132 results about "Raspberry pi" patented technology

The invention relates to a network environmental monitoring system based on Arduino and raspberry Pi. The network environmental monitoring system comprises an environment data monitoring terminal, a transmission network, a database server and a Web server, the environment data monitoring terminal comprises an optical dust sensor, a humiture sensor, a digital buzzer, a LED lamp, a LCD display, an Arduino UNO plate and a raspberry Pi plate, the optical dust sensor, humiture sensor, the digital buzzer, the LED lamp, and the LCD display are connected with the Arduino UNO plate; dust particle quantity in air can be measured and calculated through the optical dust sensor by the Arduino UNO plate, and environment humiture data is obtained by reading a signal transmitted by the humiture sensor by the Arduino UNO plate. The network environmental monitoring system realizes the acquisition of temperature, humidity, and PM 2.5 environment data of network, on one hand, environment data can be displayed at a environment data monitoring terminal with real time, and local acousto-optic alarm information can be emitted according to the condition, on the other hand, the environment data is transmitted to a database server through network, and check and query can be carried out through a browser.

The invention belongs to the technical field of detection equipment and particularly relates to an amphibious detection robot. The robot is suitable for the two environments of water and land, free travelling on the land and underwater can be achieved, detection of the water surface and the water bottom can further be achieved, and SLAM on the land, on the water surface and at the water bottom is achieved. According to the robot, a sealed waterproof shell is integrally formed through a machine shell, a front end cover and a rear end cover. Four driving wheels are mounted outside the sealed shell. Each driving wheel is provided with a motor which is arranged in the sealed shell. A fixed chassis is mounted in the sealed shell. Sonar, a Raspberry Pi and an ultra-distance transmission module Xbee-pro used for data transmission are mounted on the chassis. An ROS operation system is transplanted in the Raspberry Pi, a SLAM algorithm is set in the Raspberry Pi, target detection and information collection can be achieved, and SLAM is completed autonomously. The amphibious detection robot provided by the invention has the advantages of being high in operation ability, high in intelligent degree, good in airtightness and the like.

The invention provides a Raspberry PI-based intelligent cargo examination express box, comprising a box body, wherein the upper end of the box body is provided with a delivering opening which is provided with a delivering door, the side wall of the box body is provided with a picking opening at a position close to the lower end of the box body, the picking opening is provided with a picking door, electromagnetic locks are respectively mounted on the delivering door and the picking door, the box body is provided with an electronic scale, a barcode scanning assembly and a control assembly, the electromagnetic locks, the electronic scale and the barcode scanning assembly are respectively connected with the control assembly, and an operation time sequence of the electromagnetic locks and the barcode scanning assembly is controlled by the control assembly. As the barcode scanning assembly is arranged, a barcode of an express mail can be scanned, and the control assembly judges whether the barcode is consistent with an order form number or an express number preset in the control assembly or not, so as to determine whether the delivering door is opened or not, thus achieving aims of identifying the identity of a courier and preventing delivery errors.

The invention discloses a four-rotor unmanned aerial vehicle visual navigation system and method based on Raspberry Pi. The system comprises a flight control panel, a driving module, a power supply detection module, a visual processing module, a height measurement module, a remote controller control module and a wireless communication module, wherein the flight control panel comprises a flight control processor, a communication module and an attitude acquisition module. The method comprises the following steps: controlling the unmanned aerial vehicle to fly to a stable height, and switching toan automatic control mode; acquiring a camera image by using the Raspberry Pi to extract a target, calculating the position deviation of a target relative to the unmanned aerial vehicle, transmittingthe position deviation information to a flight control processor, and compensating the position deviation information by using attitude angle information to acquire final deviation information; and finally, calculating a control output value by using a PID algorithm and the deviation information, and controlling the unmanned aerial vehicle to perform tracking flight. The system is high in transportability and low in cost, the method is simple and convenient, and autonomous navigation of the unmanned aerial vehicle can be performed without GPS signals.

