CN111798020B - Information processing method and device, storage medium and electronic equipment - Google Patents
Information processing method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The embodiment of the application discloses an information processing method, an information processing device, a storage medium and electronic equipment, wherein the embodiment of the application acquires average travel rate corresponding to travel scenes according to motion data by acquiring the motion data of a user in different travel scenes; collecting current motion data of a user, and acquiring a first travel rate and a target travel scene of the user according to the current motion data; obtaining an average travel rate corresponding to a target travel scene, and calculating to obtain the target travel rate by combining the first travel rate and the average travel rate; and acquiring destination position information, and generating target time information according to the target travel rate and the destination position information. By combining the historical average travel rate of the current travel scene and the current first travel rate, the target travel rate which is closer to the current travel scene is generated, and therefore accuracy of travel time information prediction is improved.
Description
Technical Field
The present application relates to the field of information processing technologies, and in particular, to an information processing method, an information processing device, a storage medium, and an electronic device.
Background
With the development of cities and mobile terminals, related applications such as electronic maps play an increasingly important role in user output, wherein one of the core functions in the electronic maps is planning of travel routes and prediction of travel time. The prediction of travel time by the related application is mainly calculated by predicting the distance between the position of the user and the target position and the preset walking speed of the system according to the walking behavior example of the user, so as to obtain the final travel time.
However, since the walking speeds of different users are different, a large error exists between the estimated travel time and the actual travel time in the related application, so that the prediction of the travel time according to the preset walking speed of the system is inaccurate.
Disclosure of Invention
The embodiment of the application provides an information processing method, an information processing device, a storage medium and electronic equipment, which can more accurately estimate the travel time of a user.
In a first aspect, an embodiment of the present application provides a method for processing information, including:
acquiring motion data of a user in different travel scenes, and acquiring average travel rate corresponding to the travel scenes according to the motion data;
Collecting current motion data of a user, and acquiring a first travel rate and a target travel scene of the user according to the current motion data;
obtaining an average travel rate corresponding to the target travel scene, and calculating the target travel rate by combining the first travel rate and the average travel rate;
and acquiring destination position information, and generating target time information according to the target travel rate and the destination position information.
In a second aspect, an embodiment of the present application provides an information processing apparatus, including:
the acquisition module is used for acquiring motion data of a user in different travel scenes and acquiring average travel rate corresponding to the travel scenes according to the motion data;
the acquisition module is used for acquiring current motion data of the user and acquiring a first travel rate and a target travel scene of the user according to the current motion data;
the calculation module is used for obtaining the average travel rate corresponding to the target travel scene and calculating the target travel rate by combining the first travel rate and the average travel rate;
and the generation module is used for acquiring destination position information and generating target time information according to the target travel rate and the destination position information.
In a third aspect, a storage medium provided by an embodiment of the present application has a computer program stored thereon, which when executed on a computer causes the computer to perform the method for processing information as provided by any of the embodiments of the present application.
In a fourth aspect, an electronic device provided by an embodiment of the present application includes a processor and a memory, where the memory has a computer program, and the processor is configured to execute a method for processing information provided by any of the embodiments of the present application by calling the computer program.
According to the embodiment of the application, the average travel rate corresponding to the travel scene is obtained according to the motion data by collecting the motion data of the user in different travel scenes; collecting current motion data of a user, and acquiring a first travel rate and a target travel scene of the user according to the current motion data; obtaining an average travel rate corresponding to the target travel scene, and calculating the target travel rate by combining the first travel rate and the average travel rate; and acquiring destination position information, and generating target time information according to the target travel rate and the destination position information. The target travel rate is generated by combining the historical average travel rate and the current travel rate, so that more accurate prediction of the arrival time of the user is realized.
Drawings
The technical solution and other advantageous effects of the present application will be made apparent by the following detailed description of the specific embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is an application scenario schematic diagram of an information processing method according to an embodiment of the present application.
Fig. 2 is a flow chart of a method for processing information according to an embodiment of the present application.
Fig. 3 is another flow chart of a method for processing information according to an embodiment of the present application.
Fig. 4 is a schematic block diagram of an information processing apparatus according to an embodiment of the present application.
Fig. 5 is another schematic block diagram of an information processing apparatus according to an embodiment of the present application.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 7 is a schematic diagram of another structure of an electronic device according to an embodiment of the present application.
Detailed Description
Referring to the drawings, wherein like reference numbers refer to like elements throughout, the principles of the present application are illustrated in an appropriate computing environment. The following description is based on illustrative embodiments of the application and should not be taken as limiting other embodiments of the application not described in detail herein.
The term "module" as used herein may be considered as a software object executing on the computing system. The various components, modules, engines, and services described herein may be viewed as implementing objects on the computing system. The apparatus and method described herein are preferably implemented in software, but may of course also be implemented in hardware, all within the scope of the application.
Referring to fig. 1, fig. 1 is an application scenario schematic diagram of an information processing method according to an embodiment of the present application. The processing of this information applies to electronic devices. The electronic equipment is provided with a panoramic sensing architecture. The panoramic awareness architecture is an integration of hardware and software in an electronic device for implementing the processing method of the information.
The panoramic sensing architecture comprises an information sensing layer, a data processing layer, a feature extraction layer, a scene modeling layer and an intelligent service layer.
