A wearable artificial intelligence wireless IoT security system

Abstract

The present application discloses a wearable artificial intelligence wireless Internet of Things security system, which relates to the field of wearable wireless Internet of Things security technology. The present application includes a data acquisition module, a basic data analysis module, an environmental data analysis module, a position data analysis module, a functional data analysis module, and a visual early warning module. First, the basic data, environmental data, position data, and functional data of the user's smart translation glasses are acquired, and the user's basic data factors are combined with the environmental data and position data for analysis, and then the user's environment or position is analyzed to see whether it is abnormal. At the same time, a visual translation function is provided to detect the danger assessment coefficient of each target person, so as to determine whether each target person is at risk, and an efficient visual early warning function is provided, which solves the limitations existing in the current feasibility analysis process of the safety development of wearable artificial intelligence wireless Internet of Things.

Description

Technical Field

[0001] The present application relates to the field of wearable wireless Internet of Things security technology, and specifically to a wearable artificial intelligence wireless Internet of Things security system. Background Art

[0002] With the development of technology, wearable devices have become diverse. At the same time, wearable devices have also added artificial intelligence wireless Internet of Things security technology, which can not only ensure the safety of users but also improve the security of users' data.

[0003] The prior art, such as the invention application patent with announcement number: CN103330554B, discloses a wearable artificial intelligence wireless Internet of Things security system, including: an expert system, a communication network and multiple wearable terminals; each of the wearable terminals exchanges information with the expert system through the communication network; each of the wearable terminals includes a central processing unit, a memory, a wireless communication interface, a positioning module, a charging module and multiple types of sensors; the central processing unit is respectively connected to the memory, the wireless communication interface, the positioning module, the charging module and multiple types of sensors. It can comprehensively and accurately report the health status of the ward and the current environment information to the expert system. The expert information can accurately determine whether the ward has an accident based on the expert knowledge, and when an accident occurs, it can promptly notify the guardian or alarm, thereby ensuring that the ward can receive timely and effective rescue.

[0004] With respect to the above scheme, the inventors of the present application have at least discovered the following technical problems with the above technology: 1. Currently, there is a lack of customization of the safety zone, and it is impossible to issue an early warning when the user is far away from the safety zone, thereby failing to more accurately understand the actual situation of the current user location. At the same time, it is also impossible to ensure the comprehensiveness and authenticity of the user location data analysis process, and thus it is impossible to ensure the reference and accuracy of the analysis results.

[0005] 2. Currently, there is a lack of analysis of the distance between target persons, the dangerousness of their behaviors, and the dangerousness of the translated data. This makes it impossible to guarantee the safety of users, to truly display the user's situation, and thus to ensure that users can predict danger in advance, which reduces the effectiveness of the use of wearable devices to a certain extent. Summary of the invention

[0006] In view of the above-mentioned technical deficiencies, the purpose of this application is to provide a wearable artificial intelligence wireless Internet of Things security system.

[0007] To solve the above technical problems, the present application adopts the following technical solution: The present application provides a wearable artificial intelligence wireless Internet of Things security system, comprising the following modules: a data acquisition module, used to obtain the basic data, environmental data, location data and functional data of the user's smart translation glasses.

[0008] The basic data analysis module is used to analyze the user's basic data and obtain the user's basic data factors.

[0009] The environmental data analysis module is used to analyze the user's environmental assessment coefficient based on the user's environmental data and basic data factors, and then determine whether the user's environment is abnormal.

[0010] The location data analysis module is used to analyze the user's location evaluation coefficient based on the user's location data and basic data factors, and then determine whether the user's location is abnormal.

[0011] The functional data analysis module is used to encrypt, transmit and store the user's translation data based on the user's functional data, and at the same time analyze and obtain the risk assessment coefficient of each target person, and then determine whether each target person is at risk.

