CN116958130A - Vehicle detection system and method based on machine learning - Google Patents
Vehicle detection system and method based on machine learning Download PDFInfo
- Publication number
- CN116958130A CN116958130A CN202311198826.9A CN202311198826A CN116958130A CN 116958130 A CN116958130 A CN 116958130A CN 202311198826 A CN202311198826 A CN 202311198826A CN 116958130 A CN116958130 A CN 116958130A
- Authority
- CN
- China
- Prior art keywords
- data
- machine learning
- characteristic data
- vehicle
- characteristic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000010801 machine learning Methods 0.000 title claims abstract description 94
- 238000001514 detection method Methods 0.000 title claims abstract description 63
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000012549 training Methods 0.000 claims abstract description 43
- 238000004458 analytical method Methods 0.000 claims abstract description 4
- 238000012423 maintenance Methods 0.000 claims description 11
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 230000001502 supplementing effect Effects 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims 1
- 230000002159 abnormal effect Effects 0.000 abstract description 3
- 230000008569 process Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 238000012935 Averaging Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000002826 coolant Substances 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Quality & Reliability (AREA)
- Medical Informatics (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The application discloses a vehicle detection system and method based on machine learning, and relates to the technical field of vehicle detection. The detection system comprises a machine learning model obtained through training of first characteristic data, a characteristic acquisition module for acquiring second characteristic data related to the first characteristic data, a difference analysis module for acquiring third characteristic data meeting a difference rule with the first characteristic data, a model training module for performing machine learning based on the third characteristic data and the second characteristic data, and a vehicle detection module for detecting instant data acquired on a vehicle based on the machine learning model. The detection method is suitable for the detection system. According to the application, the first characteristic data, the second characteristic data and the third characteristic data are used as training data for machine learning, redundant data which are generated conventionally are removed, and meanwhile, the data which possibly cause abnormal vehicles can be trained, so that the accuracy and the reliability of vehicle detection results are ensured.
Description
Technical Field
The application relates to the technical field of vehicle detection, in particular to a vehicle detection system and method based on machine learning.
Background
In the related art, a manual inspection method is generally used for inspecting safety of a vehicle, and commonly used functions of the vehicle, such as fuel, coolant, brake fluid, electric quantity, rearview mirror, etc., are inspected. However, during safety inspection of a vehicle, some functions may be omitted, such as inspection and maintenance of tires, so that a user cannot make a correct judgment on the overall health of the vehicle in time. In contrast, in the vehicle detection performed by the intelligent detection technique, for example, the vehicle detection performed by the machine learning, in the machine learning, when the number of training data is large, the learning accuracy becomes high. As a method of adding training data, however, if all of the data collected from various devices is used for machine learning, a decrease in learning accuracy may be incurred instead. Therefore, by optimizing the training data in the machine learning process, the accuracy of the vehicle detection result can be improved.
Disclosure of Invention
The application aims to provide a vehicle detection system and method based on machine learning, which are used for solving the technical problems in the background technology.
In order to achieve the above purpose, the present application discloses the following technical solutions:
in a first aspect, the present application discloses a machine learning based vehicle detection system comprising: the vehicle detection system comprises a machine learning model obtained through training of first characteristic data, a characteristic acquisition module for acquiring second characteristic data related to the first characteristic data, a difference analysis module for acquiring third characteristic data meeting a difference rule with the first characteristic data, a model training module for performing machine learning based on the third characteristic data and the second characteristic data, and a vehicle detection module for detecting instant data acquired on a vehicle based on the machine learning model; and after the model training module completes the model training task, carrying out model updating on the machine learning model, wherein the model updating comprises the step of supplementing second characteristic data and/or third characteristic data used in the model training task into the first characteristic data, and the difference rule comprises a preset characteristic difference rule and a preset result difference rule.
Preferably, the characteristic difference rule includes: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,,
the numerical differences include:or->A1 is the characteristic data corresponding to the third characteristic data, and A2 is the characteristic data corresponding to the first characteristic data.
Preferably, the method further comprises: and the data acquisition module is used for acquiring the second characteristic data and the third characteristic data.