The information sensing layer is used for acquiring information of the electronic equipment or information in an external environment. The information sensing layer may include a plurality of sensors. For example, the information sensing layer includes a plurality of sensors such as a distance sensor, a magnetic field sensor, a light sensor, an acceleration sensor, a fingerprint sensor, a hall sensor, a position sensor, a gyroscope, an inertial sensor, a posture sensor, a barometer, a heart rate sensor, and the like.
Wherein the distance sensor may be used to detect a distance between the electronic device and an external object. The magnetic field sensor may be used to detect magnetic field information of an environment in which the electronic device is located. The light sensor may be used to detect light information of an environment in which the electronic device is located. The acceleration sensor may be used to detect acceleration data of the electronic device. The fingerprint sensor may be used to collect fingerprint information of a user. The Hall sensor is a magnetic field sensor manufactured according to the Hall effect and can be used for realizing automatic control of electronic equipment. The location sensor may be used to detect the geographic location where the electronic device is currently located. Gyroscopes may be used to detect angular velocities of an electronic device in various directions. Inertial sensors may be used to detect motion data of the electronic device. The gesture sensor may be used to sense gesture information of the electronic device. Barometers may be used to detect the air pressure of an environment in which an electronic device is located. The heart rate sensor may be used to detect heart rate information of the user.
The data processing layer is used for processing the data acquired by the information sensing layer. For example, the data processing layer may perform data cleaning, data integration, data transformation, data reduction, and the like on the data acquired by the information sensing layer.
The data cleaning refers to cleaning a large amount of data acquired by the information sensing layer to remove invalid data and repeated data. The data integration refers to integrating a plurality of single-dimensional data acquired by an information sensing layer into a higher or more abstract dimension so as to comprehensively process the plurality of single-dimensional data. The data transformation refers to performing data type conversion or format conversion on the data acquired by the information sensing layer, so that the transformed data meets the processing requirement. Data reduction refers to maximally simplifying the data volume on the premise of keeping the original appearance of the data as much as possible.
The feature extraction layer is used for extracting features of the data processed by the data processing layer so as to extract features included in the data. The extracted features can reflect the state of the electronic equipment itself or the state of the user or the environmental state of the environment where the electronic equipment is located, etc.
The feature extraction layer may extract features by filtration, packaging, integration, or the like, or process the extracted features.
Filtering means that the extracted features are filtered to delete redundant feature data. Packaging methods are used to screen the extracted features. The integration method is to integrate multiple feature extraction methods together to construct a more efficient and accurate feature extraction method for extracting features.
The scene modeling layer is used for constructing a model according to the features extracted by the feature extraction layer, and the obtained model can be used for representing the state of the electronic equipment or the state of a user or the state of the environment and the like. For example, the scenario modeling layer may construct a key value model, a pattern identification model, a graph model, a physical relationship model, an object-oriented model, and the like from the features extracted by the feature extraction layer.
The intelligent service layer is used for providing intelligent service for users according to the model constructed by the scene modeling layer. For example, the intelligent service layer may provide basic application services for users, may perform system intelligent optimization for electronic devices, and may provide personalized intelligent services for users.
In addition, the panoramic sensing architecture can also comprise a plurality of algorithms, each algorithm can be used for analyzing and processing data, and the algorithms can form an algorithm library. For example, the algorithm library may include a markov algorithm, an implicit dirichlet distribution algorithm, a bayesian classification algorithm, a support vector machine, a K-means clustering algorithm, a K-nearest neighbor algorithm, a conditional random field, a residual network, a long-short term memory network, a convolutional neural network, a cyclic neural network, and the like.
In some embodiments, the information sensing layer acquires motion data of a user in different travel scenes, the data processing layer acquires average travel rate corresponding to the travel scenes according to the motion data, the information sensing layer acquires current motion data of the user, the data processing layer acquires first travel rate and target travel scenes of the user according to the current motion data, acquires average travel rate corresponding to the target travel scenes, calculates to obtain target travel rate in combination with the first travel rate and the average travel rate, acquires destination location information, and generates target time information according to the target travel rate and the destination location information, so that predicted target time information is more accurate.
The embodiment of the application provides an information processing method, and an execution main body of the information processing method can be an information processing device provided by the embodiment of the application or an electronic device integrated with the information processing device, wherein the information processing device can be realized in a hardware or software mode. The electronic device may be a smart phone, a tablet computer, a palm computer (PDA, personal Digital Assistant), etc. For convenience of description, hereinafter, embodiments of the method will be described taking an execution subject of the method as an example of a terminal device capable of executing the method. It will be appreciated that the implementation of the method is merely an exemplary illustration of the terminal device and should not be construed as limiting the method.
Specific analysis is described below.
Referring to fig. 2, fig. 2 is a flow chart of a method for processing information according to an embodiment of the present application, where the method for processing information includes the following steps:
in step S101, motion data of a user in different travel scenes is collected, and an average travel rate corresponding to the travel scenes is obtained according to the motion data.
The information sensing layer in the panorama sensing module is utilized to collect motion data of a user in different travel scenes in real time, and it is required to be noted that the travel scenes refer to travel states of the user, such as travel states of vehicles such as driving automobiles and riding high-speed rails, walking or running, and the like, and the motion data refer to motion track data, such as altitude change track data, angle change track data, acceleration change track data, geographic position change track data and the like. It can be understood that the waveforms of the motion trail data curves in different travel scenes are different to some extent, for example, three-axis acceleration sensors are used for collecting motion data of a user in 3 coordinate axes (X, Y, Z), wherein the direction of the Z axis is vertical to the upward direction of the ground, at the moment, the acceleration change trail data curve of the Z axis is gentle when the user drives the automobile to go out, small fluctuation exists in the acceleration change trail data curve when the automobile goes up and down, and the acceleration change trail data curve of the Z axis is fluctuating along with the walking rhythm of the user, so that the motion trail data in different travel scenes are different, namely the motion trail data of different travel scenes have corresponding motion trail characteristics. At this time, the motion track features of the collected motion data can be extracted by using a corresponding algorithm, the travel scenes corresponding to the collected motion data are determined according to the motion track features, and the motion data corresponding to each travel scene are obtained to obtain the average travel rate under each travel scene.
In some embodiments, the step of collecting motion data of the user in different travel scenes may include:
(1) Starting a plurality of sensors by using the panoramic sensing module to detect the travel state of the user;
(2) Acquiring detection data of a plurality of sensors according to time sequence to generate motion data of a user, and determining corresponding motion trail characteristics of the motion data;
(3) And determining a corresponding travel scene according to the motion track characteristics, and storing the motion data in association with the travel scene.
Wherein, the sensor for detecting the user travel state may include: the system comprises an acceleration sensor, a gyroscope, a magnetic induction sensor, a barometer and the like, wherein the sensors can be specifically a triaxial acceleration sensor, a triaxial gyroscope, a triaxial magnetic induction sensor, a barometer and the like, and a plurality of sensor data corresponding to the sensors, microphone array data of a terminal and global positioning system (GPS, global Positioning System) data are acquired according to time sequence and combined to generate movement track data of user travel. The acquired motion trail data can be subjected to convolution processing by using a convolution neural network to generate corresponding motion trail features, corresponding travel scenes are determined according to the motion trail features, and the motion trail data and the travel scenes are stored in an associated mode. Further, the step of obtaining the average travel rate corresponding to the travel scene according to the motion data may include:
(1.1) acquiring corresponding travel distance information and travel time information in motion data corresponding to each travel scene;
and (2.1) calculating the average travel rate corresponding to the corresponding travel scene according to the travel distance information and the travel time information.
The method comprises the steps of respectively obtaining motion data corresponding to each travel scene to calculate the average travel rate of a user in each travel scene, wherein at the moment, the average travel rate is related to the travel habit of the user, namely, the average travel rates of different users in the same travel scene are different.
In step S102, current motion data of a user is collected, and a first travel rate and a target travel scene of the user are obtained according to the current motion data.
When a user starts a preset application such as an electronic map or a related application with a navigation function, the panoramic sensing module starts to collect current motion data of the user after the user inputs destination information. It will be appreciated that when the user enters destination information, the user's state at this time may not be an accurate travel state, for example, the user is sitting in a car to enter destination information, where the user is approaching a stationary state but the user's real travel state is driving the car for travel. Therefore, when the destination information is received, route scheme recommendation can be performed according to distance information of travel tools commonly used by users and the destination, and travel time information can be predicted preliminarily.
Further, whether the user is in a formal trip state or not is judged according to the motion data collected by the panoramic sensing module, and when the user is in the formal trip state, the current motion data of the user is collected. It can be understood that when the motion data of the user in the preset time has a similar motion track, the user can be judged to be in a formal trip state, and the step of acquiring the first trip rate and the target trip scene of the user according to the current motion data is performed. At this time, part of motion track data such as GPS data of the current motion data may be acquired, and the current first travel rate of the user may be acquired according to the GPS data and the sampling time information, and meanwhile, motion track features corresponding to the current motion data such as altitude change track data, angle change track data, acceleration change track data, and geographic position change track data may be extracted, and the current target travel scene may be determined according to the motion track features.
In some embodiments, the step of obtaining the first trip rate and the target trip scene of the user according to the current motion data may include:
(1) Acquiring motion trail features of current motion data, and determining a target travel scene matched with the motion trail features;
(2) And calculating a first travel rate of the user according to the motion trail characteristics of the current motion data.
And comparing the motion track characteristics of the current motion data with the motion track characteristics corresponding to each travel scene to determine a matched target travel scene.
In step S103, an average travel rate corresponding to the target travel scene is obtained, and the target travel rate is calculated by combining the first travel rate and the average travel rate.
The future travel speed is also not accurately predicted because the future traffic condition information is unknown, such as the situation that the future road is congested or unblocked cannot be obtained, and when congestion occurs on the front road, for example, the value of the future travel speed must be smaller than the value of the current first travel speed, and at this time, the time information of reaching the destination cannot be accurately estimated according to the current first travel speed. Therefore, the average travel rate corresponding to the target travel scene can be obtained from the database, and the target travel rate can be generated by combining the current first travel rate and the average travel rate corresponding to the current target travel scene.
In some embodiments, the step of calculating the target travel rate by combining the first travel rate and the average travel rate may include:
(1) Processing the first travel rate and the average travel rate by a quasi-Newton method to obtain an attenuation coefficient of the first travel rate;
(2) And calculating a target travel rate according to the attenuation coefficient and the first travel rate.
The first travel rate and the average travel rate can be calculated according to the quasi-newton method so as to obtain the target travel rate. Specifically, the first travel rate and the average travel rate can be summed together with the traffic condition of the road to obtain a loss value of the travel rate, the loss value is used as an attenuation coefficient of the first travel rate at this time, and finally the estimated target travel rate can be obtained by multiplying the first travel rate and the attenuation coefficient.
In step S104, destination location information is acquired, and target time information is generated from the target travel rate and the destination location information.
After the destination position information is acquired, current position information is acquired, travel distance information is determined according to the position information and the destination position information, and corresponding target time information is determined according to the ratio of the distance information to the target travel speed.
In some embodiments, the step of generating the target time information according to the target travel rate and the destination location information may include;
(1) Acquiring current position information, and calculating target distance information according to the current position information and destination position information;
(2) And generating target time information according to the target distance information and the target travel rate.
The target time information may be time information of reaching the destination, trip remaining time, or the like, and the target time information is updated in real time according to the target distance information and the target trip rate.
In some embodiments, the step of obtaining destination location information and generating the target time information according to the target travel rate and the destination location information may further include: and generating an updated average travel rate according to the target travel rate and the historical average travel rate.
As can be seen from the above, according to the information processing method provided by the embodiment of the application, the motion data of the user in different travel scenes is collected, and the average travel rate corresponding to the travel scenes is obtained according to the motion data; collecting current motion data of a user, and acquiring a first travel rate and a target travel scene of the user according to the current motion data; obtaining an average travel rate corresponding to the target travel scene, and calculating to obtain a target travel rate by combining the first travel rate and the average travel rate; and acquiring destination position information, and generating target time information according to the target travel rate and the destination position information. By combining the historical average travel rate of the current travel scene and the current first travel rate, the target travel rate which is closer to the current travel scene is generated, and therefore accuracy of travel time information prediction is improved.
The method described in the above examples is described in further detail below by way of example.
Referring to fig. 3, fig. 3 is another flow chart of a method for processing information according to an embodiment of the application. Specifically, the information processing method includes:
in step S201, a plurality of sensors are activated by the panorama sensing module to detect a travel state of a user.
The information sensing layer in the panoramic sensing module can start a plurality of sensors to detect motion states, such as an acceleration sensor, a gyroscope, a magnetic induction sensor, a barometer and the like, and the panoramic sensing module monitors travel states of a user in real time by detecting the motion states of the terminal equipment.
In step S202, detection data of a plurality of sensors are acquired in time sequence to generate motion data of a user, and motion trajectory features corresponding to the motion data are determined.
The plurality of sensors generate detection data according to a preset sampling frequency, at this time, the plurality of sensors can be a triaxial acceleration sensor, a triaxial gyroscope, a triaxial magnetic induction sensor, a barometer combination and the like, the detection data of the plurality of sensors are obtained according to time sequence, meanwhile, microphone array data and global positioning system data of the terminal equipment are obtained to generate motion data of a user trip, namely motion track data, further, a data processing layer in the panoramic sensing module can be utilized to process the motion track data, such as data cleaning, data integration, data transformation, data reduction and the like, the processed motion track data are obtained, and a feature extraction layer is utilized to extract features of the motion track data processed by the data processing layer, so that corresponding motion track features are extracted.
In step S203, a corresponding travel scene is determined according to the motion trail feature, and the motion data is stored in association with the travel scene.
The motion track features extracted from the acquired motion data are modeled through a scene modeling layer in the panoramic sensing module through a corresponding algorithm, and a corresponding travel scene model is constructed, wherein the travel scene model represents different travel states of a user, such as driving an automobile, taking a bus or subway, walking or running, and the like. At this time, the motion trail features extracted according to the previous step are matched with the travel scene model, so as to determine a corresponding travel scene model and store the corresponding motion data in association with the travel scene model.
In step S204, corresponding travel distance information and travel time information in the motion data corresponding to each travel scene are obtained.
The method comprises the steps of obtaining motion data corresponding to each travel scene model, and extracting travel distance information and travel time information under each travel scene from the motion data. For example, travel distance information and travel time information in the motion data of all walking trips in the walking trip scene model are acquired.
In step S205, an average travel rate corresponding to the corresponding travel scenario is calculated according to the travel distance information and the travel time information.
The average travel rate corresponding to the travel scene can be calculated according to the travel distance information and the travel time information obtained in the previous step and the calculation formula of the average rate. For example, the average travel rate is directly calculated according to travel distance information and travel time information in the acquired motion data of all the walking travel in the walking travel scene.
In step S206, current motion data of the user is collected, motion track features of the current motion data are obtained, and a target travel scene matched with the motion track features is determined.
When the motion data of the user in the preset time has a similar motion track, the user can be judged to be in a formal trip state, the motion track data of the user in the formal trip state, namely the current motion data, is collected, the current motion track data is subjected to data cleaning, data integration, data transformation, data reduction and the like by utilizing a data processing layer in the panorama sensing module to obtain processed motion track data, and further, the feature extraction layer is utilized to perform feature extraction on the current motion track data processed by the data processing layer so as to extract corresponding motion track features, and the matched target trip scene is determined by comparing the motion track features of the current motion data with the motion track features corresponding to each trip scene model.
In step S207, a first travel rate of the user is calculated according to the motion trail feature of the current motion data.
The motion trail feature of the motion data can comprise GPS information and sampling time information of a user, and distance information under a plurality of sampling time intervals can be obtained according to the GPS information, so that the current first travel rate of the user is calculated according to a calculation formula of the combination rate of the GPS information and the sampling time information of the motion trail feature of the current motion data.
In step S208, an average travel rate corresponding to the target travel scene is obtained.
Wherein, the average travel rate corresponding to the target travel scene determined in step S205 may be acquired according to the target travel scene determined in step S207.