A Raspberry Pi-based power grid equipment video monitoring device is disclosed and comprises a Raspberry Pi server, an IP camera, a PC termnial end and a display screen. The Raspberry Pi server, the IP camera and the PC client terminal are connected to the router; the Raspberry Pi server is connected to the router through an Ethernet interface, and the display screen is connected to the Raspberry Pi server through an HDMI interface. TCP / IP protocol communication is adopted among the Raspberry Pi server, the IP camera and the PC client terminal; the Raspberry Pi server adopts mechanisms under Linux , such as shared memories, message queues, semaphores and the like, to finish internal data processing operation. The device disclosed in the invention is characterized in that the device can be mounted near power grid equipment which requires real time video monitoring, angle and position adjustment of the device can be realized, and important parts or typical characteristics can be monitored. The device disclosed in the invention is small in size, low in cost and high in speed; power grid equipment can be monitored in real time; the device is convenient to install and easy to use; by employing the device, power grid operation and maintenance departments can quickly and accurately know about the appearance state of the power grid equipment in real time.

The invention discloses a remote police WiFi network investigation and evidence collection system based on the raspberry pi and a method thereof. The system comprises a remote evidence collection terminal, the remote evidence collection terminal comprises a raspberry pi controller, and a plurality of WiFi crack schemes are stored in the raspberry pi controller; the raspberry pi controller is connected with a first WiFi wireless network card; the WiFi wireless network card is used for calling a WiFi crack scheme matched with a suspect WiFi network in the raspberry pi controller to crack the suspect WiFi network, monitor a data packet in the WiFi network and form evidence collection data, and transmitting the evidence collection data to the raspberry pi controller for storage, when a monitoring mode is opened; and the raspberry pi controller is further connected with an environment detection module, the environment detection module is used for detecting surrounding environment information of the evidence collection system and transmitting the surrounding environment information to the raspberry pi controller, and when the environment information received by the raspberry pi controller is not within a preset environment information safety threshold range, the raspberry pi controller deletes the evidence collection data.

The invention discloses an efficient and safe industrial Internet of Things data exchange method based on Raspberry Pi, belonging to the technical field of information, mainly comprising: connection between the PLC and the Raspberry Pi, connection between the sensor and the Raspberry Pi, connection between the camera and the Raspberry Pi, collection of the acquired data, encryption and transmission of the acquired data, data receiving and data decryption. In the invention, the connection between the PLC and the Raspberry Pi is a connection by RS485 to the TTL chip, the connection between the camera and the sensor may be performed by using an ordinary helper line, and the DES algorithm is used for encryption and decryption in the process of encryption and decryption. By adopting the Raspberry Pi as the information transfer equipment, the invention is capable of storing large-capacity data, and simultaneously accepting the data of the PLC, sensor and video, thereby obtaining powerful functions. The host computer and the Raspberry Pi transmit through the network cable with stable transmission and low transmission cost, and may work in harsh environments. The received data is written in the form of a dynamic link library, which is convenient for the writing of the host computer, so that the software of the host computer has better flexibility.

The invention discloses an intelligent desk lamp with a child health learning supervision function, which is mainly based on image recognition and combined with a deep learning algorithm which is popular at present and has an obvious application effect, and an open-source Tensorflow framework is combined with a DeepLab technology to realize monitoring of child autonomous learning. Meanwhile, opensource hardware Raspberry Pi is used for internal control, a table lamp is used as a carrier, and the designed system module comprises an image processing module, a communication module, a front-end control module, a cloud data module and a mobile terminal display module. The image processing module is responsible for image collection, model training and image recognition. The communication moduleis responsible for Bluetooth communication and WIFI communication. The front-section control module is responsible for interactive control and induction automatic control. The cloud data module is responsible for data storage, data query and data communication. And the mobile terminal display module is responsible for data configuration and binding registration of the mobile terminal. The methodis combined with the built-in voice module to monitor the learning state of the child and help the child to learn autonomously.

The invention provides an education and entertainment robot based on an ROS framework. A speech recognition module, a power management module, a sensor module, a function module and a motion control module are connected with a decision making module provided with an ROS. A human-computer interaction platform provided with an ROS is connected with the decision making module through a communication module. Meanwhile, identical ROS masters are arranged on a Raspberry Pi 3 of the human-computer interaction platform and a Raspberry Pi 3 of the decision making module, and mutual communication of nodes on the human-computer interaction platform and nodes on the robot is achieved. The communication module comprises a USB communication terminal and a data transmission communication terminal. Based on the ROS framework, a command is sent to the robot through the human-computer interaction platform, then bottom-driven control, remote control, autonomous obstacle avoidance, autonomous line patrol, automatic following, various LED lamp control, multipath signal collecting, ultrasonic distance measurement and other functions are achieved, in addition, open source codes are adopted, secondary development is facilitated, the development difficulty is lowered, and the development process is accelerated.

The invention discloses an intelligent monitoring robot based on Raspberry Pi. The intelligent monitoring robot comprises a motor driving module, a power source voltage-stabilizing module, an electronic gyroscope for assisting the robot in making a turn and detecting the direction, an ultrasonic sensor for avoiding collision of the robot, a voice module used for recognizing a voice instruction, aRaspberry Pi development board used for processing data, a camera used for collecting video information and a carbon fiber vertical column used for fixing the camera, wherein the motor driving module,the power source voltage-stabilizing module, the electronic gyroscope, the ultrasonic sensor, the voice module, the Raspberry Pi development board, the camera and the carbon fiber vertical column arearranged on a movable base. A control part of the intelligent monitoring robot comprises the Raspberry Pi development board, a driving unit, a sensor detection unit, a monitoring unit and a voice recognition unit. The size of a whole security and protection monitoring system is smaller, the structure is simple, the intelligent monitoring robot is suitable for development and utilization of smarthome, the effect of real-time monitoring of unmanned home is achieved, and the home safety of a user is ensured.

The invention discloses an access control system for a large traffic scene based on two-dimensional face recognition, the access control system comprises an access control terminal disposed at an entrance and exit, wherein the access control terminal is connected to a remote server, a user access record management website and a remote real-time monitoring terminal through a communication network.The access control terminal is provided with a Raspberry Pi camera, a face detection module, a face recognition module and an access control module, wherein a face information table is set in the remote server, the face information table includes user's photo code information, and the photo code information is obtained by performing 68 key point detection adjustment on a user's front face or sideface photo, and then encoding 128 parameters through a FaceNet deep convolutional neural network. The access control system has a good effect on recognition rate and security, does not require a largeamount of face data, and can greatly improve access control efficiency without strict user cooperation in the case of security. Data mining is performed by using user access information, and user's living habits are analyzed by recorded entrance and exit data for detection of user's abnormal states.

The invention belongs to the technical field of computer vision and target detection, and relates to a lightweight deep neural network method for personnel detection and people counting in an elevatorcar. According to the method, a convolutional neural network model is lightened, a Raspberry Pi 4B is taken as a development system, and embedded resources are fully utilized to locally realize personnel detection and people counting of an elevator car. According to the invention, a convolutional neural network (CNN) model structure comprises 11 blocks and totally comprises 23 convolution layers,region recommendation is carried out on an advanced semantic feature map, and the recommended region carries out dichotomy on a human head part and a background through a full connection layer; and on the basis of a multi-scale diversity target detection algorithm SSD, depth separable convolution is added to the convolution layer of each block of a network structure, the detection speed is obviously increased, video detection in a Raspberry Pi 4B system reaches FPS of 2.1, and the requirement that information feedback is smaller than 3S is met.

