A method and system for preventing leakage of file data of intelligent device

Abstract

A method and system for preventing file data leakage of smart devices, relating to the technical field of device monitoring and management. In the method, a first picture taken by a camera set on a first smart device is obtained; the first picture is analyzed to obtain a picture analysis result, which includes a first face recognition result; based on the first face recognition result, the personnel information corresponding to all persons in the first picture is determined; a behavior recognition algorithm corresponding to the personnel information is used to identify all persons in the first picture, and the personnel characteristics corresponding to all persons in the first picture are determined; based on the personnel information and personnel characteristics corresponding to all persons, it is determined whether the first smart device has a risk of file data leakage; when the first smart device has a risk of file data leakage, the first smart device is turned off and a warning message is sent to the first associated smart device. Implementing the technical solution of the present application can effectively reduce the probability of file data leakage.

Description

Technical Field

[0001] The present application relates to the technical field of equipment monitoring and management, and in particular to a method and system for preventing file data leakage of smart devices. Background Art

[0002] Nowadays, smart devices are becoming more and more important in people's work and life. Most people store file data in smart devices so that they can check them at any time.

[0003] However, data leakage of these file data stored in smart devices often occurs. Among them, the reason for data leakage is largely due to the fact that when users use smart devices to check file data, the file data is peeped by other people, resulting in the leakage of the file data. Therefore, how to reduce the probability of file data leakage has become an urgent problem to be solved.

[0004] Therefore, there is an urgent need for a smart device file data leakage prevention method and system to solve the problems existing in current technology. Summary of the invention

[0005] The present application provides a method and system for preventing file data leakage of a smart device, which can effectively reduce the probability of file data leakage. :

[0006] In a first aspect, the present application provides a method for preventing file data leakage of a smart device, the method comprising: obtaining a first picture taken by a camera set on a first smart device; wherein the first smart device is a smart device that has not performed any substantial operation; analyzing the first picture to obtain a picture analysis result, the picture analysis result including a first face recognition result; based on the first face recognition result, determining personnel information corresponding to all persons in the first picture; using a behavior recognition algorithm corresponding to the personnel information, identifying all persons in the first picture, and determining personnel features corresponding to all persons in the first picture; based on the personnel information and personnel features corresponding to all persons, determining whether the first smart device has a risk of file data leakage; when the first smart device has a risk of file data leakage, shutting down the first smart device and sending a warning message to a first associated smart device associated with the first smart device.

[0007] By adopting the above technical solution, a first picture taken by a camera set on the first smart device is obtained to realize comprehensive monitoring of the situation within the surrounding range of the first smart device; then a first face recognition result is obtained by analyzing the first picture to realize comprehensive recognition of the situation of people within the surrounding range of the first smart device; based on the recognized personnel information, a behavior recognition algorithm corresponding to the personnel information is adopted to recognize all the people in the first picture, and the personnel features corresponding to all the people in the first picture are determined, so that the extracted personnel features are more consistent with the personnel situation, thereby making the extracted personnel features more accurate; finally, based on the personnel information and personnel features corresponding to all the people, it is determined whether the first smart device has a risk of file data leakage, so that the judgment of the risk of file data leakage is more accurate; when it is identified that the first smart device has a risk of file data leakage, the first smart device is turned off and a warning message is sent to a first associated smart device associated with the first smart device, thereby effectively reducing the probability of file data leakage.

[0008] Optionally, the personnel characteristics include interactive behavior sub-features, language sub-features and warning behavior sub-features; the use of a behavior recognition algorithm corresponding to the personnel information to identify all personnel in the first screen and determine the personnel characteristics corresponding to all personnel in the first screen specifically includes: judging whether all the personnel information is in a preset personnel list database; when all the personnel information is in the preset personnel list database, performing behavioral interaction detection on all personnel based on the first behavior recognition algorithm to determine the interactive behavior sub-features corresponding to all personnel in the first screen, and performing voice detection on all personnel based on the voice recognition algorithm to determine the language sub-features corresponding to all personnel in the first screen; when the information of at least one person is not in the preset personnel list database, performing warning behavior detection on all personnel based on the second behavior recognition algorithm to determine the warning behavior sub-features corresponding to all personnel in the first screen.

[0009] By adopting the above technical solution, all persons in the first screen are identified by using a behavior recognition algorithm to obtain their features, and to check whether the information of all persons is in a preset personnel list database to verify their identities. If the information of all persons is in the database, their interactive behaviors are analyzed and voice recognition is performed through the first behavior recognition algorithm to determine each person's interactive behavior sub-features and language sub-features. If the information of a person is not in the database, their warning behavior is detected through the second behavior recognition algorithm to determine the warning behavior sub-features. By distinguishing and identifying the features of a person based on whether the person's information is in the database and the behavior, the accuracy of extracting the person's features can be effectively improved.

[0010] Optionally, the determining whether there is a risk of file data leakage in the first smart device based on the personnel information and the personnel characteristics corresponding to all personnel specifically includes: when the personnel information corresponding to all personnel is in a preset personnel list database, based on the interactive behavior sub-feature and the language sub-feature, performing behavior prediction on all personnel to obtain a behavior prediction result; judging whether the behavior prediction result is consistent with at least one leakage risk behavior in a preset leakage risk behavior library; when the behavior prediction result is consistent with at least one leakage risk behavior in the preset leakage risk behavior library, determining that there is a risk of file data leakage in the first smart device; when the information of at least one person is not in the preset personnel list database, calculating the total behavior warning value corresponding to each person based on the warning behavior sub-feature; when there is at least one person whose total behavior warning value is greater than the warning threshold, determining that there is a risk of file data leakage in the first smart device.

[0011] By adopting the above technical solution, potential risks are determined through personnel information and characteristics, as well as behavior analysis and prediction. If the behavior conforms to the behavior in the preset leakage risk behavior library, or the total value of the behavior warning is higher than the warning threshold, it is confirmed that the first smart device has a file data leakage risk, thereby effectively improving the accuracy of identifying the file data leakage risk of the first smart device.

[0012] Optionally, the method also includes: obtaining a second picture taken by a camera set on a second smart device; wherein the second smart device is a smart device performing substantial operations; performing face recognition on the second picture to obtain a second face recognition result; based on the second face recognition result, determining a basic authority coefficient corresponding to an operator in the second picture; in response to an operator's file data operation request, obtaining the operator's current file data operation mode; based on the current file data operation mode, determining an operation impact coefficient; based on the basic authority coefficient and the operation impact coefficient, obtaining an actual authority coefficient corresponding to the operator; calculating the security level coefficient corresponding to each file data in the second smart device, and determining the maximum security level coefficient among the multiple security level coefficients; when the actual authority coefficient is greater than the maximum security level coefficient, allowing the operator to operate on multiple file data in the second smart device in the current file data operation mode.

[0013] By adopting the above technical solution, it is determined whether to allow the operator to perform file data operations based on the operator's identity, request and operation method, as well as the security level of the file data. By comprehensively considering the authority, risk and security level, the security of the file data can be ensured, and the operation can be restricted or authorized when necessary, thereby helping to maintain the confidentiality and integrity of the data.

[0014] Optionally, the calculation of the security level coefficient corresponding to each file data in the second smart device specifically includes: obtaining a confidentiality measurement value corresponding to each file data; obtaining a time interval between a previous operation time of each file data and a current time; based on the time interval, determining a time attenuation factor corresponding to the time interval; and calculating the security level coefficient corresponding to each file data in the second smart device by the following formula: ; Wherein, K is the security level coefficient, x is the confidentiality measurement value, t is the time interval, and α is the time attenuation factor.

[0015] Optionally, the screen analysis result also includes a device identification result; after analyzing the first screen to obtain the screen analysis result, the method also includes: based on the device identification result, determining whether there is a suspicious device in the first screen; when there is a suspicious device in the first screen, determining that there is a risk of file data leakage in the first smart device, shutting down the first smart device and sending a warning message to a first associated smart device associated with the first smart device.

[0016] By adopting the above technical solution, suspicious or unauthorized devices are detected to assess the risk of file data leakage. If a suspicious device is detected, measures are taken to protect the security of the data, including shutting down the device and notifying related devices, thereby preventing the file data from being secretly photographed by suspicious or unauthorized devices, thereby helping to maintain data confidentiality and avoid file data leakage.

[0017] Optionally, after shutting down the first smart device and sending a warning message to a first associated smart device associated with the first smart device, the method further includes: determining whether there is a risk of file data leakage on the first associated smart device; and when there is a risk of file data leakage on the first associated smart device, sending a warning message to a second associated smart device associated with the first associated smart device.

[0018] By adopting the above technical solution, the risk management scope is expanded through a chain warning system. Once the risk of file data leakage is detected in the first smart device and the device is turned off, it will be further checked whether the first associated smart device associated with the device also has similar risks; if the risk is found, the second associated smart device associated with the first associated smart device will be notified in time by sending a warning message to ensure that the risk of file data leakage can be notified to the user at the first time, thereby effectively reducing the probability of file data leakage.

[0019] In a second aspect of the present application, a smart device file data leakage prevention system is provided, the system comprising an acquisition module, a processing module and an early warning module; the acquisition module is used to acquire a first picture taken by a camera set on a first smart device; wherein the first smart device is a smart device that has not performed any substantial operation; the processing module is used to analyze the first picture to obtain a picture analysis result, and the picture analysis result includes a first face recognition result; the processing module is also used to determine the personnel information corresponding to all persons in the first picture based on the first face recognition result; the processing module is also used to use a behavior recognition algorithm corresponding to the personnel information to identify all persons in the first picture and determine the personnel characteristics corresponding to all persons in the first picture; the processing module is also used to determine whether the first smart device has a file data leakage risk based on the personnel information and the personnel characteristics corresponding to all persons; the early warning module is used to shut down the first smart device and send a warning message to a first associated smart device associated with the first smart device when the first smart device has a file data leakage risk.

[0020] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any method as described in the first aspect of the present application.

[0021] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program that can be loaded by a processor and execute the method as described in any one of the first aspects of the present application.

[0022] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0023] 1. By acquiring a first picture taken by a camera set on a first smart device, a comprehensive monitoring of the situation within the surrounding range of the first smart device is achieved; then, by analyzing the first picture, a first face recognition result is obtained to achieve a comprehensive recognition of the situation of people within the surrounding range of the first smart device; based on the recognized personnel information, a behavior recognition algorithm corresponding to the personnel information is adopted to recognize all persons in the first picture, and the personnel features corresponding to all persons in the first picture are determined, so that the extracted personnel features are more consistent with the personnel situation, thereby making the extracted personnel features more accurate; finally, based on the personnel information and personnel features corresponding to all personnel, it is determined whether the first smart device has a risk of file data leakage, so that the judgment of the risk of file data leakage is more accurate; when it is identified that the first smart device has a risk of file data leakage, the first smart device is turned off and a warning message is sent to a first associated smart device associated with the first smart device, thereby effectively reducing the probability of file data leakage.

[0024] 2. By using the behavior recognition algorithm, all personnel in the first screen are identified to obtain their features, and the information of all personnel is checked to see if it is in the preset personnel list database to verify their identities. If the information of all personnel is in the database, the first behavior recognition algorithm is used to analyze their interactive behaviors and perform voice recognition to determine each person's interactive behavior sub-features and language sub-features. If the information of some personnel is not in the database, the second behavior recognition algorithm is used to detect their warning behaviors to determine the warning behavior sub-features. By distinguishing and identifying the characteristics of personnel based on whether their information is in the database and their behavior, the accuracy of extracting personnel features can be effectively improved.

[0025] 3. Determine potential risks through personnel information and characteristics, as well as behavioral analysis and prediction. If the behavior matches the behavior in the preset leakage risk behavior library, or the total value of the behavior warning is higher than the warning threshold, it is confirmed that the first smart device has a file data leakage risk, thereby effectively improving the accuracy of identifying the file data leakage risk of the first smart device.

[0026] 4. Decide whether to allow the operator to perform file data operations based on the operator's identity, request, operation method, and the security level of the file data. By comprehensively considering permissions, risks, and security levels, the security of file data can be ensured, and operations can be restricted or authorized when necessary, which helps maintain the confidentiality and integrity of the data.

[0027] 5. Assess the risk of file data leakage by detecting suspicious or unauthorized devices. If a suspicious device is detected, take measures to protect the security of the data, including shutting down the device and notifying related devices, thereby preventing file data from being secretly photographed by suspicious or unauthorized devices, which helps maintain data confidentiality and avoid file data leakage.

[0028] 6. Expand the scope of risk management through a chain warning system. Once the risk of file data leakage is detected in the first smart device and the device is shut down, it will be further checked whether the first associated smart device associated with the device also has similar risks. If the risk is found, the second associated smart device associated with the first associated smart device will be notified in time by sending a warning message to ensure that the risk of file data leakage can be notified to the user in the first place, thereby effectively reducing the probability of file data leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is one of the flow charts of a method for preventing file data leakage of a smart device provided in an embodiment of the present application;

[0030] Figure 2 This is a second flow chart of a method for preventing file data leakage of a smart device provided in an embodiment of the present application;

[0031] Figure 3 This is a flowchart of a method for preventing file data leakage of a smart device provided in an embodiment of the present application;

[0032] Figure 4 It is a structural diagram of a smart device file data leakage prevention system provided by an embodiment of the present application;

[0033] Figure 5 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0034] Explanation of the reference numerals: 1. Acquisition module; 2. Processing module; 3. Warning module; 500. Electronic device; 501. Processor; 502. Communication bus; 503. User interface; 504. Network interface; 505. Memory. DETAILED DESCRIPTION

[0035] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0036] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0037] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" can explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0038] This application provides a method for preventing file data leakage of smart devices, referring to Figure 1 , which shows one of the flow charts of a method for preventing file data leakage of a smart device provided in an embodiment of the present application. The method includes steps S11-S16, which are as follows:

[0039] Step S11: obtaining a first picture taken by a camera set on a first smart device; wherein the first smart device is a smart device that has not performed any substantial operation.

[0040] Specifically, in this technical solution, the server obtains a first picture taken by a camera set on the first smart device.

[0041] Smart devices include but are not limited to desktop computers, laptops, mobile phones, tablets and other electronic devices. Substantial operations refer to interactive operations on smart devices. For computers, substantive operations can be mouse clicks, keyboard input, copying file data and other operations; for mobile phones or tablets, substantive operations can be touch screens, on-screen keyboard input, copying file data and other operations.

[0042] Therefore, for smart devices that have not performed any substantial operations, the server will obtain the first picture taken by the camera set on the first smart device. The camera set on the first smart device can be a camera built into the smart device itself, or a camera installed on the smart device. The number of cameras set on the first smart device may be multiple. The first picture obtained is the picture within the shooting range of the camera. If multiple cameras are installed on the smart device, the first picture refers to all pictures within the shooting range of the multiple cameras.

[0043] Step S12: Analyze the first picture to obtain a picture analysis result, which includes a first face recognition result.

[0044] Specifically, in this technical solution, the server analyzes the first picture to obtain a picture analysis result, and the picture analysis result includes a first face recognition result.

[0045] The first face recognition result can be obtained by analyzing the first picture using computer vision technology, including using image processing algorithms and machine learning models to detect faces, such as using facial detection algorithms. Once a face is detected, a facial recognition algorithm can be used for recognition.

[0046] Before obtaining the first face recognition result, it is necessary to establish a preset personnel list database and match the recognition result with it. The preset personnel list database stores the face data, name, age, file data access rights and other related information of a preset number of people. Among them, the people in the personnel list database represent people with a high degree of trust. For example, the computers in the R&D department of a company store a large amount of highly confidential file data, so the people stored in the established personnel list database can be managers in the company's R&D department.

[0047] Therefore, the first face recognition result includes the following two results, namely, identifying that the person is in the preset personnel list database, or identifying that the person is not in the preset personnel list database.

[0048] Step S13: Based on the first face recognition result, determine the personnel information corresponding to all persons in the first picture.

[0049] Specifically, in this technical solution, the server determines the personnel information corresponding to all persons in the first picture based on the first face recognition result.

[0050] When the first face recognition result obtained by the server is that the person is in the preset personnel list database, the personnel information corresponding to the person is determined to be the person's name, age, file data access rights and other related information. When the first face recognition result obtained by the server is that the person is not in the preset personnel list database, the personnel information corresponding to the person is determined to be empty, that is, the personnel information corresponding to the person cannot be determined.

[0051] Step S14: using a behavior recognition algorithm corresponding to the personnel information to identify all personnel in the first picture, and determining personnel features corresponding to all personnel in the first picture.

[0052] Specifically, in this technical solution, the server uses a behavior recognition algorithm corresponding to the personnel information to recognize all personnel in the first screen and determine the personnel features corresponding to all personnel in the first screen. The personnel features include interactive behavior sub-features, language sub-features, and warning behavior sub-features.

[0053] Different behavior recognition algorithms are used for different personnel information. That is, when the personnel information is the name, age, file data access rights and other related information of the personnel, a preset behavior recognition algorithm is used; when the personnel information is empty, another preset behavior recognition algorithm is used.

[0054] The following embodiment will explain in detail a method for identifying all persons in the first picture by using a behavior recognition algorithm corresponding to the personnel information and determining the personnel features corresponding to all persons in the first picture.

[0055] In one possible implementation, refer to Figure 2 , which shows a second flow chart of a method for preventing file data leakage of a smart device provided in an embodiment of the present application, wherein step S14 specifically includes steps S141-S143:

[0056] Step S141: Determine whether all personnel information is in the preset personnel list database.

[0057] Specifically, in this technical solution, the server determines whether all personnel information is in a preset personnel list database.

[0058] Step S142: When the information of all personnel is in the preset personnel list database, behavioral interaction detection is performed on all personnel based on the first behavior recognition algorithm to determine the interactive behavior sub-features corresponding to all personnel in the first screen, and voice detection is performed on all personnel based on the voice recognition algorithm to determine the language sub-features corresponding to all personnel in the first screen.

[0059] Specifically, in the present technical solution, when the server determines that all personnel information is in the preset personnel list database, it first performs behavioral interaction detection on all personnel based on the first behavior recognition algorithm, and determines the interactive behavior sub-features corresponding to all personnel in the first screen. That is, when multiple personnel in the preset personnel list database have interactive actions, the features with interactive actions are extracted. The specific implementation method can be to perform pre-processing operations such as removing noise, adjusting image brightness and contrast, and cropping the area of ​​interest on the collected first screen, and then use joint point detection to extract the posture features of the people in the screen after pre-processing, and use a machine learning algorithm to classify the different behavioral action features of the people in the screen. When the posture features and behavioral action features meet the pre-set interactive posture features and interactive behavior action features, such as actions such as talking to each other, the server will record these posture features and behavioral action features, that is, obtain interactive behavior sub-features. It should be noted that there may be multiple interactive behavior sub-features and language sub-features.

[0060] After determining the interactive behavior sub-features corresponding to all persons in the first screen, voice detection is performed on all persons based on the voice recognition algorithm to determine the language sub-features corresponding to all persons in the first screen. The specific implementation method may be to perform pre-processing operations such as noise reduction, removal of silent segments, equalization, and sampling rate adjustment on the collected voices of all persons, and then use voice processing tools such as MFCC (Mel-frequency cepstral coefficients) or FBANK (filter bank features) on the pre-processed voices to extract acoustic features from the voices. And use an acoustic model, such as a deep learning recurrent neural network (RNN) or convolutional neural network (CNN), to convert the acoustic features into text. When the text meets the pre-set interactive language text, such as matching certain preset keywords, the text is recorded to obtain the language sub-features.

[0061] Step S143: When the information of at least one person is not in the preset personnel list database, warning behavior detection is performed on all persons based on the second behavior recognition algorithm to determine the warning behavior sub-features corresponding to all persons in the first screen.

[0062] Specifically, in this technical solution, when the server determines that the information of at least one person is not in the preset personnel list database, it performs warning behavior detection on all personnel based on the second behavior recognition algorithm, and determines the warning behavior sub-features corresponding to all personnel in the first screen. That is, the behavior of each person among all personnel is detected, and its detection method is similar to the method of interactive behavior detection in the above embodiment. When the posture characteristics and behavioral action characteristics of each person meet the pre-set warning posture characteristics and warning behavior action characteristics, such as staring at the screen of a smart device or taking pictures, the server will record these posture characteristics and behavioral action characteristics, that is, obtain the warning behavior sub-features. It should be noted that there may be multiple warning behavior sub-features.

[0063] Step S15: Based on the personnel information and personnel characteristics corresponding to all personnel, determine whether the first smart device has a risk of file data leakage.

[0064] Specifically, in this technical solution, the server determines whether there is a risk of file data leakage in the first smart device based on the personnel information and personnel characteristics corresponding to all personnel.

[0065] In a possible implementation, step S15 specifically includes the following steps:

[0066] When the personnel information corresponding to all personnel is in the preset personnel list database, behavior prediction is performed on all personnel based on the interactive behavior sub-features and language sub-features to obtain behavior prediction results.

[0067] Specifically, in the present technical solution, when the server determines that the personnel information corresponding to all personnel is in the preset personnel list database, the interactive behavior sub-features and language sub-features extracted in step S142 are input into the preset behavior prediction model, and the behavior prediction results are output. The behavior prediction results include the classification of personnel behavior, such as "normal" or "abnormal". The behavior prediction model will be constructed based on the test interactive behavior sub-features and test language sub-features obtained from a large number of tests, wherein the test interactive behavior sub-features include interactive behavior sub-features that will not cause risks to file data leakage and interactive behavior sub-features that will cause risks to file data leakage, and the test language sub-features include test language sub-features that will not cause risks to file data leakage and test language sub-features that will cause risks to file data leakage. The machine learning model will learn based on these sub-features to determine which sub-features will cause risks to file data leakage.

[0068] Determine whether the behavior prediction result conforms to at least one leakage risk behavior in a preset leakage risk behavior library.

[0069] Specifically, in this technical solution, the server will determine whether the behavior prediction result conforms to at least one leakage risk behavior in the preset leakage risk behavior library. That is, the behavior prediction result will be compared with the preset leakage risk behavior library. The preset leakage risk behavior library contains known behaviors that may cause data leakage.

[0070] When the behavior prediction result matches at least one leakage risk behavior in a preset leakage risk behavior library, it is determined that the first smart device has a file data leakage risk.

[0071] Specifically, in this technical solution, when the server determines that the behavior prediction result matches at least one leakage risk behavior in the preset leakage risk behavior library, it is determined that the first smart device has a file data leakage risk. If the behavior prediction result matches any leakage risk behavior in the library, it can be determined that there is a potential data leakage risk.

[0072] When the information of at least one person is not in the preset personnel list database, the total behavior warning value corresponding to each person is calculated based on the warning behavior sub-features.

[0073] Specifically, in this technical solution, when the server determines that at least one person's information is not in the preset personnel list database, the total behavior warning value corresponding to each person is calculated based on the warning behavior sub-feature. That is, the server calculates the total behavior warning value of each person based on the warning behavior sub-feature extracted in step S143. The total behavior warning value is a score, which is calculated based on different warning behavior sub-features and their relative importance coefficients.

[0074] When there is at least one person whose corresponding behavior warning total value is greater than the warning threshold, it is determined that the first smart device has a file data leakage risk.

[0075] Specifically, in this technical solution, when the server determines that there is at least one person whose corresponding behavior warning total value is greater than the warning threshold, it is determined that the first smart device has a file data leakage risk. The warning threshold can be specifically set according to actual needs.

[0076] Furthermore, the server will screen out blacklisted personnel from all personnel and add the blacklisted personnel to a blacklisted personnel database, wherein the total behavior warning value corresponding to the blacklisted personnel is greater than or equal to a preset upper limit threshold.

[0077] Step S16: When there is a risk of file data leakage on the first smart device, shut down the first smart device and send a warning message to a first associated smart device associated with the first smart device.

[0078] Specifically, in this technical solution, when the server determines that the first smart device has a risk of file data leakage, the first smart device is turned off and a warning message is sent to a first associated smart device associated with the first smart device.

[0079] Further, when the server sends the warning information to the first associated smart device associated with the first smart device, if the first associated smart device does not process the warning information within a preset time period, the warning information is sent to another associated smart device associated with the first smart device. The preset time period is preferably 10 seconds.

[0080] It should be noted that when the server extracts the personnel information from the first screen and determines that the personnel information is in the blacklist personnel database, the first smart device is directly shut down and a warning message is sent to a first associated smart device associated with the first smart device.

[0081] In one possible implementation, refer to Figure 3 , which shows a third flow chart of a method for preventing file data leakage of a smart device provided in an embodiment of the present application. The method includes steps S21-S28, which are as follows:

[0082] Step S21: obtaining a second picture taken by a camera set on a second smart device; wherein the second smart device is a smart device that is performing a substantial operation.

[0083] Specifically, in this technical solution, the server obtains the second picture taken by the camera set on the second smart device. The method for obtaining the second picture taken by the camera set on the second smart device is consistent with the method in step S11, so it will not be repeated here.

[0084] Step S22: performing face recognition on the second picture to obtain a second face recognition result.

[0085] Specifically, in this technical solution, the server performs face recognition on the second picture to obtain a second face recognition result, that is, the face recognition technology is used to detect and recognize the face in the second picture to obtain the identity or characteristics of the operator.

[0086] Step S23: Based on the second face recognition result, determine the basic authority coefficient corresponding to the operator in the second screen.

[0087] Specifically, in this technical solution, the server determines the basic authority coefficient corresponding to the operator in the second screen based on the second face recognition result. That is, the server matches the second face recognition result with the preset personnel list database. Since the preset personnel list database stores the corresponding personnel's face data, name, age, file data access rights and other related information, the server can directly match and obtain the basic authority coefficient corresponding to the operator in the second screen, that is, the file data access rights. The higher the level of the operator, the larger the corresponding basic authority coefficient. The value range of the basic authority coefficient is 0-1. The basic authority coefficient corresponding to each person in the preset personnel list database can be specifically set according to the actual situation.

[0088] It should be noted that if the second face recognition result is empty, that is, the personnel information corresponding to the person cannot be determined, the server directly shuts down the smart device and sends a warning message to the associated smart device associated with the smart device.

[0089] Step S24: In response to the operator's file data operation request, the operator's current file data operation mode is obtained.

[0090] Specifically, in this technical solution, the server responds to the operator's file data operation request and obtains the operator's current file data operation mode, including but not limited to viewing, editing, copying, transferring, deleting, etc.

[0091] Step S25: Determine the operation impact coefficient based on the current file data operation mode.

[0092] Specifically, in this technical solution, the server determines the operation impact coefficient based on the current file data operation mode. Each file data operation mode corresponds to a different operation impact coefficient, wherein the operation mode with a greater impact on the file data corresponds to the largest operation impact coefficient, and the value range of the operation impact coefficient is 0-1. The operation impact coefficient corresponding to each file data operation mode can be specifically set according to the actual situation.

[0093] Step S26: Based on the basic authority coefficient and the operation influence coefficient, the actual authority coefficient corresponding to the operator is obtained.

[0094] Specifically, in this technical solution, the server obtains the actual authority coefficient corresponding to the operator based on the basic authority coefficient and the operation impact coefficient. The actual authority coefficient = f*basic authority coefficient / operation impact coefficient, where f is the proportional adjustment coefficient, which is a fixed value, and the setting of the f value can be specifically set according to the actual situation.

[0095] Step S27: Calculate the security level coefficient corresponding to each file data in the second smart device, and determine the maximum security level coefficient among the multiple security level coefficients.

[0096] Specifically, in the present technical solution, the server first calculates the security level coefficient corresponding to each file data in the second smart device, and determines the maximum security level coefficient from multiple security level coefficients.

[0097] In a possible implementation, step S27 specifically includes the following steps:

[0098] Obtain the confidentiality measurement value corresponding to each file data; obtain the time interval between the last operation time of each file data and the current time; based on the time interval, determine the time attenuation factor corresponding to the time interval; calculate the security level coefficient corresponding to each file data in the second smart device by the following formula: ; Where K is the security level coefficient, x is the confidentiality measurement value, t is the time interval, and α is the time attenuation factor.

[0099] Specifically, in this technical solution, the server obtains the confidentiality measurement value corresponding to each file data, that is, the value manually set based on the importance of each file data, where the value range of the confidentiality measurement value is 0-1, and the greater the importance of the file, the greater the corresponding confidentiality measurement value. The server then obtains the time interval between the previous operation time of each file data and the current time, where the unit of the time interval t is seconds. The server then determines the time decay factor corresponding to the time interval based on the time interval, where the setting of the time decay factor α value can be specifically set according to the actual situation.

[0100] Step S28: When the actual authority coefficient is greater than the maximum security level coefficient, the operator is allowed to operate the multiple file data in the second smart device in the current file data operation mode.

[0101] Specifically, in this technical solution, when the server determines that the actual authority coefficient is greater than the maximum security level coefficient, the operator is allowed to operate multiple file data in the second smart device in the current file data operation mode, so as to reduce the probability of malicious operation and leakage of file data, thereby improving the security and confidentiality of file data.

[0102] It should be noted that when the server determines that the actual authority coefficient is less than the maximum security level coefficient, the operator is not allowed to operate multiple file data in the second smart device in the current file data operation mode. At the same time, the second smart device is shut down and a warning message is sent to the associated smart device associated with the second smart device.

[0103] Furthermore, the server performs facial recognition on the second screen, and if the personnel information obtained by the second facial recognition result is not in the preset personnel list database, or the personnel information obtained is in the blacklist personnel database, the operator is denied from operating the multiple file data in the second smart device, and at the same time, the second smart device is shut down and a warning message is sent to the associated smart device associated with the second smart device.

[0104] In a possible implementation, after step S12, the method further includes the following steps:

[0105] Based on the device identification result, it is determined whether there is a suspicious device in the first image.

[0106] Specifically, in this technical solution, the image analysis results also include device identification results. The server will identify the type of device based on computer vision technology and determine whether there is a suspicious device among the identified devices. Suspicious devices include but are not limited to camera devices, video recording devices, etc.

[0107] When there is a suspicious device in the first screen, it is determined that there is a risk of file data leakage in the first smart device, the first smart device is shut down, and a warning message is sent to a first associated smart device associated with the first smart device.

[0108] Specifically, in this technical solution, when the server determines that there is a suspicious device in the first screen, it determines that there is a risk of file data leakage in the first smart device, shuts down the first smart device and sends a warning message to a first associated smart device associated with the first smart device.

[0109] In a possible implementation, after step S16, the method further includes the following steps:

[0110] Determine whether there is a risk of file data leakage in the first associated smart device.

[0111] Specifically, in this technical solution, the server determines whether the first associated smart device has a risk of file data leakage. The method for determining whether the first associated smart device has a risk of file data leakage is consistent with the method of steps S11-S15 and steps S21-S28, so no further details are given here.

[0112] When the first associated smart device has a risk of file data leakage, a warning message is sent to a second associated smart device associated with the first associated smart device.

[0113] Specifically, in the present technical solution, when the server determines that the first associated smart device has a risk of file data leakage, it sends a warning message to the second associated smart device associated with the first associated smart device.

[0114] Furthermore, in this solution, when the smart device does not perform any substantial operation, the server will also obtain the positioning data of the displacement sensor set on the smart device, that is, the longitude, latitude, and altitude coordinates. When it is detected that the positioning data of the smart device has changed significantly, and the duration of the positioning data change is greater than the preset duration, it is determined that the smart device has a risk of file data leakage. At this time, the server sends a warning message to the associated smart device associated with the smart device.

[0115] Reference Figure 4 , which shows a structural schematic diagram of a smart device file data leakage prevention system provided by an embodiment of the present application, the system includes: an acquisition module 1, a processing module 2 and an early warning module 3; the acquisition module 1 is used to acquire a first picture taken by a camera set on a first smart device; wherein the first smart device is a smart device that has not performed any substantial operation; the processing module 2 is used to analyze the first picture to obtain a picture analysis result, and the picture analysis result includes a first face recognition result; the processing module 2 is also used to determine the personnel information corresponding to all persons in the first picture based on the first face recognition result; the processing module 2 is also used to use a behavior recognition algorithm corresponding to the personnel information to identify all persons in the first picture and determine the personnel characteristics corresponding to all persons in the first picture; the processing module 2 is also used to determine whether the first smart device has a file data leakage risk based on the personnel information and personnel characteristics corresponding to all persons; the early warning module 3 is used to shut down the first smart device and send a warning message to a first associated smart device associated with the first smart device when the first smart device has a file data leakage risk.

[0116] In one possible implementation, processing module 2 is also used to determine whether the information of all personnel is in a preset personnel list database; processing module 2 is also used to, when the information of all personnel is in the preset personnel list database, perform behavioral interaction detection on all personnel based on the first behavior recognition algorithm to determine the interactive behavior sub-features corresponding to all personnel in the first screen, and perform voice detection on all personnel based on the voice recognition algorithm to determine the language sub-features corresponding to all personnel in the first screen; processing module 2 is also used to, when the information of at least one person is not in the preset personnel list database, perform warning behavior detection on all personnel based on the second behavior recognition algorithm to determine the warning behavior sub-features corresponding to all personnel in the first screen.

[0117] In a possible implementation, processing module 2 is also used to predict the behavior of all personnel based on interactive behavior sub-features and language sub-features to obtain behavior prediction results when the personnel information corresponding to all personnel is in a preset personnel list database; processing module 2 is also used to determine whether the behavior prediction results are consistent with at least one leakage risk behavior in a preset leakage risk behavior library; processing module 2 is also used to determine that the first smart device has a file data leakage risk when the behavior prediction result is consistent with at least one leakage risk behavior in the preset leakage risk behavior library; processing module 2 is also used to calculate the total behavior warning value corresponding to each person based on the warning behavior sub-features when the information of at least one person is not in the preset personnel list database; processing module 2 is also used to determine that the first smart device has a file data leakage risk when there is at least one person whose corresponding behavior warning total value is greater than the warning threshold.

[0118] In a possible implementation, the acquisition module 1 is also used to acquire a second picture taken by a camera set on a second smart device; wherein the second smart device is a smart device performing substantial operations; the processing module 2 is also used to perform face recognition on the second picture to obtain a second face recognition result; the processing module 2 is also used to determine the basic authority coefficient corresponding to the operator in the second picture based on the second face recognition result; the acquisition module 1 is also used to respond to the operator's file data operation request and acquire the operator's current file data operation mode; the processing module 2 is also used to determine the operation influence coefficient based on the current file data operation mode; the processing module 2 is also used to obtain the actual authority coefficient corresponding to the operator based on the basic authority coefficient and the operation influence coefficient; the processing module 2 is also used to calculate the security level coefficient corresponding to each file data in the second smart device, and determine the maximum security level coefficient among multiple security level coefficients; the processing module 2 is also used to allow the operator to operate multiple file data in the second smart device in the current file data operation mode when the actual authority coefficient is greater than the maximum security level coefficient.

[0119] In a possible implementation, the acquisition module 1 is further used to obtain the confidentiality measurement value corresponding to each file data; the acquisition module 1 is further used to obtain the time interval between the previous operation time of each file data and the current time; the processing module 2 is further used to determine the time attenuation factor corresponding to the time interval based on the time interval; the processing module 2 is further used to calculate the security level coefficient corresponding to each file data in the second smart device by the following formula: ; Where K is the security level coefficient, x is the confidentiality measurement value, t is the time interval, and α is the time attenuation factor.

[0120] In a possible implementation, the processing module 2 is also used to determine whether there is a suspicious device in the first screen based on the device identification result; the processing module 2 is also used to determine that there is a risk of file data leakage in the first smart device when there is a suspicious device in the first screen; the early warning module 3 is also used to shut down the first smart device and send a warning message to a first associated smart device associated with the first smart device.

[0121] In a possible implementation, the processing module 2 is further used to determine whether the first associated smart device has a risk of file data leakage; the early warning module 3 is further used to send a warning message to a second associated smart device associated with the first associated smart device when the first associated smart device has a risk of file data leakage.

[0122] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0123] The present application also discloses an electronic device. Figure 5 , Figure 5 The electronic device 500 may include: at least one processor 501 , at least one network interface 504 , a user interface 503 , a memory 505 , and at least one communication bus 502 .

[0124] The communication bus 502 is used to realize the connection and communication between these components.

[0125] The user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0126] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0127] Among them, the processor 501 may include one or more processing cores. The processor 501 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 505, and calling data stored in the memory 505. Optionally, the processor 501 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 501 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 501, and it can be implemented separately through a chip.

[0128] Among them, the memory 505 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 505 may optionally be at least one storage device located away from the aforementioned processor 501. Refer to Figure 5 , the memory 505 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module and an application program.

[0129] exist Figure 5In the electronic device 500 shown, the user interface 503 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 501 can be used to call an application stored in the memory 505, and when executed by one or more processors 501, the electronic device 500 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0130] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0134] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0135] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0136] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for preventing file data leakage of a smart device, characterized in that: The method comprises: Acquire a first picture taken by a camera set on a first smart device; wherein the first smart device is a smart device that does not perform substantial operations; Analyze the first picture to obtain a picture analysis result, wherein the picture analysis result includes a first face recognition result; Based on the first face recognition result, determining personnel information corresponding to all persons in the first picture; Using a behavior recognition algorithm corresponding to the personnel information, all persons in the first picture are recognized, and personnel features corresponding to all persons in the first picture are determined; Based on the personnel information and the personnel characteristics corresponding to all personnel, determining whether the first smart device has a risk of file data leakage; When the first smart device has a risk of file data leakage, shutting down the first smart device and sending a warning message to a first associated smart device associated with the first smart device; Acquire a second picture taken by a camera set on a second smart device; wherein the second smart device is a smart device that is performing a substantial operation; Performing face recognition on the second picture to obtain a second face recognition result; Based on the second face recognition result, determining a basic authority coefficient corresponding to the operator in the second screen; In response to a file data operation request of an operator, obtaining a current file data operation mode of the operator; Determining an operation impact coefficient based on the current file data operation mode; Based on the basic authority coefficient and the operation influence coefficient, obtaining an actual authority coefficient corresponding to the operator; Calculating the security level coefficient corresponding to each file data in the second smart device, and determining the maximum security level coefficient among the multiple security level coefficients; When the actual authority coefficient is greater than the maximum security level coefficient, the operator is allowed to operate the plurality of file data in the second smart device in the current file data operation mode; The calculating of the security level coefficient corresponding to each file data in the second smart device specifically includes: Get the confidential measurement value corresponding to each file data; Get the time interval between the last operation time and the current time of each file data; Based on the time interval, determining a time decay factor corresponding to the time interval; The security level coefficient corresponding to each file data in the second smart device is calculated by the following formula: ； Wherein, K is the security level coefficient, x is the confidentiality measurement value, t is the time interval, and α is the time attenuation factor.

