Travel state recognition method and device, electronic equipment and storage medium

Abstract

The invention discloses a travel state recognition method and device, electronic equipment and a storage medium. The method comprises: obtaining at least one speed feature, the at least one speed feature being obtained according to collected positioning information and / or network information, the positioning information comprising at least one of GPS information, Beidou information and base station information, and the network information comprising WIFI information and / or Bluetooth information; and according to the at least one speed feature, carrying out travel state identification to obtain a travel state result, the travel state result being used for indicating at least one of whether the user takes a vehicle during travel, the type of the vehicle taken and the current station. According to the method, the travel state of the user can be identified according to the at least one speed feature obtained according to the collected positioning information and / or network information, identification can still be performed under the condition that one or more information dimensions are missing, and the travel state identification accuracy and real-time performance are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of user status recognition, and relate to, but are not limited to, a travel status recognition method and device, an electronic device, and a storage medium. Background Art

[0002] The existing method of identifying a user's travel status is to use positioning information to identify the user's current location. This identification method produces relatively simple results, and if the user is in an area with poor positioning signals, the identification result may not be obtained in time.

[0003] Therefore, there is an urgent need to provide a method that can accurately and real-timely obtain the user's current travel status. Summary of the Invention

[0004] In view of this, the travel status identification method and device, electronic device, and storage medium provided in the embodiments of the present application can identify the user's travel status based on at least one speed feature obtained from the collected positioning information and / or network information, and can still be identified when one or more information dimensions are missing, thereby improving the accuracy and real-time performance of travel status identification.

[0005] In a first aspect, the travel status identification method provided in an embodiment of the present application is applied to an electronic device, including:

[0006] obtaining at least one speed characteristic, where the at least one speed characteristic is obtained based on collected positioning information and / or network information, the positioning information including at least one of GPS information, Beidou information, and base station information, and the network information including Wi-Fi information and / or Bluetooth information;

[0007] Travel status identification is performed based on the at least one speed feature to obtain a travel status result, where the travel status result is used to indicate at least one of whether the user is traveling by means of transportation, the type of transportation, and the current station where the user is located.

[0008] In a second aspect, the travel status identification device provided in an embodiment of the present application is applied to an electronic device, including:

[0009] a speed feature acquisition module, configured to acquire at least one speed feature, wherein the at least one speed feature is acquired based on collected positioning information and / or network information, wherein the positioning information includes at least one of GPS information, Beidou information, and base station information, and the network information includes Wi-Fi information and / or Bluetooth information;

[0010] The travel status identification module is used to identify the travel status according to the at least one speed feature to obtain a travel status result, wherein the travel status result is used to indicate whether the user travels by means of transportation, the type of transportation, and at least one of the current stations.

[0011] In a third aspect, an electronic device provided in an embodiment of the present application includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the steps of the travel status identification method provided in the first aspect of the embodiment of the present application are implemented.

[0012] In a fourth aspect, a computer-readable storage medium is provided in an embodiment of the present application, on which a computer program is stored. When the computer program is executed by a processor, the steps of the travel status identification method provided in the first aspect of the embodiment of the present application are implemented.

[0013] In a fifth aspect, the computer program product provided in an embodiment of the present application includes a computer program, which, when executed by a processor, implements the steps of the travel status identification method provided in the first aspect of the embodiment of the present application.

[0014] The travel status identification method, device, electronic device and computer-readable storage medium provided in the embodiments of the present application can identify the user's travel status based on at least one speed feature obtained from the collected positioning information and / or network information, and can still be identified when one or more information dimensions are missing, thereby improving the accuracy and real-time performance of travel status identification, thereby solving the technical problems raised in the background technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application and, together with the specification, are used to illustrate the technical solutions of the present application.

[0016] Figure 1 A schematic diagram of an application scenario of a travel status identification system provided in an embodiment of the present application;

[0017] Figure 2 A schematic diagram of the implementation flow of a travel status identification method provided in an embodiment of the present application;

[0018] Figure 3 A schematic diagram of the implementation flow of another travel status identification method provided in an embodiment of the present application;

[0019] Figure 4 A flow chart of a method for obtaining speed characteristics provided in an embodiment of the present application;

[0020] Figure 5 A schematic diagram of a method for calculating a target speed at the current moment provided in an embodiment of the present application;

[0021] Figure 6 A schematic diagram of the overall process of travel status identification provided in an embodiment of the present application;

[0022] Figure 7 A schematic diagram of an algorithm for identifying travel status provided in an embodiment of the present application;

[0023] Figure 8 A travel status recognition effect diagram provided in an embodiment of the present application;

[0024] Figure 9 A schematic diagram of the structure of a travel status identification device provided in an embodiment of the present application;

[0025] Figure 10 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the specific technical solutions of the present application will be further described in detail below in conjunction with the drawings in the embodiments of the present application. The following embodiments are used to illustrate the present application but are not intended to limit the scope of the present application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0028] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0029] It should be pointed out that the terms "first\second\third" involved in the embodiments of the present application are used to distinguish similar or different objects, and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0030] The existing method for identifying a user's travel status relies on location information to determine their current location. This method yields relatively limited results, and if the user is in an area with poor positioning signals, the identification result may not be available in a timely manner. Therefore, there is an urgent need for a method that can accurately and real-timely determine a user's current travel status.

[0031] In view of this, an embodiment of the present application provides a method for identifying travel status by obtaining at least one speed feature, wherein the at least one speed feature is obtained based on collected positioning information and / or network information, the positioning information including at least one of GPS information, Beidou information, and base station information, and the network information including Wi-Fi information and / or Bluetooth information; based on the at least one speed feature, travel status is identified to obtain a travel status result, which is used to indicate whether the user is traveling by means of transportation, the type of transportation, and at least one of the current stations. This method can identify a user's travel status even when one or more information dimensions are missing, thereby improving the accuracy and real-time nature of travel status identification.

