused by poor communication) exists.
In summary, how to accurately detect the abnormality of the vehicle and timely inform the vehicle owner becomes a technical problem to be solved at present.
Disclosure of Invention
In view of the above, the present application aims to provide a method, a device and an electronic device for detecting a vehicle abnormality, so as to alleviate the technical problem of poor accuracy of the existing method for detecting a vehicle abnormality.
In a first aspect, an embodiment of the present application provides a method for detecting an abnormality of a vehicle, including:
acquiring preset original characteristic data in the current journey of the target vehicle;
performing mathematical calculation on the original characteristic data, and performing standardized treatment on the characteristic data after the mathematical calculation to obtain the characteristic data to be detected;
performing anomaly detection on the feature data to be detected by adopting a first vehicle anomaly detection model, a second vehicle anomaly detection model and a third vehicle anomaly detection model to obtain a first vehicle anomaly detection result, a second vehicle anomaly detection result and a third vehicle anomaly detection result, wherein the first vehicle anomaly detection model is a gradient lifting tree algorithm, the second vehicle anomaly detection model is a naive Bayesian algorithm, and the third vehicle anomaly detection model is a support vector machine algorithm;
and determining a target vehicle detection result according to the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result, determining an alarm level based on the target vehicle detection result, and further determining a maintenance suggestion according to the alarm level so as to prompt a user.
Further, the raw characteristic data includes: user behavior feature data, battery condition feature data, vehicle basic information data and external data, wherein the mathematical calculation comprises: maximum value calculation, minimum value calculation, quartile calculation, average value calculation, and standard deviation calculation.
Further, determining a target vehicle detection result according to the first vehicle abnormality detection result, the second vehicle abnormality detection result, and the third vehicle abnormality detection result includes:
if at least two vehicle abnormality detection results among the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result show that the vehicle is abnormal, the target vehicle detection result is that the vehicle is abnormal, otherwise, the target vehicle detection result is that the vehicle is not abnormal.
Further, determining an alert level based on the target vehicle detection result includes:
acquiring a historical alarm level of the target vehicle;
if the target vehicle detection result shows that the vehicle is abnormal, the alarm level is an alarm level which is obtained by adding one step to the historical alarm level;
and if the target vehicle detection result shows that the vehicle is not abnormal, the alarm level is the historical alarm level.
Further, determining a maintenance recommendation according to the alarm level includes:
and if the alarm level reaches a preset alarm level threshold, determining that the target vehicle needs maintenance, otherwise, determining that the target vehicle does not need maintenance.
Further, the method further comprises:
acquiring a first original characteristic data sample of a preset time period before maintenance of a maintenance vehicle according to after-sale data, and acquiring a second original characteristic data sample of any preset time period of a non-abnormal vehicle;
performing mathematical computation on the first original characteristic data sample and the second original characteristic data sample respectively, and performing standardization processing on the first original characteristic data sample and the second original characteristic data sample after the mathematical computation to obtain a first characteristic data sample and a second characteristic data sample;
training an original first vehicle abnormality detection model, an original second vehicle abnormality detection model and an original third vehicle abnormality detection model by adopting the first characteristic data sample, an abnormal vehicle label corresponding to the first characteristic data sample, the second characteristic data sample and an abnormal-free vehicle label corresponding to the second characteristic data sample to obtain the first vehicle abnormality detection model, the second vehicle abnormality detection model and the third vehicle abnormality detection model.
Further, before performing the mathematical computation on the raw feature data, the method further includes:
preprocessing the original characteristic data, and performing mathematical calculation on the preprocessed original characteristic data.
In a second aspect, an embodiment of the present application further provides a device for detecting an abnormality of a vehicle, including:
the acquisition unit is used for acquiring preset original characteristic data in the current journey of the target vehicle;
the mathematical calculation unit is used for carrying out mathematical calculation on the original characteristic data and carrying out standardized processing on the characteristic data after the mathematical calculation to obtain the characteristic data to be detected;
the anomaly detection unit is used for carrying out anomaly detection on the feature data to be detected by adopting a first vehicle anomaly detection model, a second vehicle anomaly detection model and a third vehicle anomaly detection model to obtain a first vehicle anomaly detection result, a second vehicle anomaly detection result and a third vehicle anomaly detection result, wherein the first vehicle anomaly detection model is a gradient lifting tree algorithm, the second vehicle anomaly detection model is a naive Bayesian algorithm, and the third vehicle anomaly detection model is a support vector machine algorithm;
and the determining unit is used for determining a target vehicle detection result according to the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result, determining an alarm level based on the target vehicle detection result, and further determining a maintenance suggestion according to the alarm level so as to prompt a user.
In a third aspect, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method according to any one of the first aspects when the processor executes the computer program.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium storing machine-executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any one of the first aspects.
In an embodiment of the present application, a method for detecting an abnormality of a vehicle is provided, including: acquiring preset original characteristic data in the current journey of the target vehicle; performing mathematical calculation on the original characteristic data, and performing standardization processing on the characteristic data after the mathematical calculation to obtain the characteristic data to be detected; performing anomaly detection on the feature data to be detected by adopting a first vehicle anomaly detection model, a second vehicle anomaly detection model and a third vehicle anomaly detection model to obtain a first vehicle anomaly detection result, a second vehicle anomaly detection result and a third vehicle anomaly detection result, wherein the first vehicle anomaly detection model is a gradient lifting tree algorithm, the second vehicle anomaly detection model is a naive Bayesian algorithm, and the third vehicle anomaly detection model is a support vector machine algorithm; determining a target vehicle detection result according to the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result, determining an alarm level based on the target vehicle detection result, and determining a maintenance suggestion according to the alarm level so as to prompt a user. According to the vehicle anomaly detection method, vehicle anomaly detection is achieved according to the original characteristic data of the target vehicle and the vehicle anomaly detection model, namely, the vehicle anomaly detection is driven by data, the obtained detection result is more accurate, a user can be prompted timely, and the technical problem that the existing vehicle anomaly detection method is poor in accuracy is solved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for detecting a vehicle abnormality according to an embodiment of the present application;
fig. 2 is a schematic diagram of a device for detecting an abnormality of a vehicle according to an embodiment of the present application;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions of the present application will be clearly and completely described in connection with the embodiments, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The existing detection method for the vehicle abnormality is poor in accuracy.
Based on the above, in the vehicle abnormality detection method, the vehicle abnormality detection is realized according to the original characteristic data of the target vehicle and the vehicle abnormality detection model, namely, the vehicle abnormality detection is driven by data, the obtained detection result is more accurate, and the user can be prompted timely.
For the convenience of understanding the present embodiment, a method for detecting a vehicle abnormality disclosed in the embodiment of the present application will be described in detail.
Embodiment one:
according to an embodiment of the present application, there is provided an embodiment of a method of detecting a vehicle abnormality, it being noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in an order different from that herein.
Fig. 1 is a flowchart of a method for detecting a vehicle abnormality according to an embodiment of the present application, as shown in fig. 1, the method including the steps of:
step S102, obtaining preset original characteristic data in the current journey of a target vehicle;
in an embodiment of the present application, the target vehicle may be any vehicle using the method for detecting a vehicle abnormality according to the present application, and the journey includes: the charging process and the discharging process are one-time processes, namely one-time processes, and the current process can be understood as the current ongoing one-time charging process or the current ongoing one-time discharging process.
Step S104, carrying out mathematical computation on the original characteristic data, and carrying out standardization processing on the characteristic data after the mathematical computation to obtain the characteristic data to be detected;
step S106, carrying out anomaly detection on the feature data to be detected by adopting a first vehicle anomaly detection model, a second vehicle anomaly detection model and a third vehicle anomaly detection model to obtain a first vehicle anomaly detection result, a second vehicle anomaly detection result and a third vehicle anomaly detection result, wherein the first vehicle anomaly detection model is a gradient lifting tree algorithm, the second vehicle anomaly detection model is a naive Bayesian algorithm, and the third vehicle anomaly detection model is a support vector machine algorithm;
step S108, determining a target vehicle detection result according to the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result, determining an alarm level based on the target vehicle detection result, and further determining a maintenance suggestion according to the alarm level so as to prompt a user.
In an embodiment of the present application, a method for detecting an abnormality of a vehicle is provided, including: acquiring preset original characteristic data in the current journey of the target vehicle; performing mathematical calculation on the original characteristic data, and performing standardization processing on the characteristic data after the mathematical calculation to obtain the characteristic data to be detected; performing anomaly detection on the feature data to be detected by adopting a first vehicle anomaly detection model, a second vehicle anomaly detection model and a third vehicle anomaly detection model to obtain a first vehicle anomaly detection result, a second vehicle anomaly detection result and a third vehicle anomaly detection result, wherein the first vehicle anomaly detection model is a gradient lifting tree algorithm, the second vehicle anomaly detection model is a naive Bayesian algorithm, and the third vehicle anomaly detection model is a support vector machine algorithm; determining a target vehicle detection result according to the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result, determining an alarm level based on the target vehicle detection result, and determining a maintenance suggestion according to the alarm level so as to prompt a user. According to the vehicle anomaly detection method, vehicle anomaly detection is achieved according to the original characteristic data of the target vehicle and the vehicle anomaly detection model, namely, the vehicle anomaly detection is driven by data, the obtained detection result is more accurate, a user can be prompted timely, and the technical problem that the existing vehicle anomaly detection method is poor in accuracy is solved.
The foregoing briefly describes the method for detecting a vehicle abnormality of the present application, and the detailed description will be given below with reference to specific matters.
In an alternative embodiment of the application, the method further comprises, prior to performing the mathematical calculation on the raw characteristic data:
preprocessing the original characteristic data, and performing mathematical calculation on the preprocessed original characteristic data.
Specifically, the preprocessing refers to processing an invalid value and a null value.
In an alternative embodiment of the application, the raw characteristic data comprises: user behavior characteristic data, battery condition characteristic data, vehicle basic information data and external data, and mathematical calculation comprises: maximum value calculation, minimum value calculation, quartile calculation, average value calculation, and standard deviation calculation.
Specifically, the user behavior feature data includes: acceleration, deceleration, depth of pedal, steering, etc.;
the battery condition characteristic data includes: temperature, current, voltage of the battery, alarms of the battery, etc.; since the battery is the core component of the new energy vehicle and is most easily damaged, monitoring the working condition of the battery is an important point of attention for the vehicle system.
The above-mentioned vehicle basic information data includes: total mileage, total duration, charge-discharge rate, etc. of vehicle running;
the external data includes: ambient temperature, region, gradient, humidity, etc.;
the user behavior characteristic data and the battery working condition characteristic data are obtained by periodically collecting vehicles, and the vehicle basic information data are obtained by adding and subtracting the vehicle delivery years, the first data and the last data (the total mileage is the information of the vehicle); the external data is collected according to map data.
In an alternative embodiment of the present application, the method for determining the target vehicle detection result according to the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result specifically includes the steps of:
if at least two vehicle abnormality detection results among the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result show that the vehicle is abnormal, the target vehicle detection result is that the vehicle is abnormal, otherwise, the target vehicle detection result is that the vehicle is not abnormal.
In an alternative embodiment of the present application, the alert level is determined based on the detection result of the target vehicle, and specifically includes the following steps:
(1) Acquiring a historical alarm level of a target vehicle;
(2) If the target vehicle detection result is that the vehicle is abnormal, the alarm level is the historical alarm level plus one alarm level;
(3) If the target vehicle detection result is that the vehicle is not abnormal, the alarm level is a historical alarm level.
For example, if an abnormality of the vehicle has been detected 2 times before the current trip, the historical alert level is 2, if the detection result of the target vehicle at this time is that the vehicle is abnormal, the alert level is 3, otherwise, the alert level is still 2.
In an alternative embodiment of the present application, the maintenance recommendation is determined according to the alarm level, and specifically includes the following steps:
if the alarm level reaches the preset alarm level threshold, determining that the target vehicle needs maintenance, otherwise, determining that the target vehicle does not need maintenance.
Specifically, the preset alarm level threshold may be 4 levels, and if the preset alarm level threshold reaches 4 levels, it is determined that the target vehicle needs maintenance, and a prompt for suggesting maintenance is sent; otherwise, the target vehicle does not need maintenance. After the target vehicle is maintained according to the prompt, the historical warning level is cleared, and the calculation is carried out again.
In an alternative embodiment of the application, the method further comprises the steps of:
(1) Acquiring a first original characteristic data sample of a preset time period before maintenance of a maintenance vehicle according to after-sale data, and acquiring a second original characteristic data sample of any preset time period of a non-abnormal vehicle;
in the embodiment of the present application, the preset duration may be one month, and the embodiment of the present application does not specifically limit the preset duration, where the first original feature data sample is specifically a first original feature data sample of each trip of a previous month, and the second original feature data sample is also a first original feature data sample of each trip.
The inventor considers that if a machine learning model is adopted to detect the abnormality of the vehicle, the abnormal data needs to be marked manually, after the abnormality occurs in the vehicle, the work load of marking which data point is abnormal is large, and it is difficult to define which data point is abnormal, for example, one second is abnormal, the next second is not abnormal, and then the next second is abnormal.
In addition, the inventor compares the detection precision of the model after training the model by manually marking the abnormal data sample and directly taking the first original characteristic data sample of the maintenance vehicle for one month as the abnormal data sample, and the inventor can know that the detection precision of the model after training the model by directly taking the first original characteristic data sample of the maintenance vehicle for one month as the abnormal data sample is higher. The inventors analyzed that the reason is: the quantity of the manually marked abnormal data samples is small, the data quantity of the positive samples corresponding to the manually marked abnormal data samples is large, the proportion of the positive and negative samples is too unbalanced, so that the accuracy of a trained model is poor, and on the contrary, after the model is trained by the samples obtained through the mode of the application, the accuracy of the model can be good, and the accuracy of classification detection is improved.
After the first original characteristic data sample and the second original characteristic data sample are obtained, preprocessing is carried out on the first original characteristic data sample and the second original characteristic data sample, and mathematical calculation is carried out on the preprocessed first original characteristic data sample and the preprocessed second original characteristic data sample respectively.
(2) Respectively carrying out mathematical computation on the first original characteristic data sample and the second original characteristic data sample, and carrying out standardization processing on the first original characteristic data sample and the second original characteristic data sample after the mathematical computation to obtain a first characteristic data sample and a second characteristic data sample;
(3) Training an original first vehicle abnormality detection model, an original second vehicle abnormality detection model and an original third vehicle abnormality detection model by adopting the first characteristic data sample, an abnormal vehicle label corresponding to the first characteristic data sample, a second characteristic data sample and an abnormal-free vehicle label corresponding to the second characteristic data sample, so as to obtain the first vehicle abnormality detection model, the second vehicle abnormality detection model and the third vehicle abnormality detection model.
According to the vehicle anomaly detection method, the battery working condition is considered, the potential anomaly of the battery can be identified, and potential hidden hazards of the vehicle can be effectively excavated.
Embodiment two:
the embodiment of the application also provides a vehicle abnormality detection device which is mainly used for executing the vehicle abnormality detection method provided in the first embodiment of the application, and the vehicle abnormality detection device provided in the embodiment of the application is specifically described below.
Fig. 2 is a schematic diagram of a vehicle abnormality detection apparatus according to an embodiment of the present application, as shown in fig. 2, the apparatus mainly includes: an acquisition unit 10, a mathematical calculation unit 20, an abnormality detection unit 30, and a determination unit 40, wherein:
the acquisition unit is used for acquiring preset original characteristic data in the current journey of the target vehicle;
the mathematical calculation unit is used for carrying out mathematical calculation on the original characteristic data and carrying out standardized processing on the characteristic data after the mathematical calculation to obtain the characteristic data to be detected;
the anomaly detection unit is used for carrying out anomaly detection on the feature data to be detected by adopting a first vehicle anomaly detection model, a second vehicle anomaly detection model and a third vehicle anomaly detection model to obtain a first vehicle anomaly detection result, a second vehicle anomaly detection result and a third vehicle anomaly detection result, wherein the first vehicle anomaly detection model is a gradient lifting tree algorithm, the second vehicle anomaly detection model is a naive Bayesian algorithm, and the third vehicle anomaly detection model is a support vector machine algorithm;
and the determining unit is used for determining a target vehicle detection result according to the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result, determining an alarm level based on the target vehicle detection result, and further determining a maintenance suggestion according to the alarm level so as to prompt a user.
In an embodiment of the present application, there is provided a vehicle abnormality detection apparatus including: acquiring preset original characteristic data in the current journey of the target vehicle; performing mathematical calculation on the original characteristic data, and performing standardization processing on the characteristic data after the mathematical calculation to obtain the characteristic data to be detected; performing anomaly detection on the feature data to be detected by adopting a first vehicle anomaly detection model, a second vehicle anomaly detection model and a third vehicle anomaly detection model to obtain a first vehicle anomaly detection result, a second vehicle anomaly detection result and a third vehicle anomaly detection result, wherein the first vehicle anomaly detection model is a gradient lifting tree algorithm, the second vehicle anomaly detection model is a naive Bayesian algorithm, and the third vehicle anomaly detection model is a support vector machine algorithm; determining a target vehicle detection result according to the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result, determining an alarm level based on the target vehicle detection result, and determining a maintenance suggestion according to the alarm level so as to prompt a user. As can be seen from the above description, in the vehicle anomaly detection device of the present application, the vehicle anomaly detection is implemented according to the original feature data of the target vehicle and the vehicle anomaly detection model, that is, the detection result obtained by the data-driven vehicle anomaly detection is more accurate, so that the user can be timely prompted, and the technical problem of poor accuracy of the existing vehicle anomaly detection method is solved.
Optionally, the raw feature data includes: user behavior characteristic data, battery condition characteristic data, vehicle basic information data and external data, and mathematical calculation comprises: maximum value calculation, minimum value calculation, quartile calculation, average value calculation, and standard deviation calculation.
Optionally, the determining unit is further configured to: if at least two vehicle abnormality detection results among the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result show that the vehicle is abnormal, the target vehicle detection result is that the vehicle is abnormal, otherwise, the target vehicle detection result is that the vehicle is not abnormal.
Optionally, the determining unit is further configured to: acquiring a historical alarm level of a target vehicle; if the target vehicle detection result is that the vehicle is abnormal, the alarm level is the historical alarm level plus one alarm level; if the target vehicle detection result is that the vehicle is not abnormal, the alarm level is a historical alarm level.
Optionally, the determining unit is further configured to: if the alarm level reaches the preset alarm level threshold, determining that the target vehicle needs maintenance, otherwise, determining that the target vehicle does not need maintenance.
Optionally, the device is further configured to: acquiring a first original characteristic data sample of a preset time period before maintenance of a maintenance vehicle according to after-sale data, and acquiring a second original characteristic data sample of any preset time period of a non-abnormal vehicle; respectively carrying out mathematical computation on the first original characteristic data sample and the second original characteristic data sample, and carrying out standardization processing on the first original characteristic data sample and the second original characteristic data sample after the mathematical computation to obtain a first characteristic data sample and a second characteristic data sample; training an original first vehicle abnormality detection model, an original second vehicle abnormality detection model and an original third vehicle abnormality detection model by adopting the first characteristic data sample, an abnormal vehicle label corresponding to the first characteristic data sample, a second characteristic data sample and an abnormal-free vehicle label corresponding to the second characteristic data sample, so as to obtain the first vehicle abnormality detection model, the second vehicle abnormality detection model and the third vehicle abnormality detection model.
Optionally, the device is further configured to: preprocessing the original characteristic data, and performing mathematical calculation on the preprocessed original characteristic data.
The device provided by the embodiment of the present application has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned.
As shown in fig. 3, an electronic device 600 provided in an embodiment of the present application includes: the system comprises a processor 601, a memory 602 and a bus, wherein the memory 602 stores machine-readable instructions executable by the processor 601, when the electronic device is running, the processor 601 communicates with the memory 602 through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the vehicle anomaly detection method.
Specifically, the memory 602 and the processor 601 can be general-purpose memories and processors, and are not particularly limited herein, and the method for detecting a vehicle abnormality can be performed when the processor 601 runs a computer program stored in the memory 602.
The processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 601 or instructions in the form of software. The processor 601 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but may also be a digital signal processor (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 602, and the processor 601 reads information in the memory 602 and performs the steps of the above method in combination with its hardware.
Corresponding to the above method for detecting a vehicle abnormality, the embodiment of the present application further provides a computer-readable storage medium storing machine-executable instructions that, when invoked and executed by a processor, cause the processor to execute the steps of the above method for detecting a vehicle abnormality.
The device for detecting the abnormality of the vehicle provided by the embodiment of the application can be specific hardware on equipment or software or firmware installed on the equipment and the like. The device provided by the embodiment of the present application has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned. It will be clear to those skilled in the art that, for convenience and brevity, the specific operation of the system, apparatus and unit described above may refer to the corresponding process in the above method embodiment, which is not described in detail herein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
As another example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments provided in the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the vehicle marking method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: like reference numerals and letters in the following figures denote like items, and thus once an item is defined in one figure, no further definition or explanation of it is required in the following figures, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above examples are only specific embodiments of the present application, and are not intended to limit the scope of the present application, but it should be understood by those skilled in the art that the present application is not limited thereto, and that the present application is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit of the corresponding technical solutions. Are intended to be encompassed within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. A method for detecting a vehicle abnormality, comprising:
acquiring preset original characteristic data in the current journey of the target vehicle;
performing mathematical calculation on the original characteristic data, and performing standardized treatment on the characteristic data after the mathematical calculation to obtain the characteristic data to be detected;
performing anomaly detection on the feature data to be detected by adopting a first vehicle anomaly detection model, a second vehicle anomaly detection model and a third vehicle anomaly detection model to obtain a first vehicle anomaly detection result, a second vehicle anomaly detection result and a third vehicle anomaly detection result, wherein the first vehicle anomaly detection model is a gradient lifting tree algorithm, the second vehicle anomaly detection model is a naive Bayesian algorithm, and the third vehicle anomaly detection model is a support vector machine algorithm;
and determining a target vehicle detection result according to the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result, determining an alarm level based on the target vehicle detection result, and further determining a maintenance suggestion according to the alarm level so as to prompt a user.
2. The method of claim 1, wherein the raw feature data comprises: user behavior feature data, battery condition feature data, vehicle basic information data and external data, wherein the mathematical calculation comprises: maximum value calculation, minimum value calculation, quartile calculation, average value calculation, and standard deviation calculation.
3. The method of claim 1, wherein determining a target vehicle detection result from the first vehicle abnormality detection result, the second vehicle abnormality detection result, and the third vehicle abnormality detection result comprises:
if at least two vehicle abnormality detection results among the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result show that the vehicle is abnormal, the target vehicle detection result is that the vehicle is abnormal, otherwise, the target vehicle detection result is that the vehicle is not abnormal.
4. The method of claim 1, wherein determining an alert level based on the target vehicle detection result comprises:
acquiring a historical alarm level of the target vehicle;
if the target vehicle detection result shows that the vehicle is abnormal, the alarm level is an alarm level which is obtained by adding one step to the historical alarm level;
and if the target vehicle detection result shows that the vehicle is not abnormal, the alarm level is the historical alarm level.
5. The method of claim 1, wherein determining a maintenance recommendation based on the alert level comprises:
and if the alarm level reaches a preset alarm level threshold, determining that the target vehicle needs maintenance, otherwise, determining that the target vehicle does not need maintenance.
6. The method according to claim 1, wherein the method further comprises:
acquiring a first original characteristic data sample of a preset time period before maintenance of a maintenance vehicle according to after-sale data, and acquiring a second original characteristic data sample of any preset time period of a non-abnormal vehicle;
performing mathematical computation on the first original characteristic data sample and the second original characteristic data sample respectively, and performing standardization processing on the first original characteristic data sample and the second original characteristic data sample after the mathematical computation to obtain a first characteristic data sample and a second characteristic data sample;
training an original first vehicle abnormality detection model, an original second vehicle abnormality detection model and an original third vehicle abnormality detection model by adopting the first characteristic data sample, an abnormal vehicle label corresponding to the first characteristic data sample, the second characteristic data sample and an abnormal-free vehicle label corresponding to the second characteristic data sample to obtain the first vehicle abnormality detection model, the second vehicle abnormality detection model and the third vehicle abnormality detection model.
7. The method of claim 1, wherein prior to mathematically calculating the raw feature data, the method further comprises:
preprocessing the original characteristic data, and performing mathematical calculation on the preprocessed original characteristic data.
8. A vehicle abnormality detection device, characterized by comprising:
the acquisition unit is used for acquiring preset original characteristic data in the current journey of the target vehicle;
the mathematical calculation unit is used for carrying out mathematical calculation on the original characteristic data and carrying out standardized processing on the characteristic data after the mathematical calculation to obtain the characteristic data to be detected;
the anomaly detection unit is used for carrying out anomaly detection on the feature data to be detected by adopting a first vehicle anomaly detection model, a second vehicle anomaly detection model and a third vehicle anomaly detection model to obtain a first vehicle anomaly detection result, a second vehicle anomaly detection result and a third vehicle anomaly detection result, wherein the first vehicle anomaly detection model is a gradient lifting tree algorithm, the second vehicle anomaly detection model is a naive Bayesian algorithm, and the third vehicle anomaly detection model is a support vector machine algorithm;
and the determining unit is used for determining a target vehicle detection result according to the first vehicle abnormality detection result, the second vehicle abnormality detection result and the third vehicle abnormality detection result, determining an alarm level based on the target vehicle detection result, and further determining a maintenance suggestion according to the alarm level so as to prompt a user.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of the preceding claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing machine executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any one of the preceding claims 1 to 7.
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| CN202311191068.8A CN117208003A (en) | 2023-09-14 | 2023-09-14 | Vehicle abnormality detection method and device and electronic equipment |
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