CN109547476B - Self-generating outdoor mobile identity recognition system - Google Patents
Self-generating outdoor mobile identity recognition system Download PDFInfo
- Publication number
- CN109547476B CN109547476B CN201811600185.4A CN201811600185A CN109547476B CN 109547476 B CN109547476 B CN 109547476B CN 201811600185 A CN201811600185 A CN 201811600185A CN 109547476 B CN109547476 B CN 109547476B
- Authority
- CN
- China
- Prior art keywords
- module
- user
- data
- information
- intelligent control
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
- H04L63/0861—Network architectures or network communication protocols for network security for authentication of entities using biometrical features, e.g. fingerprint, retina-scan
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/70—Multimodal biometrics, e.g. combining information from different biometric modalities
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Multimedia (AREA)
- Biomedical Technology (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Computer Security & Cryptography (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Human Computer Interaction (AREA)
- Medical Informatics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Collating Specific Patterns (AREA)
Abstract
The invention provides a self-generating outdoor mobile identity recognition system which comprises at least one user data entry system, at least one background data computing system, at least one intelligent control system, an energy self-supply system and a cloud platform server. Compared with the traditional mobile handheld equipment, the mobile handheld equipment has the advantages that the identity recognition mode is more free and random, and the user experience is improved; meanwhile, the user identification is realized by adopting a feature extraction method, the accuracy of the user identification algorithm is improved by utilizing the feature quantity threshold value for classification, and compared with other mobile handheld devices, the method has the advantages that the calculation amount is small, the complex template establishing process is omitted, and the individual adaptability of the mobile identity identification algorithm is improved.
Description
Technical Field
The invention relates to the field of outdoor identity mobile authentication electronics, in particular to a self-generating outdoor mobile identity recognition system.
Background
New regulations for chinese network payments have been implemented for nearly two years. The new blueprint of the intelligent life opened by the internet plus 'network payment' and 'mobile payment' of the era is small in exposed potential safety hazard. Whether traditional third party payment mechanisms or increasingly sophisticated payment interfaces, users who are not authenticated by real names nowadays have limited functions even if products come out successively. This benefits from the rapid development of mobile internet and the wide spread of smart phone industry in our country.
In recent years, mobile identification payment electronic devices embedded with various Micro-Electro-Mechanical systems (MEMS) sensors, such as mobile phones, computers, smart watches, and the like, have developed rapidly, and application scenes include urban large public spaces, urban comprehensive business spaces, and community-level point-like public service points distributed in cities, such as urban public display spaces of museums, science and technology museums, libraries, convention and exhibition centers, and the like. Main exhibition areas of the large-scale comprehensive venue in the city are provided with passenger group identification monitoring equipment, and daily visiting behaviors and data of the passenger groups of the venue are analyzed. The method has the advantages that the operation condition of the venue is digitalized and accurate, trend prediction is formed for venue operation, and finally important assessment and decision basis is generated for future accurate service of the venue. Aiming at the scattered point arrangement requirement, a cloud + end mode is adopted, so that the light weight and low cost arrangement is realized. And data of all the points are gathered in real time, and the cloud analyzes the whole data, so that accurate service and decision basis is finally formed for the operation condition of the customer group of the network point. And (3) passenger group identification monitoring equipment is arranged in a main exhibition area of a large-scale comprehensive commercial space of a city, and daily customer visiting behaviors and data of a venue are analyzed. The business space operation condition is digitalized and accurate, trend prediction is formed for business space operation, and finally important evaluation and decision basis is generated for future accurate service of the business space. Meanwhile, recognition technology plays an important role in the field of human-computer interaction.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the invention provides a self-generating outdoor mobile identity recognition system to solve the problems that the existing identity recognition mode is relatively backward, the identity recognition period is long, the accuracy is low, and spontaneous power supply cannot be realized.
The technical scheme adopted by the invention for solving the technical problem is explained by adopting a layer-by-layer progressive method;
a self-generating outdoor mobile identity recognition system comprises at least one user data entry system, at least one background data computing system, at least one intelligent control system, an energy self-supply system and a cloud platform server,
the user data input system can identify user data information through voice identification, fingerprint identification, gesture identification and face identification, and sends the user data information to the intelligent control system and the cloud platform server through the background data computing system;
the background data computing system performs classification confirmation on the data input by the user data input system, performs computing, and feeds back a computing result to the intelligent control system and the cloud platform server;
the intelligent control system confirms user information and executes service items according to results obtained by the background data computing system, and feeds the user information and the service items back to the cloud platform server for confirmation;
the energy self-sufficient supply system can collect energy and is used for supplying energy to the user data entry system, the background data computing system and the intelligent control system at the same time;
the cloud platform server is used for receiving the background data computing system data, generating a user database, analyzing and classifying user behavior big data, generating a first feedback signal to the intelligent control system, and enabling or disabling the intelligent control system to perform targeted service on the user according to the first feedback signal;
the targeted service is that the data of a user data entry system is monitored and scheduled in real time through a network platform, the service content is pushed in real time according to the operation result through the real-time monitoring of the data used by the user according to user authentication information, character planning and service content preference factors, a background manager carries out mining analysis on the data, the data is shared in a multi-direction and cross mode, and convenience is brought to the user while the experience is not reduced. The accurate pushing of the service content is automatically completed; and the background data computing system, the intelligent control system and the cloud platform server are in signal transmission through a GPS transmission network.
Furthermore, the user data entry system comprises a voice recognition module, a fingerprint recognition module, a gesture recognition module and a face recognition module;
the voice recognition module converts the voice signal of the user into corresponding text information and feeds the text information back to the background data computing system for operation processing.
The voice recognition module comprises a sound signal filtering module and a framing preprocessing working module;
the sound signal filtering module mainly comprises a feature extraction module and an acoustic model module;
the framing preprocessing working module mainly comprises a language model module, a dictionary and a decoding module;
the feature extraction module extracts a signal to be analyzed from an original signal of a user, converts a sound signal from a time domain to a frequency domain and provides a proper feature vector for the acoustic model module; the acoustic model module calculates the score of each feature vector on the acoustic features according to the acoustic characteristics; the language model module calculates the probability of the sound signal corresponding to the possible phrase sequence according to the theory related to linguistics; and finally, the dictionary and decoding module decodes the phrase sequence according to the existing dictionary to obtain the final possible text representation.
