CN114005277B - Information extraction method and device of Internet of vehicles and readable medium - Google Patents

Information extraction method and device of Internet of vehicles and readable medium Download PDF

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CN114005277B
CN114005277B CN202111243705.2A CN202111243705A CN114005277B CN 114005277 B CN114005277 B CN 114005277B CN 202111243705 A CN202111243705 A CN 202111243705A CN 114005277 B CN114005277 B CN 114005277B
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CN114005277A (en
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侯琛
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Tencent Technology Shenzhen Co Ltd
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Abstract

The embodiment of the application provides an information extraction method and device of a vehicle networking and a readable medium. The information extraction method of the Internet of vehicles comprises the following steps: acquiring information of a component field in a composite field of a driving area of a vehicle, and determining a field function of the composite field of the driving area based on the information of the component field; sampling information of the component fields to obtain sampling data; and determining frequency spectrum information corresponding to the sampling data based on the field function and the sampling data, and extracting abnormal information from the sampling data according to the frequency spectrum information so as to perform traffic early warning on vehicles in the Internet of vehicles. According to the technical scheme of the embodiment of the application, the abnormal information in the composite field of the driving area is extracted through the frequency spectrum information based on the sampling data, so that the accuracy of data acquisition is improved, and meanwhile, the completeness of the acquired data is guaranteed.

Description

Information extraction method and device of Internet of vehicles and readable medium
The application is a divisional application of a Chinese patent application CN201911129669.X, which is filed on 11, 18 and 2019 and is entitled "information extraction method and device of Internet of vehicles".
Technical Field
The application relates to the technical field of computers and communication, in particular to an information extraction method and device of an internet of vehicles and a readable medium.
Background
In the monitoring process of a plurality of composite fields, the prior art usually monitors a single component field, and acquires the data of the single component field for analysis to obtain the analysis results of all the composite fields. However, in practical applications, if one component field in the composite field changes, other component fields are often triggered to change. Therefore, the desired information may not be extracted from only one component field, or the extracted information may be incomplete, which may result in one-sided data acquisition and further affect the analysis result of the entire composite field.
Disclosure of Invention
The embodiment of the application provides an information extraction method, device and readable medium of the Internet of vehicles, so that the accuracy of data acquisition can be improved at least to a certain extent, and meanwhile, the completeness of the acquired data is guaranteed.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.
According to an aspect of an embodiment of the present application, there is provided an information extraction method for a vehicle networking, including: acquiring information of a component field in a driving area composite field of a vehicle, wherein the driving area composite field comprises at least one component field, and determining a field function of the driving area composite field based on the information of the component field; sampling the information of the component field to obtain sampling data; determining spectrum information corresponding to the sampling data based on the field function and the sampling data, wherein the spectrum information is used for representing the change speed of the sampling data; and extracting abnormal information from the sampling data according to the frequency spectrum information, wherein the abnormal information is used for carrying out traffic early warning on vehicles in the Internet of vehicles.
According to an aspect of an embodiment of the present application, there is provided an information extraction apparatus of a vehicle networking, including:
an acquisition unit configured to acquire information of component fields in a travel area composite field of a vehicle, the travel area composite field including at least one of the component fields, and determine a field function of the travel area composite field based on the information of the component fields;
the sampling unit is used for sampling the information of the component field to obtain sampling data;
the spectrum unit is used for determining spectrum information corresponding to the sampling data based on the field function and the sampling data, and the spectrum information is used for representing the change speed of the sampling data;
and the extraction unit is used for extracting abnormal information from the sampling data according to the frequency spectrum information, and the abnormal information is used for carrying out traffic early warning on vehicles in the Internet of vehicles.
In some embodiments of the present application, based on the foregoing scheme, the sampling data includes a sampling number and a sampling value; the spectrum unit is configured to: and determining the frequency spectrum information corresponding to the sampling data in a multi-dimensional discrete Fourier transform mode based on the field function, the sampling number and the sampling value.
In some embodiments of the present application, based on the foregoing scheme, the extraction unit includes: the first identification unit is used for identifying a region, in a spectrogram corresponding to the spectrum information, of the sampled data, wherein the amplitude parameter of the sampled data is larger than a preset first threshold value, as a target region; a second identification unit, configured to determine a frequency boundary between the target region and the remaining region from the spectrogram, and identify an extreme value of a frequency corresponding to the frequency boundary as a second threshold; and a first extraction unit, configured to extract, as the abnormality information, sampling data with a frequency greater than the second threshold value, according to the spectrum information.