Preferably, the second feature data and the third feature data are obtained by averaging at least two instant data acquired by the data acquisition module at the same time.
Preferably, when the ratio of the instant data M to the instant data N exceeds a preset threshold ρ when the instant data is averaged, the instant data M and the instant data N are respectively used as the corresponding second feature data or the third feature data.
Preferably, the model training module performs iterative training based on the first feature data, and the machine learning model obtained after training includes:
acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data;
generating a target data set from the vehicle data and data described in a factory/maintenance manual;
and performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain the machine learning model.
Preferably, the vehicle detection module inputs the instant data acquired from the vehicle into the machine learning model to obtain an output result of the machine learning model, and takes the output result as a vehicle detection result.
In a second aspect, the present application discloses a machine learning-based vehicle detection method, the method comprising a model building section and a vehicle detection section;
the model building section includes the steps of:
generating first characteristic data, wherein the first characteristic data comprises vehicle data acquired through big data and/or vehicle data acquired through a vehicle delivery/maintenance manual;
training through the first characteristic data to obtain a machine learning model;
acquiring second characteristic data associated with the first characteristic data;
acquiring third characteristic data which meets a difference rule with the first characteristic data;
performing machine learning based on the third feature data and the second feature data, and performing model updating on the machine learning model after finishing machine learning, wherein the model updating comprises supplementing second feature data and/or third feature data used in the model training task into the first feature data, and the difference rule comprises a preset feature difference rule and a preset result difference rule;
the vehicle detecting section includes the steps of:
and detecting the instant data acquired on the vehicle based on the machine learning model, and acquiring a vehicle detection result.
Preferably, the characteristic difference rule includes: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,,
the numerical differences include:or->A1 is the characteristic data corresponding to the third characteristic data, and A2 is the characteristic data corresponding to the first characteristic data.
Preferably, the machine learning model obtained includes:
acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data;
generating a target data set from the vehicle data and data described in a factory/maintenance manual;
performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain a machine learning model;
and, the vehicle detecting portion specifically includes: and inputting the instant data acquired from the vehicle into the machine learning model to obtain an output result of the machine learning model, and taking the output result as a vehicle detection result.
The beneficial effects are that: according to the vehicle detection system and method based on machine learning, the first characteristic data, the second characteristic data and the third characteristic data are used as training data of machine learning, redundant data which are generated conventionally are removed, meanwhile, the fact that data possibly causing vehicle abnormality can be trained is ensured, and further accuracy and reliability of vehicle detection results are ensured.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a machine learning based vehicle detection system in an embodiment of the present application;
fig. 2 is a flow chart of a vehicle detection method based on machine learning in an embodiment of the application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be clear and complete, and it is obvious that the described embodiments are only 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.
In this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The present embodiment discloses a vehicle detection system based on machine learning as shown in fig. 1, comprising: the vehicle detection system comprises a machine learning model obtained through training of first characteristic data, a characteristic acquisition module for acquiring second characteristic data related to the first characteristic data, a difference analysis module for acquiring third characteristic data meeting a difference rule with the first characteristic data, a model training module for performing machine learning based on the third characteristic data and the second characteristic data, and a vehicle detection module for detecting instant data acquired on a vehicle based on the machine learning model; and after the model training module completes the model training task, carrying out model updating on the machine learning model, wherein the model updating comprises the step of supplementing second characteristic data and/or third characteristic data used in the model training task into the first characteristic data, and the difference rule comprises a preset characteristic difference rule and a preset result difference rule. It is possible that the first characteristic data is vehicle data obtained by big data and/or vehicle data obtained by a vehicle delivery/maintenance manual, such data generally enabling normal data/data range of the vehicle; the second characteristic data is data of detected normal operation of the vehicle, and the third characteristic data is data generated when the vehicle is abnormal.
As a possible implementation manner of this embodiment, the feature difference rule includes: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,,
the numerical differences include:or->A1 is the characteristic data corresponding to the third characteristic data, and A2 is the characteristic data corresponding to the first characteristic data.