The fingerprint identification module is an optical fingerprint module, including: the fingerprint identification module utilizes the refraction and reflection principles of light, the light irradiates to a prism from the bottom and is emitted out through the prism, the angle of the emitted light refracted on the uneven line of the fingerprint on the surface of the finger and the light reflected back are different in brightness, the fingerprint identification module adopts a SIC MOSFET to collect picture information with different brightness degrees to complete the collection of the fingerprint, the fingerprint acquisition module adopts an OLED screen, and the OLED screen can emit light.
The gesture recognition module includes: the device comprises a main controller module, a posture module, a data acquisition and uploading module, a coordinate system conversion module, an upper computer module and a gesture operation module; the gesture recognition module receives three-axis acceleration and angular velocity acquired by the attitude module sensor and three-axis attitude angles acquired by attitude resolving through the operation of the modules controlled by the main controller module, then transmits data to the upper computer module through the data acquisition and uploading module, the gesture operation module intercepts gesture data and performs gesture classification through characteristic quantities of extracted motion gestures, and the gesture recognition module adopts an ultrasonic gesture recognition technology.
The face recognition module carries out recognition based on visual features, pixel statistical features, face image transformation coefficient features and face image algebraic features; the face recognition module consists of a display unit, a main control unit, an acquisition module, a network interface and an identity card reading module for authenticating the public security department; the display unit is a 10-inch high-definition display screen; the main control unit is an Intel Core i7 processor, an 8G memory and a 500G SSD hard disk; the acquisition module is a 500 ten thousand pixel camera;
the visual characteristics and the pixel statistical characteristics of the user are displayed on the display unit, the acquisition module feeds back face image transformation coefficient characteristics and face image algebraic characteristics to the main control unit for face characteristic operation, meanwhile, the identity card reading module acquires related user identity information from a public security department authentication network through a network interface, the two groups of user information are compared, if the two groups of user information are matched, the authentication is passed, and if the two groups of user information are not matched, the authentication is failed.
Further, the background data computing system comprises a user data management module and a database computing module, wherein the user data management module can continuously update and improve the content of the database, and the database computing module is used for dynamically computing the data automatically acquired by the user data entry system based on three algorithms of a symbol sequence, a Hopfield neural network, a speed increment and a symbol sequence matching template;
the user data management module comprises the following components:
the data management and maintenance unit is used for taking charge of the acquisition and construction of user data and comprises a user data classification system and knowledge point keywords;
the database searching unit is used for being responsible for definition and state detection of daily detection of a user and acquiring the data entry information condition of the user;
the knowledge base content package issuing unit is used for regularly generating the latest knowledge base content package available for issuing, distributing the version number according to rules and informing an authorized user of new version issuing information based on various predefined communication modes;
the database computing module comprises an upgrading unit, and when the cloud platform server generates a first feedback signal to the intelligent control system, the upgrading unit upgrades user data to the latest state and feeds back the record of the version to the intelligent control system.
Preferably, the user data entry system and the cloud platform server acquire user information or import data to a user data control module integrated in the intelligent control system through a connection mode such as a GPS mode, near field communication or bluetooth.
Preferably, the user data entry system and the cloud platform server are in close contact with each other by adopting the RFID tag sensing module, the RFID tag sensing module comprises a sensor unit and an RFID tag unit, the two units can be physically integrated together or physically separated from each other, and the two units can realize the functions of indoor or outdoor information acquisition and wireless transmission together.
Furthermore, the RFID tag unit comprises a microprocessor, and a power supply, an RF chip, a temperature monitoring circuit and an infrared monitoring circuit which are respectively connected with the microprocessor, the sensor unit can be correspondingly provided with an infrared sensor and a temperature sensor, and the sensors are connected with the temperature monitoring circuit and the infrared monitoring circuit in a conventional mode to transmit monitored infrared information and temperature information to the electronic tag.
Further, the user data entry system calculates user traffic and viscosity according to the following formula,
user traffic and viscosity = (user current year login frequency-user last year login frequency) × service content cost rate
Furthermore, setting the service content cost rate weight, and obtaining the user flow and the viscosity value by adopting a weighted average method calculation formula.
Further, the intelligent control system comprises a user data control module and a user data intelligent feedback module,
the user data control module comprises the following components:
the system comprises a user information establishing unit, a user information obtaining unit and a user profile generating unit, wherein the user information establishing unit is used for establishing an independent user profile for all users signed with application authorization agreements, and the user profile comprises user identity information, first input time, user service content and user service frequency;
the user data intelligent feedback module is used for being deployed to the upper computer and the encryption machine to be responsible for verifying the consistency of the user information of the unique user key;
the user data intelligent feedback module comprises the following components:
the user data identification unit is used for supporting that the uKey of a database of a Hopfield neural network is accessed to a special port of an upper computer, checking the consistency of user information and the information of the authentication identity card of the ministry of public security, comparing the user information stored in the database management system and determining whether the user information needs to be updated.
Furthermore, the energy self-supply system comprises a solar thin-film battery pack protection shell, an energy storage system, an energy efficient management system, an intelligent microcontroller and a photosensitive sensor, wherein the solar thin-film battery pack protection shell consists of a GaAs thin film, a shell and a graphene modified silica gel material, and the graphene modified silica gel is uniformly distributed between the GaAs thin film and the shell and plays roles of bonding, heat preservation and current collection; the energy storage system is formed by connecting at least one lithium rechargeable battery in series; the energy efficient management system realizes information interaction with the intelligent control system and electric energy calculation scheduling through an intelligent microcontroller; the photosensitive sensor is used for identifying the illumination of light and feeding back the illumination to the intelligent control system through the intelligent microcontroller;
the energy efficient management system further comprises a power distribution module consisting of a voltage stabilizer, an inverter and a plurality of relays.
