CN112381126A

CN112381126A - Indoor and outdoor scene recognition method and device, electronic equipment and storage medium

Info

Publication number: CN112381126A
Application number: CN202011205236.0A
Authority: CN
Inventors: 才正国; 高国松; 赵明喜
Original assignee: Anhui Huami Information Technology Co Ltd
Current assignee: Anhui Huami Information Technology Co Ltd
Priority date: 2020-11-02
Filing date: 2020-11-02
Publication date: 2021-02-19
Anticipated expiration: 2040-11-02
Also published as: CN112381126B

Abstract

The invention discloses an indoor and outdoor scene recognition method and device, electronic equipment and a storage medium. The indoor and outdoor scene identification method comprises the following steps: acquiring a visible satellite message statement in a current updating period; analyzing the visible satellite message sentences to determine the signal-to-noise ratio characteristic of the current visible satellite; inputting the signal-to-noise ratio characteristic of the current visible satellite into a preset classification model to obtain a type label output by the classification model; and determining the current indoor and outdoor scenes according to the type labels. The method classifies the characteristics of the signal-to-noise ratio difference of the visible satellite under the indoor and outdoor scenes to judge whether the visible satellite is in the indoor or outdoor scene, has the advantages of simple identification mode, less calculation amount, higher accuracy and reliability, less influence of factors such as environment, position, wearing mode and the like on the message sentences of the visible satellite, and wider application range.

Description

Indoor and outdoor scene recognition method and device, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of electronic equipment, in particular to an indoor and outdoor scene recognition method and device, electronic equipment and a storage medium.

Background

With the development of electronic technology, electronic devices nowadays have many functions, and various types of services can be provided to users. For example, by recognizing whether the current location is indoors or outdoors, to provide each application in the electronic device with an auxiliary analysis of the scene where the user is located, the corresponding function is assisted.

In the related art, the current indoor and outdoor scenes are usually identified by acquiring environmental data such as ambient light intensity, air pressure change, and temperature through various sensors disposed in the electronic device, or by detecting behavior characteristics such as walking, stopping, and turning behaviors of the user through devices such as an acceleration sensor, a gyroscope, and a magnetic sensor in the electronic device.

However, the applicant finds that, in the above method, when the sensor collects the environmental data, the sensor is easily affected by factors such as shielding of clothes of the user, difference of wearing manners, interference of surrounding environments and the like, and when the user performs complicated motions in different environments, devices such as an acceleration sensor and a gyroscope need to perform a large amount of data calculation, calculation deviation is easily generated, and the sensor is interfered by factors such as geographical positions and weather conditions, so that behavior characteristics cannot be accurately detected. Therefore, the method for detecting indoor and outdoor scenes by the electronic equipment in the related art is low in accuracy and practicability, and the detection process is complex.

Disclosure of Invention

The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.

Therefore, the first objective of the present invention is to provide an indoor and outdoor scene recognition method, which classifies the characteristics representing the signal-to-noise ratio differences of visible satellites in the indoor and outdoor scenes to determine whether the current scene is in the indoor or outdoor scene, and has the advantages of simple recognition mode, less calculation amount, higher accuracy and reliability, less influence of factors such as environment, position and wearing mode on visible satellite message sentences, and wider application range.

The second purpose of the invention is to provide an indoor and outdoor scene recognition device.

A third object of the invention is to propose an electronic device.

A fourth object of the present invention is to propose a non-transitory computer-readable storage medium.

In order to achieve the above object, a first aspect of the present invention provides an indoor and outdoor scene recognition method, including the following steps:

acquiring a visible satellite message statement in a current updating period;

analyzing the visible satellite message sentences to determine the signal-to-noise ratio characteristic of the current visible satellite;

inputting the signal-to-noise ratio characteristic of the current visible satellite into a preset classification model to obtain a type label output by the classification model;

and determining the current indoor and outdoor scenes according to the type labels.

The indoor and outdoor scene identification method comprises the steps of firstly obtaining visible satellite message sentences in a current updating period, then analyzing the visible satellite message sentences to determine current visible satellite signal-to-noise ratio characteristics, then inputting the current visible satellite signal-to-noise ratio characteristics into a preset classification model to obtain type labels output by the classification model, and finally determining the current indoor and outdoor scene according to the type labels. The method judges whether the current scene is indoor or outdoor by classifying the characteristics which are used for representing the satellite signal difference under the indoor and outdoor scenes based on the signal-to-noise ratio, has the advantages of simple identification mode, less calculation amount, higher accuracy and reliability, less influence of factors such as environment, place, wearing mode and the like on visible satellite message sentences, and wide application range.

To achieve the above object, a second aspect of the present invention provides an indoor/outdoor scene recognition apparatus, including:

the first acquisition module is used for acquiring visible satellite message sentences in the current updating period;

the first determining module is used for analyzing the visible satellite message statements to determine the signal-to-noise ratio characteristic of the current visible satellite;

the second acquisition module is used for inputting the signal-to-noise ratio characteristics of the current visible satellite into a preset classification model so as to acquire a type label output by the classification model;

and the second determining module is used for determining the current indoor and outdoor scenes according to the type labels.

The indoor and outdoor scene recognition device of the embodiment of the invention firstly obtains the visible satellite message sentences in the current updating period, then analyzes the visible satellite message sentences to determine the current signal-to-noise ratio characteristics of the visible satellite, inputs the current signal-to-noise ratio characteristics of the visible satellite into the preset classification model to obtain the type labels output by the classification model, and finally determines the current indoor and outdoor scene according to the type labels. The device judges whether the current scene is indoor or outdoor by classifying the characteristics for representing the signal-to-noise ratio difference of the visible satellite in the indoor and outdoor scenes, has the advantages of simple identification mode, less calculation amount, higher accuracy and reliability, smaller influence of factors such as environment, position, wearing mode and the like on the message sentences of the visible satellite, and wider application range.

To achieve the above object, a third aspect of the present invention provides an electronic device, comprising: a processor; and a memory communicatively coupled to the processor; wherein the memory stores instructions executable by the processor, and the instructions when invoked and executed by the processor enable the indoor and outdoor scene recognition method of any one of the above aspects.

