CN111880848A - Operating system switching method, device, terminal and readable storage medium - Google Patents

Abstract

The present invention relates to the technical field of operating system switching, and discloses an operating system switching method, device, terminal and readable storage medium. The method includes: obtaining fingerprint information of a user; obtaining fingerprint features to be matched of the fingerprint information according to the fingerprint information parsing; judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database pre-stored in the terminal; if so, obtaining the identity recognition information of the user; obtaining identity recognition features to be matched of the identity recognition information according to the identity recognition information parsing; judging whether the identity recognition features to be matched are standard identity recognition features in an identity recognition feature database pre-stored in the terminal; if so, switching the operating system of the terminal; the above-mentioned operating system switching method has strong confidentiality, will not expose that the terminal has dual systems, and meets the user's usage requirements.

Description

Operating system switching method, device, terminal and readable storage medium

technical field

The present invention relates to the technical field of operating system switching, and in particular, to a method, device, terminal and readable storage medium for switching operating systems.

Background technique

With the rapid development of the Internet, users' usage environment is constantly changing. In order to ensure that important data and private files in user terminals cannot be leaked, the use of dual-system terminals is favored by everyone. Users can save important confidential files in the security system in the dual system, which provides conditions for ensuring the privacy and security of the terminal.

In the prior art, the switching method of dual-system terminals is usually completed by relying on the system switching icon in the terminal. This method exposes the terminal as a dual-system terminal when the user switches systems, which has poor confidentiality and has certain security risks.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide an operating system switching method, device, terminal and readable storage medium, aiming to solve the problem that in the prior art, when a dual-system terminal completes system switching by relying on the system switching icon in the terminal, the terminal is exposed as a Dual-system terminal, the problem of poor confidentiality.

In a first aspect, an embodiment of the present invention provides a method for switching an operating system, which is applied to a terminal, where the terminal at least has a first operating system and a second operating system that can be switched back and forth, and the switching method includes:

Obtain the user's fingerprint information;

Obtain the fingerprint feature to be matched of the fingerprint information according to the analysis of the fingerprint information;

Judging whether the fingerprint feature to be matched is a standard fingerprint feature in the fingerprint feature database pre-stored by the terminal;

If so, obtain the user's identification information;

Parsing the identification information according to the identification information to obtain the identification features to be matched of the identification information;

Judging whether the identification feature to be matched is a standard identification feature in the identification feature database pre-stored by the terminal;

If so, switch the operating system of the terminal.

In some embodiments, the handover method further includes:

Obtain the pressing duration of the user's fingerprint continuously pressing the fingerprint sensing element of the terminal;

judging whether the pressing duration exceeds a preset threshold;

If it exceeds, obtain the fingerprint information of the user.

In some embodiments, the first operating system is a normal mode system; the second operating system is a security mode system, and the security mode system is used to encrypt important data.

In some embodiments, the identification information includes one or more of voiceprint information, facial information, iris information, and body impedance information.

In some embodiments, the handover method further includes:

If the fingerprint feature to be matched is not the standard fingerprint feature in the fingerprint feature database pre-stored by the terminal, the verification is exited, and the terminal maintains the original system.

In some embodiments, the handover method further includes:

If the identification feature to be matched is not a standard identification feature in the identification feature database pre-stored by the terminal, the verification is exited, and the terminal maintains the original system.

In a second aspect, an embodiment of the present application provides an operating system switching device, which is applied to a terminal, where the terminal at least has a first operating system and a second operating system that can be switched back and forth, and the switching device includes:

a first acquiring unit, used for acquiring the fingerprint information of the user;

a first parsing unit, configured to parse and obtain the fingerprint features to be matched of the fingerprint information according to the fingerprint information;

a first judging unit, configured to judge whether the fingerprint feature to be matched is a standard fingerprint feature in a fingerprint feature database pre-stored by the terminal;

a second obtaining unit, configured to obtain the user's identification information when the fingerprint feature to be matched is a standard fingerprint feature in the fingerprint feature database pre-stored by the terminal;

a second parsing unit, configured to parse and obtain the to-be-matched identification feature of the identification information according to the identification information;

a second judging unit, configured to judge whether the identity recognition feature to be matched is a standard identity recognition feature in the identity recognition feature database pre-stored by the terminal;

A switching unit, configured to switch the operating system of the terminal when the second judging unit judges that the identification feature is a standard identification feature in the identification feature database pre-stored by the terminal.