In step S209, the first trip rate and the average trip rate are processed by the quasi-newton method to obtain the attenuation coefficient of the first trip rate.
The average travel rate of the obtained target travel scene refers to the historical average travel rate in the travel scene, but the travel rate of the user is greatly influenced by the selection of different roads or the congestion of the roads in the same travel scene, for example, the user drives an automobile to travel, and the travel rate of the road with traffic congestion is necessarily smaller than the travel rate of the road with smooth traffic. At this time, the road path can be modeled according to the historical motion data of the user or the historical collected data of a plurality of road traffic conditions, so as to obtain the road path parameters of each historical road, and the traffic conditions of different roads are reflected through the road path parameters. Because the traffic conditions of different roads are different, such as traffic jam or traffic smooth traffic, the target travel rate can be predicted more accurately through the road path parameters, so that the target travel rate is closer to the future travel rate.
Further, the step of processing the first travel rate and the average travel rate by the quasi-newton method may include: acquiring current position information of a user, namely GPS information, carrying out road matching through the position information to acquire current target road path parameters, and processing a first travel rate, an average travel rate and a target road path reference coefficient through a quasi-Newton method to generate an attenuation coefficient of the first travel rate, wherein the calculation formula of the attenuation coefficient is as follows:
the method comprises the steps of obtaining a first travel rate, calculating the average travel rate, calculating the logarithm of the average travel rate, summing the logarithm of the average travel rate, the road path coefficient and the first travel rate, and obtaining the attenuation coefficient of the first travel rate, wherein v_current is the first travel rate, v_history is the average travel rate, road_land is the road path coefficient.
In some embodiments, the step of obtaining the road path parameters may include: the method comprises the steps of obtaining the congestion condition of each historical road, wherein the road congestion condition is in ten grades of [0-10], and the congestion condition is 0 when the congestion grade of the road is 0, and the road is smooth to travel and has no congestion condition, and the congestion probability is 0; when the road congestion level is 3, the road is occasionally congested, and the congestion probability is low, such as less than 30%; when the road congestion level is 10, this road congestion probability is indicated to be high and close to 100%. At this time, the congestion level is processed to quantify the congestion condition between [0-1] to generate a parameter a; in addition, the length of the road is obtained, wherein the length of the road takes kilometers as a unit, the length of the road is quantized to be between 0 and 1 to generate the parameter b, in addition, the distance of the road is calculated in a cutting-off way due to the fact that the length of different roads is greatly changed, so that the length of the road is in a preset range, the data of the length of the road is ensured to be in Gaussian distribution, and the road can be cut off through an inflection point of a road path. At this time, the calculation formula of the road path coefficient is as follows:
road_land＝[a×log(b×100)]/(b×n)
Where n is the number of inflection points of the road path, a is the congestion level of the road path, and b is the length of the road path, as can be seen from the above formula, the road path coefficient road_land and the congestion level a of the road path are directly related to the number n of inflection points of the road path, and the larger the road path coefficient is when the road congestion level is higher, the smaller the road path coefficient is when the number of inflection points of the road path is higher.
According to the calculation formula, after the current position information is obtained, the current road information can be determined, the current road information is matched with the historical road path according to the current road information to obtain the matched road path parameters, and at the moment, under the condition that the first travel rate and the average travel rate are obtained, the first travel rate and the average travel rate are combined with the road path parameters to carry out summation operation to generate a loss value, and the loss value is used as an attenuation coefficient of the first travel rate.
In step S210, a target travel rate is calculated according to the attenuation coefficient and the first travel rate.
The future travel speed is not accurately predicted as the future traffic condition information is unknown, such as the future road congestion or unblocked condition cannot be obtained, and a large difference may exist between the future travel speed and the first travel speed.
In step S211, destination location information and current location information are acquired, and target distance information is calculated from the current location information and the destination location information.
The method comprises the steps of obtaining position information of a target place and current position information, combining road information according to the position information to generate target distance information in a travel scene, wherein the target distance information can be multiple according to different route recommendation schemes.
In step S212, target time information is generated from the target distance information and the target travel rate.
The target distance information and the target travel rate are updated in real time according to the motion data of the user, and at the moment, the target time information is updated in real time.
In some embodiments, the route recommendation scheme obtained by the user may include a plurality of travel scenes, for example, a destination which can be reached by taking a bus for 2 km after 500 meters is needed to walk, wherein when the user is in a walking stage, the first target travel rate can be generated by obtaining the current first travel rate and the average rate of the travel scenes in walking, so as to obtain corresponding first time information, in addition, the average travel rate of the travel scenes in taking the bus is obtained so as to obtain second time information in the travel scenes in taking the bus, and the target time information can be obtained by combining the first time information and the second time information.
In some embodiments, the step of obtaining destination location information and generating the target time information according to the target travel rate and the destination location information may further include: and generating an updated average travel rate according to the target travel rate and the historical average travel rate.
As can be seen from the above, according to the information processing method provided by the embodiment of the application, the motion data of the user in different travel scenes is collected, and the average travel rate corresponding to the travel scenes is obtained according to the motion data; collecting current motion data of a user, and acquiring a first travel rate and a target travel scene of the user according to the current motion data; obtaining an average travel rate corresponding to the target travel scene, and calculating to obtain a target travel rate by combining the first travel rate and the average travel rate; and acquiring destination position information, and generating target time information according to the target travel rate and the destination position information. By combining the historical average travel rate of the current travel scene and the current first travel rate, the target travel rate which is closer to the current travel scene is generated, and therefore accuracy of travel time information prediction is improved.