In some embodiments of the present application, based on the foregoing solution, the first extraction unit includes: setting the second threshold value as a preset filtering threshold value of a first high-pass filter; and inputting the sampling data into the first high-pass filter to obtain the abnormal information.
In some embodiments of the present application, based on the foregoing scheme, the sampling unit includes: sampling the information of the component field to obtain preliminary sampling data; and filtering low-frequency data in the preliminary sampling data by passing the preliminary sampling data through a preset second high-pass filter to obtain the sampling data.
In some embodiments of the present application, based on the foregoing solution, the obtaining unit includes: acquiring video data in the running process of the vehicle; identifying an object in the video data, and determining a component field in the composite field of the driving area according to the characteristics of the object; information of the component fields is extracted from the video data based on the features of the object.
In some embodiments of the present application, based on the foregoing, the field function comprises a field strength function; the acquisition unit includes: determining a field strength function of each component field based on the information of the component field; and combining the field intensity functions to obtain the field intensity function of the composite field of the driving area.
In some embodiments of the present application, based on the foregoing solution, the information extraction device of the internet of vehicles further includes: predicting the driving risk of the vehicle in the driving area composite field according to the abnormal information; and when the driving risk is predicted, risk early warning information is sent to the vehicle.
In some embodiments of the present application, based on the foregoing solution, the information extraction device of the internet of vehicles further includes: generating a vehicle control command according to the abnormal information; and sending the control command to a control device in the vehicle to control the vehicle to avoid danger automatically.
According to an aspect of the embodiments of the present application, there is provided a computer-readable medium on which a computer program is stored, the computer program, when executed by a processor, implementing the information extraction method of the internet of vehicles as described in the above embodiments.
According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the information extraction method of the internet of vehicles as described in the above embodiments.
In the technical scheme provided by some embodiments of the application, information of a component field in a composite field of a driving area of a vehicle is acquired, and a field function of the composite field of the driving area is determined based on the information of the component field; sampling information of the component fields to obtain sampling data; and determining frequency spectrum information corresponding to the sampling data based on the field function and the sampling data, and extracting abnormal information from the sampling data according to the frequency spectrum information so as to perform traffic early warning on vehicles in the Internet of vehicles. Abnormal information in a composite field of a driving area is extracted through frequency spectrum information based on sampling data, so that the accuracy of data acquisition is improved, and the completeness of the acquired data is guaranteed.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 shows a schematic diagram of an exemplary system architecture to which aspects of embodiments of the present application may be applied;
fig. 2 schematically shows a schematic diagram of an exemplary system architecture according to an embodiment of the present application;
FIG. 3 schematically illustrates a flow diagram of an information extraction method for a vehicle networking, according to one embodiment of the present application;
FIG. 4 schematically illustrates a schematic view of a driving area complex of a vehicle according to one embodiment of the present application;
FIG. 5 schematically illustrates an information flow diagram for obtaining component fields in a composite field of a driving area of a vehicle according to one embodiment of the present application;
FIG. 6 schematically illustrates a flow diagram for determining a field function for a composite field of travel area based on information of component fields according to an embodiment of the present application;
FIG. 7 schematically illustrates a flow diagram for extracting anomaly information from sampled data according to one embodiment of the present application;
FIG. 8 schematically illustrates a flow diagram of a method for information extraction of a complex field of travel zones based on a multi-dimensional discrete Fourier transform, according to an embodiment of the present application;
FIG. 9 schematically illustrates a flow diagram for driving safety lot burst information extraction according to one embodiment of the present application;
fig. 10 schematically shows a schematic view of an application environment of a travel information extraction method according to an embodiment of the present application;
FIG. 11 schematically illustrates a block diagram of an information extraction device of a vehicle networking, according to one embodiment of the present application;
FIG. 12 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the embodiments of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Fig. 1 shows a schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied.
As shown in fig. 1, the system architecture may include an acquisition device, a network 104, and a server 105. The acquisition device in this embodiment may be one or more of the terminal apparatus 101, the in-vehicle device 102, and the position sensor 103. The terminal device 101 may include a smart phone, a tablet computer, a computer, and the like; the in-vehicle device 102 may include an in-vehicle terminal, an in-vehicle radar, or the like; the position sensor 103 may include a pickup device, a radar apparatus, etc. disposed on both sides of the road, which is not limited herein. The network 104 is used to provide the medium of a communication link between the acquisition device and the server 105. Network 104 may include various connection types, such as wired communication links, wireless communication links, and so forth.
It should be understood that the number of acquisition devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of acquisition devices, networks, and servers, as desired for an implementation. For example, server 105 may be a server cluster comprised of multiple servers, and the like.
The acquisition device of the embodiment interacts with the server 105 through the network 104, so that the server 105 acquires information of component fields in a composite field of a driving area of a vehicle, and determines a field function of the composite field of the driving area based on the information of the component fields; sampling information of the component fields to obtain sampling data; and determining frequency spectrum information corresponding to the sampling data based on the field function and the sampling data, and extracting abnormal information from the sampling data according to the frequency spectrum information so as to perform traffic early warning on vehicles in the Internet of vehicles. Abnormal information in a composite field of a driving area is extracted through frequency spectrum information based on sampling data, so that the accuracy of data acquisition is improved, and meanwhile, the completeness of the acquired data is ensured.
It should be noted that the information extraction method of the internet of vehicles provided by the embodiment of the present application is generally executed by the server 105, and accordingly, the information extraction device of the internet of vehicles is generally disposed in the server 105. However, in other embodiments of the present application, the terminal device may also have a similar function as the server, so as to execute the scheme of information extraction of the internet of vehicles provided by the embodiments of the present application.
Fig. 2 shows a schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied.
As shown in fig. 2, the system architecture may include a vehicle 201, an in-vehicle terminal 202, and/or a vehicle control device 203. The vehicle-mounted terminal 202 in the vehicle 201 can acquire data in the driving process of the vehicle, and the vehicle-mounted terminal is used for directly or indirectly acquiring road condition data in the driving process of the vehicle 201, specific road condition data types are not limited, such as positioning data and the like, driving environment videos in the driving process of the vehicle can be shot in real time, and the driving environment videos are analyzed to obtain the road condition data. The vehicle-mounted terminal 202 acquires information of a component field in a composite field of a driving area of the vehicle, and determines a field function of the composite field of the driving area based on the information of the component field; sampling information of the component fields to obtain sampling data; and determining frequency spectrum information corresponding to the sampling data based on the field function and the sampling data, and extracting abnormal information from the sampling data according to the frequency spectrum information so as to perform traffic early warning on vehicles in the Internet of vehicles. Abnormal information in a composite field of a driving area is extracted through frequency spectrum information based on sampling data, so that the accuracy of data acquisition is improved, and the completeness of the acquired data is guaranteed.
In the process of performing the warning, a warning notification or the like may be issued by the in-vehicle terminal 202 to notify the driver of the attention to driving.
In addition, in an application scenario of automatic driving, after the vehicle-mounted terminal 202 extracts the abnormal information from the sampled data, a vehicle control command is generated according to the abnormal information, and the control command is sent to the vehicle control device 203, so that the vehicle control device controls the vehicle to safely run based on the control command, so as to control the vehicle to automatically avoid danger. The control instruction is generated according to the extracted abnormal information, so that the accuracy of the control instruction is improved, the stability and the safety of automatic driving are effectively guaranteed, and the method can be used in the fields of car networking, vehicle road cooperation, safe auxiliary driving, automatic driving and the like.
The implementation details of the technical solution of the embodiment of the present application are set forth in detail below:
FIG. 3 illustrates a flow diagram of a method for extracting information from a vehicle networking, which may be performed by a server, which may be the server shown in FIG. 1, according to an embodiment of the present application; or by a terminal device, which may be a vehicle-mounted terminal as shown in fig. 2. Referring to fig. 3, the information extraction method for the internet of vehicles at least includes steps S310 to S340, which are described in detail as follows:
in step S310, information of a component field in a travel region composite field of a vehicle is acquired, and a field function of the travel region composite field is determined based on the information of the component field.
As shown in fig. 4, fig. 4 is a schematic view of a composite field of a driving area of a vehicle provided in an embodiment of the present application. In one embodiment of the present application, the composite field of the driving area is composed of at least one component field, and because sampling, transmission, calculation, and the like take time, the burst information of one component field may trigger the burst information of the other component fields in the period of time, so that no burst information may be extracted or the extracted information may be incomplete from only one component field. Therefore, the component fields in this embodiment are composite results of different types of component fields, such as composite fields of multiple types of driving areas, that is, the component fields in the actual road are functions of different types of component fields, and thus composite results of different types of component fields are obtained.
The method is characterized in that the risk brought by a non-moving object (such as a vehicle parked at the roadside) which is possibly involved in the collision is simulated through a gravity field theory, the risk brought by the non-moving object (such as a traffic sign) which is not involved in the collision and can apply pressure to a driver can be simulated through a spring field theory, the risk brought by the moving object can be simulated through the gravity field theory fused with the Doppler effect, and the risk brought by the behavior of the driver can be simulated through the gravity field theory fused with the Doppler effect and the risk factor of the driver. The component fields are component fields, and the component fields are combined to form a composite field of the driving area. In the present embodiment, the field function of the travel area composite field is determined based on the information of the component field in the travel area composite field of the vehicle.