The invention belongs to the technical field of intelligent signing, and discloses a WIFI signing-in system and method based on Raspberry Pi. The method comprises the following steps: customizing and starting signing-in WIFI through the Raspberry Pi, and leading signing-in information into a MongoDB database in advance; gathering and storing signing-in equipment information in the Raspberry Pi through a background program after the WIFI is started; ensuring that the background program calculates out a list of signed personnel and a list of unsigned personnel by matching signing-in equipment with mac stored into the database; ensuring that signing personnel or management personnel inquire the signing-in information through a signing-in interface and correct personal information correspondingly; sending the signing-in information to a designated site. The system and method can reduce waste of time caused by signing in, improve the authenticity of attendance records, completely eradicate the fact that signed personnel surf the Internet through mobile phones, and are convenient for checking-in personnel to manage signing-in records and inquire the attendance records.

The invention discloses a rescue robot based on Raspberry Pi. The rescue robot comprises a rescue robot module and a remote terminal module; the rescue robot module comprises a tricycle, a Raspberry Pi controller, a driving circuit, two motors, a visual camera, an alarming machine and a first wireless receiving and transmitting module, wherein the Raspberry Pi controller, the driving circuit, the two motors, the visual camera, the alarming machine and the first wireless receiving and transmitting module are mounted on the tricycle; the two motors, the visual camera and the alarming machine are separately connected with the Raspberry Pi controller through the driving circuit; the Raspberry Pi controller is connected with the first wireless receiving and transmitting module; the visual camera is arranged at the head part of the tricycle; the two motors are separately used for driving the two rear wheels of the tricycle to rotate; and the remote terminal module comprises a second wireless receiving and transmitting module, a control terminal and a displayer, wherein the second wireless receiving and transmitting module and the displayer are respectively connected with the control terminal. The rescue robot based on the Raspberry Pi, disclosed by the invention, has the functions of walking forwards by virtue of wireless control, transmitting back the image, displaying the image and the like, is applicable to a rescue site and can provide helps for rescue workers.

The invention relates to an unmanned aerial vehicle image and video transmission and distribution system and method based on a 4G network. The system comprises an unmanned aerial vehicle, corresponding flight control hardware, and a Raspberry Pi board, wherein the Raspberry Pi board is connected with the flight control hardware of the unmanned aerial vehicle, and obtains the flight control information of the unmanned aerial vehicle. The system comprises: a cloud server for receiving the unmanned aerial vehicle flight control information transmitted based on the 4G network, wherein a pluralityof specified mobile phones access the server to download the flight control information, and the mobile phones which use a 4G network and are provided with ground station apps developed autonomously,wherein the unmanned aerial vehicle ground stations can receive return signals from different unmanned aerial vehicles at the same time and control the multiple unmanned aerial vehicles at the same time. According to the unmanned aerial vehicle image and video transmission and distribution system and method based on the 4G network, unmanned aerial vehicle flight control information is sent to a cloud server by using a mavlink protocol, video and flight control information can be received by a multi-mobile phone end ground station app, and the limitation that only one device can receive unmanned aerial vehicle video and flight control information in a traditional method is broken through.

The invention relates to a cloud server-based automatic meteorological data monitoring system. The system comprises a data acquisition module, a network sharing part and a network device; the data acquisition module comprises a plurality of different data acquisition devices and a main control chip; the network sharing part comprises a Raspberry Pi device and a cloud server; the data acquisition devices are connected with the main control chip; the main control chip is connected with the Raspberry Pi device; the Raspberry Pi device is connected with the cloud server through a wireless network;the network device can access the cloud server through the wireless network; the data acquisition module is used for acquiring meteorological data and transmitting the meteorological data to the maincontrol chip; the main control chip is used for transmitting the meteorological data to the Raspberry Pi device; and the Raspberry Pi device is used for uploading the meteorological data to the cloudserver. With the system adopted, the real-time monitoring of the meteorological data can be realized, and users can access the meteorological data through the wireless network.