In some embodiments, the apparatus further comprises:

a pressing duration acquisition unit, configured to acquire the pressing duration of the user's fingerprint continuously pressing the fingerprint sensing element of the terminal;

A pressing duration judging unit, configured to determine whether the pressing duration exceeds a preset threshold, wherein, if the pressing duration judging unit determines that the pressing duration exceeds a preset threshold, the first acquiring unit will acquire the fingerprint information of the user.

In a third aspect, a terminal provided by an embodiment of the present application includes a memory and a processor;

the memory stores a computer program;

The processor is configured to execute the computer program and implement the above-mentioned method for switching an operating system when the computer program is executed.

In a fourth aspect, a readable storage medium provided by an embodiment of the present application stores a computer program, and when the computer program is executed by a processor, the processor implements one of the operations described above System switching method.

Compared with the prior art, the present invention mainly has the following beneficial effects:

The method for switching an operating system provided above is to obtain the fingerprint information of the user; to analyze and obtain the fingerprint feature to be matched of the fingerprint information according to the fingerprint information; to determine whether the fingerprint feature to be matched is the standard fingerprint feature in the fingerprint feature database pre-stored by the terminal; , obtain the user's identification information; parse the identification information to obtain the identification features to be matched according to the identification information; determine whether the identification features to be matched are the standard identification features in the identification feature database pre-stored by the terminal; if so, switch the terminal operating system. By presetting the fingerprint feature database and the identification feature database on the terminal, when the acquired fingerprint information is parsed into fingerprint features and can match the standard fingerprint features in the fingerprint feature database, the identification information is obtained, and when the acquired identification information is analyzed When it is an identification feature and can match the standard identification feature in the identification feature database, the conditions for system switching are met, and the system is switched from the first system to the second system; this method of switching systems is highly confidential and will not expose the terminal. With dual systems, it meets the requirements of users.

Description of drawings

1 is a schematic flowchart of a method for switching an operating system according to an embodiment of the present invention;

2 is a schematic flowchart of steps S10 and S20 in an operating system switching method provided by an embodiment of the present invention;

3 is a schematic block diagram of the structure of an operating system switching device provided by an embodiment of the present invention;

FIG. 4 is a schematic block diagram of the structure of an operating system switching device provided by an embodiment of the present invention having a pressing duration acquiring unit and a pressing duration determining unit.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

The implementation of the present invention will be described in detail below with reference to specific embodiments.

FIG. 1 shows a schematic flowchart of an operating system switching method provided by the present invention. Referring to FIG. 1 , the method includes steps S30 , S40 , S50 , S60 , S70 , S80 and S90 .

Step S30: Obtain the fingerprint information of the user.

It should be noted that fingerprint identification refers to identification by comparing the minutiae feature points of different fingerprints. Fingerprint recognition technology involves image processing, pattern recognition, computer vision, mathematical morphology, wavelet analysis and many other disciplines. Since each person's fingerprints are different, that is, between the ten fingers of the same person, the fingerprints are also significantly different, so the fingerprints can be used for identification.

Specifically, there are currently two main types of fingerprint collection methods: sliding and pressing. Swipe capture is to slide a finger on the sensor, so that the mobile phone obtains the fingerprint image of the finger. Sliding acquisition has the advantages of relatively low cost and the ability to acquire large-area images. However, this collection method has the problem of poor experience. The user needs a continuous and standardized sliding action to achieve successful collection, and the probability of collection failure is greatly increased. A certain brand of mobile phone has used this collection method, and it has been criticized for the shortcomings of sliding collection. As the name implies, press-type acquisition is to press on the sensor to realize fingerprint data acquisition. Of course, this acquisition method has a better user experience, but the cost is higher than that of sliding acquisition, and the technical difficulty is relatively higher. In addition, since the area of the fingerprint collected at one time is smaller than that of the sliding collection, it has to be collected multiple times, and a larger area of the fingerprint image is spelled out through "patchwork". This must rely on advanced algorithms and use software algorithms to make up for the relatively small area of fingerprints obtained by sliding and pressing collection to ensure the accuracy of identification.