2. The method according to claim 1, characterized in that: The personnel features include interactive behavior sub-features, language sub-features, and warning behavior sub-features; the use of a behavior recognition algorithm corresponding to the personnel information to recognize all personnel in the first screen and determine the personnel features corresponding to all personnel in the first screen specifically includes: Determine whether all the personnel information is in the preset personnel list database; When all the personnel information is in the preset personnel list database, behavioral interaction detection is performed on all the personnel based on the first behavior recognition algorithm to determine the interactive behavior sub-features corresponding to all the personnel in the first screen, and voice detection is performed on all the personnel based on the voice recognition algorithm to determine the language sub-features corresponding to all the personnel in the first screen; When the information of at least one of the personnel is not in the preset personnel list database, warning behavior detection is performed on all personnel based on the second behavior recognition algorithm to determine the warning behavior sub-features corresponding to all personnel in the first screen.

3. The method according to claim 2, characterized in that The determining whether the first smart device has a risk of file data leakage based on the personnel information and the personnel characteristics corresponding to all personnel specifically includes: When the personnel information corresponding to all personnel is in the preset personnel list database, behavior prediction is performed on all personnel based on the interaction behavior sub-feature and the language sub-feature to obtain a behavior prediction result; Determining whether the behavior prediction result conforms to at least one leakage risk behavior in a preset leakage risk behavior library; When the behavior prediction result matches at least one leakage risk behavior in a preset leakage risk behavior library, determining that the first smart device has a file data leakage risk; When the information of at least one of the personnel is not in the preset personnel list database, the total behavior warning value corresponding to each person is calculated based on the warning behavior sub-feature; When the total behavior warning value corresponding to at least one person is greater than the warning threshold, it is determined that the first smart device has a file data leakage risk.

4. The method according to claim 1, characterized in that The picture analysis result also includes a device identification result; after analyzing the first picture to obtain the picture analysis result, the method further includes: Based on the device identification result, determining whether there is a suspicious device in the first picture; When there is a suspicious device in the first screen, it is determined that there is a risk of file data leakage in the first smart device, and the first smart device is shut down and a warning message is sent to a first associated smart device associated with the first smart device.

5. The method according to claim 1, characterized in that: After shutting down the first smart device and sending a warning message to a first associated smart device associated with the first smart device, the method further includes: Determining whether the first associated smart device has a risk of file data leakage; When the first associated smart device has a risk of file data leakage, a warning message is sent to a second associated smart device associated with the first associated smart device.

6. A smart device file data leakage prevention system, characterized in that: The system includes an acquisition module, a processing module and an early warning module; The acquisition module is used to acquire a first picture taken by a camera set on a first smart device; wherein the first smart device is a smart device that has not performed any substantial operation; The processing module is used to analyze the first picture to obtain a picture analysis result, wherein the picture analysis result includes a first face recognition result; The processing module is further used to determine the personnel information corresponding to all persons in the first picture based on the first face recognition result; The processing module is further configured to use a behavior recognition algorithm corresponding to the personnel information to recognize all persons in the first picture and determine personnel features corresponding to all persons in the first picture; The processing module is further used to determine whether there is a risk of file data leakage on the first smart device based on the personnel information and personnel characteristics corresponding to all personnel; The early warning module is used to shut down the first smart device and send a warning message to a first associated smart device associated with the first smart device when there is a risk of file data leakage on the first smart device; The acquisition module is further used to acquire a second picture taken by a camera set on the second smart device; wherein the second smart device is a smart device that is performing substantial operations; the processing module is further used to perform face recognition on the second picture to obtain a second face recognition result; the processing module is further used to determine the basic authority coefficient corresponding to the operator in the second picture based on the second face recognition result; the acquisition module is further used to respond to the file data operation request of the operator and acquire the current file data operation mode of the operator; the processing module is further used to determine the operation influence coefficient based on the current file data operation mode; the processing module is further used to obtain the actual authority coefficient corresponding to the operator based on the basic authority coefficient and the operation influence coefficient; the processing module is further used to calculate the security level coefficient corresponding to each file data in the second smart device, and determine the maximum security level coefficient among multiple security level coefficients; the processing module is further used to allow the operator to operate multiple file data in the second smart device in the current file data operation mode when the actual authority coefficient is greater than the maximum security level coefficient; The acquisition module is further used to obtain the confidentiality measurement value corresponding to each file data; the acquisition module is further used to obtain the time interval between the previous operation time of each file data and the current time; the processing module is further used to determine the time attenuation factor corresponding to the time interval based on the time interval; the processing module is further used to calculate the security level coefficient corresponding to each file data in the second intelligent device through the following formula: ; Where K is the security level coefficient, x is the confidentiality measurement value, t is the time interval, and α is the time attenuation factor.

7. An electronic device, characterized in that: The electronic device (500) comprises a processor (501), a memory (505), a user interface (503) and a network interface (504), wherein the memory (505) is used to store instructions, the user interface (503) and the network interface (504) are used to communicate with other devices, and the processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device (500) executes the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 5 is performed.

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