Further, the cloud platform server includes: the system comprises a transmission monitoring module, a visual monitoring module, a data storage module and a big data analysis module;
the visual monitoring module stores data in the data storage module through user data fed back by the intelligent control system and displays the data on the visual monitoring module of the cloud platform server in real time;
the visual monitoring module sets acquisition parameters of one or some data acquisition systems by using a graphical interface and sends the acquisition parameters to the corresponding intelligent control system in a wireless mode; the self-generating outdoor mobile identity recognition system can be further distributed with collection tasks in the graphical interface operation mode, and data in a background database are imported into the self-generating outdoor mobile identity recognition system;
the data displayed in real time by the visual monitoring module can provide support for judging or early warning users and background workers and fault prediction and diagnosis at the same time;
the big data analysis module counts the use time and frequency of the user and working parameters of service content to generate a user database pool for character situation writing; and providing corresponding service content and sending the service content to the intelligent control system through a second control signal to carry out intelligent pushing on the service content.
Further, the intelligent control system is used for forwarding a parameter setting command sent by the cloud platform server or acquisition information sent to the cloud platform server by the intelligent control system;
the data transmission module of the intelligent control system is static, the forwarding data of the cloud platform server is obtained by setting a fixed data mode, the transmission monitoring module monitors the acquisition information from the intelligent control system and delivers the acquisition information to the visual monitoring module, the acquisition setting command sent by the visual monitoring module is sent out, and the acquisition setting command is fed back to the user port by the intelligent control system.
Preferably, the visual monitoring module includes:
a data monitoring device for acquiring said further, said visual monitoring module comprising:
the data monitoring device is used for acquiring working parameters of the self-generating outdoor mobile identity recognition system, and the working parameters comprise user information, user frequency, service content, service cost and service income;
a radio frequency unit for transceiving signals;
the microprocessor is used for generating a state signal of the self-generating outdoor mobile identity recognition system;
further, the cloud platform server analyzes all user use historical working parameters and working curves to generate a second control signal and a user credit level indication signal;
and the cloud platform server sends the second control signal to the intelligent control system, and sends the user credit level indication signal to a user control terminal for early warning or system protection through a third signal.
The big data analysis module counts the use time and frequency of the user and working parameters of service content to generate a user database pool for character situation writing; and providing corresponding service content and sending the service content to the intelligent control system through a second control signal to carry out intelligent pushing on the service content.
Further, when the cloud platform server exchanges data with the intelligent control system and the user control terminal, the data exchange process includes the following steps:
(s 1) the access user authenticates the identity information through the user data entry system, and the access user can log in the cloud server after passing the identity authentication information;
(s 2) the access user initiates an access control request, the cloud platform server detects whether the access control request of the access user is private data, and if the access control request is not private information, the access user is allowed to perform an access process; otherwise, go to step (s 3);
(s 3), according to the identity authentication information and the personal privacy policy of the access user, the cloud platform server allocates roles for the access user and judges the purpose of the access user;
(s 4), the cloud platform server detects whether the purpose of the access user and the personal privacy policy are consistent, and if so, the step is carried out (s 5); otherwise, the access flow is ended;
(s 5) the cloud platform server detects whether the authority owned by the access user allows the operation data, if not, the access process is ended; otherwise, go to step (s 6);
(s 6) the cloud platform server detects the consistency of the IP in the request purpose and the IP of the requested data, and if the IP in the request purpose and the IP of the requested data are not consistent, the access flow is ended; otherwise, the cloud platform server activates the role of the access user, the access request of the access user takes effect, and the service content is allowed to be accessed.
Compared with the traditional mobile handheld equipment, the mobile handheld equipment has the advantages that the identity recognition mode is more free and random, and the user experience is improved; meanwhile, the method for extracting the features is adopted to realize user identification, the accuracy rate of a user identification algorithm is improved by utilizing the feature quantity threshold value for classification, the calculation amount is small compared with other mobile handheld devices, the complex template establishing process is omitted, the individual adaptability of the mobile identity identification algorithm is improved, and compared with the traditional identity identification device, the mobile identity identification method can realize self-power generation and power supply, and the later maintenance cost is extremely low. The invention realizes the identity authentication of user data by a multidimensional identification technology, improves the authentication accuracy, establishes an intelligent management system by utilizing an informatization means to realize the high-efficiency reasonable utilization of energy, monitors and dispatches user data input system data in real time through a network platform, and automatically finishes the accurate pushing of service content according to factors such as user authentication information, personality writing, service content preference and the like. Meanwhile, the invention is directly connected with the user, the user can freely set service content and manage personal information according to the actual situation, in addition, an intelligent big data operation system is established on the basis of big data, the user uses data to monitor in real time, the service content is pushed in real time according to the operation result, a background manager carries out mining analysis on the data, the data is shared in a multi-direction and cross way, and convenience is brought to the user while the experience is not reduced.
Drawings
The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is an overall block diagram of a self-generating outdoor mobile identity recognition system.
Fig. 2 is a block diagram of a voice recognition module system of a self-generating outdoor mobile identity recognition system.
Fig. 3 is a system block diagram of a fingerprint identification module of the self-generating outdoor mobile identity identification system.
Fig. 4 is a system block diagram of a gesture recognition module of a self-generating outdoor mobile identity recognition system.
Fig. 5 is a system block diagram of a face recognition module of the self-generating outdoor mobile identity recognition system.
Fig. 6 is a block diagram of a background data calculation system of a self-generating outdoor mobile identity recognition system.
Fig. 7 is a block diagram of an intelligent control system of a self-generating outdoor mobile identity recognition system.
Fig. 8 is a block diagram of an energy self-supply system of the self-generating outdoor mobile identity recognition system.
Fig. 9 is a block diagram of a cloud platform server system of the self-generating outdoor mobile identity recognition system.