To achieve the above object, a fourth aspect of the present invention provides a non-transitory computer readable storage medium storing computer instructions, wherein the computer instructions, when executed, implement the indoor and outdoor scene recognition method according to any one of the above aspects.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

Fig. 1 is a schematic flow chart of an indoor and outdoor scene recognition method according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart illustrating a method for determining a signal-to-noise ratio characteristic of a current satellite in view according to an embodiment of the present invention;

FIG. 3 is a flowchart illustrating a specific method for determining a signal-to-noise ratio of a current visible satellite according to an embodiment of the present invention;

fig. 4 is a flowchart illustrating a specific method for identifying indoor and outdoor scenes according to an embodiment of the present invention;

fig. 5 is a schematic structural diagram of an indoor and outdoor scene recognition apparatus according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of a specific indoor and outdoor scene recognition apparatus according to an embodiment of the present invention;

fig. 7 shows a block diagram of an exemplary electronic device suitable for use with an indoor and outdoor scene recognition method implementing an embodiment of the invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.

An indoor and outdoor scene recognition method according to an embodiment of the present invention is described below with reference to the accompanying drawings.

The indoor and outdoor scene recognition method provided by the embodiment of the present invention may be implemented by an electronic device provided with a satellite Positioning module, where the electronic device may be a wearable device or a mobile terminal device, and the built-in satellite Positioning module may be a Global Positioning System (GPS) module or a beidou Positioning module, and the present disclosure is not limited thereto.

Fig. 1 is a schematic flow chart of an indoor and outdoor scene recognition method provided in an embodiment of the present invention, and as shown in fig. 1, the indoor and outdoor scene recognition method includes the following steps:

step 101, obtaining a visible satellite message statement in a current updating period.

The visible Satellite message statement (Global Navigation Satellite System Satellite in View, abbreviated as GSV) is a message output statement conforming to a standard protocol of a Global Navigation Satellite System, and is used for indicating information of a visible Satellite that can be received by a positioning module, including a number (ID) of the Satellite, an elevation angle, an azimuth angle, a signal-to-noise ratio, and the like.

Specifically, when a current indoor and outdoor scene needs to be identified, a visible satellite message statement in a current update period may be obtained by the satellite positioning module, where the update period may be determined according to a period of update data when the satellite positioning system performs positioning, for example, the update period may be 1 second.

For example, when the wearable device identifies a current indoor and outdoor scene, a GPS module built in the device is started first, then the GPS module searches for a satellite signal, and obtains a returned message sentence conforming to a National Marine Electronics Association (NMEA) 0183 protocol of the GPS module, and then screens out all GSV sentences returned within 1 second currently.

In an embodiment of the present invention, in order to improve accuracy of scene recognition, visible satellite message statements sent by different types of satellite systems may be obtained, for example, any one of visible satellite message statements of types such as a visible satellite message statement (GPSGSV) sent by a global positioning system, a visible satellite message statement (GLGSV) sent by a GLONASS system, a visible satellite message statement (BDGSV) sent by a beidou navigation system, and a visible satellite message statement (GAGSV) sent by a galileo system may be obtained. The indoor and outdoor scene recognition method can aim at GSV sentences of different types and quantities, so that the indoor and outdoor scene recognition method can be realized through various types of GPS modules such as single mode, double mode or multi mode at present, and the practicability and universality of the indoor and outdoor scene recognition method are improved.

Step 102, parsing the visible satellite message statements to determine the current visible satellite signal-to-noise ratio characteristics.

Specifically, all the visible satellite message statements in the current update period are analyzed to obtain information contained in the visible satellite message statements. As a possible way, the display of each visible satellite message statement is converted, the visible satellite message statements are displayed in a way of combining a plurality of fields, and a message list corresponding to the group of visible satellite message statements is generated, so that the visible satellite information corresponding to each visible satellite message statement is read according to the message list.

In order to describe the visible satellite message statement analyzed by the embodiment of the present invention more clearly, a specific explanation is given below by taking an analyzed GPGSV statement as an example:

$GPGSV,3,1,10,20,78,331,45,01,59,235,47,22,41,069,,13,32,252,45*70

as shown above, the visible satellite message statement includes a plurality of fields after being analyzed, and each field corresponds to different information, where a field 0 is "$ GPGSV", which indicates that the statement ID is GPS Satellites in View; the field 1 is '3', which indicates that the total GPGSV statement number is 3; the field 2 is '1', which indicates that the GSV statement is the 1 st in the total GSV statement; field 3 is "10", indicating that the total number of visible satellites is 10; the field 4 is "20", which indicates that the satellite number is 20, i.e., information of the visible satellite number 20 follows; field 5 is "78" indicating that the satellite is at 78 degrees in elevation; a field 6 of "331" indicates that the satellite has an azimuth of 331 degrees; field 7 is "45" indicating that the signal-to-noise ratio from the satellite to the current device is 45; the field 8 is "01" indicating that the satellite number is 01, which is followed by information for satellite number 01, and so on, the elevation, azimuth and signal-to-noise ratio information for satellites number 01, 22 and 13 can be obtained as described above. Where field 15 is null indicating that the signal-to-noise ratio for satellite number 22 is null.

Further, after the visible satellite message statements are analyzed, the signal-to-noise ratio characteristics of the current visible satellite can be calculated according to the information in the obtained visible satellite message statements, such as the number of visible satellites and the signal-to-noise ratio of each visible satellite.

It should be noted that the signal-to-noise ratios of the visible satellite message statements received by the electronic device in different scenes are different. For example, the signal-to-noise ratios of satellites to wearable equipment are different in indoor or outdoor scenes, so that the signal-to-noise ratios of the visible satellites contained in the visible satellite message sentences are calculated, the signal-to-noise ratio characteristics of the visible satellites used for representing the difference between the indoor and outdoor scenes based on the signal-to-noise ratios can be obtained, and then the calculated signal-to-noise ratio characteristics can be conveniently classified through a preset classification model subsequently, so that the indoor and outdoor scenes corresponding to the signal-to-noise ratio characteristics can be determined.

In one embodiment of the invention, when the signal-to-noise ratio characteristic of the current visible satellite is determined, the signal-to-noise ratio of each visible satellite can be counted in different modes, and the signal-to-noise ratio characteristic of the multi-dimensional satellite is determined, for example, the mean value, the variance and the standard deviation of the signal-to-noise ratio of each visible satellite are respectively counted to obtain the signal-to-noise ratio characteristic of the three-dimensional satellite, so that the signal-to-noise ratio of the current visible satellite can be more comprehensively evaluated, and the accuracy of indoor and outdoor scene recognition can.