In order to better implement the information processing method provided by the embodiment of the present application, the embodiment of the present application further provides an apparatus based on the information processing method, where the meaning of a noun is the same as that in the information processing method, and specific implementation details may refer to the description in the method embodiment.
Referring to fig. 4, fig. 4 is a schematic block diagram of an information processing apparatus according to an embodiment of the application. Specifically, the information processing apparatus 300 includes: the device comprises an acquisition module 31, an acquisition module 32, a calculation module 33 and a generation module 34.
The acquisition module 31 is configured to acquire motion data of a user in different travel scenes, and acquire an average travel rate corresponding to the travel scenes according to the motion data.
The collecting module 31 collects motion data of the user in different travel scenes in real time through the information sensing layer in the panorama sensing module, and it should be noted that the travel scenes refer to travel states of the user, such as driving a car, taking a high-speed rail, etc., walking or running, etc., wherein the motion data refer to motion track data, such as altitude change track data, angle change track data, acceleration change track data, geographic position change track data, etc. The acquisition module 31 extracts corresponding motion track features in the acquired motion data by using a corresponding algorithm, determines travel scenes corresponding to the motion data according to the motion track features, and acquires the motion data under each travel scene to obtain an average travel rate under each travel scene.
In some embodiments, referring to fig. 5, fig. 5 is another block diagram of an information processing apparatus according to an embodiment of the present application, where the acquisition module 31 may include: the detection sub-module 311, the acquisition sub-module 312 and the determination sub-module 313.
The detection sub-module 311 is configured to start a plurality of sensors to detect a travel state of a user by using the panorama sensing module;
an acquisition sub-module 312, configured to acquire detection data of a plurality of sensors according to a time sequence, so as to generate motion data of a user, and determine motion track features corresponding to the motion data;
and the determining submodule 313 is used for determining a corresponding travel scene according to the motion trail characteristics and storing the motion data and the travel scene in a correlated way.
In some embodiments, the collecting module 31 is further specifically configured to obtain corresponding travel distance information and travel time information in the motion data corresponding to each travel scene; and calculating the average travel rate corresponding to the corresponding travel scene according to the travel distance information and the travel time information.
The obtaining module 32 is configured to collect current motion data of the user, and obtain a first trip rate and a target trip scene of the user according to the current motion data.
After the user starts a preset application, such as an electronic map or a related application with a navigation function, and inputs destination information, the obtaining module 32 determines that the motion data of the user in the preset time has a similar motion track, that is, the user is in a formal trip state, and the obtaining module 32 starts to collect the current motion data of the user through the panorama sensing module. And extracting motion track characteristics according to motion track data in the current motion data, and comparing the motion track characteristics of the current data with the motion track characteristics corresponding to each travel scene to determine a matched target travel scene.
In some embodiments, the obtaining module 32 is specifically configured to obtain a motion trajectory feature of the current motion data, and determine a target trip scenario matched with the motion trajectory feature; and calculating a first travel rate of the user according to the motion trail characteristics of the current motion data.
The calculating module 33 is configured to obtain an average travel rate corresponding to the target travel scene, and calculate a target travel rate in combination with the first travel rate and the average travel rate.
The calculation module 33 obtains the average travel rate corresponding to the target travel scene from the database, and combines the current first travel rate and the average travel rate corresponding to the current target travel scene to generate the target travel rate, so that the target travel rate can be more fit with the travel habit of the current travel scene of the user compared with the travel rate preset according to the first travel rate or the system, and the travel time information can be estimated better according to the target travel rate.
In some embodiments, the calculating module 33 is specifically configured to process the first travel rate and the average travel rate by using a quasi-newton method, so as to obtain an attenuation coefficient of the first travel rate; and calculating a target travel rate according to the attenuation coefficient and the first travel rate.
The generating module 34 is configured to obtain destination location information, and generate target time information according to the target travel speed and the destination location information.
The target time information generated by the generating module 34 may be time information of reaching a destination, travel remaining time, and the like, and the target time information is updated in real time according to the target distance information and the target travel rate.
In some embodiments, the generating module 34 is specifically configured to obtain current location information, and calculate target distance information according to the current location information and the destination location information; and generating target time information according to the target distance information and the target travel rate.
As can be seen from the above, in the information processing device provided by the embodiment of the present application, the motion data of the user in different travel scenes is collected through the collection module 31, and the average travel rate corresponding to the travel scenes is obtained according to the motion data; the acquisition module 32 acquires current motion data of the user, and acquires a first travel rate and a target travel scene of the user according to the current motion data; the calculation module 33 obtains the average travel rate corresponding to the target travel scene, and calculates the target travel rate by combining the first travel rate and the average travel rate; the generation module 34 obtains destination location information and generates target time information based on the target travel rate and the destination location information. By combining the historical average travel rate of the current travel scene and the current first travel rate, the target travel rate which is closer to the current travel scene is generated, and therefore accuracy of travel time information prediction is improved.
The embodiment of the application also provides electronic equipment. Referring to fig. 6, an electronic device 500 includes a processor 501 and a memory 502. The processor 501 is electrically connected to the memory 502.
The processor 500 is a control center of the electronic device 500, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device 500 and processes data by running or loading computer programs stored in the memory 502 and calling data stored in the memory 502, thereby performing overall monitoring of the electronic device 500.
