CN113111942A - Method and device for extracting driving condition data and electronic equipment - Google Patents
Method and device for extracting driving condition data and electronic equipment Download PDFInfo
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Abstract
The invention provides a method and a device for extracting driving condition data and electronic equipment, wherein the method comprises the following steps: acquiring original driving condition data at each moment; extracting the characteristics of the original driving condition data at each moment to obtain the characteristics of the original driving condition at each moment; the importance judgment is carried out on the original driving condition characteristics at each moment by adopting an information influence judgment model to obtain an importance judgment result; and extracting target driving condition data from the original driving condition data according to the importance judgment result. The method can extract the target driving condition data with driving condition representativeness from the original driving condition data with large noise, has small data volume and driving condition representativeness, and is more beneficial to the analysis and detection of the target vehicle by engineering personnel according to the target driving condition data.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a method and a device for extracting driving condition data and electronic equipment.
Background
With the enhancement of environmental awareness of people, the new energy automobile industry develops rapidly, and new energy automobiles such as electric automobiles gradually enter thousands of households.
In the use process of the electric automobile, an engineer often needs to analyze and detect the electric automobile according to the driving condition data of the electric automobile, and the driving condition data of the electric automobile is generally collected by a data collection system arranged on the electric automobile. However, when the electric vehicle is running, the electric vehicle may run discontinuously, so that the original driving condition data acquired by its own data acquisition system in real time is poor in continuity, the data volume of the original driving condition data is huge, and the data volume of the original driving condition data includes many noise data which do not have driving condition representativeness, and an engineer cannot effectively analyze and detect the vehicle according to the original driving condition data with large noise and huge data volume (for example, continue a journey of the vehicle, calculate the service life of a vehicle battery pack, perform early warning analysis on the vehicle, and the like), but the analysis and the detection of the vehicle are very important. Therefore, how to extract the driving condition data with driving condition representativeness from the original driving condition data with large noise and huge data amount becomes a problem which needs to be solved urgently at present.
Disclosure of Invention
In view of this, the present invention provides a method, an apparatus, and an electronic device for extracting driving condition data, so as to alleviate the technical problem that the prior art cannot extract driving condition data having driving condition representativeness from original driving condition data with large noise and large data volume.
In a first aspect, an embodiment of the present invention provides a method for extracting driving condition data, including:
acquiring original driving condition data of a target vehicle at each moment acquired in the driving process;
extracting the characteristics of the original driving condition data at each moment to obtain the characteristics of the original driving condition at each moment;
judging the importance of the original driving condition characteristics at each moment by adopting an information influence judgment model to obtain an importance judgment result;
and extracting target driving condition data from the original driving condition data according to the importance judgment result.
Further, the feature extraction of the original driving condition data at each moment includes:
acquiring a predefined target characteristic;
and extracting the characteristics of the original driving condition data at each moment according to the predefined target characteristics to obtain the original driving condition characteristics at each moment.
Further, the information influence determination model includes: the first information influence judgment module, the second information influence judgment module and the output module adopt an information influence judgment model to judge the importance of the original driving condition characteristics at each moment, and the judgment comprises the following steps:
inputting the original driving condition characteristics of each moment into the first information influence judgment module to obtain time slice characteristics corresponding to the original driving condition characteristics of each moment and an importance result of each moment on a time slice to which the time slice characteristics belong, wherein the time slice characteristics represent vector representation of the working condition information in each time slice;
determining time information characteristics and speed change characteristics corresponding to the time slice characteristics based on the original driving condition data at each time;
splicing the time slice characteristics with the time information characteristics and the speed change characteristics respectively;
inputting the characteristics obtained after splicing into the second information influence judgment module to obtain the integral time characteristics and the importance results of each time slice on the integral time, wherein the integral time characteristics represent the vector representation of the working condition information in the integral time;
and inputting the overall time characteristics, the importance results of the moments on the time slices to which the moments belong and the importance results of the time slices on the overall time to an output module to obtain the importance judgment result.
Further, the importance determination result includes: extracting target driving condition data from the original driving condition data according to the importance judgment result, wherein the importance of each moment in the overall time and/or the importance of each time slice in the overall time comprises the following steps:
when the importance judgment result is the importance of each moment in the whole time, extracting target driving condition data of a target moment from the original driving condition data according to the importance of each moment in the whole time;
and when the importance judgment result is the importance of each time slice in the whole time, extracting target driving condition data of a target time slice from the original driving condition data according to the importance of each time slice in the whole time.
Further, the method further comprises:
deploying an original information influence judgment model;
obtaining a training sample set of driving condition data, wherein the training sample set comprises: original driving condition data samples and labeled target driving condition data samples;
and training the original information influence judgment model through the training sample set to obtain the information influence judgment model.
Further, the first information influence determination module includes: a first time series module and a first attention mechanism module, the second information influence determination module comprising: a second time series module and a second attention mechanism module.
In a second aspect, an embodiment of the present invention further provides a device for extracting driving condition data, including:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring original driving condition data of a target vehicle at each moment acquired in the driving process;
the characteristic extraction unit is used for extracting the characteristics of the original driving condition data at each moment to obtain the original driving condition characteristics at each moment;
the importance judgment unit is used for judging the importance of the original driving condition characteristics at each moment by adopting an information influence judgment model to obtain an importance judgment result;
and the data extraction unit is used for extracting target driving condition data from the original driving condition data according to the importance judgment result.
Further, the feature extraction unit includes:
the acquisition module is used for acquiring predefined target characteristics;
and the characteristic extraction module is used for extracting the characteristics of the original driving condition data at each moment according to the predefined target characteristics to obtain the original driving condition characteristics at each moment.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to any one of the above first aspects when executing the computer program.
In a fourth aspect, the present invention also provides a computer-readable storage medium storing machine executable instructions, which when called and executed by a processor, cause the processor to execute the processing method of any one of the first aspect.
In an embodiment of the present invention, a method for extracting driving condition data is provided, including: acquiring original driving condition data of a target vehicle at each moment acquired in the driving process; then, extracting the characteristics of the original driving condition data at each moment to obtain the characteristics of the original driving condition at each moment; further, an information influence judgment model is adopted to judge the importance of the original driving condition characteristics at each moment to obtain an importance judgment result; and finally, extracting target driving condition data from the original driving condition data according to the importance judgment result. According to the above description, the method for extracting driving condition data can extract the target driving condition data from the original driving condition data according to the importance judgment result of the original driving condition characteristics at each moment, that is, the method can extract the target driving condition data with driving condition representativeness from the original driving condition data with large noise, the target driving condition data has small data volume and driving condition representativeness, so that engineering personnel can analyze and detect the target vehicle according to the target driving condition data, and the technical problem that the driving condition data with driving condition representativeness cannot be extracted from the original driving condition data with large noise and large data volume in the prior art is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for extracting driving condition data according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating feature extraction performed on original driving condition data at various times according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating an importance determination of an original driving condition characteristic at each time by using an information influence determination model according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating extracting target driving condition data from original driving condition data according to an importance determination result according to an embodiment of the present invention;
FIG. 5 is a flowchart of a training method of an information influence determination model according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a driving condition data extraction device according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, the data volume of original driving condition data collected by a data collection system on an electric automobile is huge, and a large amount of noise data which does not have driving condition representativeness is contained, so that the application value of the collected original driving condition data is not high, and engineers cannot directly analyze and detect the vehicle according to the original driving condition data.
Therefore, the embodiment provides the method for extracting the driving condition data, and the method can extract the target driving condition data with the driving condition representativeness from the original driving condition data with large noise, and is more favorable for an engineer to analyze and detect the target vehicle according to the target driving condition data.
Embodiments of the present invention are further described below with reference to the accompanying drawings.
The first embodiment is as follows:
in accordance with an embodiment of the present invention, there is provided an embodiment of a method for extracting driving condition data, it should be noted that the steps illustrated in the flowchart of the drawings may be executed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be executed in an order different from that herein.
Fig. 1 is a flowchart of a driving condition data extraction method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
step S102, acquiring original driving condition data of each moment acquired by a target vehicle in the driving process;
in an embodiment of the present invention, the target vehicle may be any electric vehicle, the original driving condition data may be acquired by a data acquisition system on the target vehicle in real time during multiple driving of the target vehicle, and the original driving condition data specifically may include: the driving condition data comprises vehicle frame number information, sampling time, EVCC feedback charging state, current vehicle speed, mileage, probe highest temperature, probe lowest temperature, single highest voltage, single lowest voltage, current, battery pack SOC, insulation resistance, outdoor temperature, small battery voltage, longitude, latitude, parking state, total power, acceleration, vehicle used age, temperature difference between battery cores, voltage difference between battery cores and the like.
Step S104, extracting the characteristics of the original driving condition data at each moment to obtain the characteristics of the original driving condition at each moment;
specifically, the extracted original driving condition features at each time point represent the driving condition at the corresponding time point, and the detailed description of the feature extraction process is provided below, which is not repeated herein.
S106, judging the importance of the original driving condition characteristics at each moment by adopting an information influence judgment model to obtain an importance judgment result;
the information influence judgment model is a trained model and is used for judging the influence of the original driving condition characteristics at each moment on the whole driving condition, so that the importance of the original driving condition characteristics at each moment is determined.
And S108, extracting target driving condition data from the original driving condition data according to the importance judgment result.
In an embodiment of the present invention, a method for extracting driving condition data is provided, including: acquiring original driving condition data of a target vehicle at each moment acquired in the driving process; then, extracting the characteristics of the original driving condition data at each moment to obtain the characteristics of the original driving condition at each moment; further, an information influence judgment model is adopted to judge the importance of the original driving condition characteristics at each moment to obtain an importance judgment result; and finally, extracting target driving condition data from the original driving condition data according to the importance judgment result. According to the above description, the method for extracting driving condition data can extract the target driving condition data from the original driving condition data according to the importance judgment result of the original driving condition characteristics at each moment, that is, the method can extract the target driving condition data with driving condition representativeness from the original driving condition data with large noise, the target driving condition data has small data volume and driving condition representativeness, so that engineering personnel can analyze and detect the target vehicle according to the target driving condition data, and the technical problem that the driving condition data with driving condition representativeness cannot be extracted from the original driving condition data with large noise and large data volume in the prior art is solved.
The above description briefly describes the method for extracting driving condition data according to the present invention, and the following description details the specific contents thereof.
In an optional embodiment of the present invention, referring to fig. 2, in the step S104, the feature extraction is performed on the original driving condition data at each time, which specifically includes the following steps:
step S201, obtaining predefined target characteristics;
some of the target characteristics may be original data items in original driving condition data, for example, the characteristics include temperature, current, voltage, vehicle service life, mileage, and the like, which are classified into barrels; the other characteristics may be data items obtained by performing statistical analysis on the original driving condition data by a statistical analysis calculation method, for example, positive and negative of the acceleration are used as one characteristic, and the vehicle standing time, the temperature before and after the vehicle is standing, the SOC before and after the vehicle is standing, the number of battery cells reaching the maximum/minimum voltage value, the number of probes reaching the maximum/minimum temperature, and the like may be used as the characteristics.
Step S202, according to the predefined target characteristics, performing characteristic extraction on the original driving condition data at each moment to obtain the original driving condition characteristics at each moment.
In an alternative embodiment of the present invention, the information influence determination model includes: referring to fig. 3, the step S106 is to perform importance determination on the original driving condition characteristics at each time by using an information influence determination model, and specifically includes the following steps:
step S301, inputting the original driving condition characteristics of each moment into a first information influence judgment module to obtain time slice characteristics corresponding to the original driving condition characteristics of each moment and importance results of each moment on the time slice to which the time slice characteristics belong, wherein the time slice characteristics represent vector representation of the working condition information in each time slice;
in the embodiment of the present invention, the vector representation includes data information, timing information, and the like.
Specifically, the first information influence determination module includes: a first time sequence module (including but not limited to LSTM, BilTM, transform, etc.) and a first attention mechanism module, wherein the time slice characteristics are obtained by the first time sequence module, and the importance result of each time on the time slice to which the time slice belongs is obtained by the first attention mechanism module.
Step S302, determining time information characteristics and speed change characteristics corresponding to the time slice characteristics based on the original driving condition data at each time;
specifically, after obtaining the time slice feature from the original driving condition feature at each time, the time information included in the time slice may be determined according to the time slice obtained, and then the time information feature may be obtained, or in addition, some speed change features may also be determined according to the speed information in the original driving condition data in the time slice, for example, the speed information in a certain time slice may be plotted, so as to obtain the maximum speed, the minimum speed, the slope of the speed, and other features therein, so that the obtained features are used as the speed change features.
Step S303, splicing the time slice characteristics with the time information characteristics and the speed change characteristics respectively;
the time sequence and continuity can be ensured by introducing the time information characteristic and the speed change characteristic.
Step S304, inputting the spliced characteristics to a second information influence judgment module to obtain the importance results of the overall time characteristics and each time slice on the overall time, wherein the overall time characteristics represent the vector representation of the working condition information in the overall time;
specifically, the overall time refers to time formed by start and stop moments of all moments corresponding to all original driving condition data.
The second information influence determination module includes: a second time series module (including but not limited to LSTM, BilSTM, transform, etc.) and a second attention mechanism module, wherein the overall time characteristics are obtained by the second time series module, and the importance result of each time slice on the overall time is obtained by the second attention mechanism module.
In step S305, the overall time characteristic, the importance result of each time in the time slice to which the time belongs, and the importance result of each time slice in the overall time are input to the output module, so as to obtain an importance determination result.
In an alternative embodiment of the present invention, the importance determination result includes: referring to fig. 4, the step S108 of extracting target driving condition data from the original driving condition data according to the importance determination result includes the following steps:
step S401, when the importance judgment result is the importance of each moment in the whole time, extracting target driving condition data of a target moment from the original driving condition data according to the importance of each moment in the whole time;
the target time is the time corresponding to the importance of each time in the whole time after the importance of each time is arranged according to a descending order and is located at the preset number.
Step S402, when the importance judgment result is the importance of each time slice on the whole time, extracting the target driving condition data of the target time slice from the original driving condition data according to the importance of each time slice on the whole time.
The target time slices are time slices corresponding to the importance of the time slices in the whole time after the importance of the time slices in the whole time is arranged according to a descending order and are positioned in the preset number.
The above-mentioned content describes in detail the application process of the information influence determination model of the present invention, and the training process thereof is described below.
In an alternative embodiment of the present invention, referring to fig. 5, the training process of the information influence determination model specifically includes the following steps:
step S501, deploying an original information influence judgment model;
step S502, a training sample set of driving condition data is obtained, wherein the training sample set comprises: original driving condition data samples and labeled target driving condition data samples;
step S503, training the original information influence judgment model through the training sample set to obtain an information influence judgment model.
The evaluation result shows that the finally extracted target driving condition data changes the problems of large noise and poor continuity of original driving condition data, the obtained target driving condition data is stable and continuous and has driving condition representativeness (namely accuracy), and when subsequent engineers analyze and detect the target vehicle based on the target driving condition data, the method is convenient and rapid, and the application value of the data is improved.
Example two:
the embodiment of the invention also provides a device for extracting the driving condition data, which is mainly used for executing the method for extracting the driving condition data provided by the first embodiment of the invention, and the device for extracting the driving condition data provided by the embodiment of the invention is specifically described below.
Fig. 6 is a schematic diagram of a driving condition data extraction device according to an embodiment of the present invention, and as shown in fig. 6, the device mainly includes: an acquisition unit 10, a feature extraction unit 20, an importance determination unit 30, and a data extraction unit 40, wherein:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring original driving condition data of a target vehicle at each moment acquired in the driving process;
the characteristic extraction unit is used for extracting the characteristics of the original driving condition data at each moment to obtain the original driving condition characteristics at each moment;
the importance judgment unit is used for judging the importance of the original driving condition characteristics at each moment by adopting an information influence judgment model to obtain an importance judgment result;
and the data extraction unit is used for extracting target driving condition data from the original driving condition data according to the importance judgment result.
In an embodiment of the present invention, there is provided a driving condition data extraction device, including: acquiring original driving condition data of a target vehicle at each moment acquired in the driving process; then, extracting the characteristics of the original driving condition data at each moment to obtain the characteristics of the original driving condition at each moment; further, an information influence judgment model is adopted to judge the importance of the original driving condition characteristics at each moment to obtain an importance judgment result; and finally, extracting target driving condition data from the original driving condition data according to the importance judgment result. According to the above description, the extraction device of the driving condition data can extract the target driving condition data from the original driving condition data according to the importance judgment result of the original driving condition characteristics at each moment, that is, the extraction device of the driving condition data can extract the target driving condition data with driving condition representativeness from the original driving condition data with large noise, the data volume of the target driving condition data is small, the target driving condition data has driving condition representativeness, and engineering personnel can analyze and detect the target vehicle according to the target driving condition data, so that the technical problem that the driving condition data with driving condition representativeness cannot be extracted from the original driving condition data with large noise and large data volume in the prior art is solved.
Optionally, the feature extraction unit includes: the acquisition module is used for acquiring predefined target characteristics; and the characteristic extraction module is used for extracting the characteristics of the original driving condition data at each moment according to the predefined target characteristics to obtain the original driving condition characteristics at each moment.
Optionally, the information influence determination model includes: the importance determination unit is further used for: inputting the original driving condition characteristics of each moment into the first information influence judgment module to obtain time slice characteristics corresponding to the original driving condition characteristics of each moment and an importance result of each moment on a time slice to which the time slice characteristics belong, wherein the time slice characteristics represent vector representation of the working condition information in each time slice; determining time information characteristics and speed change characteristics corresponding to the time slice characteristics based on the original driving condition data at each time; splicing the time slice characteristics with the time information characteristics and the speed change characteristics respectively; inputting the characteristics obtained after splicing into the second information influence judgment module to obtain the integral time characteristics and the importance results of each time slice on the integral time, wherein the integral time characteristics represent the vector representation of the working condition information in the integral time; and inputting the overall time characteristics, the importance results of the moments on the time slices to which the moments belong and the importance results of the time slices on the overall time to an output module to obtain the importance judgment result.
Optionally, the importance determination result includes: the importance of each time instant over the entire time and/or the importance of each time slice over the entire time, the data extraction unit is further configured to: when the importance judgment result is the importance of each moment in the whole time, extracting target driving condition data of a target moment from the original driving condition data according to the importance of each moment in the whole time; when the importance judgment result is the importance of each time slice on the whole time, extracting the target driving condition data of the target time slice from the original driving condition data according to the importance of each time slice on the whole time
Optionally, the apparatus is further configured to: deploying an original information influence judgment model; obtaining a training sample set of driving condition data, wherein the training sample set comprises: original driving condition data samples and labeled target driving condition data samples; and training the original information influence judgment model through the training sample set to obtain the information influence judgment model.
Optionally, the first information influence determination module includes: a first time series module and a first attention mechanism module, the second information influence determination module comprising: a second time series module and a second attention mechanism module.
The device provided by the embodiment of the present invention has the same implementation principle and technical effect as the method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the method embodiments without reference to the device embodiments.
As shown in fig. 7, an electronic device 600 provided in an embodiment of the present application includes: the driving condition data extracting method 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 runs, the processor 601 and the memory 602 communicate through the bus, and the processor 601 executes the machine-readable instructions to execute the steps of the driving condition data extracting method.
Specifically, the memory 602 and the processor 601 can be general memories and processors, which are not limited to the specific embodiments, and the method for extracting the driving condition data 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 having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 601. The Processor 601 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed 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 directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 602, and the processor 601 reads the information in the memory 602 and completes the steps of the method in combination with the hardware thereof.
Corresponding to the method for extracting the driving condition data, the embodiment of the application also provides a computer readable storage medium, wherein a machine executable instruction is stored in the computer readable storage medium, and when the computer executable instruction is called and executed by a processor, the computer executable instruction causes the processor to execute the step of the method for extracting the driving condition data.