The collection device mainly has the following principles:

First, optical type, which uses light reflection imaging to identify user fingerprints. This type of fingerprint module has certain requirements on the temperature and humidity of the operating environment, and the recognition accuracy is not ideal. In addition, this type of module generally occupies more space. The large space makes it difficult to make a difference on the mobile phone.

Second, capacitive, capacitive fingerprint modules use silicon crystals and conductive subcutaneous electrolyte to form an electric field. The fluctuation of the fingerprint will cause different changes in the pressure difference between the two, thereby realizing accurate fingerprint determination. . This method has strong adaptability and no special requirements for the use environment. At the same time, the space occupation of silicon wafers and related sensing components is within the acceptable range of mobile phone design, so this technology has been well promoted in mobile phones. . The current capacitive fingerprint module is also divided into two types: wipe type and push type. Although the former occupies a small volume, it has great disadvantages in terms of recognition rate and convenience. Press-type capacitive fingerprint module with more casual operation and higher recognition rate. Capacitance pattern recognition module is mainly composed of chip, "sapphire", metal ring, flexible board, carrier board, etc. The chip is also the sensor part, and "sapphire" is responsible for the protective layer (some manufacturers choose other materials as the protective layer, The cost will be reduced accordingly), and the metal ring is used as a trigger for fingerprint recognition.

The third is the radio frequency type, which includes radio wave detection and ultrasonic detection. The principle is similar to that of sonar for detecting seabed materials. The RF fingerprint module technology is to obtain the best fingerprint image by emitting a small amount of RF signal from the sensor itself, penetrating the epidermis layer of the finger to control and measure the texture of the inner layer. The biggest advantage of this type of fingerprint module is that the finger does not need to be in contact with the fingerprint module, so it will not cause much impact on the appearance of the mobile phone. Based on this, the RF fingerprint module has also become one of the main development directions of fingerprint identification in the future.

Step S40 , analysing the fingerprint information according to the fingerprint information to obtain the fingerprint features to be matched of the fingerprint information.

Specifically, the features of fingerprint images are divided into global features and local features. Global features refer to macroscopic features that can be directly observed, and are generally used for fast classification and rough matching stages. Local features refer to several effective features in the fingerprint topology map, such as whether the fingerprint lines are continuous, whether the direction is consistent, and specific details are expressed as breakpoints, bifurcation points, intersections, bridges, rings, etc., These are usually called feature points. These feature points, between the feature points and the surrounding ridges, etc., contain rich information, such as the type, direction, location, and so on of the feature points. Feature matching is performed using this information.

There are two most commonly used feature point extraction algorithms: one is to directly extract feature points from grayscale images; the other is to extract feature points from pre-processed and refined images.

The principle of direct grayscale image fingerprint feature extraction is to use pattern recognition to track the direction of the ridges on the grayscale image. The ridges of the fingerprint in the normal area should be continuous. When there is a break and the separation is terminated, it will stop and judge the feature points according to the rules. The algorithm is mainly composed of several closely related modules: (1) Tracking step module, which is mainly responsible for predicting the next tracking direction and step size, and is used to advance one step along the ridge. (2) The center point determination module is responsible for determining the center point of the ridge of the ridge, so that the tracking direction is continuously adjusted and always advances along the center of the ridge. (3) The marking module is used to mark the tracked lines, so as to avoid repeated tracking and falling into an infinite loop. (4) The feature determination module is responsible for determining whether it is a feature point and the type of the feature point when the tracking reaches the abnormal area of the ridge flow direction. The specific algorithm is as follows: (1) Calculate the direction map of the fingerprint image, and generally take the block direction as the direction of the fingerprint. (2) Starting from the initial point, according to the direction information of the fingerprint image, in the normal direction of the place, within half the fingerprint cycle, obtain the maximum and minimum values of the grayscale distribution, and use the pixel at the maximum value. point as a new starting point. (3) Starting from a new starting point, advance a certain step in the direction of the direction map of the fingerprint image (the algorithm was initially tracked with a fixed step, and later developed to adaptive step tracking), and continue to find gray in the direction of discovery. The maximum and minimum values of the degree distribution still take the pixel at the maximum value as the new starting point. (4) Repeat step 3 continuously to realize ridge line tracking, until the maximum value of the obtained gray distribution becomes significantly smaller, which is similar to the minimum value, or even equal, stop tracking, indicating that it has reached the end of the ridge line, This is the end point of the feature point. If the tracked ridge line intersects with the previously tracked ridge line, stop tracking, and find the position of the intersection of the two ridge lines, which is the bifurcation point of the feature point. The algorithm for extracting features directly from a grayscale image is generally to track the grayscale fingerprint ridges, and find the location of the feature and determine the type of the feature according to the tracking result. This method saves the complex fingerprint image preprocessing process, but the feature extraction algorithm is very complicated, and due to the influence of noise and other factors, the extracted feature information (position, direction, etc.) is not accurate enough.

The template matching method based on the thinned image firstly obtains the thinned fingerprint image by a series of preprocessing such as image normalization, enhancement, binarization and thinning, and then constructs the 3×3 adjacent pixels of the fingerprint image. domain to extract feature points from fingerprint images.

Step S50, judging whether the fingerprint feature to be matched is a standard fingerprint feature in the fingerprint feature database pre-stored by the terminal.

Specifically, in the verification mode, the fingerprint matching mainly compares whether the two sets of feature points conform to the same structural pattern; in the identification mode, the fingerprints are first classified, and then the fingerprint database is searched for the same category according to different categories. Fingerprints for final accurate comparison. Among them, the matching of minutiae points is generally a pattern formed by comparing the feature points of two images. The similarity of the two feature point patterns is compared by the number of matched feature points, and the matching result is obtained by comparing with the preset interrogation value.

Therefore, the core idea of the matching algorithm is: through some coordinate transformations (such as translation, rotation, and scaling transformation), to eliminate the influence of fingerprint displacement, rotation and roughness on the feature points; then compare the relevant information of the fingerprint feature points after the coordinate transformation. (position, type, angle, etc.). Due to the influence of various factors, it is difficult for the feature templates obtained by two inputs of the same fingerprint to be exactly the same. Therefore, when the features of the input fingerprint are compared to the template features in the fingerprint database to a certain degree of similarity, the two fingerprints are considered to match. success. It is generally believed that if two fingerprints can match 13 pairs of feature points, the conclusion of successful matching can be made, that is, it can be concluded that the two fingerprints are from the same finger of the same person.

In addition, in the actual comparison process, the comparison between the two fingerprints is not a rigid, unbiased comparison, but an elastic comparison that allows certain positional and directional deviations. Therefore, the matching result is represented by "matching degree". When the matching degree is greater than a certain threshold, it is considered that the two fingerprints match; when it is less than the threshold, it is considered that they do not match. The matching algorithm is different, and the calculation method of the matching degree is also different, and the size of the threshold is usually set artificially according to factors such as experience and system security level. When the threshold is larger, the system security increases, but the false rejection rate will increase. When the threshold is small, the ease of use of the system increases, but the false acceptance rate will increase.

Step S60: If the fingerprint feature to be matched is a standard fingerprint feature in the fingerprint feature database pre-stored by the terminal, obtain the user's identity identification information.

Specifically, the identification information includes one or more of voiceprint information, face information, iris information, and human body impedance information.

The so-called voiceprint is the sound wave spectrum that carries speech information displayed by electroacoustic instruments. Modern scientific research shows that voiceprints are not only specific, but also relatively stable. After adulthood, the human voice can remain relatively stable for a long time. Experiments have shown that whether the speaker deliberately imitates the voice and tone of others, or whispers softly, even if the imitation is vivid, the voiceprint is always different.

Standard voiceprint collection equipment, you can refer to the standard voiceprint collection equipment launched on the market. It is a voiceprint collection equipment specially developed for standard voiceprint collection scenarios. It adopts intelligent microphone cluster and supports unidirectional/omnidirectional pickup, various The text collection method ensures the integrity and authenticity of the voiceprint information collection content, and improves the quality and efficiency of the voiceprint collection.

Face recognition is a kind of biometric identification technology based on human facial feature information. A series of related technologies that use cameras or cameras to capture images or video streams containing human faces, and automatically detect and track human faces in the images, and then recognize the detected faces, also known as portrait recognition and facial recognition.

Different face images can be collected through the camera lens, such as static images, dynamic images, different positions, different expressions, etc., can be well collected. When the user is within the shooting range of the collecting device, the collecting device will automatically search for and shoot the user's face image. In practice, face detection is mainly used for the preprocessing of face recognition, that is, to accurately calibrate the position and size of the face in the image. The pattern features contained in face images are very rich, such as histogram features, color features, template features, structural features, etc. Face detection is to pick out the useful information and use these features to achieve face detection. The image preprocessing of the face is the process of processing the image and finally serving the feature extraction based on the face detection result. The original image obtained by the system cannot be used directly due to various limitations and random interference, and it must be pre-processed such as grayscale correction and noise filtering in the early stage of image processing. For face images, the preprocessing process mainly includes light compensation, grayscale transformation, histogram equalization, normalization, geometric correction, filtering and sharpening of face images.

Iris recognition technology is based on the iris in the eye for identification. The structure of the human eye consists of the sclera, iris, pupil lens, retina and other parts. The iris is the annular portion located between the black pupil and the white sclera, which contains many detailed features of interlaced spots, filaments, crowns, striations, crypts, etc. And after the iris is formed during fetal development, it will remain the same throughout life. These features determine the uniqueness of iris features, and also determine the uniqueness of identification. Therefore, the iris feature of the eye can be used as the identification object of each person.

The acquisition of the iris image is to shoot the entire human eye through a specific camera, and transmit the captured image to the image preprocessing software of the iris recognition system. The acquired iris image is processed as follows to meet the extraction requirements. Requirements for iris features: (1) Iris localization: determine the positions of the inner circle, outer circle and quadratic curve in the image. The inner circle is the boundary between the iris and the pupil, the outer circle is the boundary between the iris and the sclera, and the quadratic curve is the boundary between the iris and the upper and lower eyelids. (2) Normalization of iris image: Adjust the size of the iris in the image to the fixed size set by the recognition system. (3) Image enhancement: For the normalized image, the brightness, contrast and smoothness are processed to improve the recognition rate of iris information in the image.

Step S70, analysing the identification information according to the identification information to obtain the identification features to be matched of the identification information.

It should be noted that, for voiceprint recognition, timbre is the attribute that can best reflect a person's identity information, and the difference in timbre can be expressed as the difference in energy in different frequency bands in the frequency domain at the level of signal processing. The energy value on the frequency band can represent the nature of the spectrum in this short-term speech range. Usually, we will design appropriate short-term acoustic features by comprehensively considering the auditory properties of the human ear, equalizing the energy differences in different frequency bands, noise robustness, and subsequent calculation convenience. Through a series of complex signal processing level transformations, a period of 20-50 A millisecond-long speech can be mapped to a 39-60-dimensional vector. In order to fully retain the original information in the speech without increasing the computational burden, usually a short period of speech is taken in sequence at intervals of 15-20 milliseconds, and then features are extracted. In the field of voiceprint recognition, including speech recognition, traditional acoustic features including Mel cepstral coefficient MFCC, perceptual linear prediction coefficient PLP, deep feature DeepFeature, which has gradually attracted attention in recent years, and energy normalized spectral coefficient, etc., can be used as Voiceprint recognition is an optional and well-performing acoustic feature at the feature extraction level. To sum up, a piece of speech is mapped to a series of vector sets on the time axis, and these sets can become feature sets reflecting the characteristics of speech after some regular operations. The next step is to model the voiceprint to form features. The voiceprint recognition system is a typical pattern recognition framework. In order for the computer to recognize the identity of a user, the target user needs to provide a training voice first. A series of operations such as feature extraction and model training will be mapped to the user's voiceprint model.

The methods of face feature extraction are summarized as follows: (1) Method based on subspace (algebraic features): an image is regarded as a matrix, and the algebraic features of the face can be extracted by doing matrix transformation or linear projection; ( 2) Methods based on geometric features: The human face is composed of parts such as eyes, nose, mouth, and chin. The geometric description of these parts and their structural relationships can be used as an important feature for face recognition. (3) Neural network method: artificial neural network can be used for pattern recognition due to its inherent parallel operation mechanism and distributed global storage of patterns, and is not affected by pattern deformation. (4) Deformation model method: select some sparse reference points on the edge of the face feature to describe the shape features of the face, and then change the shape to the average shape of all face images, and then perform texture grayscale according to the deformed shape. Deformation to form a shape-independent face image. (5) Based on elastic model matching method: The idea of elastic model matching method is to use some feature points on the face as reference points to form an elastic map, each reference point stores a series of representative feature vectors, and adopts the elasticity of the hierarchical structure. In the graph, some redundant nodes are removed to form a sparse face description structure. Recognition is done by elastic matching of test samples and feature samples.

For iris recognition, iris images contain rich detailed features. If the preprocessed iris image is used as a texture image, many texture analysis methods can be used to extract iris features. There are three typical methods: (1) Gabor filtering: One of the effective strategies for extracting texture information from an image is to convolve the image with a band-pass filter, where the band-pass filter can choose a 2DGabor filter. The 2DGabor filter provides the maximum resolution of spatial frequency, direction, and spatial position, so it has good co-localization capability in both spatial and frequency domains. (2) Two-dimensional wavelet transform: Wavelet transform is a common image analysis method, and has many applications in texture recognition. A two-dimensional wavelet transform can be regarded as two consecutive one-dimensional wavelet transforms. The two-dimensional wavelet transform decomposes an image into a series of low-frequency sub-images. (3) Wavelet transform zero-crossing detection algorithm: The iris image is sampled at intervals with concentric circles with the center of the iris as the center. Convert a two-dimensional iris image into a one-dimensional signal.

Step S80, judging whether the to-be-matched identification feature is a standard identification feature in an identification feature database pre-stored by the terminal.

Specifically, for voiceprint feature recognition, in the verification stage, an unknown voice will also be mapped to a test feature through a series of operations, and the test feature will be calculated with a certain similarity with the target model to obtain a confidence The score is usually compared with our artificially set expectation value. If it is higher than this expectation value, we consider that the identity corresponding to the test voice matches the target user identity and passes the verification; otherwise, the test identity is rejected.

For face recognition, the methods of face recognition have a similar process, that is, they all use a well-classified training data set (face database, each person has many samples) for training. The detected faces are analyzed and determined from two aspects: whether the target is recognized and a measure of the confidence that the target is actually recognized, which is also known as the confidence score. The methods are as follows: (1) Eigenfaces algorithm realizes face recognition, and Eigenfaces are processed by PCA. PCA is the most mentioned mathematical concept in computer vision. The essence of PCA is to identify the principal components on a training set (such as a face database), and calculate the divergence of the training set (faces detected in images or frames) relative to the database, and output a value. The smaller the value, the smaller the difference between the face database and the detected faces; a value of 0 indicates an exact match. (2) The Fisherfaces algorithm realizes face recognition. Fisherfaces is a concept derived and developed from PCA, which adopts more complex logic. Although more computationally intensive, it is easier to get accurate results than Eigenfaces. (3) The LocalBinaryPatternHistogram (LBPH) algorithm implements face recognition. LBPH roughly (at a very high level) divides the detected face into small units and compares them with the corresponding units in the model. produces a histogram of matching values. Due to the flexibility of this approach, LBPH is the only face recognition algorithm that allows model sample faces and detected faces to be different in shape and size.

For iris recognition, iris recognition based on extracted iris features is a typical pattern matching problem. Two commonly used algorithms are: (1) Hamming distance: After converting the iris texture into an effective iris code, it is judged by the Hamming distance of the iris code, that is, different iris codes are compared by bitwise XOR. (2) Variance reciprocal weighted Euclidean distance classification method: compare the feature vector of the unknown iris with the iris feature vector of the known class that has been trained, if and only if its feature vector is the variance of the K-th feature vector When the reciprocal weighted Euclidean distance is the smallest, the input iris is classified as the K-th iris.

The iris feature database stores feature vectors of known iris textures. In the process of iris recognition, the iris image to be recognized is first processed and analyzed to obtain the iris signature, and then the extracted signature is compared with the signature template in the database, and finally the classification result is obtained. In order to use the iris for identity recognition, a large iris feature database is required in the background of the iris recognition system, so as to store and query the iris feature code.

Step S90 , if the identification feature to be matched is a standard identification feature in the identification feature database pre-stored by the terminal, switch the operating system of the terminal.

Specifically, the first operating system is a normal mode system; the second operating system is a security mode system, and the security mode system is used to encrypt important data.

Here, terminals include but are not limited to mobile phones, tablet computers, notebook computers, desktop computers, in-vehicle computers, smart watches, smart bracelets, other wearable smart devices, and POS machines.