And 103, inputting the signal-to-noise ratio characteristic of the current visible satellite into a preset classification model to obtain a type label output by the classification model.

And step 104, determining the current indoor and outdoor scenes according to the type labels.

The classification model is a model that maps the acquired data (i.e., the signal-to-noise ratio characteristics of the visible satellites in the invention) to one of given categories (i.e., one of an indoor scene or an outdoor scene), and outputs a corresponding type tag according to the mapping result, thereby predicting the category to which the data belongs.

In specific implementation, as a possible implementation manner, the current signal-to-noise ratio characteristic of the visible satellite is input into a preset two-class classifier for judging whether the visible satellite is indoor or outdoor, and whether the current signal-to-noise ratio characteristic of the visible satellite corresponds to an indoor scene or an outdoor scene is determined according to an indoor scene label or an outdoor scene label output by the classifier.

Specifically, a plurality of groups of visible satellite message sentences under different conditions of time, place, weather and the like are collected in advance, then the collected sentences are analyzed, feature extracted, vectorized and the like according to the method in the embodiment, and then the processed data set is split to form a training set and a verification set. And then selecting a corresponding algorithm for machine learning training, for example, selecting algorithms such as a decision tree, logistic regression, naive Bayes, neural network and the like for training, wherein the output of the classifier is set as a set indoor scene label or an outdoor scene label, namely, a final classification decision made by the two classes of classifiers is determined, and further, a classifier model is finally obtained through continuous evaluation and parameter adjustment. And then, inputting the signal-to-noise ratio characteristics of the current visible satellite into a trained classifier, and outputting the corresponding type label by the classifier.

Further, the current indoor and outdoor scenes are determined according to the type labels. Specifically, if the classifier outputs an indoor scene tag, it is determined that the electronic device is currently located in an indoor scene according to the tag, and if the classifier outputs an outdoor scene tag, it is determined that the electronic device is currently located in an outdoor scene according to the tag. Thus, the recognition result of the indoor and outdoor scenes is obtained.

It should be noted that, in the embodiment of the present invention, in order to reduce the amount of data computation, a decision tree algorithm may be selected to train a classifier model, and a decision tree classifier with a low computational complexity is used to identify the current indoor and outdoor scenes, so as to reduce the complexity of identifying the indoor and outdoor scenes. Of course, the invention can also select other machine learning methods such as a naive Bayes algorithm, a support vector machine algorithm or an artificial neural network algorithm and the like according to actual needs, and the invention is not limited here.

As another possible implementation manner, a large number of experiments for determining the signal-to-noise ratio characteristics of the visible satellites are performed indoors and outdoors in advance, and the signal-to-noise ratio characteristics of the standard visible satellites in an indoor scene and the signal-to-noise ratio characteristics of the standard visible satellites in an outdoor scene are determined by performing statistics, analysis and other processing on the experiment results. Then, after the signal-to-noise ratio characteristic of the current visible satellite is obtained, the signal-to-noise ratio characteristic of the current visible satellite is subjected to similar operation with the two standard characteristics respectively, the distance between the signal-to-noise ratio characteristic of the current visible satellite and the two standard characteristics is obtained, and the indoor and outdoor scenes to which the current visible satellite belongs are determined according to the magnitude relation of the two distance values. For example, if the distance between the current visible satellite signal-to-noise ratio feature and the standard visible satellite signal-to-noise ratio feature in the indoor scene is smaller than the distance between the current visible satellite signal-to-noise ratio feature and the standard visible satellite signal-to-noise ratio feature in the outdoor scene, it is indicated that the current visible satellite signal-to-noise ratio feature belongs to the standard visible satellite signal-to-noise ratio feature range in the indoor scene, and it is determined that the current visible satellite signal-to-.

Therefore, the indoor and outdoor scene identification method determines the current indoor and outdoor scene according to the signal-to-noise ratio characteristics of the current visible satellite, and because the factors such as the shielding of obstacles, the geographic position, the weather conditions and the like have little influence on the visible satellite message sentences sent by the receiving satellite and can be ignored, the identification method is not limited by the conditions such as environment, places and the like, the accuracy of the identification result is higher, the identification result can be obtained by analyzing, extracting the characteristics and the like of fewer visible satellite message sentences, the calculation complexity is lower, and the method is easy to realize.

In summary, in the indoor and outdoor scene recognition method according to the embodiment of the present invention, the visible satellite message statements in the current update period are obtained, then the visible satellite message statements are analyzed to determine the current visible satellite signal-to-noise ratio characteristic, the current visible satellite signal-to-noise ratio characteristic is input into the preset classification model to obtain the type label output by the classification model, and finally the current indoor and outdoor scene is determined according to the type label. The method judges whether the current scene is indoor or outdoor by classifying the characteristics for representing the signal-to-noise ratio difference of the visible satellite in the indoor and outdoor scenes, has the advantages of simple identification mode, less calculation amount, higher accuracy and reliability, less influence of factors such as environment, position, wearing mode and the like on the message sentences of the visible satellite, and wide application range.

Based on the above embodiment, in order to describe a specific process of analyzing a visible satellite message statement to determine a current visible satellite signal-to-noise ratio characteristic more clearly, the present invention further provides a method for determining a current visible satellite signal-to-noise ratio characteristic.

Fig. 2 is a schematic flowchart of a method for determining a signal-to-noise ratio characteristic of a current visible satellite according to an embodiment of the present invention, as shown in fig. 2, the method includes:

step 201, the visible satellite message statement is analyzed to obtain the number of visible satellites included in the message statement and the signal-to-noise ratio of each visible satellite.

Specifically, with reference to the method in the foregoing embodiment, all the obtained visible satellite message statements in the current update period are analyzed, a complete message list corresponding to the group of visible satellite message statements is generated, and the number of visible satellites and the signal-to-noise ratio of each visible satellite are obtained from the message list. Since in practical applications different types and numbers of GSV statements can be obtained in general, the process of obtaining the number of visible satellites and the signal-to-noise ratio of each visible satellite contained in the message statement is described in detail below with two examples.

In a first example, when the obtained GSV statement includes two GPSGSV statements and one GLGSV statement, the three GSV statements are converted, and a message list corresponding to the group of visible satellite message statements is generated as shown in table 1 below.

TABLE 1

$GPGSV，2，1，07	01,01,186，，	04,39,232，，	07,18,316，15，	08,68,239，，
					$GPGSV，2，2，07	18,02,035，，	21,10,042，00，	31,09,132，，
$GLGSV，1，1，03	69,10,247，，	70,08,302，14，	88,24,032，，

As shown in table 1, as can be seen from the data in each field in each converted visible satellite message statement, the number of visible satellites returned to the GPSGSV statement is 7, and the number of visible satellites returned to the GLGSV statement is 3, so that the number of visible satellites included in the group of message statements obtained by adding 7 to 3 is 10. In the signal-to-noise ratios of the visible satellites, the number of the satellites with data in the field indicating the signal-to-noise ratio is 3, namely, the number of the satellites is number 07, number 21 and number 70, and the corresponding signal-to-noise ratios are 15, 0 and 14.

As a second example, when the obtained GSV statement includes two GPSGSV statements, the two GPSGSV statements are converted respectively to generate a message list corresponding to the group of visible satellite message statements as shown in table 2 below.

TABLE 2

$GPGSV，2，1，08	01,28,179，19，	04,16,209，13，	07,44,312，26，	08,70,352，28，
					$GPGSV，2，2，08	09,22,244，33，	11,66,191，25，	23,21,254，，	30,13,318,00，

As shown in table 2, as can be seen from the data in each field in each translated visible satellite message statement, the number of visible satellites returned to the GPSGSV statement is 8, and there are no other satellites, so the number of visible satellites included in the set of message statements is 8. In the snr of each visible satellite, the number of satellites with data in the field indicating the snr is 7, which are respectively satellites 01, 04, 07, 08, 09, 11, and 30, whose snrs are respectively 19, 13, 26, 28, 33, 25, and 0, and only the snr of satellite 23 is null.

Step 202, determining the signal-to-noise ratio characteristic of the current visible satellite according to the number of the visible satellites and the signal-to-noise ratio of each visible satellite.

The visible satellite signal-to-noise ratio characteristics in the embodiment of the invention comprise various characteristics which can be used for representing indoor and outdoor satellite signal differences based on the signal-to-noise ratio, and the dimension of the visible satellite signal-to-noise ratio characteristics can be determined according to actual needs.

As one possible implementation, the following is an exemplary description of a specific method for determining the signal-to-noise ratio of the current visible satellite according to the present invention

Fig. 3 is a schematic flow chart of a specific method for determining a signal-to-noise ratio characteristic of a current visible satellite according to an embodiment of the present invention, and as shown in fig. 3, after acquiring the number of visible satellites and the signal-to-noise ratio of each visible satellite included in a message statement, the method further includes:

step 301, determining the number of the current effective visible satellites according to the signal-to-noise ratio of each visible satellite.

The effective visible satellites are satellites capable of performing operation according to the signal-to-noise ratio of the effective visible satellites to determine the signal-to-noise ratio characteristics of the current visible satellites.

With continuing reference to both examples in step 201, the number of valid visible satellites is 3 in the first example and 7 in the second example.

Step 302, determining whether the number of the current valid visible satellites is greater than a first threshold, if so, performing step 303, and if not, performing step 304.

The first threshold value may be set according to the requirement of detection accuracy. For example, in the present embodiment, when it is determined that the mobile terminal is currently located indoors or outdoors and the requirement for accuracy of the coordinates of the specific location is low, the first threshold may be set to 3.

Continuing with the two examples in step 201, in the first example, the number of valid visible satellites is 3, and if the first threshold is 3, the number of valid visible satellites is not greater than the first threshold, step 304 is performed subsequently. In the second example, the number of valid visible satellites is 7, and if the first threshold is 3, the number of valid visible satellites is greater than the first threshold, then step 303 is performed subsequently.

Step 303, calculating the mean, variance and median of the signal-to-noise ratio of the current effective visible satellite according to the signal-to-noise ratio of each effective visible satellite.

Specifically, the signal-to-noise ratio of each effective visible satellite is subjected to corresponding statistical operation, and the mean, variance and median of the signal-to-noise ratio of the current effective visible satellite are calculated. For example, referring to the second example in step 201, the mean, variance and median of the snr 19, 13, 26, 28, 33, 25 and 0 of the valid visible satellites are calculated, and the mean, variance and median of the snr of the valid visible satellites are obtained as 20.5714, 11.1184 and 25, respectively.

And step 304, determining that the mean value, the variance and the median of the signal-to-noise ratio of the current effective visible satellite are preset values.

Specifically, the preset value may be any value outside the range of the effective signal-to-noise ratio of the visible satellite. For example, the effective snr of the visible satellite ranges from 0 to 99, and the preset value can be 100, 101, 102, or-1, -2, -3, etc. In practical use, normally, the signal-to-noise ratio is empty and is only an accidental event, and in order to avoid the influence of the accidental event on a long-term detection result, in the embodiment of the application, the preset value may be a value which is slightly different from the effective signal-to-noise ratio, for example, the preset value may be-1, -2, and the like. Accordingly, referring to the first example in step 201, the mean, variance and median number of signal-to-noise ratios of the current valid visible satellites may all be set to-1.

And 305, determining the signal-to-noise ratio characteristic of the current visible satellite according to the mean value, the variance and the median of the signal-to-noise ratio of the current effective visible satellite.

Specifically, the signal-to-noise ratio characteristics of the visible satellites with different dimensions are obtained after vectorization is carried out on the obtained mean value, variance and median of the signal-to-noise ratio of the effective visible satellites. For example, a 3-dimensional visible satellite signal-to-noise ratio feature vector (mean) can be generated directly from the mean, variance, and median of the signal-to-noise ratio.

In an embodiment of the present invention, the number of invalid visible satellites may be determined according to the current number of visible satellites and the number of valid visible satellites, and then the number of invalid visible satellites is compared with the number of visible satellites to determine the current occupation ratio of invalid visible satellites. With continuing reference to both examples in step 201, in the first example, if the number of invalid visible satellites with empty snr is 7, the current invalid visible satellite ratio is 7/10. In a second example, if the number of inactive visible satellites with empty snr is 1 and the number of visible satellites is 8, then the current inactive visible satellite ratio is 1/8.

Furthermore, the signal-to-noise ratio characteristic of the current visible satellite is determined according to the mean value, the variance and the median of the signal-to-noise ratio of the current effective visible satellite and the occupation ratio of the invalid visible satellite. Therefore, the dimensionality of the signal-to-noise ratio characteristic of the visible satellite is expanded, and the accuracy of indoor and outdoor scene recognition is improved.

To sum up, the method for determining the signal-to-noise ratio characteristic of the current visible satellite according to the embodiments of the present invention analyzes the visible satellite message statements to obtain the number of visible satellites and the signal-to-noise ratio of each visible satellite contained in the message statements, and then determines the signal-to-noise ratio characteristic of the current visible satellite according to the number of visible satellites and the signal-to-noise ratio of each visible satellite, wherein the mean, the variance, and the median of the signal-to-noise ratio of the current effective visible satellite can be calculated according to the signal-to-noise ratio of the effective visible satellite to determine the signal-to-noise ratio characteristic of the. The method can accurately identify the indoor and outdoor scenes by analyzing, extracting information and carrying out statistical operation on a small number of visible satellite message sentences, and has the advantages of small data calculation amount, low complexity and high accuracy of indoor and outdoor scene identification.

Based on the embodiment, in practical application, in order to avoid the influence of unexpected factors in the operation process of identifying the indoor and outdoor scenes, the accuracy of identifying the indoor and outdoor scenes is further improved, and the invention further provides a specific indoor and outdoor scene identification method.

Fig. 4 is a schematic flowchart of a specific indoor and outdoor scene recognition method according to an embodiment of the present invention, and as shown in fig. 4, after the step of determining the signal-to-noise ratio of the current visible satellite, the method further includes:

step 401, obtaining N groups of visible satellite message statements respectively corresponding to the previous N update periods adjacent to the current update period, where N is a positive integer greater than 1.

Specifically, after determining the visible satellite signal-to-noise ratio characteristic corresponding to the visible satellite message statement in the current update period, the visible satellite message statement in each update period in N update periods before the current update period, that is, N groups of visible satellite message statements corresponding to N periods before the current update period on the time axis, are obtained. N may be set according to a requirement for identifying accuracy of indoor and outdoor scenes, for example, N is set to 3.

In an embodiment of the present invention, after the visible satellite message statements in each update period are obtained, the visible satellite message statements in each update period may be cached, so that the visible satellite message statements in the current update period may still be queried in the subsequent update period. In order to reduce the occupied storage space, when it is determined that the interval between the update period and the current update period in the stored data is greater than a preset threshold, the data corresponding to the update period exceeding the preset threshold may be deleted.

Of course, the visible satellite message statements corresponding to the first N update cycles may also be obtained in other manners, for example, after the application in the electronic device obtains the visible satellite message statement in each update cycle, the visible satellite message statement is sent to the cloud platform to be stored, and when the visible satellite message statement in the cycle needs to be obtained, a request for obtaining the corresponding visible satellite message statement is sent to the cloud platform according to the identifier of the update cycle.

Step 402, analyzing the N groups of visible satellite message statements to obtain the number of visible satellites corresponding to each group of visible satellite message statements and the signal-to-noise ratio of each visible satellite.

And step 403, determining the signal-to-noise ratio characteristics of the N groups of visible satellites according to the number of the N groups of visible satellites and the signal-to-noise ratio of each visible satellite.

Specifically, N groups of visible satellite message statements are analyzed to obtain the number of visible satellites corresponding to each group of visible satellite message statements and the signal-to-noise ratio of each visible satellite, and then a specific implementation manner of the signal-to-noise ratio characteristics of the N groups of visible satellites is determined according to the number of the N groups of visible satellites and the signal-to-noise ratio of each visible satellite.

In an embodiment of the present invention, after determining the signal-to-noise ratio characteristics of the visible satellites corresponding to each update period, the determined signal-to-noise ratio characteristics of the visible satellites may be cached, so that the signal-to-noise ratio characteristics of the visible satellites corresponding to the previous N periods may be directly queried according to the time sequence.

And step 404, correcting the signal-to-noise ratio characteristic of the current visible satellite according to the signal-to-noise ratio characteristics of the N groups of visible satellites to generate the signal-to-noise ratio characteristic of the target visible satellite.

Specifically, the correction of the current visible satellite signal-to-noise ratio characteristic comprises the steps of calculating the visible satellite signal-to-noise ratio characteristic for the second time on the N groups of visible satellite signal-to-noise ratios and the current visible satellite signal-to-noise ratio characteristic to obtain a visible satellite signal-to-noise ratio characteristic with more dimensionality, and taking the visible satellite signal-to-noise ratio characteristic as the corrected target visible satellite signal-to-noise ratio characteristic.

In an embodiment of the invention, in the N groups of visible satellite signal-to-noise bits and the current visible satellite signal-to-noise ratio characteristics, if each group of visible satellite signal-to-noise ratio characteristics includes an effective visible satellite signal-to-noise ratio mean value, an effective visible satellite signal-to-noise ratio variance, an effective visible satellite signal-to-noise median and an ineffective visible satellite percentage, there are N +1 groups of effective visible satellite signal-to-noise ratios, effective visible satellite signal-to-noise ratio variances, effective visible satellite signal-to-noise median and ineffective visible satellite percentage. And then, respectively calculating a first mean value, a first variance and a first median corresponding to the N +1 groups of effective visible satellite signal-to-noise ratio mean values, a second mean value, a second variance and a second median corresponding to the N +1 groups of effective visible satellite signal-to-noise ratio variances, a third mean value, a third variance and a third median corresponding to the N +1 groups of effective visible satellite signal-to-noise ratio medias, and a fourth mean value, a fourth variance and a fourth median corresponding to the N +1 groups of ineffective visible satellite signal-to-noise ratio. Therefore, after the first mean value, the first variance, the first median, the second mean value, the second variance, the second median, the third mean value, the third variance, the third median, the fourth mean value, the fourth variance and the fourth median are vectorized, the signal-to-noise ratio characteristic of the target visible satellite with the dimension of 12 is generated.

Step 405, inputting the signal-to-noise ratio characteristics of the target visible satellite into a preset classification model to obtain a type label output by the classification model.

Specifically, the method for inputting the signal-to-noise ratio characteristic of the target visible satellite into the preset classification model to obtain the type label output by the classification model may refer to the related description in the above embodiment, and details are not repeated here.

Therefore, according to the indoor and outdoor scene identification method, the signal-to-noise ratio characteristics of N adjacent groups of visible satellites before the current update period are obtained, and then the signal-to-noise ratio characteristics of the current visible satellites are corrected according to the signal-to-noise ratio characteristics of the N groups of visible satellites, so that the signal-to-noise ratio characteristics of the target visible satellites with more dimensions are generated. Due to the fact that the updating period corresponding to the N groups of visible satellite signal-to-noise ratio characteristics is continuous with the current updating period in time, the corrected target visible satellite signal-to-noise ratio characteristics are smoother in time dimension, accidental errors and result jumping possibly occurring in the operation process of identifying indoor and outdoor scenes are fewer, and accuracy and reliability of indoor and outdoor scene identification are further improved.

Based on the above embodiment, after the current indoor and outdoor scene is determined by the indoor and outdoor scene recognition method of the embodiment of the invention, each function provided by the electronic equipment can be improved and adjusted according to the current scene recognition result, so that each function provided by the electronic equipment better conforms to the current scene, and the satisfaction degree of a user is improved. The indoor and outdoor scene recognition method according to the embodiment of the present invention is applied to wearable devices.

In an embodiment of the present invention, before the wearable device acquires the visible satellite message statement in the current update period, an application scenario of the current wearable device is determined, that is, a function executed by the wearable device in the current scenario is determined.

For example, when the user is in motion, the wearable device performs a motion recognition detection function, such as the wearable device recognizing that the user is running and recording the number of steps and mileage the user is running; when a user needs to listen to music, the wearable device executes a music playing function and determines that the current application scene of the wearable device is multimedia playing; when the wearable device sends reminding information such as incoming call reminding, short message reminding and sedentary reminding to a user, determining that an application scene of the wearable device is a reminding scene and the like.

Then, after the current indoor and outdoor scene is determined by the method in the above embodiment, each corresponding function provided by the wearable device in the current application scene is improved and optimized according to the indoor and outdoor scene recognition result.

Continuing with the above example, when the user is moving, after determining the current indoor and outdoor scene, the current movement statistics are modified according to the current indoor and outdoor scene.

Specifically, the modification of the current motion statistical parameter includes refinement of the motion category and modification of the motion index parameter. For example, after the current indoor and outdoor scene is determined, the original running motion is divided into indoor running and outdoor running, the original swimming motion is divided into indoor swimming pool swimming and open water area swimming, and the original riding motion is divided into indoor spinning and outdoor riding, so that the detected motion index parameters can be conveniently reevaluated according to the motion types, the motion types in the motion statistical parameters displayed currently can be updated, and the satisfaction degree of the user is improved.

Furthermore, the statistical motion index is corrected according to the refined motion category. For example, after the running exercise is divided into indoor running or outdoor running according to the current indoor and outdoor scenes, the counted step length and the estimated distance of the user are corrected, the step length and the distance before the indoor and outdoor scenes are identified are corrected, and the new step length and the new distance under the indoor running scene are obtained, so that the optimization of the statistical exercise index parameters is realized, and the accuracy of the statistical exercise index parameters is improved.

When the current application scene of the wearable device is multimedia playing, after the current indoor and outdoor scene is determined, the current playing volume is adjusted according to the current indoor and outdoor scene.

Specifically, when the wearable device is playing music, if it is recognized that the current scene is switched from indoor to outdoor, the volume is automatically increased to counteract the ambient noise sound or the like, and if it is recognized that the current scene is switched from outdoor to indoor, the volume is automatically decreased to protect hearing and save power, or the like.

Certainly, after the current indoor and outdoor scene is determined according to the indoor and outdoor scene recognition method of the embodiment of the present invention, other functions that can be integrated into the indoor and outdoor scene recognition results provided by the wearable device can be improved according to the current scene recognition results, for example, when the wearable device is applied to a reminding scene, the heart rate voice broadcast frequency is automatically adjusted according to the indoor and outdoor scene recognition results, and the voice broadcast frequency is increased in an outdoor environment, so that the user is more closely reminded of the change of physiological indexes, and the risk of motion accidents is reduced.

Therefore, according to the indoor and outdoor scene recognition method provided by the embodiment of the invention, the functions of the electronic equipment, such as motion statistics, multimedia playing and the like, are corrected according to the recognition result of the current indoor and outdoor scene, the optimization of various functions, such as scene analysis of motion recognition and detection, adaptive control of multimedia playing and the like, can be realized on the basis of the original functions, the functions provided by the electronic equipment are enriched, and the satisfaction degree of users is improved.

In order to implement the above embodiments, the present invention further provides an indoor and outdoor scene recognition apparatus. Fig. 5 is a schematic structural diagram of an indoor and outdoor scene recognition apparatus according to an embodiment of the present invention.

As shown in fig. 5, the indoor/outdoor scene recognition apparatus includes: a first obtaining module 100, a first determining module 200, a second obtaining module 300, and a second determining module 400.

The first obtaining module 100 is configured to obtain a visible satellite message statement in a current update period.

A first determining module 200, configured to parse the visible satellite message statement to determine a current visible satellite signal-to-noise ratio characteristic.

The second obtaining module 300 is configured to input the signal-to-noise ratio characteristic of the current visible satellite into a preset classification model, so as to obtain a type tag output by the classification model.

A second determining module 400, configured to determine a current indoor and outdoor scene according to the type tag.

In a possible implementation manner of the embodiment of the present invention, the first determining module 200 is specifically configured to analyze a visible satellite message statement to obtain the number of visible satellites included in the message statement and a signal-to-noise ratio of each visible satellite; and determining the signal-to-noise ratio characteristic of the current visible satellite according to the number of the visible satellites and the signal-to-noise ratio of each visible satellite.

In a possible implementation manner of the embodiment of the present invention, when the user is moving, after determining the current indoor and outdoor scenes, the second determining module 400 is further configured to modify the current motion statistical parameter according to the current indoor and outdoor scenes, in a possible implementation manner of the embodiment of the present invention, if the current application scene is multimedia playing, after determining the current indoor and outdoor scenes, the second determining module 400 is further configured to adjust the current playing volume according to the current indoor and outdoor scenes.

In an embodiment of the present invention, on the basis of fig. 5, in the indoor and outdoor scene recognition apparatus shown in fig. 6, the first determining module 200 further includes: a first determination unit 210, a first calculation unit 220, a second calculation unit 230, and a second determination unit 240. The second obtaining module 300 further includes: a first obtaining unit 310, an analyzing unit 320, a third determining unit 330, a correcting unit 340 and a second obtaining unit 350.

The first determining unit 210 is configured to determine the current number of valid visible satellites according to the signal-to-noise ratio of each visible satellite; the first calculating unit 220 is configured to calculate, if the number of the current effective visible satellites is greater than a first threshold, a mean value, a variance, and a median of signal-to-noise ratios of the current effective visible satellites according to the signal-to-noise ratios of the effective visible satellites; the second calculating unit 230 is configured to determine that the mean, variance, and median of the signal-to-noise ratio of the current effective visible satellite are preset values if the number of the current effective visible satellites is less than or equal to the first threshold; the second determining unit 240 is configured to determine the signal-to-noise ratio characteristic of the current visible satellite according to the signal-to-noise ratio mean, the variance, and the median of the current effective visible satellite.

Further, the second determining unit 240 is further configured to determine a current invalid visible satellite proportion according to the current number of visible satellites and the number of valid visible satellites; and determining the signal-to-noise ratio characteristic of the current visible satellite according to the mean value, the variance and the median of the signal-to-noise ratio of the current effective visible satellite and the occupation ratio of the invalid visible satellite.

Continuing to refer to the apparatus shown in fig. 6, the first obtaining unit 310 is configured to obtain N sets of visible satellite message statements respectively corresponding to N previous update periods adjacent to the current update period, where N is a positive integer greater than 1; the analysis unit 320 is configured to analyze the N groups of visible satellite message statements to obtain the number of visible satellites corresponding to each group of visible satellite message statements and a signal-to-noise ratio of each visible satellite; a third determining unit 330, configured to determine signal-to-noise ratio characteristics of the N groups of visible satellites according to the number of the N groups of visible satellites and signal-to-noise ratios of the visible satellites; the correcting unit 340 is configured to correct the signal-to-noise ratio characteristic of the current visible satellite according to the signal-to-noise ratio characteristics of the N groups of visible satellites to generate a signal-to-noise ratio characteristic of the target visible satellite; and a second obtaining 350, configured to input the signal-to-noise ratio characteristic of the target visible satellite into a preset classification model, so as to obtain a type tag output by the classification model.

In one embodiment of the invention, the visible satellite signal-to-noise ratio features include: the mean value of the signal-to-noise ratios of the effective visible satellites, the variance of the signal-to-noise ratios of the effective visible satellites, the median of the signal-to-noise ratios of the effective visible satellites, and the percentage of the invalid visible satellites, wherein the correcting unit 340 is specifically configured to: calculating a first mean value, a first variance and a first median corresponding to the mean value of the signal-to-noise ratios of the N +1 groups of effective visible satellites; calculating a second mean value, a second variance and a second median corresponding to the signal-to-noise ratio variance of the N +1 groups of effective visible satellites; calculating a third mean value, a third variance and a third median corresponding to the median of the signal-to-noise ratios of the N +1 groups of effective visible satellites; calculating a fourth mean value, a fourth variance and a fourth median corresponding to the occupation ratio of the N +1 groups of invalid visible satellites; and determining the signal-to-noise ratio characteristic of the target visible satellite according to the first mean value, the first variance, the first median, the second mean value, the second variance, the second median, the third mean value, the third variance, the third median, the fourth mean value, the fourth variance and the fourth median.

It should be noted that the foregoing explanation of the indoor and outdoor scene recognition method embodiment is also applicable to the indoor and outdoor scene recognition apparatus of this embodiment, and the implementation principle and process of each module may refer to the above method embodiment, and therefore will not be described herein again.

In summary, the indoor and outdoor scene recognition apparatus according to the embodiment of the present invention first obtains the visible satellite message statements in the current update period, then analyzes the visible satellite message statements to determine the current signal-to-noise ratio characteristic of the visible satellite, further inputs the current signal-to-noise ratio characteristic of the visible satellite into the preset classification model to obtain the type label output by the classification model, and finally determines the current indoor and outdoor scene according to the type label. The device judges whether the current scene is indoor or outdoor by classifying the characteristics for representing the signal-to-noise ratio difference of the visible satellite in the indoor and outdoor scenes, has the advantages of simple identification mode, less calculation amount, higher accuracy and reliability, smaller influence of factors such as environment, position, wearing mode and the like on the message sentences of the visible satellite, and wider application range.

In order to implement the above embodiments, the embodiment of the present invention further provides a wearing device. As shown in fig. 7, the wearable device 1000 includes: a processor 2000, and a memory 3000 communicatively coupled to the processor 2000; the memory stores instructions executable by the processor 2000, and the instructions, when called and executed by the processor 2000, can implement the indoor and outdoor scene recognition method according to any one of the above embodiments.

In order to implement the foregoing embodiments, an embodiment of the present invention further proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions, when executed, implement the indoor and outdoor scene recognition method in any one of the foregoing embodiments.

It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.

In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.

Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims

1. An indoor and outdoor scene recognition method, comprising:

acquiring a visible satellite message statement in a current updating period;

analyzing the visible satellite message statement to determine the signal-to-noise ratio characteristic of the current visible satellite;

2. The method of claim 1, wherein said parsing the visible satellite message statement to determine a current visible satellite signal-to-noise ratio signature comprises:

analyzing the visible satellite message statements to acquire the number of visible satellites contained in the message statements and the signal-to-noise ratio of each visible satellite;

and determining the signal-to-noise ratio characteristic of the current visible satellite according to the number of the visible satellites and the signal-to-noise ratio of each visible satellite.

3. The method of claim 2, wherein determining the signal-to-noise ratio characteristics of the current visible satellites based on the current number of visible satellites and the signal-to-noise ratio of each visible satellite comprises:

determining the number of the current effective visible satellites according to the signal-to-noise ratio of each visible satellite;

if the number of the current effective visible satellites is larger than a first threshold value, calculating the mean value, the variance and the median of the signal-to-noise ratio of the current effective visible satellites according to the signal-to-noise ratio of each effective visible satellite;

and determining the signal-to-noise ratio characteristic of the current visible satellite according to the mean value, the variance and the median of the signal-to-noise ratio of the current effective visible satellite.