The driving condition data extraction device provided by the embodiment of the application can be specific hardware on equipment or software or firmware installed on the equipment. The device provided by the embodiment of the present application has the same implementation principle and technical effect as the foregoing method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiments where no part of the device embodiments is mentioned. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the foregoing systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
For 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 parts may or may not be physically separate, and parts displayed 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 can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into 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 or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing an electronic device (which may be a personal computer, a server, or a network device) to execute 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: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, 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-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the scope of the embodiments of the present application. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for extracting driving condition data is characterized by comprising the following steps:
acquiring original driving condition data of a target vehicle at each moment acquired in the driving process;
extracting the characteristics of the original driving condition data at each moment to obtain the characteristics of the original driving condition at each moment;
judging the importance of the original driving condition characteristics at each moment by adopting an information influence judgment model to obtain an importance judgment result;
and extracting target driving condition data from the original driving condition data according to the importance judgment result.
2. The method of claim 1, wherein the feature extraction of the raw driving condition data at each time comprises:
acquiring a predefined target characteristic;
and extracting the characteristics of the original driving condition data at each moment according to the predefined target characteristics to obtain the original driving condition characteristics at each moment.
3. The method of claim 1, wherein the information influence determination model comprises: the first information influence judgment module, the second information influence judgment module and the output module adopt an information influence judgment model to judge the importance of the original driving condition characteristics at each moment, and the judgment comprises the following steps:
inputting the original driving condition characteristics of each moment into the first information influence judgment module to obtain time slice characteristics corresponding to the original driving condition characteristics of each moment and an importance result of each moment on a time slice to which the time slice characteristics belong, wherein the time slice characteristics represent vector representation of the working condition information in each time slice;
determining time information characteristics and speed change characteristics corresponding to the time slice characteristics based on the original driving condition data at each time;
splicing the time slice characteristics with the time information characteristics and the speed change characteristics respectively;
inputting the characteristics obtained after splicing into the second information influence judgment module to obtain the integral time characteristics and the importance results of each time slice on the integral time, wherein the integral time characteristics represent the vector representation of the working condition information in the integral time;
and inputting the overall time characteristics, the importance results of the moments on the time slices to which the moments belong and the importance results of the time slices on the overall time to an output module to obtain the importance judgment result.
4. The method according to claim 1, wherein the importance determination result comprises: extracting target driving condition data from the original driving condition data according to the importance judgment result, wherein the importance of each moment in the overall time and/or the importance of each time slice in the overall time comprises the following steps:
when the importance judgment result is the importance of each moment in the whole time, extracting target driving condition data of a target moment from the original driving condition data according to the importance of each moment in the whole time;
and when the importance judgment result is the importance of each time slice in the whole time, extracting target driving condition data of a target time slice from the original driving condition data according to the importance of each time slice in the whole time.
5. The method of claim 1, further comprising:
deploying an original information influence judgment model;
obtaining a training sample set of driving condition data, wherein the training sample set comprises: original driving condition data samples and labeled target driving condition data samples;
and training the original information influence judgment model through the training sample set to obtain the information influence judgment model.
6. The method of claim 3, wherein the first information influence determination module comprises: a first time series module and a first attention mechanism module, the second information influence determination module comprising: a second time series module and a second attention mechanism module.
7. An extraction device of driving condition data, characterized by comprising:
the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring original driving condition data of a target vehicle at each moment acquired in the driving process;
the characteristic extraction unit is used for extracting the characteristics of the original driving condition data at each moment to obtain the original driving condition characteristics at each moment;
the importance judgment unit is used for judging the importance of the original driving condition characteristics at each moment by adopting an information influence judgment model to obtain an importance judgment result;
and the data extraction unit is used for extracting target driving condition data from the original driving condition data according to the importance judgment result.
8. The apparatus of claim 7, wherein the feature extraction unit comprises:
the acquisition module is used for acquiring predefined target characteristics;
and the characteristic extraction module is used for extracting the characteristics of the original driving condition data at each moment according to the predefined target characteristics to obtain the original driving condition characteristics at each moment.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1 to 6 are implemented when the computer program is executed by the processor.
10. A computer readable storage medium having stored thereon machine executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any of claims 1 to 6.
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