Fig. 10 is a flow chart of a user access system of the self-generating outdoor mobile identity recognition system.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, the terms "first", "second", and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "connected" and "connected" are to be interpreted broadly, e.g., as being fixed or detachable or integrally connected; can be mechanically or electrically connected; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
As shown in fig. 1 to 10, the self-generating outdoor mobile identity recognition system includes at least one user data entry system, at least one background data computing system, at least one intelligent control system, an energy self-supply system and a cloud platform server,
the user data input system can identify user data information through voice identification, fingerprint identification, gesture identification and face identification, and sends the user data information to the intelligent control system and the cloud platform server through the background data computing system;
the background data computing system performs classification confirmation on the data input by the user data input system, performs computing, and feeds back a computing result to the intelligent control system and the cloud platform server;
the intelligent control system confirms user information and executes service items according to results obtained by the background data computing system, and feeds the user information and the service items back to the cloud platform server for confirmation;
the energy self-sufficient supply system can collect energy and is used for supplying energy to the user data entry system, the background data computing system and the intelligent control system at the same time;
the cloud platform server is used for receiving the background data computing system data, generating a user database, analyzing and classifying user behavior big data at the same time, generating a first feedback signal to the intelligent control system,
the intelligent control system starts or closes the intelligent control system to perform targeted service on the user according to the first feedback signal;
and the background data computing system, the intelligent control system and the cloud platform server are in signal transmission through a GPS transmission network.
As shown in fig. 1 to 5, the user data entry system includes a voice recognition module, a fingerprint recognition module, a gesture recognition module, and a face recognition module;
the voice recognition module converts the voice signal of the user into corresponding text information and feeds the text information back to the background data computing system for operation processing,
the voice recognition module comprises a sound signal filtering module and a framing preprocessing working module;
the sound signal filtering module mainly comprises a feature extraction module and an acoustic model module;
the framing preprocessing working module mainly comprises a language model module, a dictionary and a decoding module;
the feature extraction module extracts a signal to be analyzed from an original signal of a user, converts a sound signal from a time domain to a frequency domain and provides a proper feature vector for the acoustic model module; the acoustic model module calculates the score of each feature vector on the acoustic features according to the acoustic characteristics; the language model module calculates the probability of the sound signal corresponding to the possible phrase sequence according to the theory related to linguistics; and finally, the dictionary and decoding module decodes the phrase sequence according to the existing dictionary to obtain the final possible text representation.
Preferably, the speech recognition module selected by the present invention is a SYN7318 chinese speech interaction module, and the SYN7318 chinese speech interaction module integrates speech recognition, speech synthesis, and speech wake-up function modules. And receiving the command frame in a UART interface communication mode. Such as control command frames, text data to be synthesized, text-to-speech, speech-to-text conversion, and voice wake-up functions. The module supports speech recognition of 10000 entries. The user can flexibly correspond the vocabulary item ID and the command ID to the similar vocabulary items according to the semantic category appointed by the user. As for "please turn on the light 1", "turn on the light 1", a command ID =1 designated for the user may be fed back. The module can also support a voice awakening function, and six interesting awakening names are supported inside the module. In the wake-up mode a simple voice call can wake up. Non-specific human command word recognition, six voice awakening functions with specific command words, Chinese, English and Chinese-English mixed recognition and natural language processing, 10000 voice commands can be defined, the identification command words can be dynamically updated, the entry updating supports two modes, the overtime detection text synthesis function, the text intelligent analysis processing, the polyphone processing and the Chinese family name processing capability support 10-level volume adjustment, 10-level speed adjustment and 10-level tone adjustment, 77 voice prompt tones are integrated in the module, 14 chord music is integrated in the module, the self-defined addition prompt tone capability is supported, 6 Chinese pronunciations of two men, two women, an effector and a girl voice are provided, various text control marks are supported, and the voice awakening function MP3 supporting six specific command words plays local MP3 music.
The fingerprint identification module is an optical fingerprint module, including: the fingerprint identification module utilizes the refraction and reflection principles of light, the light irradiates to a prism from the bottom and is emitted out through the prism, the angle of the emitted light refracted on the uneven line of the fingerprint on the surface of the finger and the light reflected back are different in brightness, the fingerprint identification module adopts a SIC MOSFET to collect picture information with different brightness degrees to complete the collection of the fingerprint, the fingerprint acquisition module adopts an OLED screen, and the OLED screen can emit light.
Preferably, the fingerprint identification module is an ZAZ-010-B optical fingerprint module, and the DSP and the FPGA are used as fingerprint sensors, flash memories, RS-232 interfaces and LED interfaces of peripheral equipment. Operating frequency 500MHz, data acquisition from fingerprint sensor at 20MHz rate, sensor lifetime: more than 100 ten thousand times, antistatic index: 15KV, fingerprint image recording time: <250 ms, operating temperature: -25 ℃ to 70 ℃, relative humidity: 20% -90%, communication interface: support for USB and UART (TTL logic level), communication baud rate: 9600 BPS-115200 BPS is adjustable.
The gesture recognition module includes: the device comprises a main controller module, a posture module, a data acquisition and uploading module, a coordinate system conversion module, an upper computer module and a gesture operation module; the gesture recognition module receives three-axis acceleration and angular velocity acquired by the attitude module sensor and three-axis attitude angles acquired by attitude resolving through the operation of the modules controlled by the main controller module, then transmits data to the upper computer module through the data acquisition and uploading module, the gesture operation module intercepts gesture data and performs gesture classification through characteristic quantities of extracted motion gestures, and the gesture recognition module adopts an ultrasonic gesture recognition technology.
Preferably, the main controller module of the gesture recognition module selects ATmega328 to control the operation of each module, and the gesture module selects MPU6050 sensor for collection.
Preferably, the upper computer module selects the following programs for operation:
the/Python code is as follows:
X_train = train.drop(['label'],axis=1)
X_test = test.drop(['label'], axis=1)
X_train = np.array(X_train.iloc[:,:])
X_train = np.array([np.reshape(i, (28,28)) for i in X_train])
X_test = np.array(X_test.iloc[:,:])
X_test = np.array([np.reshape(i, (28,28)) for i in X_test])
num_classes = 26
y_train = np.array(y_train).reshape(-1)
y_test = np.array(y_test).reshape(-1)
y_train = np.eye(num_classes)[y_train]
y_test = np.eye(num_classes)[y_test]
X_train = X_train.reshape((27455, 28, 28, 1))
X_test = X_test.reshape((7172, 28, 28, 1))
model
We will construct a simple CNN (convolutional neural network) using Keras.