The method for switching an operating system provided above is to obtain the fingerprint information of the user; to analyze and obtain the fingerprint feature to be matched of the fingerprint information according to the fingerprint information; to determine whether the fingerprint feature to be matched is the standard fingerprint feature in the fingerprint feature database pre-stored by the terminal; , obtain the user's identification information; parse the identification information to obtain the identification features to be matched according to the identification information; determine whether the identification features to be matched are the standard identification features in the identification feature database pre-stored by the terminal; if so, switch the terminal operating system. By presetting the fingerprint feature database and the identification feature database on the terminal, when the acquired fingerprint information is parsed into fingerprint features and can match the standard fingerprint features in the fingerprint feature database, the identification information is obtained, and when the acquired identification information is analyzed When it is an identification feature and can match the standard identification feature in the identification feature database, the conditions for system switching are met, and the system is switched from the first system to the second system; this method of switching systems is highly confidential and will not expose the terminal. With dual systems, it meets the requirements of users.

Referring to FIG. 2, in some embodiments, the operating system switching method further includes:

Step S10 , acquiring the pressing duration of the user's fingerprint continuously pressing the fingerprint sensing element of the terminal.

Specifically, the fingerprint sensing element is disposed between the sensing plane and the circuit board for sensing the fingerprint image of the finger placed on the sensing plane.

Step S20, judging whether the pressing duration exceeds a preset threshold.

Specifically, if the pressing duration exceeds a preset threshold, step S30 is performed.

It should be noted that the pressing duration here exceeds the preset threshold and exists as a trigger switch for operating system switching. The preset threshold can be set to a time period that is not frequently reached, so as to avoid accidentally turning on the system switch when the fingerprint sensor is pressed for other reasons.

Referring to FIG. 2, in some embodiments, the operating system switching method further includes:

If the fingerprint feature to be matched is not the standard fingerprint feature in the fingerprint feature database pre-stored by the terminal, the verification is exited, and the terminal maintains the original system.

Referring to FIG. 2, in some embodiments, the operating system switching method further includes:

If the identification feature to be matched is not the standard identification feature in the identification feature database pre-stored by the terminal, the verification is exited, and the terminal maintains the original system.

Please refer to FIG. 3. FIG. 3 shows a schematic block diagram of the structure of an operating system switching device provided by an embodiment of the present invention; an operating system switching device provided by an embodiment of the present application may be configured on a terminal or a server , for executing the aforementioned operating system switching method. The switching device of the operating system includes:

The first obtaining unit 1 is used to obtain the fingerprint information of the user;

A first parsing unit 2, configured to analyze and obtain fingerprint features to be matched of the fingerprint information according to the fingerprint information;

The first judging unit 3 is used for judging whether the fingerprint feature to be matched is a standard fingerprint feature in the fingerprint feature database pre-stored by the terminal;

a second obtaining unit 4, configured to obtain the user's identification information when the first judgment unit judges that the fingerprint feature to be matched is a standard fingerprint feature in the fingerprint feature database pre-stored by the terminal;

A second parsing unit 5, configured to parse and obtain the to-be-matched identification feature of the identification information according to the identification information;

The second judgment unit 6 is used for judging whether the identification feature to be matched is a standard identification feature in the identification feature database pre-stored by the terminal;

The switching unit 7 is configured to switch the operating system of the terminal when the second judging unit judges that the identification feature is a standard identification feature in the identification feature database pre-stored by the terminal.

It should be noted that, referring to FIG. 4 , the device further includes:

a pressing duration acquisition unit 8, configured to acquire the pressing duration of the user's fingerprint continuously pressing the fingerprint sensing element of the terminal;

The pressing duration judging unit 9 is used to judge whether the pressing duration exceeds a preset threshold, wherein, if the pressing duration judging unit determines that the pressing duration exceeds the preset threshold, the first obtaining unit will obtain the fingerprint information of the user .

It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the device and each unit described above may refer to the corresponding process in the foregoing method embodiments, which is not repeated here. Repeat.

The above-described apparatus can be implemented in the form of a computer program that can be run on a computer device.

The computer device may be a terminal. A terminal provided by an embodiment of the present invention includes a processor and a memory; wherein, the memory may include a non-volatile storage medium and an internal memory.

The processor is used to provide computing and control capabilities to support the operation of the entire computer equipment.