The invention discloses a Raspberry Pi design-based Botnet analysis method of a household router. The method comprises the following steps: A, carrying out network port monitoring by using a pyspark library in python to extract network flow information, and carrying out data storage on the extracted information; B, calculating according to the data obtained in the step A to obtain flow characteristics; C, accumulating the values of the flow characteristics calculated in the step B through weight to obtain a threat value; and D, carrying out matching detection on the threat value to judge whether the network flow is botnet flow or not. According to the method provided by the invention, the network flow of the router is extracted and calculated, and then the calculated result is compared andanalyzed, so that the problem that the existing small household router is insufficient in botnet detection and processing accuracy is solved.

The invention provides a deep learning product defect detection system based on Raspberry Pi, and the system comprises: an collection module which is used for collecting an image of a to-be-detected product; the Raspberry Pi that is in communication connection with the acquisition module, receives and preprocesses the acquired image of the product to be detected, identifies a defective product according to the preprocessed image of the product to be detected, and acquires image information of the defective product; and the display module that is in communication connection with the Raspberry Pi, receives the defective product image information, and displays a defective product image and a defective product image contour according to the defective product image information. The invention can realize detection and identification of defective products, has the advantage of low cost, and has very high popularization and use values. Correspondingly, the invention further provides a deep learning product defect detection method based on the Raspberry Pi.

The invention discloses an isometric spiral multipoint ultraviolet spectrum cryopreserved serum analyzer. The analyzer is composed of a machine body and internal components arranged in the machine body, wherein the internal components mainly include a Raspberry Pi, an ultraviolet raman laser, an ultraviolet raman probe, a spectrograph, a sample chamber, a direct-current stabilized power supply, atouch screen, a liquid nitrogen pump, a liquid nitrogen bottle, an electric controlling and moving assembly and an auxiliary component. The isometric spiral multipoint ultraviolet spectrum cryopreserved serum analyzer disclosed by the invention has the advantages that a mode that the flow is controlled by an electric valve and the liquid velocity is controlled by the liquid nitrogen pump is utilized, and low-temperature and constant-temperature control in an inner cavity is achieved; thus, test environment uniformity is ensured, and test repeatability is ensured; for a cryopreserved serum sample test, constant-low-temperature isometric spiral multipoint ultraviolet raman fluorescence spectra collection is performed, principal component analysis is performed on spectrum mean vectors and spectrum variance vectors, contingency and deviation of a single-point test are eliminated, and recognition rates of different species of serums can be effectively improved.

The invention relates to a transformer station secondary screen cabinet temperature and humidity monitoring system based on Raspberry Pis and Java language, a computer serves as a client, an instruction is input and a result is displayed, Raspberry Pis serve as servers and control temperature and humidity measurement modules connected thereto to measure, and the system is controlled by programming in JAVA language. The client and the Raspberry Pi servers are connected through a local area network, communication therebetween is performed through a TCP / IP protocol, network interface programs are compiled in the client and Raspberry Pi system respectively, and data exchange and transmission are realized through Java programs of two sides. Programs for function realization of the monitoring system include four main program modules: a control temperature and humidity measuring module, a Raspberry Pi server communication module, a client communication module and a client visual interface module. The transformer station secondary screen cabinet temperature and humidity monitoring system can enable a power grid operation maintenance department to rapidly and accurately know the temperature and humidity condition in the secondary screen cabinet in real time, timely discover the environment temperature and humidity standard-exceeding condition, and to take effective measures to dispose, thereby ensuring that secondary equipment runs at appropriate temperature and humidity.