CNN has 7 layers:
python is implemented as follows:
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot
import pydot
def model():
classifier = Sequential()
classifier.add(Convolution2D(filters=8,
kernel_size=(3,3),
strides (1,1),
padding='same',
input_shape=(28,28,1),
activation='relu',
data_format='channels_last'))
classifier.add(MaxPooling2D(pool_size=(2,2)))
classifier.add(Convolution2D(filters=16,
kernel_size=(3,3),
strides=(1,1),
padding='same',
activation='relu'))
classifier.add(MaxPooling2D(pool_size=(4,4)))
classifier.add(Flatten())
classifier.add(Dense(128, activation='relu'))
classifier.add(Dense(26, activation='sigmoid'))
classifier.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
return classifier
then fit the model to the training set and check the accuracy on the test set.
classifier.fit(X_train, y_train, batch_size = 100, epochs = 100)
y_pred = classifier.predict(X_test)。
The face recognition module carries out recognition based on visual features, pixel statistical features, face image transformation coefficient features and face image algebraic features; the face recognition module consists of a display unit, a main control unit, an acquisition module, a network interface and an identity card reading module for authenticating the public security department; the display unit is a 10-inch high-definition display screen; the main control unit is an Intel Core i7 processor, an 8G memory and a 500G SSD hard disk; the acquisition module is a 500 ten thousand pixel camera;
the visual characteristics and the pixel statistical characteristics of the user are displayed on the display unit, the acquisition module feeds back face image transformation coefficient characteristics and face image algebraic characteristics to the main control unit for face characteristic operation, meanwhile, the identity card reading module acquires related user identity information from a public security department authentication network through a network interface, the two groups of user information are compared, if the two groups of user information are matched, the authentication is passed, and if the two groups of user information are not matched, the authentication is failed.
As shown in fig. 6, the background data computing system includes a user data management module and a database computing module, wherein the user data management module continuously updates and perfects the database content, and the database computing module is configured to dynamically compute the data automatically acquired by the user data entry system based on three algorithms, i.e., a symbol sequence in combination with a Hopfield neural network, a speed increment, and a symbol sequence in combination with a template matching;
the user data management module comprises the following components:
the data management and maintenance unit is used for taking charge of the acquisition and construction of user data and comprises a user data classification system and knowledge point keywords;
the database searching unit is used for being responsible for definition and state detection of daily detection of a user and acquiring the data entry information condition of the user;
the knowledge base content package issuing unit is used for regularly generating the latest knowledge base content package available for issuing, distributing the version number according to rules and informing an authorized user of new version issuing information based on various predefined communication modes;
the database computing module comprises an upgrading unit, and when the cloud platform server generates a first feedback signal to the intelligent control system, the upgrading unit upgrades user data to the latest state and feeds back the record of the version to the intelligent control system.
Preferably, the user data entry system and the cloud platform server acquire user information or import data to a user data control module integrated in the intelligent control system through a connection mode such as a GPS mode, near field communication or bluetooth.
Preferably, the user data entry system and the cloud platform server are in close contact with each other by adopting the RFID tag sensing module, the RFID tag sensing module comprises a sensor unit and an RFID tag unit, the two units can be physically integrated together or physically separated from each other, and the two units can realize the functions of indoor or outdoor information acquisition and wireless transmission together.
Furthermore, the RFID tag unit comprises a microprocessor, and a power supply, an RF chip, a temperature monitoring circuit and an infrared monitoring circuit which are respectively connected with the microprocessor, the sensor unit can be correspondingly provided with an infrared sensor and a temperature sensor, and the sensors are connected with the temperature monitoring circuit and the infrared monitoring circuit in a conventional mode to transmit monitored infrared information and temperature information to the electronic tag.
Further, the user data entry system calculates user traffic and viscosity according to the following formula,
user traffic and viscosity = (user current year login frequency-user last year login frequency) × service content cost rate
Furthermore, setting the service content cost rate weight, and obtaining the user flow and the viscosity value by adopting a weighted average method calculation formula.
As shown in fig. 7, the intelligent control system comprises a user data control module and a user data intelligent feedback module,
the user data control module comprises the following components:
the system comprises a user information establishing unit, a user information obtaining unit and a user profile generating unit, wherein the user information establishing unit is used for establishing an independent user profile for all users signed with application authorization agreements, and the user profile comprises user identity information, first input time, user service content and user service frequency;
the user data intelligent feedback module is used for being deployed to the upper computer and the encryption machine to be responsible for verifying the consistency of the user information of the unique user key;
the user data intelligent feedback module comprises the following components:
the user data identification unit is used for supporting that the uKey of a database of a Hopfield neural network is accessed to a special port of an upper computer, checking the consistency of user information and the information of the authentication identity card of the ministry of public security, comparing the user information stored in the database management system and determining whether the user information needs to be updated.
As shown in fig. 8, the energy self-supply system includes a solar thin film battery pack protection shell, an energy storage system, an energy efficient management system, an intelligent microcontroller and a photosensitive sensor, wherein the solar thin film battery pack protection shell is composed of a GaAs thin film, a shell and a graphene modified silica gel material, and the graphene modified silica gel is uniformly distributed between the GaAs thin film and the shell and plays roles of adhesion, heat preservation and current collection; the energy storage system is formed by connecting at least one lithium rechargeable battery in series; the energy efficient management system realizes information interaction with the intelligent control system and electric energy calculation scheduling through an intelligent microcontroller; the photosensitive sensor is used for identifying the illumination of light and feeding back the illumination to the intelligent control system through the intelligent microcontroller;
the energy efficient management system further comprises a power distribution module consisting of a voltage stabilizer, an inverter and a plurality of relays.