[0032] Figure 1 The present invention provides an application scenario diagram of a travel status identification system. Figure 1 As shown, the travel status identification system includes a smart watch 10 and a network device 20, and the smart watch 10 and the network device 20 perform wireless communication. The smart watch 10 includes a wireless communication module, a positioning module, and a sensor module. The network device 20 can be a server, etc.

[0033] During a user's travel, the smartwatch 10 can obtain positioning information, such as GPS information and base station information, through the positioning module and / or mobile communication module, obtain network information, such as WIFI information and Bluetooth information, through the wireless communication module, and obtain sensor information, such as inertial measurement unit (IMU) information, gyroscope information, and magnetic information, through sensors. After the smartwatch 10 collects the network information and / or positioning information and / or sensor information, it can transmit the network information and / or positioning information and / or sensor information to the network device 20, which then performs travel status identification, i.e., the network device 20 executes the travel status identification method provided in the embodiments of the present application.

[0034] In another embodiment, after collecting network information and / or positioning information and / or sensor information, the smart watch 10 can identify the travel status by itself, that is, the smart watch 10 executes the travel status identification method provided in the embodiment of the present application.

[0035] It can be understood that the electronic device that executes the travel status identification method can be a smart watch 10, a network device 20, or other electronic device that establishes a wireless connection with the smart watch 10. The embodiment of the present application does not limit the type of electronic device that executes the travel status identification method.

[0036] The technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings. The electronic device mentioned below can be either a smart watch 10 or a network device 20.

[0037] Figure 2 The present invention provides a flow chart of an implementation of a travel status identification method according to an embodiment of the present invention. The travel status method can be applied to electronic devices, which can be various types of devices with information processing capabilities during implementation. For example, the electronic device can include a personal computer, a laptop, a PDA or a server, etc.; the electronic device can also be a wearable device, such as a smart watch, smart glasses or a smart bracelet, etc. The electronic device can also be a mobile terminal, such as a mobile phone, a car computer, a tablet computer or a projector, etc. Figure 1 As shown, the method may include the following steps 101 to 104:

[0038] Step 101: Obtain at least one speed feature, where the at least one speed feature is obtained based on collected positioning information and / or network information, where the positioning information includes at least one of GPS information, Beidou information, and base station information, and the network information includes WIFI information and / or Bluetooth information.

[0039] It should be noted that the electronic device can collect positioning information and / or network information in real time and obtain at least one speed characteristic based on the collected positioning information and / or network information. The electronic device can also receive positioning information and / or network information transmitted by other devices and obtain at least one speed characteristic based on the collected positioning information and / or network information. The acquisition method can adopt existing methods, such as through algorithm calculation, model calculation, etc. The embodiment of the present application does not limit the method of obtaining at least one speed characteristic.

[0040] It is understandable that the speed feature can be used to indicate the speed magnitude, speed change or other speed-related features, and the embodiment of the present application does not limit the content of the speed feature.

[0041] Step 102: Perform travel status identification based on the at least one speed feature to obtain a travel status result, where the travel status result is used to indicate at least one of whether the user is traveling by means of transportation, the type of transportation, and the current station where the user is located.

[0042] It should be noted that there are many ways to obtain travel status results by performing travel status identification based on the at least one speed feature. For example, a judgment condition can be preset for determination, the speed feature can be input into a model for calculation, or the speed feature can be calculated by an algorithm, etc. The embodiment of the present application does not limit the method of performing travel status identification based on the at least one speed feature to obtain travel status results.

[0043] By acquiring information about the user's electronic device, including but not limited to base station, Wi-Fi, GPS, and other information, the embodiments of the present application can proactively identify the type of vehicle being used, whether the user is currently riding a vehicle, and the current station information while the user is riding. For example, if a student is wearing a smartwatch, the information collected by the smartwatch can be used to identify the student's travel status and provide feedback to the parent, allowing the parent to keep track of the user / student's real-time riding status.

[0044] In some embodiments, a trained recognition model may be obtained based on a large number of speed feature samples, and the current travel status may be determined using the trained recognition model.

[0045] In an embodiment of the present application, the travel status identification based on the at least one speed feature to obtain the travel status result may include: inputting the at least one speed feature into a trained first state recognition model to obtain the travel status result, and the trained first state recognition model is trained based on speed feature samples.

[0046] It is understandable that using models to identify travel status can improve recognition efficiency and accuracy.

[0047] Figure 3 A schematic diagram of the implementation flow of another travel status identification method provided in an embodiment of the present application. As shown in FIG2 , the method may include the following steps 201 to 203:

[0048] Step 201: Obtain at least one speed feature, where the at least one speed feature is obtained based on collected positioning information and / or network information, where the positioning information includes at least one of GPS information, Beidou information, and base station information, and the network information includes WIFI information and / or Bluetooth information.

[0049] It should be noted that the content of step 201 is the same as that of step 101 , and reference may be made to the description of step 101 in the aforementioned embodiment.

[0050] Step 202: Obtain target features, where the target features include at least one of user behavior features, network features, scenario features, and route features. The user behavior features are extracted based on collected sensor information, the network features are extracted based on collected network information, and the scenario features and route features are obtained based on collected positioning information and / or network information.

[0051] It should be noted that the target feature may be at least one of a user behavior feature, a network feature, a scene feature, and a route feature.

[0052] The user behavior characteristics are extracted based on the collected sensor information. For example, the sensor information may include inertial measurement unit (IMU) information, gyroscope information, and magnetic information. After obtaining the sensor information, the sensor information is subjected to feature extraction through algorithm calculation or model extraction to obtain the user behavior characteristics. For example, the user behavior characteristics may include user movement characteristics, user sitting characteristics, user walking characteristics, etc. The embodiments of this application do not limit the type and acquisition method of user behavior characteristics.

[0053] The network features are extracted based on collected network information, which may include Wi-Fi information, Bluetooth information, etc. The extraction method may be information reading, etc. Network features may include timestamps, service set identifiers, MAC addresses, network attributes, etc. This embodiment of the application does not limit the type or acquisition method of network features.