4. The method of claim 3, wherein prior to said determining said current visible satellite signal-to-noise ratio signature based on said current valid visible satellite signal-to-noise ratio mean, variance, and median, further comprising:

and if the number of the current effective visible satellites is less than or equal to a first threshold value, determining that the mean value, the variance and the median of the signal to noise ratio of the current effective visible satellites are preset values.

5. The method of claim 3, wherein said determining said current visible satellite signal-to-noise ratio characteristic from said current valid visible satellite signal-to-noise ratio mean, variance, and median comprises:

determining the current invalid visible satellite proportion according to the current number of visible satellites and the number of effective visible satellites;

and determining the signal-to-noise ratio characteristic of the current visible satellite according to the mean value, the variance and the median of the signal-to-noise ratio of the current effective visible satellite and the occupation ratio of the invalid visible satellite.

6. The method of claim 1, wherein the inputting the current signal-to-noise ratio characteristics of the visible satellites into a preset classification model to obtain a type label output by the classification model comprises:

acquiring N groups of visible satellite message sentences corresponding to the previous N updating periods adjacent to the current updating period respectively, wherein N is a positive integer greater than 1;

analyzing the N groups of visible satellite message sentences to obtain the number of visible satellites corresponding to each group of visible satellite message sentences and the signal-to-noise ratio of each visible satellite;

determining the signal-to-noise ratio characteristics of the N groups of visible satellites according to the number of the N groups of visible satellites and the signal-to-noise ratio of each visible satellite;

correcting the current visible satellite signal-to-noise ratio characteristic according to the N groups of visible satellite signal-to-noise ratio characteristics to generate a target visible satellite signal-to-noise ratio characteristic;

and inputting the signal-to-noise ratio characteristics of the target visible satellite into a preset classification model to obtain a type label output by the classification model.

7. The method of claim 6, wherein the visible satellite signal-to-noise ratio characteristic comprises: the method for generating the signal-to-noise ratio of the target visible satellite comprises the following steps of correcting the signal-to-noise ratio characteristics of the current visible satellite according to the signal-to-noise ratio characteristics of the N groups of visible satellites, wherein the average value of the signal-to-noise ratios of the effective visible satellites, the variance of the signal-to-noise ratios of the effective visible satellites, the median of the signal-to-noise ratios of the effective visible satellites and the occupation ratio of:

calculating a first mean value, a first variance and a first median corresponding to the mean value of the signal-to-noise ratios of the N +1 groups of effective visible satellites;

calculating a second mean value, a second variance and a second median corresponding to the signal-to-noise ratio variance of the N +1 groups of effective visible satellites;

calculating a third mean value, a third variance and a third median corresponding to the median of the signal-to-noise ratios of the N +1 groups of effective visible satellites;

calculating a fourth mean value, a fourth variance and a fourth median corresponding to the occupation ratio of the N +1 groups of invalid visible satellites;

and determining the signal-to-noise ratio characteristic of the target visible satellite according to the first mean value, the first variance, the first median, the second mean value, the second variance, the second median, the third mean value, the third variance, the third median, the fourth mean value, the fourth variance and the fourth median.

8. An indoor and outdoor scene recognition device, comprising:

and the second determining module is used for determining the current indoor and outdoor scenes according to the type label.

9. The apparatus of claim 8, wherein the first determining module is specifically configured to:

10. The apparatus of claim 9, wherein the first determining module comprises:

the first determining unit is used for determining the number of the current effective visible satellites according to the signal-to-noise ratio of each visible satellite;

the first calculation unit is used for calculating the mean value, the variance and the median of the signal-to-noise ratio of the current effective visible satellite according to the signal-to-noise ratio of each effective visible satellite if the number of the current effective visible satellites is larger than a first threshold value;

and the second determining unit is used for determining the signal-to-noise ratio characteristic of the current visible satellite according to the signal-to-noise ratio mean value, the variance and the median of the current effective visible satellite.

11. The apparatus of claim 10, wherein the first determining module further comprises:

and the second calculating unit is used for determining that the mean value, the variance and the median of the signal-to-noise ratio of the current effective visible satellite are preset values if the number of the current effective visible satellites is less than or equal to a first threshold value.

12. The apparatus of claim 10, wherein the second determining unit is further configured to:

13. The apparatus of claim 8, wherein the second obtaining module further comprises:

a first obtaining unit, configured to obtain N groups of visible satellite message statements corresponding to previous N update cycles adjacent to the current update cycle, where N is a positive integer greater than 1;

the analysis unit is used for analyzing the N groups of visible satellite message sentences to obtain the number of visible satellites corresponding to each group of visible satellite message sentences and the signal-to-noise ratio of each visible satellite;

the third determining unit is used for determining the signal-to-noise ratio characteristics of the N groups of visible satellites according to the number of the N groups of visible satellites and the signal-to-noise ratio of each visible satellite;

the correction unit is used for correcting the current visible satellite signal-to-noise ratio characteristic according to the N groups of visible satellite signal-to-noise ratio characteristics to generate a target visible satellite signal-to-noise ratio characteristic;

and the second acquisition unit is used for inputting the signal-to-noise ratio characteristics of the target visible satellite into a preset classification model so as to acquire the type label output by the classification model.

14. The indoor and outdoor scene recognition device of claim 13, wherein the visible satellite signal-to-noise ratio features comprise: the correction unit is specifically configured to correct the mean effective visible satellite signal-to-noise ratio, the variance effective visible satellite signal-to-noise ratio, the median effective visible satellite signal-to-noise ratio, and the percentage invalid visible satellite ratio according to the signal-to-noise ratio characteristics of the N groups of visible satellites:

15. An electronic device, comprising:

a processor; and

a memory communicatively coupled to the processor; wherein,

the memory stores instructions executable by the processor, the instructions being invoked and executed by the processor to implement the indoor and outdoor scene recognition method of any one of claims 1-7.

16. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein the computer instructions, when executed, implement the indoor-outdoor scene recognition method of any one of claims 1-7.