As shown in fig. 9, the cloud platform server includes: the system comprises a transmission monitoring module, a visual monitoring module, a data storage module and a big data analysis module;
the visual monitoring module stores data in the data storage module through user data fed back by the intelligent control system and displays the data on the visual monitoring module of the cloud platform server in real time;
the visual monitoring module sets acquisition parameters of one or some data acquisition systems by using a graphical interface and sends the acquisition parameters to the corresponding intelligent control system in a wireless mode; the self-generating outdoor mobile identity recognition system can be further distributed with collection tasks in the graphical interface operation mode, and data in a background database are imported into the self-generating outdoor mobile identity recognition system;
the data displayed in real time by the visual monitoring module can provide support for judging or early warning users and background workers and fault prediction and diagnosis at the same time.
Further, the intelligent control system is used for forwarding a parameter setting command sent by the cloud platform server or acquisition information sent to the cloud platform server by the intelligent control system;
the data transmission module of the intelligent control system is static, the forwarding data of the cloud platform server is obtained by setting a fixed data mode, the transmission monitoring module monitors the acquisition information from the intelligent control system and delivers the acquisition information to the visual monitoring module, the acquisition setting command sent by the visual monitoring module is sent out, and the acquisition setting command is fed back to the user port by the intelligent control system.
Preferably, the visual monitoring module includes:
a data monitoring device for acquiring said further, said visual monitoring module comprising:
the data monitoring device is used for acquiring working parameters of the self-generating outdoor mobile identity recognition system, and the working parameters comprise user information, user frequency, service content, service cost and service income;
a radio frequency unit for transceiving signals;
the microprocessor is used for generating a state signal of the self-generating outdoor mobile identity recognition system;
further, the cloud platform server analyzes all user use historical working parameters and working curves to generate a second control signal and a user credit level indication signal;
and the cloud platform server sends the second control signal to the intelligent control system, and sends the user credit level indication signal to a user control terminal for early warning or system protection through a third signal.
The big data analysis module counts the use time and frequency of the user and working parameters of service content to generate a user database pool for character situation writing; and providing corresponding service content and sending the service content to the intelligent control system through a second control signal to carry out intelligent pushing on the service content.
As shown in fig. 10, when the cloud platform server exchanges data with the intelligent control system and the user control terminal, the data exchange process includes the following steps:
(s 1) the access user authenticates the identity information through the user data entry system, and the access user can log in the cloud server after passing the identity authentication information;
(s 2) the access user initiates an access control request, the cloud platform server detects whether the access control request of the access user is private data, and if the access control request is not private information, the access user is allowed to perform an access process; otherwise, go to step (s 3);
(s 3), according to the identity authentication information and the personal privacy policy of the access user, the cloud platform server allocates roles for the access user and judges the purpose of the access user;
(s 4), the cloud platform server detects whether the purpose of the access user and the personal privacy policy are consistent, and if so, the step is carried out (s 5); otherwise, the access flow is ended;
(s 5) the cloud platform server detects whether the authority owned by the access user allows the operation data, if not, the access process is ended; otherwise, go to step (s 6);
(s 6) the cloud platform server detects the consistency of the IP in the request purpose and the IP of the requested data, and if the IP in the request purpose and the IP of the requested data are not consistent, the access flow is ended; otherwise, the cloud platform server activates the role of the access user, the access request of the access user takes effect, and the service content is allowed to be accessed.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, a schematic representation of the term does not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (10)
1. A self-generating outdoor mobile identity recognition system comprises at least one user data entry system, at least one background data computing system, at least one intelligent control system, an energy self-supply system and a cloud platform server,
the user data input system can identify user data information through voice identification, fingerprint identification, gesture identification and face identification, and sends the user data information to the intelligent control system and the cloud platform server through the background data computing system;
the background data computing system performs classification confirmation on the data input by the user data input system, performs computing, and feeds back a computing result to the intelligent control system and the cloud platform server;
the intelligent control system confirms user information and executes service items according to results obtained by the background data computing system, and feeds the user information and the service items back to the cloud platform server for confirmation;
the energy self-sufficient supply system can collect energy and is used for supplying energy to the user data entry system, the background data computing system and the intelligent control system at the same time;
the cloud platform server is used for receiving the background data computing system data, generating a user database, analyzing and classifying user behavior big data, and generating a first feedback signal to the intelligent control system;
the intelligent control system starts or closes the targeted service provided by the intelligent control system to the user according to the first feedback signal;
the targeted service is to monitor and schedule user data input system data in real time through a network platform, push service content in real time according to operation results through user use data real-time monitoring according to user authentication information, character planning and service content preference factors, mine and analyze the data by a background manager, and perform multi-directional cross sharing of the data, so that convenience is brought to users while experience is not reduced, and accurate pushing of the service content is automatically completed;
and the background data computing system, the intelligent control system and the cloud platform server are in signal transmission through a GPS transmission network.
2. The self-generating outdoor mobile identity recognition system according to claim 1,
the user data entry system comprises a voice recognition module, a fingerprint recognition module, a gesture recognition module and a face recognition module;
the voice recognition module converts the voice signal of the user into corresponding text information and feeds the text information back to the background data computing system for operation processing,
the voice recognition module comprises a sound signal filtering module and a framing preprocessing working module;
the sound signal filtering module mainly comprises a feature extraction module and an acoustic model module;
the framing preprocessing working module mainly comprises a language model module, a dictionary and a decoding module;
the feature extraction module extracts a signal to be analyzed from an original signal of a user, converts a sound signal from a time domain to a frequency domain and provides a proper feature vector for the acoustic model module; the acoustic model module calculates the score of each feature vector on the acoustic features according to the acoustic characteristics; the language model module calculates the probability of the sound signal corresponding to the possible phrase sequence according to the theory related to linguistics; and finally, the dictionary and decoding module decodes the phrase sequence according to the existing dictionary to obtain the final possible text representation.