[0012] The visual warning module is used to display the environmental abnormality in the lenses of the smart translation glasses when the user's environment is abnormal; to display the position abnormality in the lenses of the smart translation glasses when the user's position is abnormal; and to display the target task abnormality in the lenses of the smart translation glasses when a target person is at risk.

[0013] Preferably, the basic data includes the user's age, heart rate and body temperature; the environmental data includes temperature, external pressure and smoke concentration; the location data includes the safe area distance and altitude; the functional data includes translation mode, translation data, the distance between each target person, the behavior danger level and the danger level of the translation data.

[0014] Preferably, the user's translation data is encrypted, transmitted and stored according to the user's functional data, and the specific encryption process is as follows: according to the translation mode in the user's functional data, the encryption method corresponding to the translation mode is retrieved in the database, and the encryption methods include no encryption, primary encryption and secondary encryption; when the encryption method is no encryption, the translation data is directly transmitted to the user's cloud account through the network; when the encryption method is primary encryption, the translation data is encrypted using an asymmetric encryption algorithm and transmitted to the user's cloud account, and when the user views the translation data, it jumps to identity authentication. When the user passes the identity authentication, the key is sent to the user's mailbox in the form of an email. When the user fails the identity authentication, the translation data is locked; when the encryption method is secondary encryption, asymmetric encryption is used to generate a pair of public and private keys, and then the private key is used to symmetrically encrypt the translation data and transmit it to the user's cloud account, and when the user views the translation data, it jumps to identity authentication. When the user passes the identity authentication, the key is sent to the user's mailbox in the form of an email. When the user fails the identity authentication, the translation data is locked.

[0015] The beneficial effects of the present application are: 1. A wearable artificial intelligence wireless Internet of Things security system provided by the present application first obtains the basic data, environmental data, location data and functional data of the user's smart translation glasses, combines the user's basic data factors with the environmental data and location data for analysis, and then analyzes whether the user's environment or location is abnormal. At the same time, it provides a visual translation function, detects the danger assessment coefficient of each target person, so as to whether each target person is at risk, and provides an efficient visual early warning function, which solves the limitations of the current feasibility analysis process of the security development of wearable artificial intelligence wireless Internet of Things.

[0016] 2. The smart translation glasses in this application can monitor dangers in the surrounding environment, such as possible impact due to excessive external pressure, smoke, gas leakage, etc., to ensure the safety of users and improve the early warning capability of the equipment; at the same time, users can customize the safe area, and then analyze the user's location based on the user's location information, and combine the user's basic data; these two types of analysis improve the user experience to a certain extent.

[0017] 3. In this application, when the user selects the travel mode for translation, the smart translation glasses can upload the translation data to the cloud in real time, ensuring that family members or guardians can obtain the user's status at any time and protect the user's personal safety. It can be used for real-time monitoring, setting alarms, and viewing historical data, etc.; users can adjust the functions and settings of the device according to their needs and preferences, and encrypt the translation data with different encryption methods to ensure the security of user data during transmission and storage, and prevent unauthorized access. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 This is a schematic diagram of the system structure connection for this application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] See also Figure 1 As shown, the present application provides a wearable artificial intelligence wireless Internet of Things security system, including: a data acquisition module, a basic data analysis module, an environmental data analysis module, a location data analysis module, a functional data analysis module, and a visual warning module.

[0022] The data acquisition module is used to obtain the basic data, environmental data, location data and functional data of the user's smart translation glasses.

[0023] In a specific example, the basic data includes the user's age, heart rate and body temperature; the environmental data includes temperature, external pressure and smoke concentration; the location data includes the safe area distance and altitude; the functional data includes translation mode, translation data, the distance between target persons, the behavior danger level and the danger level of translation data.

[0024] It should be noted that the smart translation glasses are equipped with temperature sensors, pressure sensors, smoke sensors, positioning devices, heart rate sensors, microphones and other devices.

[0025] It should be noted that the safety zone is user-defined, and the safety zone spacing is represented by the distance from the nearest boundary of the safety zone when the user exceeds the safety zone. When the user does not exceed the safety zone, the safety zone spacing is 0.

[0026] It should be noted that the translation modes include normal mode, confidential mode, travel mode and sharing mode.

[0027] It should be noted that the target persons are persons that can be detected by the smart translation glasses.

[0028] It should be noted that the distance of the target person is expressed as the distance from the smart translation glasses to the target person's glasses; the translation data is the data of the final translation language customized by the user. The ASR algorithm of the existing technology can translate and transcribe the speech into a translation text. The danger level of the translation data is expressed as the proportion of dangerous or bad words in the translation text; the behavioral danger level is expressed as the degree of danger of the target person's behavior, which is obtained by existing visual analysis technology.

[0029] The basic data analysis module is used to analyze the user's basic data and obtain the user's basic data factors.

[0030] In a specific example, the analysis obtains the basic data factors of the user, and the specific analysis process is as follows: the user's age, heart rate and body temperature are recorded as , and , substitute into the calculation formula Get the user's basic data factors ,in is expressed as a natural constant, Represents the age limit of users in the database. and They represent the standard value of the heart rate and the standard value of the body temperature of the user in the database, respectively. and They are respectively represented as the allowed floating value of the heart rate and the allowed floating value of the body temperature of the user in the database, and They are respectively represented as the weight factor corresponding to the heart rate and the weight factor corresponding to the body temperature in the database.

[0031] It should be noted that , , .

[0032] It should be noted that the weight factors corresponding to the heart rate and the body temperature were obtained by factor analysis. First, the information of the heart rate and body temperature was condensed, and then the variance explanation rate after rotation was obtained. The weights were obtained by dividing the cumulative variance explanation rate.

[0033] It should be noted that factor analysis is a well-known technology. It is a multivariate statistical analysis method that starts from studying the internal dependencies of variables and reduces some variables with intricate relationships to a few comprehensive factors; information concentration is expressed as calculating the median; the variance explanation rate is the amount of information extracted by the factor, and the variance explanation rate = characteristic root / total number of analysis items; the variance explanation rate after rotation is expressed as the variance explanation rate of the factor after maximum variance rotation.

[0034] The environmental data analysis module is used to analyze the user's environmental assessment coefficient based on the user's environmental data and basic data factors, and then determine whether the user's environment is abnormal.

[0035] In a specific example, the analysis obtains the user's environmental assessment coefficient, and the specific analysis process is as follows: the temperature, external pressure and smoke concentration in the environmental data are recorded as , and .

[0036] According to the calculation formula Determine the user's environmental assessment coefficient ,in , and They are respectively represented as the reference value of temperature, the reference value of external pressure and the reference value of smoke concentration in the database. and They are respectively expressed as the allowable floating value of temperature in the database and the allowable floating value of external pressure. , and They are respectively expressed as the weight factor corresponding to the temperature in the database, the weight factor corresponding to the external pressure, and the weight factor corresponding to the smoke concentration.

[0037] It should be noted that , , , .

[0038] It should be noted that the weight factors corresponding to temperature, external pressure and smoke concentration were obtained through factor analysis. First, the information of temperature, external pressure and smoke concentration was condensed, and then the variance explanation rate after rotation was obtained. The weight was obtained by dividing the cumulative variance explanation rate.

[0039] In a specific example, the process of determining whether the user's environment is abnormal is as follows: comparing the user's environment assessment coefficient with the environment assessment coefficient threshold in the database; when the user's environment assessment coefficient is greater than or equal to the environment assessment coefficient threshold in the database, it is determined that the user's environment is abnormal; otherwise, it is determined that the user's environment is not abnormal.

[0040] The location data analysis module is used to analyze the user's location evaluation coefficient based on the user's location data and basic data factors, and then determine whether the user's location is abnormal.

[0041] In a specific example, the analysis obtains the user's location evaluation coefficient, and the specific analysis process is as follows: the safety area distance and altitude in the location data are respectively recorded as and .

[0042] According to the calculation formula Get the user's location evaluation coefficient ,in is expressed as a natural constant, and They are respectively represented as the reference value of the safety area spacing and the reference value of the altitude in the database. Represents the floating value allowed for altitude in the database. and They are respectively expressed as the weight factor corresponding to the safety area spacing and the weight factor corresponding to the altitude in the database.

[0043] It should be noted that the safety zone is user-defined, and the safety zone spacing is represented by the distance from the nearest boundary of the safety zone when the user exceeds the safety zone. When the user does not exceed the safety zone, the safety zone spacing is 0.

[0044] It should be noted that , , .

[0045] It should be noted that the weight factors corresponding to the safety zone spacing and the weight factors corresponding to the altitude are obtained through factor analysis. First, the information of the safety zone spacing and altitude is condensed, and then the variance explanation rate after rotation is obtained. The weight is obtained by dividing the cumulative variance explanation rate.

[0046] In a specific example, the process of determining whether the user's location is abnormal is as follows: comparing the user's location evaluation coefficient with a location evaluation coefficient threshold in a database; when the user's location evaluation coefficient is greater than or equal to the location evaluation coefficient threshold in the database, the user's location is determined to be abnormal; otherwise, the user's location is determined to be normal.

[0047] The smart translation glasses in this application can monitor dangers in the surrounding environment, such as possible impact due to excessive external pressure, smoke, gas leakage, etc., to ensure the safety of users and improve the early warning capability of the equipment; at the same time, users can customize the safe area, and then analyze the user's location based on the user's location information, and combine the user's basic data; these two types of analysis improve the user experience to a certain extent.

[0048] The functional data analysis module is used to encrypt, transmit and store the user's translation data based on the user's functional data, and at the same time analyze and obtain the risk assessment coefficient of each target person, and then determine whether each target person is at risk.

[0049] In a specific example, the user's translation data is encrypted, transmitted and stored according to the user's functional data, and the specific encryption process is as follows: according to the translation mode in the user's functional data, the encryption method corresponding to the translation mode is retrieved in the database, and the encryption methods include no encryption, primary encryption and secondary encryption; when the encryption method is no encryption, the translation data is directly transmitted to the user's cloud account through the network; when the encryption method is primary encryption, the translation data is encrypted using an asymmetric encryption algorithm and transmitted to the user's cloud account, and when the user views the translation data, it jumps to identity authentication. When the user passes the identity authentication, the key is sent to the user's mailbox in the form of an email. When the user fails the identity authentication, the translation data is locked; when the encryption method is secondary encryption, asymmetric encryption is used to generate a pair of public and private keys, and then the private key is used to symmetrically encrypt the translation data and transmit it to the user's cloud account, and when the user views the translation data, it jumps to identity authentication. When the user passes the identity authentication, the key is sent to the user's mailbox in the form of an email. When the user fails the identity authentication, the translation data is locked.

[0050] In a specific example, the analysis obtains the risk assessment coefficient of each target person. The specific analysis process is as follows: the distance between each target person, the risk of behavior and the risk of translation data are recorded as , and ,in It represents the number corresponding to each target person. , It is represented as the total number of target persons, is a natural integer greater than or equal to 2.

[0051] According to the calculation formula Obtain the risk assessment coefficient of each target person , where e is a natural constant, and They are respectively represented as the reference value corresponding to the dangerousness of the target person's behavior in the database and the reference value corresponding to the dangerousness of the translation data. and They are respectively represented as the weight factor corresponding to the behavior risk in the database and the weight factor corresponding to the risk of the translation data.

[0052] It should be noted that , , .

[0053] It should be noted that the weight factors corresponding to the behavioral risk and the weight factors corresponding to the risk of the translated data were obtained by factor analysis. First, the information of the behavioral risk and the risk of the translated data was condensed, and then the variance explanation rate after rotation was obtained, and the weight was obtained by dividing the cumulative variance explanation rate.