[0054] The scene features and the route features are obtained based on the collected positioning information and / or network information. Scene features can be network information and / or positioning information corresponding to a particular scene, and route features can be network information and / or positioning information corresponding to a particular traffic route. For example, the currently collected positioning information and / or network information can be compared with a preset database to obtain scene features or route features, etc. The embodiments of the present application do not limit the method for obtaining the scene features and the route features.

[0055] In some embodiments, the target feature includes the scene feature, and obtaining the target feature may include: matching the acquired positioning information and / or network information with preset third-party scene information to obtain a scene feature corresponding to the acquired positioning information and / or network information, and the preset third-party scene information includes the correspondence between the preset scene feature and the preset positioning information and / or network information.

[0056] It should be noted that map scene information provided by third-party vendors can be used to help this solution quickly determine the current scene after obtaining positioning information and / or network information. It is also possible to construct scene information based on historically collected information to provide information for determining the user's travel status.

[0057] It is understandable that third-party scene information is just one dimension among many pieces of information, not the only data dimension. This solution collects multiple types of information for identification, which can improve accuracy and real-time performance.

[0058] In some embodiments, the target feature includes a route feature, and obtaining the target feature may include: matching the acquired positioning information and / or network information with a preset private database to obtain a line feature corresponding to the acquired positioning information and / or network information, and the preset private database includes a correspondence between the preset line feature and the preset positioning information and / or network information; wherein the correspondence between the preset line feature and the preset positioning information and / or network information is obtained based on the historically acquired positioning information and / or network information, and the corresponding travel status results.

[0059] It should be noted that route characteristics can be obtained using a pre-set private database. This pre-set private database can be established by collecting location information and / or network information corresponding to the current user or other users traveling along a particular route through big data. This pre-set private database can help this solution quickly determine the current route after obtaining location information and / or network information, providing information for determining the user's travel status.

[0060] It is understandable that the preset private database is one dimension among many information dimensions, not the only data dimension. This solution collects multiple information for identification, which can improve accuracy and real-time performance.

[0061] Step 203: Input the at least one speed feature and the target feature into a trained second state recognition model to obtain the travel state result, where the second state recognition model is trained based on speed feature samples and target feature samples.

[0062] It should be noted that a trained recognition model can be obtained based on a large number of speed feature samples and target feature samples, and the current travel status can be determined using the trained recognition model. It is understood that using a model for travel status recognition can improve recognition efficiency and accuracy.

[0063] It is understood that the solution provided by the embodiments of this application integrates data from base stations, Wi-Fi, GPS, Bluetooth, IMU sensors, geomagnetic sensors, and other sensors, as well as historical user preferences, to perform feature extraction and state recognition. This allows for vehicle identification and state recognition in various scenarios, even when one or more information dimensions are missing. It can accurately identify underground rail transit, surface rail transit, and road transportation.

[0064] In some embodiments, when extracting the speed characteristics of the user's ride from the acquired GPS, base station, WIFI, and Bluetooth source data, due to the uncertainty of the GPS, base station, WIFI, and Bluetooth data, their sampling rates and the type of source data appearing at the next moment are uncertain, so the speed data needs to be fused through a data fusion algorithm.

[0065] Figure 4 A flow chart of a method for obtaining speed features provided in an embodiment of the present application. The speed features include the target speed at the current moment, and the target speed is used to indicate the speed after fusion. Figure 4 As shown, obtaining at least one speed feature may include:

[0066] Step 301: Acquire the actual speed at the current moment, where the actual speed at the current moment is extracted based on at least one of the currently acquired positioning information and network information.

[0067] It should be noted that the method of extracting the actual speed at the current moment based on at least one of the currently obtained positioning information and network information can be calculated based on a preset algorithm or a preset model, etc. The embodiment of the present application does not limit the method of obtaining the actual speed at the current moment.

[0068] It is understood that obtaining the actual speed at the current moment may be performed by extracting a speed from one piece of currently collected information, or by processing at least two actual speeds at the current moment extracted from at least two pieces of currently collected information to obtain a single actual speed at the current moment. The present embodiment does not limit the method for obtaining the actual speed at the current moment.

[0069] Step 302: If the actual speed at the current moment is the speed at the starting moment, determine the actual speed at the current moment as the target speed at the current moment.

[0070] It should be noted that if the actual speed obtained at the current moment is the speed obtained for the first time, and there is no historical speed, the actual speed at the current moment can be directly determined as the target speed at the current moment.

[0071] Step 303: If the actual speed at the current moment is not the speed at the starting moment, the target speed at the current moment is determined according to the actual speed at the current moment and the target speed at the previous moment.

[0072] It should be noted that if the actual speed obtained at the current moment is not the speed at the starting moment, that is, it is not the speed obtained by the first acquisition, and there is a historical speed before that. Then the target speed at the current moment can be determined based on the actual speed at the current moment and the target speed at the previous moment. There are many methods for calculation, such as averaging, or calculating based on weights, or calculating based on the difference between the two. The embodiment of the present application does not limit the method of determining the target speed at the current moment based on the actual speed at the current moment and the target speed at the previous moment.

[0073] The "previous moment," "current moment," and "next moment" here refer to the time periods used to collect signals and calculate speed. For example, if the speed is calculated every 5 seconds, the speed obtained from GPS information collected in the last 5 seconds is the actual speed at the current moment. The speed extracted from Wi-Fi information collected 5 seconds before the current moment is the speed at the previous moment, and the speed extracted from base station information collected 5 seconds after the current moment is the speed at the next moment. The source signal at each moment may be different, but the speed is still extracted for calculation.

[0074] It is understandable that since errors may occur in speed collection, the speeds collected at other moments, except for the speed collected at the starting moment, need to be calculated based on the speed collected at the previous moment to determine the target speed at the current moment, that is, to obtain the fusion speed at the current moment. This method can further improve the accuracy of the acquired speed features.

[0075] In some embodiments, determining the target speed at the current moment based on the actual speed at the current moment and the target speed at the previous moment may include: obtaining the weight corresponding to the target speed at the previous moment and the weight corresponding to the actual speed at the current moment, the weight corresponding to the target speed at the previous moment being less than the weight corresponding to the actual speed at the current moment; obtaining a first speed and a second speed, the first speed being obtained by multiplying the target speed at the previous moment and the weight corresponding to the target speed at the previous moment, and the second speed being obtained by multiplying the actual speed at the current moment and the weight corresponding to the actual speed at the current moment; and summing the first speed and the second speed to obtain the target speed at the current moment.

[0076] It should be noted that different weights can be assigned to the actual speed at the current moment and the target speed at the previous moment to calculate the target speed at the current moment. The solution provided in the embodiment of the present application trusts the currently acquired speed more, so the weight corresponding to the target speed at the previous moment can be set to be smaller than the weight corresponding to the actual speed at the current moment. The embodiment of the present application is not limited to this approach. In actual calculations, corresponding weights can be set according to actual needs.

[0077] Figure 5 A schematic diagram of a method for calculating the target speed at the current moment provided in an embodiment of the present application is shown as follows: Figure 5 As shown, for example, the calculation formula of the target speed at the current moment can be expressed as follows:

[0078]

[0079] Among them, V p-1 is the target speed at the previous moment, W g is the weight corresponding to the actual speed of the GPS signal at the current moment, V g is the actual speed of the GPS signal at the current moment, W b is the weight corresponding to the actual speed of the base station signal at the current moment, V b is the actual speed of the base station signal at the current moment, W w is the weight corresponding to the actual speed of the WIFI signal at the current moment, V w is the actual speed of the Wi-Fi signal at the current moment. It can be seen that the method of calculating the target speed at the current moment is based on the weights corresponding to the actual speed at the current moment, the target speed at the previous moment, and the actual speed at the current moment.

[0080] For example, if the actual speed at the current moment is obtained based on the GPS signal, the target speed at the current moment is the fusion speed V p The current target speed is calculated by multiplying the previous target speed by the weight corresponding to the target speed (1 minus the weight corresponding to the GPS signal). The product of the current actual speed and the weight corresponding to the GPS signal is then added to the result. If the current actual speed is obtained based on Wi-Fi or base station signals, the calculation method is similar and is not detailed here.

[0081] In some embodiments, there are multiple ways to determine the weight corresponding to the actual speed at the current moment. For example, the weight can be determined based on the signal type of the source signal from which the actual speed at the current moment is obtained, or it can be determined after updating the weight based on the actual speed at the current moment. The embodiments of the present application do not limit the method for determining the weight corresponding to the actual speed at the current moment.

[0082] Example 1: Obtaining the weight corresponding to the actual speed at the current moment may include: determining a target information type corresponding to the target information, where the target information type is at least one of GPS information, Beidou information, base station information, WIFI information, and Bluetooth information, and the target information is the source information for obtaining the actual speed at the current moment; determining the weight corresponding to the target information type based on the target information type and the correspondence between a preset information type and a preset weight.

[0083] It can be understood that the electronic device may preset a correspondence between information type and weight, and determine the corresponding target weight according to the type of data source corresponding to the actual speed currently obtained at the current moment.

[0084] Example 2: Obtaining the weight corresponding to the actual speed at the current moment may include: determining an initial weight based on the standard deviation of the target speed at the previous moment, the standard deviation of the actual speed at the current moment, the target speed at the previous moment, and the actual speed at the current moment; and obtaining the weight corresponding to the actual speed at the current moment based on the initial weight and a preset weight range. The weight corresponding to the actual speed at the current moment and the preset weight range are determined based on the information type of target information, the target information type being at least one of GPS information, Beidou information, base station information, WIFI information, and Bluetooth information, and the target information being the source information for obtaining the actual speed at the current moment.

[0085] For example, the weight update formula can be expressed as follows:

[0086]

[0087] Among them, std(V p-1 ) is the standard deviation of the target speed at the previous moment, V p-1 is the target speed at the previous moment, std(V g ) is the standard deviation of the actual speed of the GPS signal at the current moment, V g is the actual speed of the GPS signal at the current moment, std(V b ) is the standard deviation of the actual speed of the base station signal at the current moment, V b is the actual speed of the base station signal at the current moment, std(V w ) is the standard deviation of the actual speed of the WIFI signal at the current moment, V w is the actual speed of the Wi-Fi signal at the current moment, α, β, and γ are constants. The following two formulas are respectively the formula for calculating the standard deviation std(V) of N speeds and the formula for calculating the average speed of N speeds The formula, V iIndicates the i-th actual speed among the N actual speeds obtained. In addition, a weight range is set for the weights corresponding to different signal types. The calculated weight must meet the weight range. For example, if the calculated weight is less than the minimum value of the weight range, the weight is adjusted to the minimum value. If it is greater than the maximum value of the weight range, the weight is adjusted to the maximum value.

[0088] It can be seen that the method of calculating the target speed at the current moment is based on the standard deviation of the target speed at the previous moment, the standard deviation of the actual speed at the current moment, the target speed at the previous moment, the actual speed at the current moment and the preset weight range.

[0089] Example 2, the actual speed at the current moment includes at least 2 actual speeds at the current moment, and determining the target speed at the current moment based on the actual speed at the current moment and the target speed at the previous moment may include: determining the updated actual speed based on each actual speed at the current moment, the covariance matrix corresponding to each actual speed at the current moment, the target speed at the previous moment, the covariance matrix corresponding to the target speed at the previous moment, and the Kalman coefficient; determining the target speed at the current moment based on the updated actual speed and a preset state matrix.

[0090] It should be noted that the electronic device may actually obtain at least two actual speeds at the current moment, and thus the target speed at the current moment may be obtained based on the at least two speeds at the current moment.

[0091] For example, when the GPS speed V g , base station speed V b , WIFI speed V w The data fusion method when simultaneously acquiring data can be fused by Kalman filtering to obtain V p , the calculation formula is as follows:

[0092]

[0093] In the above formula, V p Indicates the prediction speed, V p-1 Indicates the predicted speed at the last moment; K represents the Kalman coefficient, and GPS speed V g , base station speed V b , WIFI speed V w Update V′, K and other parameters, V g 、V b 、V w They are the actual speeds obtained, which can also be expressed as the mean within a certain window, μ g 、μ b 、μ w ,Σ g ,Σb ,Σ w 、R g 、R b 、R w Represent the variance of GPS, base station and WIFI speed respectively. Update the parameters V′ and P′ according to formula (1), (2) and (3) in sequence, and then predict the fusion result V according to the updated parameters. p .

[0094] In the above formula, V p ,K,V′,P′, Is constantly being updated, Formula V p =F*V′, F represents the state matrix, which can be expressed as a matrix or a constant, representing the relationship between V′ and V p The conversion relationship between them.

[0095] formula Q represents the random noise matrix, P′ represents the covariance matrix of the velocity at the previous moment, Represents the covariance matrix of the forecast at the current moment.

[0096] Formula V′=V p-1 +K(V g -H g V p ), V′ represents the fusion speed V predicted at the previous moment p-1 , the current speed V g Update V′ parameter, H g Indicates V p With V g Conversion relationship, such as H g =1.

[0097] formula In this formula, the function is to predict the value based on the covariance matrix at the current moment Update the covariance matrix P′ of the current velocity.

[0098] formula Its function is to use the covariance matrix at the current moment to predict the value R g Update parameter K, which represents V over a period of time g Variance of speed.

[0099] In formulas (2) and (3), the meanings of each formula are the same as those of formula (1), except that the GPS speed V g , becomes V b and V w , subscript g represents GPS data, subscript b represents base station data, and subscript w represents WIFI data.

[0100] It is understandable that the above-mentioned method of fusing and obtaining the three actual speeds at the current moment is only an example, and other methods may also be used to calculate the target speed at the current moment.

[0101] The method provided in the embodiment of the present application integrates base station, WIFI, GPS, Bluetooth data, IMU sensor, geomagnetic and other sensor data and historical user usage preferences to perform data fusion and status recognition, which can realize vehicle identification and status recognition in various scenarios when one or more information dimensions are missing.

[0102] It is understandable that after obtaining the fused speed, it is necessary to enter the vehicle identification algorithm to identify and analyze the status. The algorithm output results include whether the vehicle is in the vehicle, the time of the ride, whether it is at a stop, and the type of vehicle.

[0103] In some embodiments, before performing travel status identification based on the at least one speed feature and obtaining a travel status result, the method may further include: determining that the at least one speed feature satisfies a preset condition, the preset condition including that the speed corresponding to the at least one speed feature is greater than or equal to a preset threshold, and / or that the duration of the speed corresponding to the at least one speed feature is greater than or equal to a preset duration.

[0104] It is understandable that, when the at least one speed feature satisfies the preset condition, travel status identification can also be performed based on the at least one speed feature and the target feature to obtain a travel status result.

[0105] The method provided in the embodiment of the present application can actively identify the type of transportation a user is riding and whether the user is currently riding a transportation vehicle when the user is riding a transportation vehicle. This solution has high real-time requirements.

[0106] The following describes an exemplary application of the embodiments of the present application in a practical application scenario.

[0107] Figure 6 This is a schematic diagram of the overall process of travel status identification provided by the embodiment of this application. Figure 6 As shown in the figure, after the device collects data, some of the raw data is uploaded to the server. Simultaneously, features are extracted on the device and uploaded to the server for fusion calculation and ride status recognition. After the ride status is obtained, the server saves the uploaded information and builds a private database. Once the server recognizes the ride status, it transmits the recognition results to the device through the server for user display.

[0108] Data sources include wearable devices or mobile terminals, such as cell phones and smartwatches, and include positioning information from GPS, base stations, Wi-Fi, Bluetooth, and IMU accelerometer, gyroscope, and magnetometer sensors. Private information comes from historical user usage data stored during use. Third-party scenario information is derived from information provided by third-party map providers for specific scenarios, such as hospitals, subways, and high-speed trains.

[0109] Privatized database: The privatized database is used by users to customize route information during use. It consists of GPS positioning information, base station positioning information, Bluetooth information, Wi-Fi positioning information, scene information provided by a third party, and sensor data information that has been downsampled. Based on the recognized boarding sign, it is decided whether to save the above information to the server to build private data information.

[0110] Third-party scene information: map information and special scene information provided by third-party map providers.

[0111] GPS positioning information: comes from the GPS positioning information obtained by the chip, including timestamp, longitude and latitude, accuracy, etc.

[0112] Base station positioning information: comes from the positioning result information of the phone chip, including timestamp, base station name and identification, latitude and longitude, altitude, network standard, etc.

[0113] Wi-Fi network / location information: information obtained by electronic devices scanning Wi-Fi, including timestamp, SSID, MAC address, network attributes (public or private), etc.

[0114] Bluetooth information: comes from the device information obtained by the device scanning Bluetooth, including timestamp, device name, Bluetooth address, connection status, signal strength, etc.

[0115] Privatized data information: high-speed rail public WIFI information, high-speed rail line WIFI fingerprint, base station fingerprint, and geomagnetic fingerprint information.

[0116] The solution provided in the embodiment of the present application uses map scene information, route information, etc. provided by a third-party manufacturer as environmental information input, and combines the user's usage preferences / history information to propose a user-personalized identification solution, while increasing the accuracy of the recognition of this solution and reducing the probability of misidentification. At the same time, private information data belonging to the individual user is constructed as the user's usage preference information, which facilitates the accurate identification of the user's next transportation plan.

[0117] Figure 7 This is a schematic diagram of an algorithm for identifying travel status provided in an embodiment of the present application. Figure 7 As shown, the identified fusion speed is first used to judge the riding status, mainly relying on the speed and travel time of the vehicle to determine whether the user has entered the riding state; after the user enters the riding state, the positioning route features will be extracted from the privatized database, such as whether the line is a railway line or a non-railway line; geomagnetic route features, such as whether the geomagnetic changes match the current geomagnetic change features; railway WIFI features, such as whether the public WIFI on the railway matches; motion features include acceleration features and air pressure features, etc.; scene features obtained from third-party scene information include positioning information of special buildings, etc.; the above features are used to construct a state recognition model. The model used in this solution is a context-based deep learning model to identify the riding status.

[0118] Figure 8 This is a travel status recognition effect diagram provided by this application. Figure 8 As shown, the black curve represents the fused speed, combining base station speed, Wi-Fi speed, and GPS speed. The gray curve represents the identified passenger status. Highlights in the curve indicate the passenger status, while valleys indicate station locations. Based on the speed and privatized information, the vehicle is traveling by high-speed rail.

[0119] The solution provided in the embodiment of the present application will integrate base station, WIFI, Bluetooth data, IMU sensor, geomagnetic and other sensor information and historical user information to perform data fusion and status recognition. It can realize vehicle identification and status recognition in various scenarios when one or more information dimensions are missing, and can realize accurate identification in various complex environments and information missing situations.

[0120] In addition, this solution can achieve accurate identification of underground rail transit, ground rail transit, and road traffic through data fusion, construction of a private database, and integration of map and business information.

[0121] It should be understood that, although the various steps in the above flow chart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flow chart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0122] Based on the foregoing embodiments, an embodiment of the present application provides a travel status identification device, and the modules included in the device and the units included in each module can be implemented by a processor; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA), etc.

[0123] Figure 9 This is a schematic diagram of the structure of a travel status identification device provided in an embodiment of the present application. Figure 9 As shown, the device 400 includes a speed feature acquisition module 401 and a travel state identification module 402, wherein:

[0124] A speed feature acquisition module 401 is configured to acquire at least one speed feature, wherein the at least one speed feature is acquired based on collected positioning information and / or network information, wherein the positioning information includes at least one of GPS information, Beidou information, and base station information, and the network information includes Wi-Fi information and / or Bluetooth information;

[0125] The travel status identification module 402 is used to identify the travel status according to the at least one speed feature to obtain a travel status result, which is used to indicate whether the user travels by means of transportation, the type of transportation, and at least one of the current stations.

[0126] In some embodiments, the travel state recognition module 402 is specifically configured to input the at least one speed feature into a trained first state recognition model to obtain the travel state result, where the trained first state recognition model is trained based on speed feature samples.

[0127] In some embodiments, the device also includes: a target feature acquisition module, the target feature acquisition module is used to acquire target features, the target features include at least one of user behavior features, network features, scene features and route features, the user behavior features are extracted based on the collected sensor information, the network features are extracted based on the collected network information, the scene features and the route features are acquired based on the collected positioning information and / or network information; the travel state identification module is further specifically used to: input the at least one speed feature and the target feature into a trained second state recognition model to obtain the travel state result, the second state recognition model is trained based on speed feature samples and target feature samples.

[0128] In some embodiments, the target feature includes the scene feature, and obtaining the target feature includes: matching the acquired positioning information and / or network information with preset third-party scene information to obtain a scene feature corresponding to the acquired positioning information and / or network information, and the preset third-party scene information includes the correspondence between the preset scene feature and the preset positioning information and / or network information.

[0129] In some embodiments, the target feature includes a route feature, and obtaining the target feature includes: matching the acquired positioning information and / or network information with a preset private database to obtain a route feature corresponding to the acquired positioning information and / or network information, and the preset private database includes a correspondence between the preset route feature and the preset positioning information and / or network information; wherein the correspondence between the preset route feature and the preset positioning information and / or network information is obtained based on the historically acquired positioning information and / or network information, and the corresponding travel status results.

[0130] In some embodiments, the speed feature includes a target speed at a current moment, and the speed feature acquisition module 401 includes an actual speed acquisition unit and a target speed acquisition unit, wherein:

[0131] The actual speed acquisition unit is used to acquire the actual speed at a current moment, where the actual speed at the current moment is extracted based on at least one of the currently acquired positioning information and network information;

[0132] The target speed acquisition unit is used to determine the actual speed at the current moment as the target speed at the current moment if the actual speed at the current moment is the speed at the starting moment; if the actual speed at the current moment is the speed at a non-starting moment, determine the target speed at the current moment based on the actual speed at the current moment and the target speed at the previous moment.

[0133] In some embodiments, the target speed acquisition unit is specifically used to: obtain the weight corresponding to the target speed at the previous moment and the weight corresponding to the actual speed at the current moment, the weight corresponding to the target speed at the previous moment is less than the weight corresponding to the actual speed at the current moment; obtain a first speed and a second speed, the first speed is obtained by multiplying the target speed at the previous moment and the weight corresponding to the target speed at the previous moment, and the second speed is obtained by multiplying the actual speed at the current moment and the weight corresponding to the actual speed at the current moment; summing the first speed and the second speed to obtain the target speed at the current moment.

[0134] In some embodiments, the target speed acquisition unit is specifically used to: determine the target information type corresponding to the target information, the target information type is at least one of GPS information, Beidou information, base station information, WIFI information and Bluetooth information, and the target information is the source information for obtaining the actual speed at the current moment; according to the target information type, and the correspondence between the preset information type and the preset weight, determine the weight corresponding to the target information type.

[0135] In some embodiments, the target speed acquisition unit is specifically configured to: determine the initial weight according to the standard deviation of the target speed at the previous moment, the standard deviation of the actual speed at the current moment, the target speed at the previous moment, and the actual speed at the current moment;

[0136] Obtaining a weight corresponding to the actual speed at the current moment according to the initial weight and a preset weight range;

[0137] Among them, the weight corresponding to the actual speed at the current moment and the preset weight range are determined according to the information type of the target information, the target information type is at least one of GPS information, Beidou information, base station information, WIFI information and Bluetooth information, and the target information is the source information for obtaining the actual speed at the current moment.

[0138] In some embodiments, the actual speed at the current moment includes at least two actual speeds at the current moment, and the target speed determination unit is used to determine the updated actual speed based on the actual speeds at each current moment, the covariance matrix corresponding to the actual speeds at each current moment, the target speed at the previous moment, the covariance matrix corresponding to the target speed at the previous moment, and the Kalman coefficient; and determine the target speed at the current moment based on the updated actual speed and a preset state matrix.

[0139] In some embodiments, the device also includes a speed judgment module, which is used to determine that the at least one speed feature meets a preset condition, and the preset condition includes that the speed corresponding to the at least one speed feature is greater than or equal to a preset threshold, and / or that the duration of the speed corresponding to the at least one speed feature is greater than or equal to a preset duration.

[0140] In an embodiment of the present application, the user's travel status can be identified based on at least one speed feature obtained from the collected positioning information and / or network information, and can still be identified when one or more information dimensions are missing, thereby improving the accuracy and real-time performance of travel status identification.

[0141] The description of the above device embodiment is similar to the description of the above method embodiment and has similar beneficial effects as the method embodiment. For technical details not disclosed in the device embodiment of this application, please refer to the description of the method embodiment of this application for understanding.

[0142] It should be noted that in the embodiments of this application Figure 9 The module division of the travel status recognition device shown is schematic and represents only one logical functional division. Actual implementation may employ other division methods. Furthermore, the functional units in the various embodiments of the present application may be integrated into a single processing unit, physically exist separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units. Alternatively, a combination of software and hardware may be employed.

[0143] It should be noted that, in the embodiment of the present application, if the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling an electronic device to execute all or part of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0144] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 10 As shown, the electronic device 100 may include a processor 110, a memory 120, a wireless communication module 130, a mobile communication module 140, a camera 150, a sensor module 160, a display screen 170, and the like.

[0145] The processor 110 may include one or more processing units. For example, the processor 110 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application, such as one or more microprocessors (digital signal processors, DSPs) or one or more field programmable gate arrays (FPGAs). The different processing units may be independent devices or integrated into one or more processors.

[0146] The memory 120 can be used to store computer executable program code, which includes instructions. The internal memory 120 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area may store data created during the use of the electronic device 100 (such as audio data, video data, etc.), etc. In addition, the memory 120 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc. The processor 110 executes various functional applications and data processing of the electronic device 100 by running instructions stored in the memory 120, and / or instructions stored in a memory provided in the processor.

[0147] The wireless communication module 130 can provide wireless communication solutions for the electronic device 100, including WLAN, such as Wi-Fi networks, Bluetooth, NFC, IR, and the like. The wireless communication module 130 can be one or more devices that integrate at least one communication processing module. In some embodiments of the present application, the electronic device 100 can establish a wireless communication connection with other electronic devices through the wireless communication module 130.

[0148] The electronic device 100 can implement audio functions such as music playback and recording through the mobile communication module 140, the speaker 140A, the receiver 140B, the microphone 140C, the headphone jack 140D, and the application processor.

[0149] The mobile communication module 140 can provide solutions for wireless communications including 2G / 3G / 4G / 5G applied to the electronic device 100. The mobile communication module 140 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 140 can receive electromagnetic waves through the first antenna, and filter, amplify and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 140 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the first antenna. In some embodiments, at least some of the functional modules of the mobile communication module 140 can be set in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 140 can be set in the same device as at least some of the modules of the processor 110.

[0150] In some embodiments, the first antenna of the electronic device 100 is coupled to the mobile communication module 140, and the second antenna is coupled to the wireless communication module 130, so that the electronic device 100 can communicate with a network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technology. The GNSS may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a Beidou navigation satellite system (BDS), a quasi-zenith satellite system (QZSS) and / or a satellite based augmentation system (SBAS).

[0151] The camera 150 is used to capture still images or videos. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, and then passes the electrical signal to the ISP for conversion into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV or other format. In some embodiments, the electronic device 100 may include 1 or N cameras 150, where N is a positive integer greater than 1.

[0152] The sensor module 160 may include a pressure sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a distance sensor, a proximity light sensor, a fingerprint sensor, a temperature sensor, a touch sensor, an ambient light sensor, a bone conduction sensor, and the like.

[0153] Display screen 170 is used to display images, videos, and the like. Display screen 170 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a MiniLED, a MicroLED, a Micro-oLed, or a quantum dot light-emitting diode (QLED). In some embodiments, electronic device 100 may include one or N display screens 170, where N is a positive integer greater than one.

[0154] It should be understood that the structure illustrated in the embodiments of the present invention does not constitute a specific limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0155] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the travel status identification method provided in the above embodiment are implemented.

[0156] The above-mentioned computer-readable storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM) or flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0157] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0158] Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, radio frequency (RF), etc., or any suitable combination of the foregoing.

[0159] Computer program code for performing the operations of this specification may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0160] An embodiment of the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the steps of the travel status identification method provided in the above method embodiment.

[0161] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0162] It should be noted that the descriptions of the above storage medium, program product, and device embodiments are similar to the descriptions of the above method embodiments and have similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium, program product, and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0163] It should be understood that "one embodiment" or "an embodiment" or "some embodiments" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments. The above description of the various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other. For the sake of brevity, they will not be repeated here.

[0164] The term "and / or" in this article is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, object A and / or object B can mean: object A exists alone, object A and object B exist at the same time, and object B exists alone.

[0165] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0166] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical, mechanical or other forms.

[0167] The modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules; they may be located in one place or distributed across multiple network units; some or all of the modules may be selected according to actual needs to achieve the purpose of this embodiment.

[0168] In addition, all functional modules in the embodiments of the present application can be integrated into one processing unit, or each module can be a separate unit, or two or more modules can be integrated into one unit; the above-mentioned integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0169] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, and other media that can store program codes.

[0170] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling an electronic device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0171] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined, if they do not conflict, to obtain new method embodiments. The features disclosed in the several product embodiments provided in this application can be arbitrarily combined, if they do not conflict, to obtain new product embodiments. The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined, if they do not conflict, to obtain new method embodiments or device embodiments.

[0172] The above is merely an embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A travel status identification method, characterized in that: Used in electronic equipment, including: obtaining at least one speed characteristic, where the at least one speed characteristic is obtained based on collected positioning information and / or network information, the positioning information including at least one of GPS information, Beidou information, and base station information, and the network information including Wi-Fi information and / or Bluetooth information; Travel status identification is performed based on the at least one speed feature to obtain a travel status result, where the travel status result is used to indicate at least one of whether the user is traveling by means of transportation, the type of transportation, and the current station where the user is located.

2. The method according to claim 1, characterized in that The performing travel status identification according to the at least one speed feature to obtain a travel status result includes: The at least one speed feature is input into a trained first state recognition model to obtain the travel state result, where the trained first state recognition model is trained based on speed feature samples.

3. The method according to claim 1, characterized in that Before performing travel status identification according to the at least one speed feature to obtain a travel status result, the method further includes: Obtaining target features, the target features including at least one of user behavior features, network features, scenario features, and route features, the user behavior features being extracted based on collected sensor information, the network features being extracted based on collected network information, and the scenario features and route features being obtained based on collected positioning information and / or network information; The performing travel status identification according to the at least one speed feature to obtain a travel status result includes: The at least one speed feature and the target feature are input into a trained second state recognition model to obtain the travel state result, where the second state recognition model is trained based on speed feature samples and target feature samples.

4. The method according to claim 3, characterized in that The target feature includes the scene feature, and acquiring the target feature includes: The acquired positioning information and / or network information is matched with the preset third-party scene information to obtain scene features corresponding to the acquired positioning information and / or network information. The preset third-party scene information includes a correspondence between the preset scene features and the preset positioning information and / or network information.

5. The method according to claim 3, characterized in that The target features include route features, The acquiring target features includes: Matching the acquired positioning information and / or network information with a preset private database to obtain a line feature corresponding to the acquired positioning information and / or network information, wherein the preset private database includes a correspondence between preset line features and preset positioning information and / or network information; The correspondence between the preset route features and the preset positioning information and / or network information is obtained based on historically acquired positioning information and / or network information, and corresponding travel status results.

6. The method according to claim 1, characterized in that The speed feature includes a target speed at a current moment, and obtaining at least one speed feature includes: Obtaining an actual speed at a current moment, where the actual speed at the current moment is extracted based on at least one of currently acquired positioning information and network information; If the actual speed at the current moment is the speed at the starting moment, determining the actual speed at the current moment as the target speed at the current moment; If the actual speed at the current moment is not the speed at the starting moment, the target speed at the current moment is determined according to the actual speed at the current moment and the target speed at the previous moment.

7. The method according to claim 6, characterized in that Determining the target speed at the current moment according to the actual speed at the current moment and the target speed at the previous moment includes: Obtaining a weight corresponding to the target speed at the previous moment and a weight corresponding to the actual speed at the current moment, wherein the weight corresponding to the target speed at the previous moment is less than the weight corresponding to the actual speed at the current moment; Obtaining a first speed and a second speed, wherein the first speed is obtained by multiplying the target speed at the previous moment by the weight corresponding to the target speed at the previous moment, and the second speed is obtained by multiplying the actual speed at the current moment by the weight corresponding to the actual speed at the current moment; The first speed and the second speed are summed to obtain the target speed at the current moment.

8. The method according to claim 7, characterized in that The obtaining of the weight corresponding to the actual speed at the current moment includes: Determine a target information type corresponding to the target information, where the target information type is at least one of GPS information, Beidou information, base station information, WIFI information, and Bluetooth information, and the target information is source information for obtaining the actual speed at the current moment; The weight corresponding to the target information type is determined according to the target information type and the corresponding relationship between the preset information type and the preset weight.

9. The method according to claim 7, characterized in that The obtaining of the weight corresponding to the actual speed at the current moment includes: Determine the initial weight based on the standard deviation of the target speed at the previous moment, the standard deviation of the actual speed at the current moment, the target speed at the previous moment, and the actual speed at the current moment; Obtaining a weight corresponding to the actual speed at the current moment according to the initial weight and a preset weight range; Among them, the weight corresponding to the actual speed at the current moment and the preset weight range are determined according to the information type of the target information, the target information type is at least one of GPS information, Beidou information, base station information, WIFI information and Bluetooth information, and the target information is the source information for obtaining the actual speed at the current moment.

10. The method according to claim 6, characterized in that The actual speed at the current moment includes at least two actual speeds at the current moment, and determining the target speed at the current moment according to the actual speed at the current moment and the target speed at the previous moment includes: Determine an updated actual speed based on the actual speed at each current moment, the covariance matrix corresponding to the actual speed at each current moment, the target speed at the previous moment, the covariance matrix corresponding to the target speed at the previous moment, and the Kalman coefficient; The target speed at the current moment is determined according to the updated actual speed and a preset state matrix.

11. The method according to claim 1, wherein Before performing travel status identification according to the at least one speed feature to obtain a travel status result, the method further includes: Determine that the at least one speed feature meets a preset condition, where the preset condition includes that the speed corresponding to the at least one speed feature is greater than or equal to a preset threshold, and / or that the duration of the speed corresponding to the at least one speed feature is greater than or equal to a preset duration.

12. A travel status identification device, characterized in that: Used in electronic equipment, including: a speed feature acquisition module, configured to acquire at least one speed feature, wherein the at least one speed feature is acquired based on collected positioning information and / or network information, wherein the positioning information includes at least one of GPS information, Beidou information, and base station information, and the network information includes Wi-Fi information and / or Bluetooth information; The travel status identification module is used to identify the travel status according to the at least one speed feature to obtain a travel status result, wherein the travel status result is used to indicate whether the user travels by means of transportation, the type of transportation, and at least one of the current stations.

13. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the program, the steps of the travel status identification method according to any one of claims 1 to 11 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the travel status identification method according to any one of claims 1 to 11 are implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the travel status identification method according to any one of claims 1 to 11 are implemented.

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