3. The system of claim 2, wherein the fingerprint recognition module is an optical fingerprint module, comprising: the fingerprint identification module utilizes the refraction and reflection principles of light, the light irradiates to a prism from the bottom and is emitted out through the prism, the angle of the emitted light refracted on the uneven line of the fingerprint on the surface of the finger and the light reflected back are different in brightness, the fingerprint identification module adopts a SIC MOSFET to collect picture information with different brightness degrees to complete the collection of the fingerprint, the fingerprint acquisition module adopts an OLED screen, and the OLED screen can emit light.
4. The system according to claim 2, wherein the gesture recognition module comprises: the device comprises a main controller module, a posture module, a data acquisition and uploading module, a coordinate system conversion module, an upper computer module and a gesture operation module; the gesture recognition module receives three-axis acceleration and angular velocity acquired by the attitude module sensor and three-axis attitude angles acquired by attitude resolving through the operation of the modules controlled by the main controller module, then transmits data to the upper computer module through the data acquisition and uploading module, the gesture operation module intercepts gesture data and performs gesture classification through characteristic quantities of extracted motion gestures, and the gesture recognition module adopts an ultrasonic gesture recognition technology.
5. The system of claim 2, wherein the face recognition module performs recognition based on visual features, pixel statistical features, face image transformation coefficient features, and face image algebraic features; the face recognition module consists of a display unit, a main control unit, an acquisition module, a network interface and an identity card reading module for authenticating the public security department; the display unit is a 10-inch high-definition display screen; the main control unit is an Intel Core i7 processor, an 8G memory and a 500G SSD hard disk; the acquisition module is a 500 ten thousand pixel camera;
the visual characteristics and the pixel statistical characteristics of the user are displayed on the display unit, the acquisition module feeds back face image transformation coefficient characteristics and face image algebraic characteristics to the main control unit for face characteristic operation, meanwhile, the identity card reading module acquires related user identity information from a public security department authentication network through a network interface, the two groups of user information are compared, if the two groups of user information are matched, the authentication is passed, and if the two groups of user information are not matched, the authentication is failed.
6. The system of claim 1, wherein the background data computing system comprises a user data management module and a database computing module, wherein the user data management module continuously updates and perfects database contents, and the database computing module is used for dynamically computing data automatically acquired by the user data entry system based on three algorithms of symbol sequence combination with Hopfield neural network, speed increment and symbol sequence combination template matching;
the user data management module comprises the following components:
the data management and maintenance unit is used for taking charge of the acquisition and construction of user data and comprises a user data classification system and knowledge point keywords;
the database searching unit is used for being responsible for definition and state detection of daily detection of a user and acquiring the data entry information condition of the user;
the knowledge base content package issuing unit is used for regularly generating the latest knowledge base content package available for issuing, distributing the version number according to rules and informing an authorized user of new version issuing information based on various predefined communication modes;
the database computing module comprises an upgrading unit, and when the cloud platform server generates a first feedback signal to the intelligent control system, the upgrading unit upgrades user data to the latest state and feeds back the record of the version to the intelligent control system.
7. The self-generating outdoor mobile identity recognition system according to claim 1,
the intelligent control system comprises a user data control module and a user data intelligent feedback module,
the user data control module comprises the following components:
the system comprises a user information establishing unit, a user information obtaining unit and a user profile generating unit, wherein the user information establishing unit is used for establishing an independent user profile for all users signed with application authorization agreements, and the user profile comprises user identity information, first input time, user service content and user service frequency;
the user data intelligent feedback module is used for being deployed to the upper computer and the encryption machine to be responsible for verifying the consistency of the user information of the unique user key;
the user data intelligent feedback module comprises the following components:
the system comprises a user data identification unit, a database authentication server and a database management system, wherein the user data identification unit is used for supporting that a database uKey of a Hopfield neural network is accessed to a special port of an upper computer, checking the consistency of user information and the information of an authentication identity card of a public security department, comparing the user information stored in the database management system and determining whether the user information needs to be updated;
the intelligent control system is used for forwarding a parameter setting command sent by the cloud platform server or acquisition information sent to the cloud platform server by the intelligent control system;
the data transmission module of the intelligent control system is static, and the forwarding data of the cloud platform server is obtained by setting a fixed data mode.
8. The self-generating outdoor mobile identity recognition system according to claim 1,
the energy self-supply system comprises a solar thin-film battery pack protection shell, an energy storage system, an energy efficient management system, an intelligent microcontroller and a photosensitive sensor, wherein the solar thin-film battery pack protection shell is composed of a GaAs thin film, a shell and a graphene modified silica gel material, and the graphene modified silica gel is uniformly distributed between the GaAs thin film and the shell and plays roles in bonding, heat preservation and current collection; the energy storage system is formed by connecting at least one lithium rechargeable battery in series; the energy efficient management system realizes information interaction with the intelligent control system and electric energy calculation scheduling through an intelligent microcontroller; the photosensitive sensor is used for identifying the illumination of light and feeding back the illumination to the intelligent control system through the intelligent microcontroller;
the energy efficient management system further comprises a power distribution module consisting of a voltage stabilizer, an inverter and a plurality of relays.
9. The self-generating outdoor mobile identity recognition system according to claim 1,
the cloud platform server includes: the system comprises a transmission monitoring module, a visual monitoring module, a data storage module and a big data analysis module;
the visual monitoring module stores data in the data storage module through user data fed back by the intelligent control system and displays the data on the visual monitoring module of the cloud platform server in real time;
the visual monitoring module sets acquisition parameters of one or some data acquisition systems by using a graphical interface and sends the acquisition parameters to the corresponding intelligent control system in a wireless mode; the self-generating outdoor mobile identity recognition system can be further distributed with collection tasks in the graphical interface operation mode, and data in a background database are imported into the self-generating outdoor mobile identity recognition system;
the data displayed in real time by the visual monitoring module can provide support for judging or early warning users and background workers and fault prediction and diagnosis at the same time.