In an embodiment of the present application, as shown in fig. 5, the process of acquiring information of component fields in the composite field of the driving area of the vehicle in step S310 includes the following steps S510 to S530, which are described in detail as follows:
in step S510, video data during the running of the vehicle is acquired.
In one embodiment of the present application, in the process of acquiring information of component fields in a composite field of a driving area of a vehicle, the information may be acquired by means of video information. In this embodiment, video data during the driving of the vehicle may be acquired by the camera device. For example, the video data may be acquired by a vehicle tachograph, a roadside camera, a road detection radar, or a vehicle vision device, but not limited thereto.
In step S520, an object in the video data is identified, and a component field in the composite field of the driving area is determined according to the feature of the object.
In one embodiment of the present application, the component fields in the composite field of the driving area are identified in real time. For example, traffic participants, such as vehicles, pedestrians, etc.; the method can identify which traffic factors exist on the road in real time, wherein the traffic factors include non-moving objects which are possibly involved in collisions, such as vehicles parked at the roadside; non-moving objects, such as traffic signs, moving objects, etc., that are not involved in the collision but exert pressure on the driver.
In one embodiment of the present application, by identifying an object in video data, it is determined how many component fields of a target area are according to the characteristics of the object, i.e. how many component fields constitute a driving area composite field of the target area, the number of the component fields is denoted as d, and E1, E2 d Respectively represent d kinds of component fields;
in addition, after being identified, the roadside camera can upload the identification information to the traffic management platform, so that other vehicles can acquire component field information from the traffic management platform in real time.
In one embodiment of the present application, although recognition is also mentioned here, since the recognition effect is not completely reliable at all times, it cannot completely recognize burst information, and the present embodiment can obtain abnormal information by processing data. The extraction of burst information in the present embodiment may be based only on the component field data. For example, the data of the component fields is used for analyzing the burst information so as to guide the judgment of the traffic accident.
In step S530, information of the component field is extracted from the video data based on the feature of the object.
In one embodiment of the present application, after the component fields in the composite field of the driving area are identified, information of the component fields is extracted from the video data based on the characteristics of the object according to the component fields in the composite field of the driving area, that is, information of one component field is separated from the video data.
In an embodiment of the present application, as shown in fig. 6, the process of determining the field function of the driving area composite field based on the information of the component field in step S310 includes the following steps S610 to S620, which are described in detail as follows:
in step S610, a field strength function of each component field is determined based on the information of the component field.
In one embodiment of the present application, the vehicle travel area is considered a physical field in which vehicles travel with the potential risk of being collided with by other vehicles. In the present embodiment, the information of each component field is embodied by a field strength function, and in the present embodiment, the field strength function E of each component field is determined according to the information of the component field. By way of example, the driving safety field is similar to an electric field or a magnetic field, and the strength of the driving safety field is similar to that of the electric field or the magnetic field. The field strength of the driving safety field is therefore similar to the electric and magnetic field strength and is therefore also denoted by E.
Specifically, in the process of determining the field intensity function, vehicle information, such as the relative speed of vehicles, the included angle of the traveling wind directions between the vehicles, the vehicle mass, the surface viscosity, the camber and the like, is brought into an attraction force field theory model, a spring potential energy model and a Doppler effect model in the field of physics to calculate the potential collision intensity between the vehicles, and the potential collision intensity is the field intensity of safe and smooth driving. The field intensity of the driving safety field is obtained through measurement or other methods. For example, a driving safety field is sampled, and the field strength of the driving safety field is 100N/V (cattle/vehicle), that is, the average impact force of each vehicle is 100 cattle.
In step S620, the field strength functions are combined to obtain the field strength function of the composite field in the driving area.
In one embodiment of the application, sudden information is present in the component fields, i.e. in the actual traffic route or in the geographical area, such as suddenly changing vehicles, suddenly braked vehicles, suddenly breaking pedestrians, which have a great influence on the vehicle safety. In a road or a certain area, there are often different types of safety fields, and fields interact with each other. Therefore, burst information may not be extracted from only one type of security field or the extracted burst information may be incomplete.
In this embodiment, these pieces of information are extracted from different types of safety fields, that is, the burst information of the composite field in the driving area is extracted to obtain the field intensity functions of the component fields, and the field intensity functions of all the component fields are combined to obtain the field intensity function of the composite field in the driving area.