The invention discloses a Raspberry Pi-based small-scale autonomous underwater vehicle. The Raspberry Pi-based small-scale autonomous underwater vehicle comprises a control panel, a sensor module, a driving plate, a propeller, a movable plate and a power management module, wherein the control panel comprises a main control panel and an auxiliary control panel; when the main control panel is in normal working state, the main control panel controls the other parts; when the main control panel works abnormally, the auxiliary control panel controls the other parts; the sensor module comprises a depth sensor, a camera, a GPS module and an attitude sensor; the depth sensor and the camera are arranged outside a sealing cabin and are in contact with the external world; the GPS module and the attitude sensor are arranged in the sealing cabin; and the driving plate is controlled by the control panel to drive the propeller and the movable plate. According to the main control panel, the common PCplatform or PC 104 platform of Intel is replaced by Raspberry Pi, so that both the volume and power consumption are greatly decreased, and low power consumption, miniaturization and low cost are realized.

The invention discloses an embedded ranging system based on binocular machine vision, which relates to the technical field of ranging. The embedded ranging system photographs a difference image by means of a binocular camera, transmits the difference image to Raspberry Pi and processes the difference image by adopting an OpenCV computer vision library, displays a ranging numerical value after a user selects a region, and has the advantages of convenient measurement and high applicability.

The invention discloses a fixed node mobile phone automation testing device. The fixed node mobile phone automation testing device comprises a Raspberry Pi, a customized testing circuit h and more than one mobile phone clamping devices. The customized testing circuit comprises a master control chip, a power supply in signal communication with the master control chip, an USB-to-serial-port chip, an USB interface physical control A module, a current detection module, a multifunctional extension port, an OLED display screen, a rotary encoder and a first USB interface, wherein the first USB interface is in series connection with the output terminal of the USB interface physical control A module and the first USB interface, the current detection module and the USB interface physical control A module constitute a current detection loop. The fixed node mobile phone automation testing device is small in volume and light in weigh, easy to deploy in clusters and flexible to clamp and install and low in power consumption and can be used in various simple working environments. Furthermore, the fixed node mobile phone automation testing device is simple in task issuing, high in scene coverage and can satisfy wide testing requirements for different mobile phones.

The invention discloses an NCS and MS-based similar object real-time detection method and system, and the method comprises: collecting the image data of a similar object, carrying out the preprocessing, extracting edge feature parameters, and constructing a sample data set; inputting the sample data set into a recognition model for training, ending the training until a training precision thresholdis met, and outputting a recognition result; performing detection classification of corresponding object numbers on the identification result by using a decision model, advancing a detection processin real time in combination with an AI accelerator, and outputting a corresponding detection result; and importing the detection result into a Bayesian analysis model to carry out secondary verification judgment, and displaying the verification result and the detection result in real time by using a mobile terminal or a Raspberry Pi display. According to the method, the image detection object canbe rapidly propelled and the specific object of the object can be identified so that the problem that the specific object cannot be judged in the process of image detection of multiple similar objectscan be solved, and the method has relatively high real-time performance, accuracy, applicability and economy.

The invention provides a strawberry picking robot based on a Raspberry Pi, and belongs to the technical field of agricultural harvest machinery. The strawberry picking robot comprises a mobile platform module, a coordinate type mechanical arm module, a picking mechanism module, a control module and a vision module, wherein the mobile platform module is located on the bottommost layer of the picking robot and is used for driving the picking robot to move forwards in a strawberry greenhouse; the coordinate type mechanical arm module is arranged on the slight left side above a chassis and is usedfor enabling the picking mechanism module to be close to a mature strawberry fruit; the picking mechanism module is arranged on the coordinate type mechanical arm module and is used for picking the strawberry fruit; and the control module and the vision module are arranged on the chassis, the vision module is used for collecting a strawberry image, and the control module is used for processing the image and controlling the motion of the picking robot. The strawberry picking robot is capable of fully-automatically working in the strawberry greenhouse and recognizing, positioning and picking the mature strawberry.