The memory 502 may be used to store software programs and modules, and the processor 501 may execute various functional applications and data processing by executing the computer programs and modules stored in the memory 502. The memory 502 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, a computer program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created according to the use of the electronic device, etc. In addition, memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 502 may also include a memory controller to provide access to the memory 502 by the processor 501.
In the embodiment of the present application, the processor 501 in the electronic device 500 loads the instructions corresponding to the processes of one or more computer programs into the memory 502 according to the following steps, and the processor 501 executes the computer programs stored in the memory 502, so as to implement various functions, as follows:
acquiring motion data of a user in different travel scenes, and acquiring average travel rate corresponding to the travel scenes according to the motion data;
collecting current motion data of a user, and acquiring a first travel rate and a target travel scene of the user according to the current motion data;
obtaining an average travel rate corresponding to the target travel scene, and calculating to obtain a target travel rate by combining the first travel rate and the average travel rate;
and acquiring destination position information, and generating target time information according to the target travel rate and the destination position information.
In some embodiments, when the target travel rate is calculated in combination with the first travel rate and the average travel rate, the processor 501 may specifically perform the following steps:
starting a plurality of sensors by using the panoramic sensing module to detect the travel state of the user;
acquiring detection data of a plurality of sensors according to time sequence to generate motion data of a user, and determining corresponding motion trail characteristics of the motion data;
And determining a corresponding travel scene according to the motion track characteristics, and storing the motion data in association with the travel scene.
In some embodiments, when obtaining the average travel rate corresponding to the travel scene according to the motion data, the processor 501 may specifically perform the following steps:
acquiring corresponding travel distance information and travel time information in the motion data corresponding to each travel scene;
and calculating the average travel rate corresponding to the corresponding travel scene according to the travel distance information and the travel time information.
In some embodiments, when acquiring the first trip rate and the target trip scenario of the user according to the current motion data, the processor 501 may specifically perform the following steps:
acquiring motion trail features of current motion data, and determining a target travel scene matched with the motion trail features;
and calculating a first travel rate of the user according to the motion trail characteristics of the current motion data.
In some embodiments, when the target travel rate is calculated in combination with the first travel rate and the average travel rate, the processor 501 may specifically perform the following steps:
processing the first travel rate and the average travel rate by a quasi-Newton method to obtain an attenuation coefficient of the first travel rate;
And calculating a target travel rate according to the attenuation coefficient and the first travel rate.
In some embodiments, when generating the target time information from the target travel rate and the destination location information, the processor 501 may specifically perform the steps of:
acquiring current position information, and calculating target distance information according to the current position information and destination position information;
and generating target time information according to the target distance information and the target travel rate.
Referring to fig. 7, in some embodiments, the electronic device 500 may further include: a display 503, radio frequency circuitry 504, audio circuitry 505, and a power supply 506. Wherein, the display 503, the radio frequency circuit 504, the audio circuit 505 and the power supply 506 are electrically connected to the processor 501 respectively.
The display 503 may be used to display information entered by a user or provided to a user as well as various graphical user interfaces that may be composed of graphics, text, icons, video, and any combination thereof. The display 503 may include a display panel, which in some embodiments may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD), or an Organic Light-Emitting Diode (OLED), or the like.
The rf circuitry 504 may be configured to receive and transmit rf signals to and from a network device or other terminal via wireless communication to and from the network device or other terminal via wireless communication.
The audio circuit 505 may be used to provide an audio interface between a user and an electronic device through a speaker, microphone.
The power supply 506 may be used to power the various components of the electronic device 500. In some embodiments, the power supply 506 may be logically connected to the processor 501 through a power management system, so as to perform functions of managing charging, discharging, and power consumption management through the power management system.
The embodiment of the present application also provides a storage medium storing a computer program, which when executed on a computer, causes the computer to perform the information processing method in any of the above embodiments, for example: acquiring motion data of a user in different travel scenes, and acquiring average travel rate corresponding to the travel scenes according to the motion data; collecting current motion data of a user, and acquiring a first travel rate and a target travel scene of the user according to the current motion data; obtaining an average travel rate corresponding to the target travel scene, and calculating to obtain a target travel rate by combining the first travel rate and the average travel rate; and acquiring destination position information, and generating target time information according to the target travel rate and the destination position information.
In an embodiment of the present application, the storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
It should be noted that, for the information processing method according to the embodiment of the present application, it will be understood by those skilled in the art that all or part of the flow of the information processing method implementing the embodiment of the present application may be implemented by controlling related hardware by a computer program, where the computer program may be stored in a computer readable storage medium, such as a memory of an electronic device, and executed by at least one processor in the electronic device, and the execution may include the flow of the embodiment of the information processing method. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, etc.
For the information processing device according to the embodiment of the present application, each functional module may be integrated into one processing chip, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated module, if implemented as a software functional module and sold or used as a stand-alone product, may also be stored on a computer readable storage medium such as read-only memory, magnetic or optical disk, etc.
The foregoing describes in detail a method, an apparatus, a storage medium, and an electronic device for processing information provided by the embodiments of the present application, and specific examples are applied to describe the principles and implementations of the present application, where the descriptions of the foregoing embodiments are only used to help understand the method and core ideas of the present application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present application, the present description should not be construed as limiting the present application.
Claims (7)
1. A method of processing information, comprising:
starting a plurality of sensors by using the panoramic sensing module to detect the travel state of the user;
acquiring detection data of a plurality of sensors according to time sequence to generate motion data of a user in different travel scenes, and determining motion track characteristics corresponding to the motion data;
determining corresponding travel scenes according to the motion track characteristics, storing the motion data in association with the travel scenes, and acquiring average travel rate corresponding to the travel scenes according to the motion data;
judging whether the user is in a formal trip state according to the motion data of the user;
When the motion data of the user in the preset time is similar to the motion trail of the motion data of the user in different travel scenes, determining that the user is in a formal travel state; when the user is in a formal trip state, collecting current motion data of the user in a current trip scene by using a panoramic sensing module, and acquiring a first trip rate and a first target trip scene of the user according to the current motion data;
obtaining a first average travel rate corresponding to the first target travel scene by using a panoramic sensing module, obtaining current position information of a user, determining current road information, matching the current road information with a historical road path to obtain matched road path parameters, and matching the matched road path parameters, the first travel rate and the first average travel rate by using a quasi-Newton method according to a formulaProcessing to obtain an attenuation coefficient of a first travel rate, wherein v_current is the first travel rate, v_average is the first average travel rate, road_land is a road path parameter, and n is the number of inflection points of the road path; road path parameter road_land= [ a×log (b×100)](b×n), where a is the congestion level of the road path and a is the historical congestion condition quantization of the road path [0-1 ] ]The parameters generated between the two are that b is the length of the road path and b is the historical travel length of the road path is quantized to be 0-1]The value of n ensures that the distance of the road path is calculated in a truncated mode through the inflection point of the road path so that the length of the truncated road is in a preset range and is in Gaussian distribution; calculating a target travel rate according to the attenuation coefficient and the first travel rate;
acquiring a second target travel scene and a second average travel rate corresponding to the second target travel scene by using a panoramic sensing module;
the method comprises the steps of obtaining destination position information, wherein the destination position information comprises a first road section and a second road section, generating first time information according to a target travel rate and the first road section, generating second time information according to the second average travel rate and the second road section, and obtaining target time information according to the first time information and the second time information.
2. The method of claim 1, wherein the step of obtaining an average travel rate corresponding to a travel scene from the motion data comprises:
acquiring corresponding travel distance information and travel time information in the motion data corresponding to each travel scene;
And calculating the average travel rate corresponding to the corresponding travel scene according to the travel distance information and the travel time information.
3. The method of claim 1, wherein the step of obtaining the first travel rate and the first target travel scenario of the user based on the current motion data comprises:
acquiring motion trail features of current motion data, and determining a target travel scene matched with the motion trail features;
and calculating a first travel rate of the user according to the motion trail characteristics of the current motion data.
4. The method of claim 1, wherein the step of generating first time information from the target travel rate and the first segment comprises:
acquiring current position information, and calculating target distance information according to the current position information and the first road segment;
and generating first time information according to the target distance information and the target travel rate.
5. An information processing apparatus, comprising:
the detection sub-module is used for starting a plurality of sensors to detect the travel state of the user by utilizing the panoramic sensing module;
the acquisition sub-module is used for acquiring detection data of a plurality of sensors according to time sequence to generate motion data of a user in different travel scenes and determining motion track characteristics corresponding to the motion data;
The determining submodule is used for determining corresponding travel scenes according to the motion track characteristics and storing the motion data and the travel scenes in an associated mode;
the acquisition module is used for acquiring average travel rate corresponding to travel scenes according to the motion data of the user in different travel scenes;
the acquisition module is used for judging whether the user is in a formal trip state according to the motion data of the user; when the motion data of the user in the preset time is similar to the motion trail of the motion data of the user in different travel scenes, determining that the user is in a formal travel state; when the user is in a formal trip state, the panoramic sensing module is used for acquiring current motion data of the user in a current trip scene, and acquiring a first trip rate and a first target trip scene of the user according to the current motion data;
the calculation module is used for acquiring a first average travel rate corresponding to the first target travel scene by using the panorama sensing module, acquiring current position information of a user, determining current road information, matching the current road information with a historical road path to acquire matched road path parameters, and matching the matched road path parameters, the first travel rate and the first average travel rate according to a formula by using a quasi-Newton method Processing to obtain an attenuation coefficient of a first travel rate, wherein v_current is the first travel rate, v_average is the first average travel rate, road_land is a road path parameter, and n is the number of inflection points of the road path; road path parameter road_land= [ a×log (b×100)](b×n), where a is the congestion level of the road path and a is the historical congestion condition quantization of the road path [0-1 ]]The parameters generated between the two are that b is the length of the road path and b is the historical travel length of the road path is quantized to be 0-1]The value of n ensures that the distance of the road path is calculated in a truncated mode through the inflection point of the road path so that the length of the truncated road is in a preset range and is in Gaussian distribution; calculating a target travel rate according to the attenuation coefficient and the first travel rate;
the panoramic sensing module is also used for acquiring a second target travel scene and a second average travel rate corresponding to the second target travel scene; the generation module is used for acquiring destination position information, the destination position information comprises a first road section and a second road section, first time information is generated according to a target travel rate and the first road section, second time information is generated according to the second average travel rate and the second road section, and target time information is acquired according to the first time information and the second time information.
6. A storage medium having stored thereon a computer program, wherein the computer program, when run on a computer, causes the computer to perform the method of processing information according to claim 1.
7. An electronic device comprising a processor and a memory, the memory having a computer program, characterized in that the processor is adapted to perform the method of processing information according to claim 1 by invoking the computer program.
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