The nonvolatile storage medium can store operating systems and computer programs. The computer program includes program instructions, which, when executed, can cause the processor to execute any operating system switching method.

The internal memory provides an environment for running the computer program in the non-volatile storage medium, and when the computer program is executed by the processor, the processor can execute any operating system switching method.

It should be understood that the processor may be a central processing unit (Central Processing Unit, CPU), and the processor may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-available processors Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. Wherein, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like.

The embodiments of the present application further provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, the computer program includes program instructions, and the processor executes the program instructions to implement the present application Any operating system switching method provided in the embodiment. When the program is executed, it may include some or all of the steps in the various embodiments of the operating system switching method provided by the present invention.

Wherein, the computer-readable storage medium may be an internal storage unit of the terminal described in the foregoing embodiments, such as a hard disk or a memory of the terminal. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk equipped on the terminal, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, flash memory Card (FlashCard) and so on.

The above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention shall be included in the protection of the present invention. within the range.

Claims (10)

1. A switching method of an operating system is applied to a terminal, the terminal is provided with at least a first operating system and a second operating system which can be switched back and forth, and the switching method comprises the following steps:

acquiring fingerprint information of a user;

analyzing according to the fingerprint information to obtain the fingerprint characteristics to be matched of the fingerprint information;

judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal;

if so, acquiring the identity identification information of the user;

analyzing according to the identity recognition information to obtain identity recognition features to be matched of the identity recognition information;

judging whether the identification features to be matched are standard identification features in an identification feature database prestored in the terminal;

and if so, switching the operating system of the terminal.

2. The method of switching an operating system according to claim 1, wherein the switching method further comprises:

acquiring the pressing duration of the fingerprint sensing element of the terminal continuously pressed by the fingerprint of the user;

judging whether the pressing time length exceeds a preset threshold value or not;

and if so, acquiring the fingerprint information of the user.

3. The method for switching operating systems according to claim 1, wherein the first operating system is a normal mode system; the second operating system is a secure mode system for encrypting important data.

4. The method of claim 1, wherein the identification information comprises one or more of voiceprint information, facial information, iris information, and body impedance information.

5. The method of switching an operating system according to claim 1, wherein the switching method further comprises:

and if the fingerprint features to be matched are not the standard fingerprint features in the fingerprint feature database prestored in the terminal, quitting the verification, and maintaining the original system by the terminal.

6. The method of switching an operating system according to claim 1, wherein the switching method further comprises:

and if the identification features to be matched are not the standard identification features in the identification feature database prestored in the terminal, quitting the verification, and maintaining the original system by the terminal.

7. A switching device of an operating system, applied to a terminal having at least a first operating system and a second operating system that can be switched back and forth, the switching device comprising:

the first acquisition unit is used for acquiring fingerprint information of a user;

the first analysis unit is used for analyzing the fingerprint information to obtain the fingerprint features to be matched of the fingerprint information;

the first judgment unit is used for judging whether the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal;

the second acquisition unit is used for acquiring the identity identification information of the user when the first judgment unit judges that the fingerprint features to be matched are standard fingerprint features in a fingerprint feature database prestored in the terminal;

the second analysis unit is used for analyzing the identity recognition information to obtain the identity recognition features to be matched of the identity recognition information;

the second judging unit is used for judging whether the identification features to be matched are standard identification features in an identification feature database prestored in the terminal;

and the switching unit is used for switching the operating system of the terminal when the second judging unit judges that the identification features are standard identification features in an identification feature database prestored in the terminal.

8. The switching apparatus of an operating system according to claim 7, wherein said apparatus further comprises:

the pressing duration acquisition unit is used for acquiring the pressing duration of the fingerprint sensing element of the terminal continuously pressed by the fingerprint of the user;

the pressing duration judging unit is used for judging whether the pressing duration exceeds a preset threshold value, and if the pressing duration judging unit judges that the pressing duration exceeds the preset threshold value, the first acquiring unit acquires the fingerprint information of the user.

9. A terminal, characterized in that the terminal comprises a memory and a processor;

the memory stores a computer program;

the processor is configured to execute the computer program and implement the switching method of the operating system according to any one of claims 1 to 6 when executing the computer program.

10. A readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the switching method of an operating system of any one of claims 1 to 6.

Images