Then invention relates to an auxiliary lighting system for night vehicle driving based on computer vision. The auxiliary lighting system comprises an information acquisition module, a Raspberry Pi controlling module and an auxiliary lamp dynamic controlling module which are connected in order. The information acquisition module transmits acquired pavement information to the Raspberry Pi controlling module. The Raspberry Pi controlling module achieves dynamic controlling over GPIO after internal processing and completes intelligent controlling over the auxiliary lamp dynamic controlling module. By arranging effective communication integration of all the modules, mutual feedback and mutual sharing of information among all the modules are achieved, so that the problem of night driving is effectively solved, the potential safety hazard is reduced, authentic intelligent light controlling is achieved, visual visibility can be greatly improved, and the potential safety hazard is reduced.

The invention relates to a raspberry Pi-based security prevention monitoring system, comprising a raspberry Pi development board, a cloud terminal, an environmental acquisition unit and a monitoring unit, wherein the monitoring unit comprises a camera; an input end of the camera is connected with an output end of the raspberry Pi development board; the environmental acquisition unit comprises a temperature and humidity sensor and a carbon monoxide sensor, wherein input ends of the temperature and humidity sensor and the carbon monoxide sensor are connected with the output end of the raspberryPi development board; the data communication between raspberry Pi development board and the cloud terminal is realized by means of a network. The raspberry Pi-based security prevention monitoring system realizes the real-time monitoring for temperature, humidity and carbon monoxide as well as a video monitoring effect, thus guaranteeing the home safety of a user; due to the use of raspberry Pi, the whole security prevention monitoring system is smaller in volume and simpler in structure.

The invention discloses an organic waste fermentation tank information acquisition method and an intelligent system of a turning and throwing machine. The information acquisition method comprises a sensor setting scheme and a data acquisition scheme at a fermentation tank and a manure tank. The sensor setting scheme comprises a waterproof environmental sensor and a distance measuring sensor, wherein the environmental sensor is embedded into the wall of the fermentation tank, and the distance measuring sensor is arranged on the upper portion of the wall of the manure tank. The data acquisitionscheme comprises an acquisition controller which is networked with a server terminal and controlled by the server terminal, the acquisition controller acquires data from the environmental sensor through an acquisition card, the acquired data are processed by the acquisition controller and then displayed on a factory building monitoring screen or returned to the server terminal, and the acquisitioncontroller monitors the material height of the manure tank through the distance measuring sensor connected with the acquisition card and comprises a Raspberry Pi capable of initiating operation instructions to the intelligent system of the turning and throwing machine for the fermentation tank according to the detected environmental data. Information of the organic waste fermentation tank can beconveniently acquired, and turning and throwing operation can be controlled.

The invention relates to a bone age assessment method and system. The method is applied to Raspberry Pi and comprises the following steps of: receiving a bone age assessment request; acquiring a hand bone image and a user gender corresponding to the hand bone image according to the bone age assessment request; and performing cutting and histogram matching on the hand bone image, performing numerical conversion on the user gender, transmitting the user gender to a nerve calculation rod, and performing bone age assessment according to the hand bone image and the user gender through a pre-trained bone age assessment model deployed in the nerve calculation rod. By adopting the method, the safety can be improved, and meanwhile, the processing speed is increased to improve the assessment efficiency.

The invention relates to a programmable music play system based on the Raspberry Pi, and the system comprises an infrared ray output module, a camera module, a Raspberry Pi development board, a keyboard pattern projection module, a music output module and a power module. The camera module, the keyboard pattern projection module, the infrared ray output module, the music output module and the powermodule are respectively connected with the Raspberry Pi development board. The keyboard pattern projection module forms a keyboard pattern on a plane through projection, and the infrared ray output module carries out the irradiation of a projection region. The camera module receives an infrared image from the infrared ray output module, and transmits the infrared image to the Raspberry Pi development board. The Raspberry Pi development board carries out the image processing, and recognizes the control gesture or key operation of a user on the keyboard pattern, and controls the music output module to play corresponding music. Compared with the prior art, the system is programmable, is portable, greatly reduces the cost in learning the related knowledge of the music and musical instruments,and reduces the performing difficulty of the musical instruments.