In this embodiment, the field intensity function of the composite field in the driving area is obtained by summing the field intensity functions of all the component fields in the composite field in the driving area, or by weighted summation.
In step S320, the information of the component field is sampled to obtain sampled data.
In one embodiment of the application, after the component fields of the composite field of the driving area are determined, information of the component fields in the driving process of the vehicle is sampled, namely, for data characteristics in the component fields, environment data or driving data corresponding to the data characteristics in the driving process of the vehicle are collected to obtain sampled data.
In an embodiment of the present application, the sampling data in the present embodiment may be field strength data, vehicle speed, obstacle data, and the like, which is not limited herein.
In an embodiment of the present application, the process of sampling the information of the component field in step S320 to obtain the sampling data includes the following steps:
sampling the information of the component field to obtain preliminary sampling data;
and filtering low-frequency data in the preliminary sampling data by passing the preliminary sampling data through a preset second high-pass filter to obtain the sampling data.
Specifically, in an embodiment of the present application, the preliminary sampling data is obtained by sampling the component field information. The preliminary sampling data in this embodiment is complete data, and a large amount of redundant data exists in the data, so that the preliminary sampling data needs to be filtered to obtain more accurate data.
In one embodiment of the present application, after obtaining the preliminary sampling data, the preliminary sampling data is passed through a preset second high-pass filter to obtain sampling information with a higher frequency. Specifically, the preliminary sampling information is passed through a second high-pass filter, and the frequency threshold of the second high-pass filter may be selected to be lower, for example, the frequency threshold may be set to a frequency at which no burst information is normally present, so that the sampling information with a frequency lower than the threshold cannot pass through the filter, and the sampling data with a higher frequency may be obtained higher than the threshold.
In step S330, spectral information corresponding to the sampled data is determined based on the field function and the sampled data, and the spectral information is used to characterize a variation speed of the sampled data.
In one embodiment of the application, after the field function and the sampled data are obtained, spectral information corresponding to the sampled data is determined, so that the change speed of the sampled data is represented by the spectral information.
In one embodiment of the present application, the sample data includes a sample number and a sample value; in step S330, a process of determining spectrum information corresponding to the sample data based on the field function and the sample data includes the following steps:
and determining the frequency spectrum information corresponding to the sampling data in a multi-dimensional discrete Fourier transform mode based on the field function, the sampling number and the sampling value.
Specifically, in one embodiment of the present application, the component fields obtained by sampling the driving safety field are respectively E1, E2 d N1, n2, a d Denotes the pair E1, E2 d The number of sampling values obtained by sampling is represented by Ei, ji i ∈{E1,E2,...,E d J (j) th sampling i ∈{1,2,...,n i } sample values; with f (E1, E2., E) d ) And (3) representing a field function of the composite field of the driving area, and carrying out multi-dimensional discrete Fourier transform on the field function to obtain frequency spectrum information corresponding to the sampling data of the composite field of the driving area:
Figure BDA0003320171990000111
wherein the content of the first and second substances,
Figure BDA0003320171990000112
in step S340, according to the spectrum information, extracting abnormal information from the sampled data, where the abnormal information is used to perform traffic warning on vehicles in the internet of vehicles.
In one embodiment of the application, after the frequency spectrum information corresponding to the sampling data of the composite field of the driving area is obtained, abnormal information is extracted from the sampling data according to the frequency spectrum information so as to perform traffic early warning on vehicles in a vehicle network.
In an embodiment of the present application, the anomaly information in the embodiment includes burst data, and the sample data with a higher frequency may be identified as the burst data.
In an embodiment of the present application, as shown in fig. 7, the process of extracting abnormal information from the sample data according to the spectrum information in step S340 includes the following steps S710 to S730, which are described in detail as follows:
in step S710, in the spectrogram corresponding to the spectrum information, a region where the amplitude parameter of the sampled data is greater than a preset first threshold is identified as a target region.
In an embodiment of the application, fourier transform is performed on the obtained sampling data with higher frequency to obtain a frequency spectrum of information with higher frequency, and a region with high frequency and large amplitude is found out from the frequency spectrum to be used as a target region. The amplitude value represents the part of the driving safety field generated by the sampling information with the frequency, which has large field intensity and brings great danger to traffic participants.
Specifically, if u of a certain sample data 1 Amplitude (denoted as u) 1 ) Dividing the sum of the amplitudes (u 1, u2, un) of the sampling information corresponding to all the frequencies by a certain threshold, the amplitude u is 1 The corresponding sampled data is the abnormal data or burst information. I.e. if u 1/(u 1+ u2+. + un) ≧ p, where p denotes the first threshold, then the amplitude is u 1 The information of (2) is the burst information; if except for u 1 And also amplitudes u of other frequencies i Also this condition is fulfilled, then the amplitude is u 1 And u i The information of (2) is burst information, that is, the region in the spectrogram corresponding to the data is a target region.
It should be noted that, in the above scheme, the selection of the threshold p is determined in advance. If the attention degree of the potential safety hazard brought by the burst information is high, the p is set to be smaller, namely the setting is conservative, so that more burst information can be obtained; conversely, p can be set larger, so that less burst information is obtained.
In step S720, a frequency boundary between the target region and the remaining region is determined from the spectrogram, and an extreme value of a frequency corresponding to the frequency boundary is identified as a second threshold.
In an embodiment of the present application, a frequency boundary between the target region and the remaining region is then continuously found from the frequency spectrum, and a frequency value corresponding to the frequency boundary is obtained as a second threshold, i.e., a filtering threshold of the first high-pass filter.
It should be noted that, in the present embodiment, the maximum value and the minimum value exist in the frequency corresponding to the frequency boundary, and in the present embodiment, the extreme value of the frequency corresponding to the frequency boundary is recognized as the second threshold.
In step S730, according to the spectrum information, the sampling data with the frequency greater than the second threshold is extracted as the abnormal information.
In an embodiment of the present application, after the second threshold is obtained, according to the spectrum information obtained by the previous calculation, the sampling data with the frequency greater than the second threshold is extracted as the abnormal information or the burst information.
In one embodiment of the present application, the sample data includes a sample number and a sample value; in step S730, according to the spectrum information, extracting the sampling data with the frequency greater than the second threshold as the abnormal information, including the following steps:
setting the second threshold value as a preset filtering threshold value of a first high-pass filter;
and inputting the sampling data into the first high-pass filter to obtain the abnormal information.
In one embodiment of the present application, the constant information is obtained by setting the second threshold as a filtering threshold of the first high-pass filter to input the sample data into the first high-pass filter.
As shown in fig. 8, fig. 8 is a flowchart of an information extraction method for a driving area composite field based on multi-dimensional discrete fourier transform according to an embodiment of the present application. In fig. 8, driving safety fields of the target area, i.e., the kind of component fields, which may include one or more component fields, are determined in step S810; in step S820, a functional relationship between the composite driving safety field (i.e., the driving area composite field) and each component field is determined; in step S830, each driving safety field is sampled; in step S840, performing a discrete fourier transform on the driving safety field to determine spectral information of the sampled data in the driving safety field; in step S850, information with a high change frequency is extracted from the spectrum information of the sample data.
Illustratively, as shown in fig. 9, fig. 9 is a flowchart of the extraction of the burst information of the driving safety field provided by the embodiment of the present application. In fig. 9, in step S910, sampling data of a driving safety field is acquired first; performing multidimensional discrete Fourier transform on the sampled data in step S920, and determining frequency spectrum information of the sampled data; in step S930, the sampled data is passed through a high-pass filter according to the spectrum information, and burst information of the driving safety field in step S940 is obtained.
In an embodiment of the present application, in step S340, according to the spectrum information, extracting abnormal information from the sampled data, where the abnormal information is used after a process of performing traffic early warning on vehicles in the internet of vehicles, the method further includes the following steps:
predicting the driving risk of the vehicle in the driving area composite field according to the abnormal information;
and when the driving risk is predicted, risk early warning information is sent to the vehicle.
As shown in fig. 10, fig. 10 is a schematic diagram of an application environment of the driving information extraction method according to the embodiment of the present application. In one embodiment of the present application, the multidimensional discrete fourier transform module 1001 and the high-pass filtering module 1002 together extract abnormal data in a driving safety field; the shared data module 1003 mainly provides various existing road condition information; the cooperative sensing module 1004 is mainly composed of an information sending unit including a vehicle sensor, and is used for sampling a driving safety field. Writing a temperature acquisition function module by using C in a vehicle sensor of a cooperative sensing part, writing a data analysis and statistics module of traffic data and yard data by using python in a shared data part, and writing by using MATLAB in a multi-dimensional discrete Fourier transform module and a high-pass filter module; determining the composite driving safety field (namely the composite driving safety field in the driving area) of the target area, determining the functional relation between the composite driving safety field and each component field, sampling the driving safety field, recording the sampling value and the sampling number of each driving safety field, separating information with higher change frequency and information with lower change frequency by using multi-dimensional discrete Fourier transform, and taking out high-frequency information by using a low-pass filter, wherein the information is burst information.
In the practical application process of the method in the embodiment, if the driving safety field has the burst factor and is extracted, the extraction is judged to be correct. Based on the method, the rate of the method for extracting the abnormal information in the embodiment is obtained through statistics as shown in the following table:
table 1 burst information extraction accuracy of the prior art and the present embodiment
Figure BDA0003320171990000141
In summary, the method in this embodiment takes time for sampling, transmission, calculation, and the like, so that the burst information of one type of driving safety field may trigger the burst information of other types of driving safety fields in this period of time, and therefore, the burst information may not be extracted or the extracted information may not be complete from only one type of component field. Thus, the present embodiment allows for extraction of burst information from a variety of different types of driving safety fields.
The following describes an embodiment of an apparatus of the present application, which may be used to execute an information extraction method of a car networking in the above-described embodiment of the present application. For details that are not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the information extraction method of the internet of vehicles described above in the present application.
FIG. 11 shows a block diagram of an information extraction device of a vehicle networking, according to one embodiment of the present application.
Referring to fig. 11, an information extraction apparatus 1100 of a vehicle networking according to an embodiment of the present application includes:
an acquisition unit 1110 that acquires information of a component field in a travel area composite field of a vehicle, and determines a field function of the travel area composite field based on the information of the component field;
a sampling unit 1120, configured to sample information of the component field to obtain sampling data;
a spectrum unit 1130, configured to determine, based on the field function and the sampled data, spectrum information corresponding to the sampled data, where the spectrum information is used to characterize a change speed of the sampled data;
an extracting unit 1140, configured to extract abnormal information from the sampled data according to the spectrum information, where the abnormal information is used to perform traffic early warning on vehicles in the internet of vehicles.
In some embodiments of the present application, based on the foregoing scheme, the sampling data includes a sampling number and a sampling value; the spectrum unit is configured to: and determining the frequency spectrum information corresponding to the sampling data in a multi-dimensional discrete Fourier transform mode based on the field function, the sampling number and the sampling value.
In some embodiments of the present application, based on the foregoing scheme, the extracting unit 1140 includes: the first identification unit is used for identifying a region of the sampling data, of which the amplitude parameter is greater than a preset first threshold value, as a target region in a spectrogram corresponding to the spectrum information; a second identification unit, configured to determine a frequency boundary between the target region and the remaining region from the spectrogram, and identify an extreme value of a frequency corresponding to the frequency boundary as a second threshold; and a first extraction unit, configured to extract, as the abnormality information, sampling data with a frequency greater than the second threshold value, according to the spectrum information.
In some embodiments of the present application, based on the foregoing solution, the first extraction unit includes: setting the second threshold value as a preset filtering threshold value of a first high-pass filter; and inputting the sampling data into the first high-pass filter to obtain the abnormal information.
In some embodiments of the present application, based on the foregoing scheme, the sampling unit 1120 includes: sampling the information of the component field to obtain preliminary sampling data; and filtering low-frequency data in the preliminary sampling data by passing the preliminary sampling data through a preset second high-pass filter to obtain the sampling data.
In some embodiments of the present application, based on the foregoing solution, the obtaining unit 1110 includes: acquiring video data in the running process of the vehicle; identifying an object in the video data, and determining a component field in the composite field of the driving area according to the characteristics of the object; information of the component fields is extracted from the video data based on features of the object.
In some embodiments of the present application, based on the foregoing, the field function comprises a field strength function; the acquisition unit 1110 includes: determining a field strength function of each component field based on the information of the component field; and combining the field intensity functions to obtain the field intensity function of the composite field of the driving area.
In some embodiments of the present application, based on the foregoing solution, the information extraction apparatus 1100 further includes: predicting the driving risk of the vehicle in the driving area composite field according to the abnormal information; and when the driving risk is predicted, risk early warning information is sent to the vehicle.
In some embodiments of the present application, based on the foregoing solution, the information extraction apparatus 1100 of the internet of vehicles further includes: generating a vehicle control command according to the abnormal information; and sending the control command to a control device in the vehicle to control the vehicle to avoid danger automatically.
FIG. 12 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
It should be noted that the computer system 1200 of the electronic device shown in fig. 12 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 12, the computer system 1200 includes a Central Processing Unit (CPU) 1201, which can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 1202 or a program loaded from a storage section 1208 into a Random Access Memory (RAM) 1203. In the RAM 1203, various programs and data necessary for system operation are also stored. The CPU 1201, ROM 1202, and RAM 1203 are connected to each other by a bus 1204. An Input/Output (I/O) interface 1205 is also connected to bus 1204.
The following components are connected to the I/O interface 1205: an input section 1206 including a keyboard, a mouse, and the like; an output section 1207 including a Display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 1208 including a hard disk and the like; and a communication section 1209 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 1209 performs communication processing via a network such as the internet. A driver 1210 is also connected to the I/O interface 1205 as needed. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 1210 as necessary, so that a computer program read out therefrom is mounted into the storage section 1208 as necessary.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 1209, and/or installed from the removable medium 1211. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 1201.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with a computer program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. 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 or flowchart illustration, and combinations of blocks in the block diagrams 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 in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method described in the above embodiments.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (10)

1. The information extraction method of the Internet of vehicles is characterized by comprising the following steps:
acquiring information of a component field in a driving area composite field of a vehicle, wherein the driving area composite field comprises at least one component field, and determining a field function of the driving area composite field based on the information of the component field;
sampling the information of the component field to obtain sampling data, wherein the sampling data comprises a sampling number and a sampling value;
determining spectrum information corresponding to the sampling data in a multi-dimensional discrete Fourier transform mode based on the field function, the sampling number and the sampling value, wherein the spectrum information is used for representing the change speed of the sampling data;
and extracting abnormal information from the sampling data according to the frequency spectrum information, wherein the abnormal information is the sampling data with the frequency greater than a second threshold value, and the abnormal information is used for carrying out traffic early warning on vehicles in the Internet of vehicles.
2. The method of claim 1, wherein extracting anomaly information from the sampled data based on the spectral information comprises:
identifying a region, in a spectrogram corresponding to the spectrum information, of the sampled data, wherein the amplitude parameter of the sampled data is greater than a preset first threshold value as a target region;
determining a frequency boundary between the target region and the rest regions from the spectrogram, and identifying an extreme value of a frequency corresponding to the frequency boundary as the second threshold;
setting the second threshold value as a preset filtering threshold value of a first high-pass filter;
and inputting the sampling data into the first high-pass filter to obtain the sampling data with the frequency greater than the second threshold value as the abnormal information.
3. The method of claim 1, wherein sampling information of the component field to obtain sampled data comprises:
sampling the information of the component field to obtain preliminary sampling data;
and filtering low-frequency data in the preliminary sampling data by passing the preliminary sampling data through a preset second high-pass filter to obtain the sampling data.
4. The method of claim 1, wherein obtaining information of component fields in a composite field of a driving area of a vehicle comprises:
acquiring video data in the running process of the vehicle;
identifying an object in the video data, and determining a component field in the composite field of the driving area according to the characteristics of the object;
information of the component fields is extracted from the video data based on features of the object.
5. The method of claim 1, wherein the field function comprises a field strength function; determining a field function of the travel region composite field based on the information of the component fields, including:
determining a field strength function of each component field based on the information of the component field;
and combining the field intensity functions to obtain the field intensity function of the composite field of the driving area.
6. The method according to claim 1, wherein after extracting the abnormal information from the sampled data according to the spectrum information, the method further comprises:
predicting the driving risk of the vehicle in the driving area composite field according to the abnormal information;
and when the driving risk is predicted, risk early warning information is sent to the vehicle.
7. The method according to any one of claims 1 to 6, wherein after extracting abnormal information from the sampled data according to the spectrum information, the method further comprises:
generating a vehicle control command according to the abnormal information;
and sending the control command to a control device in the vehicle to control the vehicle to avoid danger automatically.
8. The utility model provides an information extraction device of car networking which characterized in that includes:
an acquisition unit configured to acquire information of a component field in a travel area composite field of a vehicle, the travel area composite field including at least one of the component fields, and determine a field function of the travel area composite field based on the information of the component field;
the sampling unit is used for sampling the information of the component field to obtain sampling data, and the sampling data comprises a sampling number and a sampling value;
the spectrum unit is used for determining spectrum information corresponding to the sampling data in a multi-dimensional discrete Fourier transform mode based on the field function, the sampling number and the sampling value, and the spectrum information is used for representing the change speed of the sampling data;
and the extraction unit is used for extracting abnormal information from the sampling data according to the frequency spectrum information, wherein the abnormal information is the sampling data with the frequency greater than a second threshold value, and the abnormal information is used for carrying out traffic early warning on vehicles in the Internet of vehicles.
9. A computer-readable medium, characterized in that a computer program is stored thereon, which when executed by a processor, implements the information extraction method for the internet of vehicles according to any one of claims 1 to 7.
10. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the information extraction method of the internet of vehicles of any one of claims 1 to 7.
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