[0054] In a specific example, the determination of whether each target person is at risk is performed as follows: the risk assessment coefficient of each target person is compared with the risk assessment coefficient threshold in the database. When the risk assessment coefficient of a target person is greater than or equal to the risk assessment coefficient threshold in the database, the target person is determined to be at risk; otherwise, the target person is determined to be not at risk, thereby determining whether each target person is at risk.

[0055] In this application, when the user chooses travel mode for translation, the smart translation glasses can upload the translation data to the cloud in real time, ensuring that family members or guardians can obtain the user's status at any time and protect the user's personal safety. It can be used for real-time monitoring, setting alarms, and viewing historical data, etc.; users can adjust the functions and settings of the device according to their needs and preferences, and encrypt the translation data with different encryption methods to ensure the security of user data during transmission and storage, and prevent unauthorized access.

[0056] The visual warning module is used to display the environmental abnormality in the lenses of the smart translation glasses when the user's environment is abnormal; to display the position abnormality in the lenses of the smart translation glasses when the user's position is abnormal; and to display the target task abnormality in the lenses of the smart translation glasses when a target person is at risk.

[0057] The present application provides a wearable artificial intelligence wireless Internet of Things security system, which first obtains the basic data, environmental data, location data and functional data of the user's smart translation glasses, combines the user's basic data factors with the environmental data and location data for analysis, and then analyzes whether the user's environment or location is abnormal. At the same time, it provides a visual translation function, detects the danger assessment coefficient of each target person, so as to determine whether each target person is at risk, and provides an efficient visual early warning function, which solves the limitations of the current feasibility analysis process of the security development of wearable artificial intelligence wireless Internet of Things.

[0058] The above contents are merely examples and explanations of the concept of the present application. The technical personnel in the relevant technical field may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present application.

Claims

1. A wearable artificial intelligence wireless Internet of Things security system, characterized in that: include: A data acquisition module, used to acquire basic data, environmental data, location data and functional data of the user's smart translation glasses; The basic data analysis module is used to analyze the basic data factors of users based on their basic data; The environmental data analysis module is used to analyze the user's environmental assessment coefficient based on the user's environmental data and basic data factors, and then determine whether the user's environment is abnormal; The location data analysis module is used to analyze the user's location evaluation coefficient based on the user's location data and basic data factors, and then determine whether the user's location is abnormal; Functional data analysis module, used to encrypt, transmit and store the user's translation data according to the user's functional data, and analyze and obtain the risk assessment coefficient of each target person, and then determine whether each target person has risks; A visual warning module is used to display environmental abnormalities on the lenses of the smart translation glasses when the user's environment is abnormal; When the user's position is abnormal, the abnormal position will be displayed in the lenses of the smart translation glasses; When a target person is at risk, the target person will be shown abnormal in the lenses of the smart translation glasses; The basic data includes the user's age, heart rate and body temperature; the environmental data includes temperature, external pressure and smoke concentration; the location data includes the safe area distance and altitude; the functional data includes the translation mode, translation data, the distance between each target person, the behavior risk and the risk of the translation data; The analysis results in the user's basic data factors, and the specific analysis process is as follows: Let the user's age, heart rate and body temperature be N, X and T respectively, and substitute them into the calculation formula The user's basic data factor φ is obtained, where e represents a natural constant, N' represents the age limit value of the user in the database, X' and T' represent the standard value of the heart rate and the standard value of the body temperature of the user in the database, ΔX and ΔT represent the allowable floating value of the heart rate and the allowable floating value of the body temperature of the user in the database, σ1 and σ2 represent the weight factor corresponding to the heart rate and the weight factor corresponding to the body temperature in the database, respectively; The analysis results in the user's environmental assessment coefficient, and the specific analysis process is as follows: The temperature, external pressure and smoke density in the environmental data are recorded as W, Y and D respectively; According to the calculation formula The user's environmental assessment coefficient α is obtained, where W′, Y′ and D′ represent the reference value of temperature, the reference value of external pressure and the reference value of smoke concentration in the database, respectively, and ΔW and ΔY represent the allowable floating value of temperature and the allowable floating value of external pressure in the database, respectively. and They are respectively expressed as the weight factor corresponding to the temperature in the database, the weight factor corresponding to the external pressure, and the weight factor corresponding to the smoke concentration; The analysis results in a user's location evaluation coefficient, and the specific analysis process is as follows: The safe area distance and altitude in the location data are recorded as A and H respectively; According to the calculation formula The user's position evaluation coefficient λ is obtained, where e is represented as a natural constant, A′ and H′ represent the reference value of the safe area spacing and the reference value of the altitude in the database, ΔH represents the allowable floating value of the altitude in the database, and τ1 and τ2 represent the weight factor corresponding to the safe area spacing and the weight factor corresponding to the altitude in the database, respectively; According to the user's functional data, the user's translation data is encrypted, transmitted and stored. The specific encryption process is as follows: According to the translation mode in the user's functional data, the encryption method corresponding to the translation mode is retrieved in the database, and the encryption methods include no encryption, primary encryption and secondary encryption; when the encryption method is no encryption, the translation data is directly transmitted to the user's cloud account through the network; when the encryption method is primary encryption, the translation data is encrypted using an asymmetric encryption algorithm and transmitted to the user's cloud account, and when the user views the translation data, it jumps to identity authentication. When the user passes the identity authentication, the key is sent to the user's mailbox in the form of an email. When the user fails the identity authentication, the translation data is locked; when the encryption method is secondary encryption, asymmetric encryption is used to generate a pair of public and private keys, and then the private key is used to symmetrically encrypt the translation data and transmit it to the user's cloud account. When the user views the translation data, it jumps to identity authentication. When the user passes the identity authentication, the key is sent to the user's mailbox in the form of an email. When the user fails the identity authentication, the translation data is locked; The analysis results in the risk assessment coefficient of each target person, and the specific analysis process is as follows: The distance between target characters, the dangerousness of their behaviors and the dangerousness of their translation data are recorded as Ji, XWi and FWi, respectively, where i represents the number corresponding to each target character, i=1, 2, ..., n, n represents the total number of target characters, and n is a natural integer greater than or equal to 2; According to the calculation formula The risk assessment coefficient βi of each target person is obtained, where e is represented by a natural constant, XW′ and FW′ represent the reference value corresponding to the behavior risk of the target person in the database and the reference value corresponding to the risk of the translation data, respectively, and ω1 and ω2 represent the weight factor corresponding to the behavior risk in the database and the weight factor corresponding to the risk of the translation data, respectively.

2. A wearable artificial intelligence wireless Internet of Things security system according to claim 1, characterized in that: The specific process of judging whether the user's environment is abnormal is as follows: The user's environmental assessment coefficient is compared with the environmental assessment coefficient threshold in the database. When the user's environmental assessment coefficient is greater than or equal to the environmental assessment coefficient threshold in the database, it is determined that the user's environment is abnormal; otherwise, it is determined that the user's environment is not abnormal.

3. A wearable artificial intelligence wireless Internet of Things security system according to claim 1, characterized in that: The specific process of judging whether the user's location is abnormal is as follows: The user's location evaluation coefficient is compared with the location evaluation coefficient threshold in the database. When the user's location evaluation coefficient is greater than or equal to the location evaluation coefficient threshold in the database, it is determined that the user's location is abnormal; otherwise, it is determined that the user's location is not abnormal.

4. A wearable artificial intelligence wireless Internet of Things security system according to claim 1, characterized in that: The specific process of judging whether each target person has a risk is as follows: The risk assessment coefficient of each target person is compared with the risk assessment coefficient threshold in the database. When the risk assessment coefficient of a target person is greater than or equal to the risk assessment coefficient threshold in the database, the target person is judged to be at risk. Otherwise, the target person is judged to be not at risk. In this way, it is determined whether each target person is at risk.

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