CN117807445A - Fog recognition method, device, equipment and storage medium - Google Patents
Fog recognition method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention belongs to the technical field of vehicles, and discloses a fog identification method, a fog identification device, fog identification equipment and a storage medium. The method comprises the steps of obtaining vehicle running data of a target vehicle; performing similarity matching according to the vehicle driving data and a target fogging database, and determining a similarity matching result; and determining a window fogging result of the target vehicle according to the similarity matching result. Through the mode, the vehicle running data of the target vehicle and the target fogging database are utilized for similarity matching, the window fogging result of the target vehicle is determined based on the obtained similarity matching result, the window fogging condition can be rapidly judged based on the vehicle running data, the judgment accuracy is ensured, the problems of misjudgment, overlarge error and the like caused by complex driving environment can be effectively solved, and meanwhile, the cost of fogging judgment is reduced.
Description
Technical Field
The present invention relates to the field of vehicle technologies, and in particular, to a fog identifying method, a fog identifying device, and a storage medium.
Background
During the running of a vehicle, a window fogging situation is easily encountered. In general, when a window fogging situation is encountered, a driver is required to manually operate and take corresponding defogging measures according to the fogging state. The existing windshield fog recognition algorithm is common to the method using visual detection and image recognition, and the existing algorithm is easy to be subjected to environmental influence and misjudgment in the environment with darker driving light at night. Meanwhile, the existing vehicle window fogging intelligent algorithm needs to be added with a camera or an image collector, so that the cost is increased.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a fog recognition method and aims to solve the technical problem of how to accurately and efficiently recognize the fog of a vehicle window on the premise of controlling cost in the prior art.
To achieve the above object, the present invention provides a fogging identification method comprising the steps of:
acquiring vehicle running data of a target vehicle;
performing similarity matching according to the vehicle driving data and a target fogging database, and determining a similarity matching result;
and determining a window fogging result of the target vehicle according to the similarity matching result.
Optionally, the acquiring vehicle driving data of the target vehicle includes:
acquiring vehicle auxiliary information, vehicle running speed, in-vehicle temperature data, out-of-vehicle temperature data and vehicle humidity data of a target vehicle;
determining a vehicle temperature difference according to the vehicle interior temperature data and the vehicle exterior temperature data;
and obtaining vehicle running data according to the vehicle temperature difference, the vehicle auxiliary information, the vehicle running speed, the vehicle internal temperature data, the vehicle external temperature data and the vehicle humidity data.
Optionally, the acquiring vehicle auxiliary information of the target vehicle includes:
acquiring vehicle sensor information, weather information and vehicle position information of a target vehicle;
determining the number of passengers in the vehicle according to the sensor information;
and obtaining vehicle auxiliary information according to the number of passengers in the vehicle, the weather information and the vehicle position information.
Optionally, before the similarity matching is performed according to the vehicle driving data and the target fogging database, the method further includes:
acquiring working condition environment data and working condition running data under a plurality of working conditions;
determining working condition environment parameters of each working condition according to the working condition environment data of each working condition;
and constructing a target fogging database according to the working condition running data of each working condition and the working condition environment parameters of each working condition.
Optionally, the determining the working condition environment parameters of each working condition according to the working condition environment data of each working condition includes:
determining the working condition dew point temperature, the working condition indoor temperature and the working condition outdoor temperature and the working condition relative humidity of each working condition according to the working condition environment data under each working condition;
determining the dew point correction temperature of each working condition according to the working condition dew point temperature of each working condition and a preset correction coefficient;
determining the working condition temperature difference of each working condition according to the working condition indoor temperature of each working condition and the working condition outdoor temperature of each working condition;
and determining the working condition environment parameters of each working condition according to the working condition temperature difference of each working condition, the dew point correction temperature of each working condition, the working condition indoor temperature of each working condition, the working condition outdoor temperature of each working condition and the working condition relative humidity of each working condition.
Optionally, the determining a similarity matching result according to the similarity matching between the vehicle driving data and the target fogging database includes:
comparing each data segment in the vehicle driving data with each data segment in the target fogging database, and determining a comparison result;
determining a working condition corresponding to the vehicle driving data in the target fogging database according to the comparison result;
and obtaining a similarity matching result according to the working condition corresponding to the vehicle running data.
Optionally, after determining the window fogging result of the target vehicle according to the similarity matching result, the method further includes:
when the window fogging result is a preset fogging result, acquiring a target window image;
determining vehicle driving visibility according to the target window image;
and when the vehicle driving visibility is lower than the visibility threshold, sending warning information to a user for fog warning.
In addition, in order to achieve the above object, the present invention also proposes a mist identifying device comprising:
the acquisition module is used for acquiring vehicle running data of the target vehicle;
the matching module is used for carrying out similarity matching according to the vehicle running data and the target fogging database and determining a similarity matching result;
and the processing module is used for determining the window fogging result of the target vehicle according to the similarity matching result.
In addition, in order to achieve the above object, the present invention also proposes a mist identifying apparatus comprising: a memory, a processor, and a fogging identification program stored on the memory and executable on the processor, the fogging identification program being configured to implement the steps of the fogging identification method as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a fogging identification program which, when executed by a processor, implements the steps of the fogging identification method as described above.
The method comprises the steps of obtaining vehicle running data of a target vehicle; performing similarity matching according to the vehicle driving data and a target fogging database, and determining a similarity matching result; and determining a window fogging result of the target vehicle according to the similarity matching result. Through the mode, the vehicle running data of the target vehicle and the target fogging database are utilized for similarity matching, the window fogging result of the target vehicle is determined based on the obtained similarity matching result, the window fogging condition can be rapidly judged based on the vehicle running data, the judgment accuracy is ensured, the problems of misjudgment, overlarge error and the like caused by complex driving environment can be effectively solved, and meanwhile, the cost of fogging judgment is reduced.
Drawings
FIG. 1 is a schematic diagram of a hardware operating environment fog recognition device according to an embodiment of the present invention;
FIG. 2 is a flow chart of a first embodiment of a haze identifying method according to the present invention;
FIG. 3 is a flow chart of a second embodiment of the mist identifying method of the present invention;
FIG. 4 is a schematic overall flow chart of an embodiment of a fog recognition method according to the present invention;
fig. 5 is a block diagram showing the construction of a first embodiment of the mist identifying means of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a fog identifying device in a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the fog recognition apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 is not limiting of the mist identification device and may include more or fewer components than shown, or certain components may be combined, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a mist identification program may be included in the memory 1005 as one type of storage medium.
In the fog recognition apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the mist identifying apparatus of the present invention may be provided in the mist identifying apparatus, which invokes the mist identifying program stored in the memory 1005 through the processor 1001, and executes the mist identifying method provided by the embodiment of the present invention.
An embodiment of the present invention provides a fogging identification method, referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the fogging identification method of the present invention.
In this embodiment, the fogging identification method includes the following steps:
step S10: vehicle travel data of a target vehicle is acquired.
The execution body of the embodiment is a fog recognition device, where the fog recognition device has functions of data processing, data communication, program running, and the like, and the fog recognition device may be an integrated controller, a control computer, or other devices with similar functions, which is not limited in this embodiment. In this embodiment, the fog recognition apparatus may be a control unit of an automobile.
It is understood that the vehicle travel data includes, but is not limited to, vehicle assistance information and vehicle real-time data, the vehicle real-time data includes, but is not limited to, inside and outside temperature and humidity data and vehicle travel speed, and the vehicle assistance information includes, but is not limited to, outside weather information, vehicle position information, and the number of occupants in the vehicle.
In a specific implementation, in order to accurately acquire the vehicle running data, further, the acquiring the vehicle running data of the target vehicle includes: acquiring vehicle auxiliary information, vehicle running speed, in-vehicle temperature data, out-of-vehicle temperature data and vehicle humidity data of a target vehicle; determining a vehicle temperature difference according to the vehicle interior temperature data and the vehicle exterior temperature data; and obtaining vehicle running data according to the vehicle temperature difference, the vehicle auxiliary information, the vehicle running speed, the vehicle internal temperature data, the vehicle external temperature data and the vehicle humidity data.
The vehicle auxiliary information, the vehicle running speed, the vehicle interior temperature data, the vehicle exterior temperature data and the vehicle humidity data of the target vehicle are acquired through a plurality of sensors on the target vehicle, the temperature difference between the interior and exterior of the target vehicle is determined based on the vehicle interior temperature data and the vehicle exterior temperature data, the temperature difference between the interior and exterior of the target vehicle is the vehicle temperature difference, the vehicle humidity data comprises the vehicle interior humidity data and the vehicle exterior humidity data, the vehicle temperature difference, the vehicle running speed, the vehicle interior temperature data, the vehicle exterior temperature data and the vehicle humidity data are taken as vehicle real-time data, and the vehicle real-time data and the vehicle auxiliary information are taken as vehicle running data together.
It may be appreciated that, to accurately obtain the vehicle auxiliary information, further, the obtaining the vehicle auxiliary information of the target vehicle includes: acquiring vehicle sensor information, weather information and vehicle position information of a target vehicle; determining the number of passengers in the vehicle according to the sensor information; and obtaining vehicle auxiliary information according to the number of passengers in the vehicle, the weather information and the vehicle position information.
In a specific implementation, weather information can embody weather conditions of the current environment of the target vehicle, vehicle sensor information refers to information collected by weight sensors installed in seats of the target vehicle, whether people sit on each seat can be determined according to the vehicle sensor information, and accordingly the number of passengers in the vehicle can be obtained, and vehicle auxiliary information of the target vehicle can be obtained according to the number of passengers in the vehicle, the weather information and the vehicle position information.
Step S20: and carrying out similarity matching according to the vehicle running data and the target fogging database, and determining a similarity matching result.
The target fogging database is a target range database constructed based on a large number of vehicle running data under the condition that the vehicle window is fogged and the vehicle running data under the condition that the vehicle window is not fogged, and the vehicle running data under different working conditions when the vehicle window is fogged and the vehicle window is not fogged in a normal working state are obtained through an environment simulation test and a road test calibration test. The target fogging database comprises vehicle running data under a plurality of working conditions in a vehicle window fogging state and vehicle running data under a plurality of working conditions in a vehicle window non-fogging state.
It can be understood that the vehicle running data and the vehicle running data stored in the target fogging database are subjected to similarity matching, so as to obtain the window state corresponding to the vehicle running data with high similarity to the current vehicle running data of the target vehicle in the target fogging database, and the window state corresponding to the vehicle running data is the similarity matching result.
In a specific implementation, in order to accurately perform similarity matching, so as to obtain a corresponding matching result, further, the step of performing similarity matching according to the vehicle driving data and the target fogging database, and determining the similarity matching result includes: comparing each data segment in the vehicle driving data with each data segment in the target fogging database, and determining a comparison result; determining a working condition corresponding to the vehicle driving data in the target fogging database according to the comparison result; and obtaining a similarity matching result according to the working condition corresponding to the vehicle running data.
It should be noted that, a comparison is performed between a data segment representing each parameter information in the vehicle driving data and each data segment in the target fogging data to obtain a comparison result, a working condition corresponding to the vehicle driving data is determined in the target fogging database according to the comparison result, and a window state corresponding to the vehicle driving data can be determined according to the working condition, so that a similarity matching result is obtained.
Step S30: and determining a window fogging result of the target vehicle according to the similarity matching result.
After determining the state of the window corresponding to the target vehicle, whether the window of the target vehicle is fogged or not can be known, so that a window fogged result of the target vehicle is obtained, wherein the window fogged result comprises two conditions of a window fogged result and a window non-fogged result.
It may be appreciated that, in order to ensure the safety of the vehicle running, further, after determining the window fogging result of the target vehicle according to the similarity matching result, the method further includes: when the window fogging result is a preset fogging result, acquiring a target window image; determining vehicle driving visibility according to the target window image; and when the vehicle driving visibility is lower than the visibility threshold, sending warning information to a user for fog warning.
In specific implementation, the preset fogging result refers to a window fogging result, when the window fogging result is the preset fogging result, an image corresponding to a window right in front of a target vehicle is acquired, the image corresponding to the window right in front of the target vehicle is a target window image, the visibility of the target vehicle when the target vehicle is currently running is determined according to the target window image and a target visibility calculation model, and the visibility of the target vehicle when the target vehicle is currently running is the vehicle running visibility. The target visibility calculation model is a model obtained by performing deep learning based on a large number of window images and the corresponding visibility, the target window images are input into the target visibility calculation model, and the target visibility calculation model can directly output the corresponding vehicle driving visibility.
When the visibility of the vehicle running is lower than the visibility threshold set by the user, it is indicated that the current running is dangerous, at this time, warning information needs to be generated, and the warning information is sent to the user to perform fog warning, so that the user can adjust the current running strategy according to the warning information, and the current running strategy can be a speed reduction strategy or other strategies.
The embodiment obtains the vehicle running data of the target vehicle; performing similarity matching according to the vehicle driving data and a target fogging database, and determining a similarity matching result; and determining a window fogging result of the target vehicle according to the similarity matching result. Through the mode, the vehicle running data of the target vehicle and the target fogging database are utilized for similarity matching, the window fogging result of the target vehicle is determined based on the obtained similarity matching result, the window fogging condition can be rapidly judged based on the vehicle running data, the judgment accuracy is ensured, the problems of misjudgment, overlarge error and the like caused by complex driving environment can be effectively solved, and meanwhile, the cost of fogging judgment is reduced.
Referring to fig. 3, fig. 3 is a flowchart illustrating a second embodiment of a fog recognition method according to the present invention.
Based on the first embodiment, the fogging identifying method of the present embodiment further includes, before the step S20:
step S21: and acquiring working condition environment data and working condition running data under a plurality of working conditions.
The vehicle running data under a plurality of working conditions in a vehicle window fogging state and the vehicle running data under a plurality of working conditions in a vehicle window non-fogging state are respectively obtained, and the vehicle running data under each working condition comprises working condition environment data and working condition running data under each working condition.
It will be appreciated that each condition has indicated that its corresponding window condition is in particular a window foggy condition or a window non-foggy condition, and that the condition environment data includes, but is not limited to, dew point temperature, vehicle interior temperature, vehicle exterior temperature, and relative humidity of the vehicle under each condition, and the condition travel data includes, but is not limited to, vehicle exterior weather information W, number of occupants in the vehicle P, vehicle travel geographic location information L, and vehicle travel speed V under each condition.
In the specific implementation, working condition environment data and working condition running data under all working conditions are obtained through an environment simulation test and a road test calibration test.
Step S22: and determining the working condition environment parameters of each working condition according to the working condition environment data of each working condition.
It should be noted that, because the working condition environment data under each working condition has parameters to be corrected, the parameters to be corrected in the working condition environment data under each working condition are corrected, so as to obtain the working condition environment parameters of each working condition.
It may be appreciated that, to obtain accurate working condition environment parameters, further, determining the working condition environment parameters of each working condition according to the working condition environment data of each working condition includes: determining the working condition dew point temperature, the working condition indoor temperature and the working condition outdoor temperature and the working condition relative humidity of each working condition according to the working condition environment data under each working condition; determining the dew point correction temperature of each working condition according to the working condition dew point temperature of each working condition and a preset correction coefficient; determining the working condition temperature difference of each working condition according to the working condition indoor temperature of each working condition and the working condition outdoor temperature of each working condition; and determining the working condition environment parameters of each working condition according to the working condition temperature difference of each working condition, the dew point correction temperature of each working condition, the working condition indoor temperature of each working condition, the working condition outdoor temperature of each working condition and the working condition relative humidity of each working condition.
In a specific implementation, according to the working conditionsCondition environmental data determine condition dew point temperature T for each condition d 1, the vehicle interior temperature (i.e., the operating condition indoor temperature T (in)), the vehicle exterior temperature (i.e., the operating condition outdoor temperature T (out)), and the relative humidity of the vehicle (i.e., the operating condition relative humidity RH).
The dew point temperature is corrected according to the working condition dew point temperature of each working condition and a preset percentage correction coefficient t%, and the preset percentage correction coefficient is used for correcting the dew point temperatureThe temperature of the working condition dew point of each working condition is corrected by utilizing the preset correction coefficient to obtain the corrected dew point temperature of each working condition, wherein the corrected dew point temperature is the dew point correction temperature, and the dew point correction temperature is +.>。
It can be understood that the temperature difference is calculated according to the temperature in the working chamber and the temperature outside the working chamber under each working condition, and the temperature difference between the vehicle chamber and the outside under each working condition is determinedThe temperature difference between the indoor and the outdoor of the vehicle is the working condition temperature difference.
In the specific implementation, the working condition temperature difference of each working condition, the dew point correction temperature of each working condition, the working condition indoor temperature of each working condition, the working condition outdoor temperature of each working condition and the working condition relative humidity of each working condition are taken as working condition environment parameters of each working condition.
Step S23: and constructing a target fogging database according to the working condition running data of each working condition and the working condition environment parameters of each working condition.
The target fogging database is constructed according to the working condition running data of each working condition and the working condition environment parameters of each working condition, and comprises dew point correction parameters Td, working condition relative humidity RH, vehicle indoor and outdoor temperatures T and working condition temperature differences under different working conditionsData such as vehicle travel speed V, vehicle travel geographical position information L, outside weather information W, and the number of occupants in the vehicle P.
It will be appreciated that as shown in fig. 4, the vehicle travel data including, but not limited to, the vehicle interior temperature of the target vehicle, the vehicle exterior temperature, the vehicle interior humidity, the vehicle exterior humidity, the vehicle travel speed, the vehicle current location, the vehicle exterior environment information including, but not limited to, the outside weather information, and the number of occupants in the vehicle, is subjected to similarity matching with the target fogging database to obtain the window fogging result of the target vehicle. Based on a target fogging database containing a large amount of data for various driving environments, compared with a method for detecting whether a vehicle window is fogged or not through images, the method reduces the influence caused by driving environment change, adjusts and optimizes data in real time, and improves accuracy.
According to the embodiment, working condition environment data and working condition running data under a plurality of working conditions are obtained; determining working condition environment parameters of each working condition according to the working condition environment data of each working condition; and constructing a target fogging database according to the working condition running data of each working condition and the working condition environment parameters of each working condition. By the method, the working condition environment parameters of the working conditions are determined based on the working condition environment data of the working conditions, and the target fogging database is constructed based on the working condition running data of the working conditions and the working condition environment parameters of the working conditions, so that the accuracy of the follow-up determination of the vehicle window fogging result is ensured.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium stores a fog identification program, and the fog identification program realizes the steps of the fog identification method when being executed by a processor.
Referring to fig. 4, fig. 5 is a block diagram showing the structure of a first embodiment of the mist identifying means of the present invention.
As shown in fig. 5, the fog recognition device provided by the embodiment of the present invention includes:
an acquisition module 10 for acquiring vehicle travel data of the target vehicle.
And the matching module 20 is used for carrying out similarity matching according to the vehicle running data and the target fogging database, and determining a similarity matching result.
And the processing module 30 is used for determining a window fogging result of the target vehicle according to the similarity matching result.
The embodiment obtains the vehicle running data of the target vehicle; performing similarity matching according to the vehicle driving data and a target fogging database, and determining a similarity matching result; and determining a window fogging result of the target vehicle according to the similarity matching result. Through the mode, the vehicle running data of the target vehicle and the target fogging database are utilized for similarity matching, the window fogging result of the target vehicle is determined based on the obtained similarity matching result, the window fogging condition can be rapidly judged based on the vehicle running data, the judgment accuracy is ensured, the problems of misjudgment, overlarge error and the like caused by complex driving environment can be effectively solved, and meanwhile, the cost of fogging judgment is reduced.
In an embodiment, the acquiring module 10 is further configured to acquire vehicle auxiliary information, vehicle running speed, in-vehicle temperature data, out-of-vehicle temperature data, and vehicle humidity data of the target vehicle;
determining a vehicle temperature difference according to the vehicle interior temperature data and the vehicle exterior temperature data;
and obtaining vehicle running data according to the vehicle temperature difference, the vehicle auxiliary information, the vehicle running speed, the vehicle internal temperature data, the vehicle external temperature data and the vehicle humidity data.
In an embodiment, the acquiring module 10 is further configured to acquire vehicle sensor information, weather information, and vehicle location information of the target vehicle;
determining the number of passengers in the vehicle according to the sensor information;
and obtaining vehicle auxiliary information according to the number of passengers in the vehicle, the weather information and the vehicle position information.
In an embodiment, the matching module 20 is further configured to obtain operating condition environment data and operating condition driving data under a plurality of operating conditions;
determining working condition environment parameters of each working condition according to the working condition environment data of each working condition;
and constructing a target fogging database according to the working condition running data of each working condition and the working condition environment parameters of each working condition.
In one embodiment, the matching module 20 is further configured to determine a working condition dew point temperature, a working condition indoor temperature, a working condition outdoor temperature, and a working condition relative humidity of each working condition according to the working condition environmental data under each working condition;
determining the dew point correction temperature of each working condition according to the working condition dew point temperature of each working condition and a preset correction coefficient;
determining the working condition temperature difference of each working condition according to the working condition indoor temperature of each working condition and the working condition outdoor temperature of each working condition;
and determining the working condition environment parameters of each working condition according to the working condition temperature difference of each working condition, the dew point correction temperature of each working condition, the working condition indoor temperature of each working condition, the working condition outdoor temperature of each working condition and the working condition relative humidity of each working condition.
In an embodiment, the matching module 20 is further configured to compare each data segment in the vehicle driving data with each data segment in the target fogging database, and determine a comparison result;
determining a working condition corresponding to the vehicle driving data in the target fogging database according to the comparison result;
and obtaining a similarity matching result according to the working condition corresponding to the vehicle running data.
In an embodiment, the processing module 30 is further configured to collect a target window image when the window fogging result is a preset fogging result;
determining vehicle driving visibility according to the target window image;
and when the vehicle driving visibility is lower than the visibility threshold, sending warning information to a user for fog warning.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the invention as desired, and the invention is not limited thereto.
It should be understood that, although the steps in the flowcharts in the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the figures may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily occurring in sequence, but may be performed alternately or alternately with other steps or at least a portion of the other steps or stages.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
Furthermore, it should be noted that, 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 system 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 system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of embodiments, it will be clear to a person skilled in the art that the above embodiment method may be implemented by means of software plus a necessary general hardware platform, but may of course also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk) and comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A fogging identification method, characterized in that the fogging identification method comprises:
acquiring vehicle running data of a target vehicle;
performing similarity matching according to the vehicle driving data and a target fogging database, and determining a similarity matching result;
and determining a window fogging result of the target vehicle according to the similarity matching result.
2. The fog recognition method according to claim 1, wherein the acquiring vehicle travel data of the target vehicle comprises:
acquiring vehicle auxiliary information, vehicle running speed, in-vehicle temperature data, out-of-vehicle temperature data and vehicle humidity data of a target vehicle;
determining a vehicle temperature difference according to the vehicle interior temperature data and the vehicle exterior temperature data;
and obtaining vehicle running data according to the vehicle temperature difference, the vehicle auxiliary information, the vehicle running speed, the vehicle internal temperature data, the vehicle external temperature data and the vehicle humidity data.
3. The fog recognition method according to claim 2, wherein the acquiring vehicle assistance information of the target vehicle includes:
acquiring vehicle sensor information, weather information and vehicle position information of a target vehicle;
determining the number of passengers in the vehicle according to the sensor information;
and obtaining vehicle auxiliary information according to the number of passengers in the vehicle, the weather information and the vehicle position information.
4. The method for identifying fog as claimed in claim 1, wherein said matching of the degree of similarity between the vehicle travel data and the target fog database is performed, and further comprising, before determining the result of the matching of the degree of similarity:
acquiring working condition environment data and working condition running data under a plurality of working conditions;
determining working condition environment parameters of each working condition according to the working condition environment data of each working condition;
and constructing a target fogging database according to the working condition running data of each working condition and the working condition environment parameters of each working condition.
5. The method of claim 4, wherein determining the operating condition environment parameters for each operating condition based on the operating condition environment data for each operating condition comprises:
determining the working condition dew point temperature, the working condition indoor temperature and the working condition outdoor temperature and the working condition relative humidity of each working condition according to the working condition environment data under each working condition;
determining the dew point correction temperature of each working condition according to the working condition dew point temperature of each working condition and a preset correction coefficient;
determining the working condition temperature difference of each working condition according to the working condition indoor temperature of each working condition and the working condition outdoor temperature of each working condition;
and determining the working condition environment parameters of each working condition according to the working condition temperature difference of each working condition, the dew point correction temperature of each working condition, the working condition indoor temperature of each working condition, the working condition outdoor temperature of each working condition and the working condition relative humidity of each working condition.
6. The method of claim 1, wherein the determining a similarity match result from the similarity match between the vehicle travel data and the target fogging database comprises:
comparing each data segment in the vehicle driving data with each data segment in the target fogging database, and determining a comparison result;
determining a working condition corresponding to the vehicle driving data in the target fogging database according to the comparison result;
and obtaining a similarity matching result according to the working condition corresponding to the vehicle running data.
7. The fogging identification method according to any one of claims 1 to 6, wherein after said determining a window fogging result of said target vehicle from said similarity matching result, further comprising:
when the window fogging result is a preset fogging result, acquiring a target window image;
determining vehicle driving visibility according to the target window image;
and when the vehicle driving visibility is lower than the visibility threshold, sending warning information to a user for fog warning.
8. A fog recognition device, characterized in that the fog recognition device comprises:
the acquisition module is used for acquiring vehicle running data of the target vehicle;
the matching module is used for carrying out similarity matching according to the vehicle running data and the target fogging database and determining a similarity matching result;
and the processing module is used for determining the window fogging result of the target vehicle according to the similarity matching result.
9. A mist identification device, characterized in that the device comprises: a memory, a processor and a fogging identification program stored on the memory and executable on the processor, the fogging identification program being configured to implement the steps of the fogging identification method according to any one of claims 1 to 7.
10. A storage medium having stored thereon a mist-generation identification program which, when executed by a processor, implements the steps of the mist-generation identification method according to any one of claims 1 to 7.
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