In this embodiment, the result difference rule may be a number of hardware structural differences obtained through image acquisition and sensor acquisition, and may be, but not limited to, detection result data corresponding to breakage of a vehicle body component, loosening of a connecting member, and the like.
As a preferred implementation manner of the present embodiment, the vehicle detection system based on machine learning further includes: and the data acquisition module is used for acquiring the second characteristic data and the third characteristic data. And the second characteristic data and the third characteristic data are obtained by averaging at least two instant data acquired at the same time by the data acquisition module. The average may be an average calculation method between numerical values, or may be a result expressed as a compromise of the result expression according to the result difference rule. Further, for the average value calculation mode, when the ratio of the instant data M to the instant data N exceeds the preset threshold ρ during the average of the instant data, the instant data M and the instant data N are respectively used as the corresponding second characteristic data or the third characteristic data, so that the data with larger difference can be ensured not to be removed, and the accuracy of the final detection result can be ensured.
As a possible implementation manner of this embodiment, the model training module performs iterative training based on the first feature data, and a machine learning model obtained after training includes:
acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data;
generating a target data set from the vehicle data and data described in a factory/maintenance manual;
and performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain the machine learning model.
Further, the vehicle detection module inputs the instant data acquired from the vehicle into the machine learning model to obtain an output result of the machine learning model, and the output result is used as a vehicle detection result.
The present embodiment discloses in a second aspect a machine learning-based vehicle detection method as shown in fig. 2, which includes a model building section and a vehicle detection section.
Specifically, the model building section includes the steps of:
s101, generating first characteristic data, wherein the first characteristic data comprises vehicle data acquired through big data and/or vehicle data acquired through a vehicle delivery/maintenance manual;
s102, training through first characteristic data to obtain a machine learning model;
s103, acquiring second characteristic data associated with the first characteristic data;
s104, obtaining third characteristic data which meets a difference rule with the first characteristic data;
s105, performing machine learning based on the third feature data and the second feature data, and performing model updating on the machine learning model after the machine learning is completed, wherein the model updating comprises supplementing second feature data and/or third feature data used in a model training task into the first feature data, and the difference rule comprises a preset feature difference rule and a preset result difference rule;
specifically, the vehicle detecting section includes the steps of:
s201-detecting the instant data acquired on the vehicle based on the machine learning model, and acquiring a vehicle detection result.
It should be noted that the vehicle detection method based on machine learning disclosed in the present embodiment is applicable to the vehicle detection system based on machine learning described above.
Thus, the feature difference rule includes: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,,
the numerical differences include:or->A1 is the characteristic data corresponding to the third characteristic data, and A2 is the characteristic data corresponding to the first characteristic data.
The machine learning model obtained comprises:
acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data;
generating a target data set from the vehicle data and data described in a factory/maintenance manual;
performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain a machine learning model;
and, the vehicle detecting portion specifically includes: and inputting the instant data acquired from the vehicle into the machine learning model to obtain an output result of the machine learning model, and taking the output result as a vehicle detection result.
By virtue of the above, the specific contents of other steps in the method can be compared with the functional descriptions of each module in the vehicle detection system based on machine learning, and the description is omitted herein.
In summary, according to the vehicle detection system and method based on machine learning of the present embodiment, the first feature data, the second feature data and the third feature data are used as training data for machine learning, so that redundant data conventionally occurring are removed, and meanwhile, data possibly causing abnormal vehicle is ensured to be trained, so that accuracy and reliability of a vehicle detection result are ensured.
In the embodiments provided by the present application, it is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For a hardware implementation, the processor may be implemented in one or more of the following units: an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to perform the functions described herein, or a combination thereof. For a software implementation, some or all of the flow of an embodiment may be accomplished by a computer program to instruct the associated hardware. When implemented, the above-described programs may be stored in or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. The computer-readable storage media may include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or 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.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present application, and although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present application.
Claims (10)
1. A machine learning based vehicle detection system, comprising: the vehicle detection system comprises a machine learning model obtained through training of first characteristic data, a characteristic acquisition module for acquiring second characteristic data related to the first characteristic data, a difference analysis module for acquiring third characteristic data meeting a difference rule with the first characteristic data, a model training module for performing machine learning based on the third characteristic data and the second characteristic data, and a vehicle detection module for detecting instant data acquired on a vehicle based on the machine learning model; and after the model training module completes the model training task, carrying out model updating on the machine learning model, wherein the model updating comprises the step of supplementing second characteristic data and/or third characteristic data used in the model training task into the first characteristic data, and the difference rule comprises a preset characteristic difference rule and a preset result difference rule.
2. The machine-learning based vehicle detection system of claim 1, wherein the feature difference rule comprises: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,,
the numerical differences include:or->A1 is the characteristic data corresponding to the third characteristic data, and A2 is the characteristic data corresponding to the first characteristic data.
3. The machine learning based vehicle detection system of claim 1 further comprising: and the data acquisition module is used for acquiring the second characteristic data and the third characteristic data.
4. The machine learning based vehicle detection system of claim 3 wherein the second and third characteristic data are each averaged by the data acquisition module from at least two instant data acquired at the same time.
5. The machine learning based vehicle detection system of claim 4, wherein when the ratio of the instant data M to the instant data N exceeds a preset threshold ρ when the instant data is averaged, the instant data M and the instant data N are respectively taken as the corresponding second feature data or the third feature data.
6. The machine-learning-based vehicle detection system of claim 1, wherein the model training module performs iterative training based on the first feature data, and the trained machine learning model comprises:
acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data;
generating a target data set from the vehicle data and data described in a factory/maintenance manual;
and performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain the machine learning model.
7. The machine learning based vehicle detection system of claim 6, wherein the vehicle detection module inputs the instant data collected on the vehicle into the machine learning model to obtain an output result of the machine learning model, and uses the output result as the vehicle detection result.
8. A vehicle detection method based on machine learning, characterized in that the method comprises a model construction part and a vehicle detection part;
the model building section includes the steps of:
generating first characteristic data, wherein the first characteristic data comprises vehicle data acquired through big data and/or vehicle data acquired through a vehicle delivery/maintenance manual;
training through the first characteristic data to obtain a machine learning model;
acquiring second characteristic data associated with the first characteristic data;
acquiring third characteristic data which meets a difference rule with the first characteristic data;
performing machine learning based on the third feature data and the second feature data, and performing model updating on the machine learning model after finishing machine learning, wherein the model updating comprises supplementing second feature data and/or third feature data used in the model training task into the first feature data, and the difference rule comprises a preset feature difference rule and a preset result difference rule;
the vehicle detecting section includes the steps of:
and detecting the instant data acquired on the vehicle based on the machine learning model, and acquiring a vehicle detection result.
9. The machine learning based vehicle detection method of claim 8 wherein the feature difference rule comprises: the characteristic data corresponding to the third characteristic data and the characteristic data corresponding to the first characteristic data have at least numerical value difference; wherein,,
the numerical differences include:or->A1 is the characteristic data corresponding to the third characteristic data, and A2 is the characteristic data corresponding to the first characteristic data.
10. The machine learning based vehicle detection method of claim 8 wherein the resulting machine learning model comprises:
acquiring vehicle data corresponding to a detected part of a vehicle; the vehicle data includes hardware module data, software module data, and electrical data;
generating a target data set from the vehicle data and data described in a factory/maintenance manual;
performing machine learning on the neural network initial model by taking the target data set as training characteristics to obtain a machine learning model;
and, the vehicle detecting portion specifically includes: and inputting the instant data acquired from the vehicle into the machine learning model to obtain an output result of the machine learning model, and taking the output result as a vehicle detection result.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202311198826.9A CN116958130B (en) | 2023-09-18 | 2023-09-18 | Vehicle detection system and method based on machine learning |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202311198826.9A CN116958130B (en) | 2023-09-18 | 2023-09-18 | Vehicle detection system and method based on machine learning |
Publications (2)
Publication Number | Publication Date |
---|---|
CN116958130A true CN116958130A (en) | 2023-10-27 |
CN116958130B CN116958130B (en) | 2024-01-05 |
Family
ID=88462323
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202311198826.9A Active CN116958130B (en) | 2023-09-18 | 2023-09-18 | Vehicle detection system and method based on machine learning |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN116958130B (en) |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20210056778A1 (en) * | 2019-08-19 | 2021-02-25 | Capital One Services, Llc | Techniques to detect vehicle anomalies based on real-time vehicle data collection and processing |
CN115169580A (en) * | 2022-06-30 | 2022-10-11 | 章鱼博士智能技术(上海)有限公司 | Training method and device for voltage difference abnormity detection model of vehicle battery pack |
CN116047164A (en) * | 2022-12-30 | 2023-05-02 | 章鱼博士智能技术(上海)有限公司 | Detection method and detection device for insulation resistance abnormality of electric automobile |
CN116502131A (en) * | 2023-06-26 | 2023-07-28 | 清华大学 | Bearing fault diagnosis model training method and device based on transfer learning |
-
2023
- 2023-09-18 CN CN202311198826.9A patent/CN116958130B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20210056778A1 (en) * | 2019-08-19 | 2021-02-25 | Capital One Services, Llc | Techniques to detect vehicle anomalies based on real-time vehicle data collection and processing |
CN115169580A (en) * | 2022-06-30 | 2022-10-11 | 章鱼博士智能技术(上海)有限公司 | Training method and device for voltage difference abnormity detection model of vehicle battery pack |
CN116047164A (en) * | 2022-12-30 | 2023-05-02 | 章鱼博士智能技术(上海)有限公司 | Detection method and detection device for insulation resistance abnormality of electric automobile |
CN116502131A (en) * | 2023-06-26 | 2023-07-28 | 清华大学 | Bearing fault diagnosis model training method and device based on transfer learning |
Also Published As
Publication number | Publication date |
---|---|
CN116958130B (en) | 2024-01-05 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
KR101967339B1 (en) | System and Method for Diagnosing Fault and Backup of ADAS Sensors based on Deep Learning | |
CN111208800B (en) | Automobile diagnosis method and device and vehicle communication interface | |
WO2019205017A1 (en) | Method, system, device, and computer readable storage medium for diagnosing vehicle | |
CN108501757B (en) | Battery management system, current sampling method and device and electric automobile | |
KR20170104705A (en) | System and method for diagnosing facility fault | |
CN112172991B (en) | Balance car fault positioning method, equipment, storage medium and device | |
KR101565030B1 (en) | Decision system for error of car using the data analysis and method therefor | |
CN116958130B (en) | Vehicle detection system and method based on machine learning | |
CN114548280A (en) | Fault diagnosis model training method, fault diagnosis method and electronic equipment | |
CN113359657B (en) | ECU diagnosis configuration code verification method and system and electronic control unit thereof | |
CN112990372A (en) | Data processing method, model training device and electronic equipment | |
KR20220068799A (en) | System for detecting error of automation equipment and method thereof | |
CN116910552A (en) | Vehicle fault detection method and device, vehicle-mounted terminal and storage medium | |
CN114625106B (en) | Method, device, electronic equipment and storage medium for vehicle diagnosis | |
CN113204994B (en) | Detection method and system for original factory electronic accessories on vehicle and cloud server | |
CN115143909A (en) | Brake pedal calibration method, system storage medium and vehicle | |
CN113763305B (en) | Method and device for calibrating defect of article and electronic equipment | |
CN111045875B (en) | Vehicle accident detection method and related equipment | |
JP2020083138A (en) | Vehicle electronic control device and diagnostic system | |
CN112887262B (en) | Automobile information safety protection method and device based on multi-source information fusion | |
CN116512821A (en) | Vehicle tire safety detection method and device, electronic equipment and storage medium | |
CN115718680B (en) | Data reading method, system, computer and readable storage medium | |
JP2020083139A (en) | Vehicle electronic control device and diagnostic system | |
CN112637210B (en) | Data detection method and device, electronic equipment and readable storage medium | |
CN117150229A (en) | Data processing method, device, equipment and medium |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |