CN111083284B - Vehicle arrival prompting method and device, electronic equipment and computer readable storage medium - Google Patents
Vehicle arrival prompting method and device, electronic equipment and computer readable storage medium Download PDFInfo
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- CN111083284B CN111083284B CN201911253934.5A CN201911253934A CN111083284B CN 111083284 B CN111083284 B CN 111083284B CN 201911253934 A CN201911253934 A CN 201911253934A CN 111083284 B CN111083284 B CN 111083284B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
- H04M1/72454—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to context-related or environment-related conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
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Abstract
The embodiment of the application discloses a vehicle arrival prompting method and a related product, wherein the method comprises the following steps: the method comprises the steps that a target riding circuit is obtained, wherein the target riding circuit is formed by connecting more than two stations in series; collecting audio data in a riding environment within a period of time; when the current station where the vehicle arrives is determined according to the audio data, determining the next station according to the current station and the target riding route; and if the next station is the preset station, performing vehicle arrival prompting operation, and thus determining that the vehicle arrives according to the audio data in the riding environment, and further more accurately performing vehicle arrival prompting when the next station is determined to be the preset station.
Description
Technical Field
The present application relates to the field of electronic technologies, and in particular, to a vehicle arrival prompting method and a related product.
Background
With the widespread use of electronic devices (such as mobile phones, tablet computers, etc.), the electronic devices have more and more applications and more powerful functions, and the electronic devices are developed towards diversification and personalization, and become indispensable electronic products in the life of users.
Under current subway scene of taking a bus, electronic equipment accessible acceleration sensor detects the subway whether be in acceleration and deceleration state to judge the subway and get into next station, and the suggestion user arrives the station and gets off the bus, however, the user grips electronic equipment with different postures, and the acceleration that electronic equipment detected also can be different, leads to the acceleration numerical value that detects through acceleration sensor probably misjudge the subway and be in acceleration or deceleration state.
Disclosure of Invention
The embodiment of the application provides a vehicle arrival prompting method and a related product, which can determine that a vehicle arrives at a station according to audio data in a riding environment, and further more accurately prompt the vehicle to arrive at the station.
In a first aspect, an embodiment of the present application provides a vehicle arrival prompting method, where the method includes:
acquiring a target riding circuit, wherein the target riding circuit is formed by connecting more than two stations in series;
collecting audio data in a riding environment within a period of time;
when the current station where the vehicle arrives is determined according to the audio data, determining the next station according to the current station and the target riding route;
and if the next station is a preset station, performing vehicle arrival prompting operation.
In a second aspect, an embodiment of the present application provides a vehicle arrival prompting device, including:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring a target riding circuit which is formed by connecting more than two stations in series;
the acquisition unit is used for acquiring audio data in a period of time under a riding environment;
the determining unit is used for determining a next station according to the current station and the target riding route when the current station where the vehicle arrives is determined according to the audio data;
and the prompting unit is used for performing prompting operation when the next station is a preset station.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program enables a computer to perform some or all of the steps described in the first aspect of the embodiment of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
According to the vehicle arrival prompting method and the related product provided by the embodiment of the application, the target riding circuit is obtained and is a circuit formed by connecting more than two stations in series; collecting audio data in a riding environment within a period of time; when the current station where the vehicle arrives is determined according to the audio data, determining the next station according to the current station and the target riding route; and if the next station is the preset station, performing vehicle arrival prompting operation, and thus determining that the vehicle arrives according to the audio data in the riding environment, and further more accurately performing vehicle arrival prompting when the next station is determined to be the preset station.
Drawings
In order to more clearly illustrate the embodiments of the present application 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, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 2A is a schematic flowchart of a vehicle arrival prompting method according to an embodiment of the present application;
fig. 2B is a schematic flow chart of feature extraction provided in the embodiment of the present application;
FIG. 2C is a schematic flow chart of MFCC feature extraction provided in the embodiments of the present application;
fig. 2D is a schematic flowchart of determining a current station where a subway arrives according to an embodiment of the present disclosure;
FIG. 3A is a schematic flow chart diagram illustrating another vehicle arrival prompting method provided by the embodiment of the present application;
FIG. 3B is a schematic flow chart illustrating a vehicle arrival prompt according to an embodiment of the present disclosure;
FIG. 4 is a schematic flow chart diagram illustrating another vehicle arrival prompting method provided by the embodiment of the present application;
fig. 5 is a schematic structural diagram of another electronic device provided in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a vehicle arrival prompting device provided in an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. 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 application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The scheme of the embodiment of the application can be used for prompting that a vehicle arrives, for example, when a user takes a subway, a bus, a motor car, a high-speed rail, a train and other vehicles, the vehicle can generate certain specific sounds to remind events of reminding the arrival of the vehicle, the opening of the door of the vehicle or the closing of the door of the vehicle, and the like.
Taking a subway riding scene as an example, after a subway arrives at a station, a subway door is prompted to close through a warning ring, then the subway door moves to the next station, because the difference between the warning ring and the speaking voice is large, even under the condition that the speaking voice of a person in the subway environment is noisy, the influence of the speaking voice of the person on the recognition result of the warning ring under the subway riding environment is small by recognizing the recognition result of the warning ring under the subway riding environment, therefore, the audio data in a period of time under the subway riding environment can be collected, if the warning ring is determined to exist according to the audio data, the arrival of a vehicle at the station can be determined, further, the next station can be determined according to the riding route of the user and the currently arriving station, the user is supposed to take a bus from an initial station P to a target station Q, the vehicle currently arrives at the station X, the next station Y can be determined according to the riding route of the user and the currently arriving, if the next station Y is the destination station Q which the user needs to reach, the user can be prompted that the vehicle is about to reach the destination station Q, or if the next station Y is the transfer station Z, the user can be prompted that the vehicle is about to reach the transfer station Z, and the user is prompted to transfer. Thus, the vehicle arrival prompt can be more accurately performed.
The electronic devices involved in the embodiments of the present application may include various handheld devices, computing devices or other processing devices connected to a wireless modem, as well as various forms of User Equipment (UE), Mobile Station (MS), terminal equipment (terminal device), and the like, which have wireless communication functions. For convenience of description, the above-mentioned devices are collectively referred to as electronic devices.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, where the electronic device includes a control circuit and an input-output circuit, and the input-output circuit is connected to the control circuit.
The control circuitry may include, among other things, storage and processing circuitry. The storage circuit in the storage and processing circuit may be a memory, such as a hard disk drive memory, a non-volatile memory (e.g., a flash memory or other electronically programmable read only memory used to form a solid state drive, etc.), a volatile memory (e.g., a static or dynamic random access memory, etc.), etc., and the embodiments of the present application are not limited thereto. Processing circuitry in the storage and processing circuitry may be used to control the operation of the electronic device. The processing circuitry may be implemented based on one or more microprocessors, microcontrollers, digital signal processors, baseband processors, power management units, audio codec chips, application specific integrated circuits, display driver integrated circuits, and the like.
The storage and processing circuitry may be used to run software in the electronic device, such as play incoming call alert ringing application, play short message alert ringing application, play alarm alert ringing application, play media file application, Voice Over Internet Protocol (VOIP) phone call application, operating system functions, and so forth. The software may be used to perform some control operations, such as playing an incoming alert ring, playing a short message alert ring, playing an alarm alert ring, playing a media file, making a voice phone call, and performing other functions in the electronic device, and the embodiments of the present application are not limited.
The input-output circuit can be used for enabling the electronic device to input and output data, namely allowing the electronic device to receive data from the external device and allowing the electronic device to output data from the electronic device to the external device.
The input-output circuit may further include a sensor. The sensors may include ambient light sensors, optical and capacitive based infrared proximity sensors, ultrasonic sensors, touch sensors (e.g., optical based touch sensors and/or capacitive touch sensors, where the touch sensors may be part of a touch display screen or may be used independently as a touch sensor structure), acceleration sensors, gravity sensors, and other sensors, etc. The input-output circuit may further include audio components that may be used to provide audio input and output functionality for the electronic device. The audio components may also include a tone generator and other components for generating and detecting sound.
The input-output circuitry may also include one or more display screens. The display screen can comprise one or a combination of a liquid crystal display screen, an organic light emitting diode display screen, an electronic ink display screen, a plasma display screen and a display screen using other display technologies. The display screen may include an array of touch sensors (i.e., the display screen may be a touch display screen). The touch sensor may be a capacitive touch sensor formed by a transparent touch sensor electrode (e.g., an Indium Tin Oxide (ITO) electrode) array, or may be a touch sensor formed using other touch technologies, such as acoustic wave touch, pressure sensitive touch, resistive touch, optical touch, and the like, and the embodiments of the present application are not limited thereto.
The input-output circuitry may further include communications circuitry that may be used to provide the electronic device with the ability to communicate with external devices. The communication circuitry may include analog and digital input-output interface circuitry, and wireless communication circuitry based on radio frequency signals and/or optical signals. The wireless communication circuitry in the communication circuitry may include radio frequency transceiver circuitry, power amplifier circuitry, low noise amplifiers, switches, filters, and antennas. For example, the wireless communication circuitry in the communication circuitry may include circuitry to support Near Field Communication (NFC) by transmitting and receiving near field coupled electromagnetic signals. For example, the communication circuit may include a near field communication antenna and a near field communication transceiver. The communications circuitry may also include cellular telephone transceiver and antennas, wireless local area network transceiver circuitry and antennas, and so forth.
The input-output circuit may further include other input-output units. Input-output units may include buttons, joysticks, click wheels, scroll wheels, touch pads, keypads, keyboards, cameras, light emitting diodes and other status indicators, and the like.
The electronic device may further include a battery (not shown) for supplying power to the electronic device.
Referring to fig. 2A, fig. 2A is a schematic flowchart of a vehicle arrival prompting method provided in the embodiment of the present application, where the method includes the following steps:
201. and acquiring a target riding circuit, wherein the target riding circuit is formed by connecting more than two stations in series.
The target riding circuit is a circuit formed by connecting more than two stations in series, wherein the first station of the target riding circuit is an initial station for riding the vehicle by the user, and the last station of the target riding circuit is a target station for riding the vehicle by the user.
In the embodiment of the application, the electronic device may obtain a target riding route for a user to ride, for example, the user goes from an origin station P to a target station Q, may obtain a target riding route between the origin station P and the target station Q, and may further include a transfer station if the user needs to change into a vehicle.
Optionally, in the step 201, acquiring the target riding route may include the following steps:
11. acquiring an initial site and the target site;
12. and determining a target riding route according to the starting station, the target station and a preset riding route map, wherein the target riding route comprises at least two stations including the starting station and the target station.
In specific implementation, the electronic device may obtain a starting station and a target station input by a user, or the electronic device may obtain a destination input by the user, determine a nearest starting station according to a preset riding route map, determine a target station according to the riding route map, and then determine a target riding route according to the preset riding route map.
Optionally, in the step 201, acquiring the target riding route may include the following steps:
13. acquiring the current position and riding time;
14. determining at least one reference riding route from a plurality of preset historical riding routes according to the current position;
15. and determining a target riding route in the at least one reference riding route according to the riding time.
In a specific implementation, the electronic device can also determine the current position through the positioning device and obtain the riding time, and the electronic device can also store a plurality of historical riding circuits, wherein each historical riding circuit in the plurality of historical riding circuits is a common riding circuit set by a user, so that at least one reference riding circuit with the current position as an initial position can be selected from the plurality of historical riding circuits, and then a target riding circuit with the historical riding time consistent with the riding time is selected from the at least one reference riding circuit, so that the circuit prediction can be carried out according to the historical riding habits of the user, and further the target riding circuit of the user can be determined.
202. Audio data is collected over a period of time in a riding environment.
In this embodiment of the application, a user may collect audio data through an electronic device during a riding process, specifically, the electronic device may collect the audio data in real time, for example, after the user gets on the vehicle, the audio data collection function may be turned on, the electronic device starts to continuously collect the audio data, for example, the riding process of the user lasts for 40 minutes, the electronic device may continuously collect audio data with a duration of 40 minutes, then, the audio data collected in real time may be sampled to obtain a plurality of sampled audio data, each sampled audio data is audio data within a period of time, the audio data within a period of time may be, for example, audio data with a duration of 3 minutes, or audio data with a duration of 5 minutes, and the like, which is not limited herein.
In a possible embodiment, the electronic device may further detect acceleration through the acceleration sensor, then predict whether the vehicle is in a deceleration state through the acceleration, if it is predicted that the vehicle is likely to be in the deceleration state through the acceleration, start audio data collection to obtain audio data within a period of time, stop audio data collection until it is predicted that the vehicle is in the acceleration state according to the acceleration again of the acceleration sensor, until the electronic device predicts that the vehicle is in the deceleration state next time, start audio data collection again, in a specific implementation, when the vehicle is about to arrive at a station, the vehicle is decelerated, when the station starts, acceleration is performed, the time length of a time interval from deceleration to acceleration may be, for example, 2 minutes, 3 minutes, 5 minutes, and the like, and collect audio data within a period of time, the audio data may be 2 minutes long, 3 minutes long or 5 minutes long, so that by intermittently acquiring the audio data in the riding process, continuous audio data acquisition is not needed, energy consumption can be saved, and memory generated by excessive audio data can be reduced.
203. And when the current station where the vehicle arrives is determined according to the audio data, determining the next station according to the current station and the target riding route.
Wherein, the vehicle can be any one of: subway, bus, motor train, high-speed rail, train, etc., without limitation herein.
In the embodiment of the application, after audio data in a period of time is collected, the audio data can be analyzed, specifically, if it is determined that alarm sound, station announcement sound, door opening prompt sound, door closing prompt sound and other prompt sounds exist according to the audio data, it can be determined that a vehicle arrives at a station, then, it can be determined that the vehicle arrives at the current station through a positioning device, or it can be determined that the vehicle arrives at the current station according to a target riding route.
Optionally, in this embodiment of the present application, after the acquiring the audio data in the vehicle, the following steps 31 to 34 may further be included:
31. performing feature extraction on the audio data to obtain an audio feature set;
in the embodiment of the present application, the algorithm for extracting the audio feature may include any one of the following algorithms, which are not limited herein: time-frequency feature fusion algorithm (Time-frequency feature fusion algorithm), Scale-invariant feature transform (SIFT) algorithm, Speeded Up Robust Features (SURF) algorithm, Fast Fourier Transform (FFT) algorithm, and the like, without limitation.
Optionally, in the step 31, performing feature extraction on the audio data to obtain an audio feature set, which may include the following steps:
3101. pre-emphasis processing is carried out on the audio data to obtain processed audio data;
3102. performing frame windowing on the processed audio data to obtain windowed audio data;
3103. performing Short Time Energy (STE) feature extraction on the windowed audio data to obtain a STE feature set; performing Mel-frequency cepstral coefficients (MFCC) feature extraction on the windowed audio data to obtain an MFCC feature set;
3104. and performing feature fusion on the STE feature set and the MFCC feature set to obtain a fused audio feature set.
Taking a time-frequency feature fusion algorithm as an example, please refer to fig. 2B, where fig. 2B is a schematic flow chart of feature extraction provided in the embodiment of the present application, where audio data in a period of time may be first input into a high-pass filter, and the high-pass filter is used to perform pre-emphasis processing, and a mathematical expression is as follows: h (z) ═ 1-az-1Wherein a is a correction coefficient, and the value range of a can be 0.95-0.97.
Then, a hamming window can be used for framing and windowing, and the hamming window formula is as follows:
where n is an integer, and n is 0, 1, 2, 3.
The sound is a non-stationary signal which changes along with time, has time-varying characteristics, but has stationary characteristics in a short time range, the audio sampling frequency adopted for framing and windowing can be 16000Hz, and furthermore, STE characteristic extraction and MFCC characteristic extraction can be carried out on the audio data after windowing.
When STE feature extraction is performed, audio data with a frame duration of 1024ms can be input, and if the parameter M in the hamming window is 128, one window corresponds to audio data with a duration of 8 ms. The STE characteristic calculation formula is as follows:
wherein x isnFor each window of audio data, wnFor Hamming window, EnFor the energy value of each window audio data, a one-dimensional vector a with 128 elements is obtained corresponding to each frame of audio data, where a is the STE feature of each frame of audio data.
Optionally, in step 3103, performing MFCC feature extraction on the windowed audio data to obtain an MFCC feature set, which may include the following steps:
3131. performing multi-precision Fourier transform on the windowed audio data to obtain a transform result;
3132. performing energy calculation on the transformation result to obtain an energy spectrum;
3133. carrying out Mel filtering on the energy spectrum to obtain a Mel spectrum;
3134. and taking the logarithm of the Mel spectrum, performing discrete cosine change, and taking the obtained discrete cosine change coefficient as an MFCC feature to obtain an MFCC feature set.
Referring to fig. 2C, fig. 2C is a schematic flow chart illustrating a process of performing MFCC feature extraction according to an embodiment of the present disclosure, wherein discrete fourier transform may be performed on windowed audio data, where the discrete fourier transform is expressed by the following formula:
wherein, N is the point number of Fourier transform, k is the frequency information of Fourier transform, and the frequency precision is f/N. For each 1024ms audio data frame, the time precision and the frequency precision of Fourier transform features obtained by taking different N are different. In the application, N can be respectively 1024, 512 and 256 to carry out Fourier transform, and a Fourier transform result is obtained.
Then, energy calculation is performed on the fourier transform result, specifically, the sum of squares of the real part and the imaginary part of the fourier transform result is calculated, and an energy spectrum is obtained.
Then, the energy spectrum is processed with Mel filtering, which is to convert the energy spectrum into Mel spectrum conforming to human ear hearing, and the mathematical expression of the Mel filtering is as follows:
wherein f is frequency, and finally, logarithm is taken to mel spectrum, Discrete Cosine Transform (DCT) is performed, and the obtained DCT coefficient is the MFCC feature, and the MFCC feature of 128 dimensions can be taken in this embodiment.
The MFCC feature obtained when N is 1024 is a two-dimensional vector B of 16 × 128, the MFCC feature obtained when N is 512 is a two-dimensional vector C of 32 × 128, and the MFCC feature obtained when N is 256 is a two-dimensional vector D of 64 × 128.
Finally, the STE feature set and the MFCC feature set are subjected to feature fusion, specifically, a feature vector a in the generated STE feature set and feature vectors B, C, and D in the generated MFCC feature set are combined to obtain a two-dimensional fusion feature vector of 113 × 128 (1+16+32+64 ═ 113).
32. Reducing the dimensions of a plurality of audio features in the audio feature set to obtain a reduced-dimension audio feature set, wherein the reduced-dimension audio feature set comprises a plurality of target audio features;
in a specific implementation, the audio features in the audio feature set extracted by the features are 113 × 128 dimensions, and the feature dimensions are too high, which results in a very large amount of calculation for the model, and it is also difficult to learn better parameters from the features with such high dimensions. The audio feature set fused by the multi-precision MFCC features and the time domain features STE has a very large information amount and certain redundancy, so that the audio features in the audio feature set can be reduced by a PCA method. Assuming m pieces of n-dimensional data, performing PCA dimension reduction may include the following steps:
3201. forming a plurality of audio features in the audio feature set into an n-row m-column matrix X according to columns;
3202. zero-averaging each row of the matrix X, i.e. subtracting the average of all elements in each row;
3203. solving a covariance matrix ∑ XXT；
3204. Solving an eigenvalue of the covariance matrix and a corresponding eigenvector;
3205. and arranging the eigenvectors into a matrix from top to bottom according to the size of the corresponding eigenvalue, and taking the first k rows to form the matrix P.
3206. And multiplying each audio feature by the matrix P to obtain feature data reduced to k dimensions.
Therefore, through the feature dimension reduction processing, the dimension reduction can be performed on the multiple audio features with high dimension to obtain the audio feature set after the dimension reduction, so that the feature classification can be performed more efficiently subsequently.
33. Sequentially inputting the target audio features into a preset classification model to obtain a plurality of probabilities that the target audio features belong to preset audio features, wherein each target audio feature corresponds to one probability;
the preset audio features may be cue sound features of any one of the following cue sounds: the alarm sound, the station announcement sound, the door opening prompt sound, the door closing prompt sound, and other prompt sounds are not limited herein.
The preset classification model may be, for example, an xgboost classification model, and the xgboost library is an efficient integration method library of the CART classification tree, and the method library has the following advantages: adding regularization to the loss function to prevent overfitting; the algorithm flow not only uses the first derivative, but also uses the second derivative, so that the loss function is more accurate; the parallel optimization is good, the parallel of the xgboost library is in the characteristic granularity, and the model training is fast; the abnormal condition that the training data are sparse values is considered, the method can specify the default direction of the branch for the missing value or the given value, the efficiency of the algorithm can be greatly improved, and the algorithm is prevented from being influenced by the abnormal value; column sampling is supported, so that overfitting can be reduced, calculation can be reduced, and efficiency is improved.
According to the embodiment of the application, the trained xgboost classification model can be obtained on the server by utilizing the xgboost library training model of Python, and then the xgboost library of Java is utilized to be deployed on the electronic equipment.
Therefore, a plurality of probabilities that the target audio features belong to the preset audio features are obtained by sequentially inputting the target audio features into the preset classification model.
34. If M probabilities corresponding to M continuous target audio features in a first preset time period include N first target probabilities larger than a preset probability threshold, determining that a vehicle reaches a current station, wherein the probabilities include the M probabilities, M and N are positive integers, and M is larger than or equal to N.
The preset probability threshold may be, for example, 80%, 85%, and the first preset time period may be set by default by the system or may be set by the user.
For example, in a subway riding scene, a subway door closing alarm generally lasts for about 4-6 seconds, a first preset time period can be set to be 5s, if the length of an audio data window in the framing and windowing is 1024ms, the audio data time length corresponding to the audio features of the preset classification model is input to be 1.056 seconds, the value of M can be 5, and the value of N can be a positive integer less than or equal to M, so that alarm rings appearing in the audio data for 5 seconds can be detected for several times, if alarm rings appearing for more than 2 times in 5 seconds continuously, it is determined that the subway is close to the door in the first preset time period, and it is determined that the subway arrives at the current station.
Referring to fig. 2D, fig. 2D is a schematic flow chart illustrating a process of determining a current station where a subway arrives according to an embodiment of the present application, where audio features of audio data within a period of time may be extracted to obtain an audio feature set, then PCA feature reduction is performed on a plurality of audio features in the audio feature set to obtain a reduced-dimension audio feature set, then the plurality of audio features in the reduced-dimension audio feature set are sequentially input into an xgboost classification model, a plurality of probabilities that the plurality of audio features belong to a warning ring tone of the subway are output, and whether the subway arrives at a station is determined according to the plurality of probabilities.
Optionally, in this embodiment of the present application, the following steps may also be included:
if N second target probabilities which are greater than a preset probability threshold value are included in M probabilities corresponding to M continuous target audio features in a second preset time period, and the time interval between the first preset time period and the second preset time period is smaller than a preset time length, it is determined that a first site which arrives in the first preset time period and a second site which arrives in the second preset time period are the same site.
In the embodiment of the application, it is considered that the same or similar prompting sounds from other sound sources may exist shortly after the vehicle arrives at the station during the driving process of the vehicle, for example, the alarm ring sounds from the mobile phones of other passengers, or the alarm ring sounds from other vehicles in the same station. If the electronic device detects that N second target probabilities greater than the preset probability threshold are included in M probabilities corresponding to M target audio features existing in the first preset time period, and determines that the vehicle reaches the first station, and immediately after a short time interval, for example, 30 seconds, the electronic device detects that N second target probabilities greater than the preset probability threshold are included in M probabilities corresponding to M consecutive target audio features also existing in the second preset time period, and determines that the vehicle reaches the second station, it may be erroneously determined that the vehicle has passed through two stations, therefore, the electronic device may preset a preset time duration, where the preset time duration may be, for example, 1 minute, 2 minutes, and so on, and no limitation is made here, so that the electronic device detects that N second target probabilities greater than the preset probability threshold are included in M probabilities corresponding to M consecutive target audio features existing in the first preset time period and the second preset time period, it may be determined that a first station arriving at a first preset time period and a second station arriving at a second preset time period are the same station.
Taking a subway riding scene as an example, if the electronic device detects that 3 times of alarm rings occur within a first preset time period of 5:00:00-5:00:05, it can be determined that one station is reached, if the electronic device detects that 2 times of alarm rings occur within a second preset time period of 5:00:35-5:00:40, it can be determined that one station is reached, then it can be determined that the time interval between the first preset time period and the second preset time period is 30 seconds, and it can be determined that the time interval is less than the preset time duration for 1 minute, so that it can be determined that the vehicles arrive at the same station.
204. And if the next station is a preset station, performing vehicle arrival prompting operation.
In this embodiment of the application, the preset station may be a target station that a user wants to reach, or may be a transfer station for transferring vehicles. If the next station is determined to be the target station, the user can be prompted to arrive at the target station, and if the next station is determined to be the transfer station, the user can be prompted to transfer at the transfer station. Wherein, the prompt operation may include at least one of the following: display a prompt message such as text or an icon, a vibration prompt, a voice prompt, and the like.
Therefore, the arrival of the vehicle can be determined through the prompting sound in the vehicle environment in the process that the user takes a bus, the vehicle is further prompted to arrive at the preset station, and compared with the scheme that the arrival of the vehicle is predicted by detecting the deceleration or acceleration running state of the vehicle through the acceleration sensor, the arrival of the vehicle can be determined more accurately through the audio data, and the arrival prompt is performed.
According to the vehicle arrival prompting method in the embodiment of the application, the target riding circuit is obtained and is a circuit formed by connecting more than two stations in series; collecting audio data in a riding environment within a period of time; when the current station where the vehicle arrives is determined according to the audio data, determining the next station according to the current station and the target riding route; and if the next station is the preset station, performing vehicle arrival prompting operation, and thus determining that the vehicle arrives according to the audio data in the riding environment, and further more accurately performing vehicle arrival prompting when the next station is determined to be the preset station.
Referring to fig. 3A, fig. 3A is a schematic flowchart of a vehicle arrival prompting method according to an embodiment of the present application, where the method includes the following steps:
301. and acquiring a target riding circuit, wherein the target riding circuit is formed by connecting more than two stations in series.
302. Audio data is collected over a period of time in a riding environment.
303. And performing feature extraction on the audio data to obtain an audio feature set.
304. And reducing the dimensions of the plurality of audio features in the audio feature set to obtain a reduced-dimension audio feature set, wherein the reduced-dimension audio feature set comprises a plurality of target audio features.
305. And sequentially inputting the target audio features into a preset classification model to obtain a plurality of probabilities that the target audio features belong to preset audio features, wherein each target audio feature corresponds to one probability.
306. If M probabilities corresponding to M continuous target audio features in a first preset time period include N first target probabilities larger than a preset probability threshold, determining that a vehicle reaches a current station, and determining a next station according to the current station and the target riding route, wherein the probabilities include the M probabilities, M and N are positive integers, and M is larger than or equal to N.
307. And if the next station is a preset station, performing vehicle arrival prompting operation.
For example, please refer to fig. 3B, where fig. 3B is a schematic flow diagram for prompting that a vehicle arrives at a station according to an embodiment of the present disclosure, a riding route diagram may be stored in advance in an electronic device, and when a user takes a car, the electronic device may obtain a target riding route of the user, and then the electronic device may collect audio data within a period of time through a microphone, and determine whether a sound is a prompting sound for prompting that the vehicle arrives at the station, for example, a warning ring tone for closing a subway, if so, determine a next station according to a current station where the vehicle arrives, if the next station is the target station, prompt the user that the vehicle is about to arrive at the target station, if not, determine whether the next station is a transfer station, and if the next station is the transfer station, prompt the user to transfer at the transfer station.
The specific implementation process of steps 301-307 can refer to the corresponding description in steps 201-204, which is not described herein again.
It can be seen that, in the embodiment of the application, by obtaining the target riding circuit, the target riding circuit is a circuit formed by connecting more than two stations in series; collecting audio data in a riding environment within a period of time; carrying out feature extraction on the audio data to obtain an audio feature set; reducing the dimensions of a plurality of audio features in the audio feature set to obtain an audio feature set after dimension reduction, sequentially inputting a plurality of target audio features into a preset classification model to obtain a plurality of probabilities that the plurality of target audio features belong to preset audio features, determining that a vehicle arrives at a current station if M probabilities corresponding to M continuous target audio features in a first preset time period include N first target probabilities greater than a preset probability threshold, and determining a next station according to the current station and a target riding line; and if the next station is the preset station, performing vehicle arrival prompting operation, and thus determining that the vehicle arrives according to the audio data in the riding environment, and further more accurately performing vehicle arrival prompting when the next station is determined to be the preset station.
Referring to fig. 4, fig. 4 is a schematic flow chart of a vehicle arrival prompting method according to an embodiment of the present application, where the method includes the following steps:
401. and acquiring a target riding circuit, wherein the target riding circuit is formed by connecting more than two stations in series.
402. Audio data is collected over a period of time in a riding environment.
403. And pre-emphasis processing is carried out on the audio data to obtain processed audio data.
404. And performing frame division and windowing on the processed audio data to obtain windowed audio data.
405. Performing STE feature extraction on the windowed audio data to obtain an STE feature set; and performing MFCC feature extraction on the windowed audio data to obtain an MFCC feature set.
406. And performing feature fusion on the STE feature set and the MFCC feature set to obtain a fused audio feature set.
407. And reducing the dimensions of the plurality of audio features in the audio feature set to obtain a reduced-dimension audio feature set, wherein the reduced-dimension audio feature set comprises a plurality of target audio features.
408. And sequentially inputting the target audio features into a preset classification model to obtain a plurality of probabilities that the target audio features belong to preset audio features, wherein each target audio feature corresponds to one probability.
409. If M probabilities corresponding to M continuous target audio features in a first preset time period include N first target probabilities larger than a preset probability threshold, determining that a vehicle reaches a current station, and determining a next station according to the current station and the target riding route, wherein the probabilities include the M probabilities, M and N are positive integers, and M is larger than or equal to N.
410. And if the next station is a preset station, performing vehicle arrival prompting operation.
The specific implementation process of steps 401-410 can refer to the corresponding description in steps 201-204, which is not described herein again.
Therefore, in the embodiment of the application, the audio feature extraction is performed by adopting the time-frequency fusion feature algorithm, and the dimension reduction processing is performed on the plurality of audio features in the audio feature set to obtain the audio feature set after the dimension reduction, so that the efficiency of processing the target audio features can be improved, and the accuracy of vehicle arrival prompting is improved.
The following is a device for implementing the vehicle arrival prompting method, and specifically includes:
in accordance with the above, please refer to fig. 5, fig. 5 is a schematic structural diagram of an electronic device 500 according to an embodiment of the present disclosure, where the electronic device 500 includes: processor 510, communication interface 530, and memory 520; the electronic device 500 further comprises one or more programs 521, the one or more programs 521 stored in the memory 520 and configured to be executed by the processor, the programs 521 including instructions for performing the steps of:
acquiring a target riding circuit, wherein the target riding circuit is formed by connecting more than two stations in series;
collecting audio data in a riding environment within a period of time;
when the current station where the vehicle arrives is determined according to the audio data, determining the next station according to the current station and the target riding route;
and if the next station is a preset station, performing vehicle arrival prompting operation.
In one possible example, after the capturing of audio data within a vehicle, the program 521 further includes instructions for performing the steps of:
performing feature extraction on the audio data to obtain an audio feature set;
reducing the dimensions of a plurality of audio features in the audio feature set to obtain a reduced-dimension audio feature set, wherein the reduced-dimension audio feature set comprises a plurality of target audio features;
sequentially inputting the target audio features into a preset classification model to obtain a plurality of probabilities that the target audio features belong to preset audio features, wherein each target audio feature corresponds to one probability;
if M probabilities corresponding to M continuous target audio features in a first preset time period include N first target probabilities larger than a preset probability threshold, determining that a vehicle reaches a current station, wherein the probabilities include the M probabilities, M and N are positive integers, and M is larger than or equal to N.
In one possible example, the program 521 further includes instructions for performing the steps of:
if N second target probabilities which are greater than a preset probability threshold value are included in M probabilities corresponding to M continuous target audio features in a second preset time period, and the time interval between the first preset time period and the second preset time period is smaller than a preset time length, it is determined that a first site which arrives in the first preset time period and a second site which arrives in the second preset time period are the same site.
In one possible example, in the aspect of performing feature extraction on the audio data to obtain an audio feature set, the program 521 includes instructions for performing the following steps:
pre-emphasis processing is carried out on the audio data to obtain processed audio data;
performing frame windowing on the processed audio data to obtain windowed audio data;
performing STE feature extraction on the windowed audio data to obtain an STE feature set; performing MFCC feature extraction on the windowed audio data to obtain an MFCC feature set;
and performing feature fusion on the STE feature set and the MFCC feature set to obtain a fused audio feature set.
In one possible example, in the MFCC feature extraction of the windowed audio data to obtain a MFCC feature set, the program 521 includes instructions for:
performing multi-precision Fourier transform on the windowed audio data to obtain a transform result;
performing energy calculation on the transformation result to obtain an energy spectrum;
carrying out Mel filtering on the energy spectrum to obtain a Mel spectrum;
and taking the logarithm of the Mel spectrum, performing discrete cosine change, and taking the obtained discrete cosine change coefficient as an MFCC feature to obtain an MFCC feature set.
In one possible example, in the obtaining the target ride route, the program 521 includes instructions for performing the following steps:
acquiring an initial site and the target site;
and determining a target riding route according to the starting station, the target station and a preset riding route map, wherein the target riding route comprises at least two stations including the starting station and the target station.
In one possible example, in the obtaining the target ride route, the program 521 includes instructions for performing the following steps:
acquiring the current position and riding time;
determining at least one reference riding route from a plurality of preset historical riding routes according to the current position;
and determining a target riding route in the at least one reference riding route according to the riding time.
It can be seen that, in the electronic device in the embodiment of the present application, the electronic device obtains a target riding circuit, where the target riding circuit is a circuit formed by connecting more than two stations in series; collecting audio data in a riding environment within a period of time; when the current station where the vehicle arrives is determined according to the audio data, determining the next station according to the current station and the target riding route; and if the next station is the preset station, performing vehicle arrival prompting operation, and thus determining that the vehicle arrives according to the audio data in the riding environment, and further more accurately performing vehicle arrival prompting when the next station is determined to be the preset station.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a vehicle arrival prompting device 600 provided in this embodiment, which is applied to an electronic device, where the electronic device includes a plurality of ambient light sensors disposed at different positions, the vehicle arrival prompting device 600 includes an obtaining unit 601, a collecting unit 602, a determining unit 603, and a prompting unit 604, where,
the obtaining unit 601 is configured to obtain a target riding line, where the target riding line is a line formed by connecting more than two stations in series;
the acquisition unit 602 is configured to acquire audio data in a riding environment for a period of time;
the determining unit 603 is configured to determine a next station according to the current station and the target riding route when determining a current station where the vehicle arrives according to the audio data;
the prompting unit 604 is configured to perform a prompting operation when the next station is a preset station. .
Optionally, after the acquiring the audio data in the vehicle, the determining unit 603 is further configured to:
performing feature extraction on the audio data to obtain an audio feature set;
reducing the dimensions of a plurality of audio features in the audio feature set to obtain a reduced-dimension audio feature set, wherein the reduced-dimension audio feature set comprises a plurality of target audio features;
sequentially inputting the target audio features into a preset classification model to obtain a plurality of probabilities that the target audio features belong to preset audio features, wherein each target audio feature corresponds to one probability;
if M probabilities corresponding to M continuous target audio features in a first preset time period include N first target probabilities larger than a preset probability threshold, determining that a vehicle reaches a current station, wherein the probabilities include the M probabilities, M and N are positive integers, and M is larger than or equal to N.
Optionally, the determining unit 603 is further configured to:
if N second target probabilities which are greater than a preset probability threshold value are included in M probabilities corresponding to M continuous target audio features in a second preset time period, and the time interval between the first preset time period and the second preset time period is smaller than a preset time length, it is determined that a first site which arrives in the first preset time period and a second site which arrives in the second preset time period are the same site.
Optionally, in the aspect of performing feature extraction on the audio data to obtain an audio feature set, the determining unit 603 is specifically configured to:
pre-emphasis processing is carried out on the audio data to obtain processed audio data;
performing frame windowing on the processed audio data to obtain windowed audio data;
performing STE feature extraction on the windowed audio data to obtain an STE feature set; performing MFCC feature extraction on the windowed audio data to obtain an MFCC feature set;
and performing feature fusion on the STE feature set and the MFCC feature set to obtain a fused audio feature set.
Optionally, in terms of performing MFCC feature extraction on the windowed audio data to obtain an MFCC feature set, the determining unit 603 is specifically configured to:
performing multi-precision Fourier transform on the windowed audio data to obtain a transform result;
performing energy calculation on the transformation result to obtain an energy spectrum;
carrying out Mel filtering on the energy spectrum to obtain a Mel spectrum;
and taking the logarithm of the Mel spectrum, performing discrete cosine change, and taking the obtained discrete cosine change coefficient as an MFCC feature to obtain an MFCC feature set.
Optionally, the obtaining unit 601 is specifically configured to:
acquiring an initial site and the target site;
and determining a target riding route according to the starting station, the target station and a preset riding route map, wherein the target riding route comprises at least two stations including the starting station and the target station.
Optionally, the obtaining unit 601 is specifically configured to:
acquiring the current position and riding time;
determining at least one reference riding route from a plurality of preset historical riding routes according to the current position;
and determining a target riding route in the at least one reference riding route according to the riding time.
It can be seen that, in the vehicle arrival prompting device described in the embodiment of the present application, by obtaining a target riding circuit, the target riding circuit is a circuit formed by connecting more than two stations in series; collecting audio data in a riding environment within a period of time; when the current station where the vehicle arrives is determined according to the audio data, determining the next station according to the current station and the target riding route; and if the next station is the preset station, performing vehicle arrival prompting operation, and thus determining that the vehicle arrives according to the audio data in the riding environment, and further more accurately performing vehicle arrival prompting when the next station is determined to be the preset station.
It can be understood that the functions of the program modules of the vehicle arrival prompting device in this embodiment can be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process thereof may refer to the related description of the foregoing method embodiment, which is not described herein again.
Embodiments of the present application also provide a computer-readable storage medium storing a computer program for electronic data exchange, the computer program causing a computer to execute part or all of the steps of any one of the vehicle arrival prompting methods as set forth in the above method embodiments.
Embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program causes a computer to execute part or all of the steps of any one of the vehicle arrival prompting methods as described in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, 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 some interfaces, devices or units, and may be an electric or other form.
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 of 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 integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (8)
1. A vehicle arrival prompting method, characterized in that the method comprises:
acquiring a target riding circuit, wherein the target riding circuit is formed by connecting more than two stations in series;
collecting audio data over a period of time in a riding environment, comprising: detecting acceleration through an acceleration sensor, starting audio data acquisition if the acceleration sensor is in a deceleration state, and stopping audio data acquisition if the acceleration sensor is in an acceleration state;
performing feature extraction on the audio data to obtain an audio feature set;
reducing the dimensions of a plurality of audio features in the audio feature set to obtain a reduced-dimension audio feature set, wherein the reduced-dimension audio feature set comprises a plurality of target audio features;
sequentially inputting the target audio features into a preset classification model to obtain a plurality of probabilities that the target audio features belong to preset audio features, wherein each target audio feature corresponds to one probability;
if M probabilities corresponding to M continuous target audio features in a first preset time period include N first target probabilities larger than a preset probability threshold, determining that a vehicle reaches a current station, wherein the probabilities include the M probabilities, M and N are positive integers, and M is larger than or equal to N;
if N second target probabilities which are greater than a preset probability threshold value are included in M probabilities corresponding to M continuous target audio features in a second preset time period, and the time interval between the first preset time period and the second preset time period is less than a preset time length, determining that a first site arriving in the first preset time period and a second site arriving in the second preset time period are the same site, and the first site is the current site;
when the current station where the vehicle arrives is determined according to the audio data, determining the next station according to the current station and the target riding route;
and if the next station is a preset station, performing vehicle arrival prompting operation.
2. The method of claim 1, wherein the performing feature extraction on the audio data to obtain an audio feature set comprises:
pre-emphasis processing is carried out on the audio data to obtain processed audio data;
performing frame windowing on the processed audio data to obtain windowed audio data;
performing STE feature extraction on the windowed audio data to obtain an STE feature set; performing MFCC feature extraction on the windowed audio data to obtain an MFCC feature set;
and performing feature fusion on the STE feature set and the MFCC feature set to obtain a fused audio feature set.
3. The method of claim 2, wherein performing MFCC feature extraction on the windowed audio data to obtain a MFCC feature set comprises:
performing multi-precision Fourier transform on the windowed audio data to obtain a transform result;
performing energy calculation on the transformation result to obtain an energy spectrum;
carrying out Mel filtering on the energy spectrum to obtain a Mel spectrum;
and taking the logarithm of the Mel spectrum, performing discrete cosine change, and taking the obtained discrete cosine change coefficient as an MFCC feature to obtain an MFCC feature set.
4. The method according to any one of claims 1-3, wherein the obtaining a target ride route comprises:
acquiring an initial site and the target site;
and determining a target riding route according to the starting station, the target station and a preset riding route map, wherein the target riding route comprises at least two stations including the starting station and the target station.
5. The method according to any one of claims 1-3, wherein the obtaining a target ride route comprises:
acquiring the current position and riding time;
determining at least one reference riding route from a plurality of preset historical riding routes according to the current position;
and determining a target riding route in the at least one reference riding route according to the riding time.
6. A vehicle arrival prompting apparatus characterized by comprising:
the system comprises an acquisition unit, a processing unit and a control unit, wherein the acquisition unit is used for acquiring a target riding circuit which is formed by connecting more than two stations in series;
the acquisition unit is used for acquiring audio data in a period of time under a riding environment, and comprises: detecting acceleration through an acceleration sensor, starting audio data acquisition if the acceleration sensor is in a deceleration state, and stopping audio data acquisition if the acceleration sensor is in an acceleration state;
the determining unit is used for determining a next station according to the current station and the target riding route when the current station where the vehicle arrives is determined according to the audio data;
the prompting unit is used for performing prompting operation when the next station is a preset station;
the determining unit is further configured to:
performing feature extraction on the audio data to obtain an audio feature set;
reducing the dimensions of a plurality of audio features in the audio feature set to obtain a reduced-dimension audio feature set, wherein the reduced-dimension audio feature set comprises a plurality of target audio features;
sequentially inputting the target audio features into a preset classification model to obtain a plurality of probabilities that the target audio features belong to preset audio features, wherein each target audio feature corresponds to one probability;
if M probabilities corresponding to M continuous target audio features in a first preset time period include N first target probabilities larger than a preset probability threshold, determining that a vehicle reaches a current station, wherein the probabilities include the M probabilities, M and N are positive integers, and M is larger than or equal to N;
if N second target probabilities greater than a preset probability threshold are included in M probabilities corresponding to M continuous target audio features in a second preset time period, and the time interval between the first preset time period and the second preset time period is smaller than a preset time length, determining that a first site arriving in the first preset time period and a second site arriving in the second preset time period are the same site, and the first site being the current site.
7. An electronic device comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-5.
8. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-5.
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