10. The self-generating outdoor mobile identity recognition system according to claim 1,
the cloud platform server analyzes all user use historical working parameters and working curves to generate a second control signal and a user credit level indicating signal;
the cloud platform server sends the second control signal to the intelligent control system, and sends the user credit level indication signal to a user control terminal as a third signal for early warning or system protection;
the big data analysis module counts the use time and frequency of the user and the working parameters of the service content to generate a user database pool for character situation writing; and providing corresponding service content and sending the service content to the intelligent control system through a second control signal to carry out intelligent pushing on the service content.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811600185.4A CN109547476B (en) | 2018-12-26 | 2018-12-26 | Self-generating outdoor mobile identity recognition system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811600185.4A CN109547476B (en) | 2018-12-26 | 2018-12-26 | Self-generating outdoor mobile identity recognition system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109547476A CN109547476A (en) | 2019-03-29 |
| CN109547476B true CN109547476B (en) | 2021-10-22 |
Family
ID=65857810
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811600185.4A Active CN109547476B (en) | 2018-12-26 | 2018-12-26 | Self-generating outdoor mobile identity recognition system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109547476B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110083723B (en) * | 2019-04-24 | 2021-07-13 | 成都大熊猫繁育研究基地 | Small panda individual identification method, equipment and computer readable storage medium |
| CN111652058B (en) * | 2020-04-27 | 2023-03-28 | 青岛百灵信息科技股份有限公司 | Computer face recognition device |
| CN112838947A (en) * | 2020-12-23 | 2021-05-25 | 广州体育职业技术学院 | Construction method of power communication scheduling system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102045162A (en) * | 2009-10-16 | 2011-05-04 | 电子科技大学 | Personal identification system of permittee with tri-modal biometric characteristic and control method thereof |
| CN104916727A (en) * | 2015-04-24 | 2015-09-16 | 京东方科技集团股份有限公司 | Solar cell, manufacturing method of solar cell, display module and display device |
| CN105893404A (en) * | 2015-11-11 | 2016-08-24 | 乐视云计算有限公司 | Natural information identification based pushing system and method, and client |
| CN106570641A (en) * | 2016-11-07 | 2017-04-19 | 成都科曦科技有限公司 | Intelligent hotel management system based on Internet of everything |
| CN107862543A (en) * | 2017-09-29 | 2018-03-30 | 江苏跃鑫科技有限公司 | Business intelligence system based on intelligent video analysis technology |
| CN108228914A (en) * | 2018-03-06 | 2018-06-29 | 中经汇通电子商务有限公司 | A kind of information transmission system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150199571A1 (en) * | 2014-01-16 | 2015-07-16 | Toshiba Tec Kabushiki Kaisha | Pos terminal apparatus and customer information acquisition method |
| US9923949B2 (en) * | 2014-03-24 | 2018-03-20 | Baldur A. Steingrimsson | All-electronic ecosystems for design and collaboration |
-
2018
- 2018-12-26 CN CN201811600185.4A patent/CN109547476B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102045162A (en) * | 2009-10-16 | 2011-05-04 | 电子科技大学 | Personal identification system of permittee with tri-modal biometric characteristic and control method thereof |
| CN104916727A (en) * | 2015-04-24 | 2015-09-16 | 京东方科技集团股份有限公司 | Solar cell, manufacturing method of solar cell, display module and display device |
| CN105893404A (en) * | 2015-11-11 | 2016-08-24 | 乐视云计算有限公司 | Natural information identification based pushing system and method, and client |
| CN106570641A (en) * | 2016-11-07 | 2017-04-19 | 成都科曦科技有限公司 | Intelligent hotel management system based on Internet of everything |
| CN107862543A (en) * | 2017-09-29 | 2018-03-30 | 江苏跃鑫科技有限公司 | Business intelligence system based on intelligent video analysis technology |
| CN108228914A (en) * | 2018-03-06 | 2018-06-29 | 中经汇通电子商务有限公司 | A kind of information transmission system |
Non-Patent Citations (3)
| Title |
|---|
| To wa rd s a data- driven IoT software a rchitecture for smart city utilities;Yogesh Simmhan ET AL;《WILEY》;20180430;全文 * |
| 可穿戴手势识别控制器;徐军等;《电子技术应用》;20161231;全文 * |
| 基于云平台的智慧旅游信息推送系统的研究;周长敏;《软件设计开发》;20180331;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109547476A (en) | 2019-03-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109756489B (en) | High-efficient thing networking user management system | |
| CN108009521B (en) | Face image matching method, device, terminal and storage medium | |
| CN109547476B (en) | Self-generating outdoor mobile identity recognition system | |
| US8660959B2 (en) | Content distribution platform | |
| US9002768B2 (en) | Human-computer interface system | |
| CN109243432A (en) | Speech processing method and the electronic equipment for supporting the speech processing method | |
| CN110753927B (en) | Synchronizing access control between computing devices | |
| CN110149806A (en) | The digital assistants of stack data structures are handled | |
| CN109711200B (en) | Precise poverty alleviation method, device, equipment and medium based on block chain | |
| CN112862516A (en) | Resource delivery method and device, electronic equipment and storage medium | |
| US10740635B2 (en) | Motion based account recognition | |
| KR20220091529A (en) | Anaphora treatment | |
| US11057673B2 (en) | Personalized content aggregation and delivery | |
| CN109272994A (en) | Speech data processing method and the electronic device for supporting the speech data processing method | |
| US20180267603A1 (en) | Physical object addition and removal based on affordance and view | |
| WO2024060951A1 (en) | Servicing method and apparatus for services | |
| CN110414624A (en) | Disaggregated model construction method and device based on multi-task learning | |
| CN112634017A (en) | Remote card opening activation method and device, electronic equipment and computer storage medium | |
| CN106462629A (en) | Direct answer triggering in search | |
| CN111639318A (en) | Wind control method based on gesture monitoring on mobile terminal and related device | |
| CN112437956B (en) | Speech recognition system | |
| KR102353687B1 (en) | Server for providing service for educating english and method for operation thereof | |
| CN113762587A (en) | Material releasing method and device and related products | |
| CN115708085A (en) | Business processing method, neural network model training method, device, equipment and medium | |
| KR102678350B1 (en) | English education method using learning level and service system therefor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |