WO2018196731A1 - 智能感知设备及感知系统 - Google Patents

智能感知设备及感知系统 Download PDF

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Publication number
WO2018196731A1
WO2018196731A1 PCT/CN2018/084201 CN2018084201W WO2018196731A1 WO 2018196731 A1 WO2018196731 A1 WO 2018196731A1 CN 2018084201 W CN2018084201 W CN 2018084201W WO 2018196731 A1 WO2018196731 A1 WO 2018196731A1
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Prior art keywords
data
sensor
sensing device
acceleration
sampling
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PCT/CN2018/084201
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English (en)
French (fr)
Inventor
李漾
王茂
王畅
张婷
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大连云动力科技有限公司
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Application filed by 大连云动力科技有限公司 filed Critical 大连云动力科技有限公司
Priority to EP18792176.2A priority Critical patent/EP3599754B1/en
Priority to US16/607,680 priority patent/US11255706B2/en
Priority to JP2019558384A priority patent/JP6912120B2/ja
Publication of WO2018196731A1 publication Critical patent/WO2018196731A1/zh

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    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G08—SIGNALLING
    • G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
    • G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
    • G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/16—Implementation or adaptation of Internet protocol [IP], of transmission control protocol [TCP] or of user datagram protocol [UDP]
    • H04L69/163—In-band adaptation of TCP data exchange; In-band control procedures
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters
    • H04W4/027—Services making use of location information using location based information parameters using movement velocity, acceleration information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Definitions

  • the invention relates to an intelligent sensing device and a sensing system.
  • the information society's requirements for communication are not limited to communication between people.
  • the development of sensing technology and network technology makes it possible to communicate between people and goods, goods and articles. Intelligent sensing devices are like this. Under the circumstances, it came into being.
  • the intellisense device in the prior art has the following defects: severe fragmentation and single sensing parameters, such as a sensing device that can only sense temperature on the market, a sensing device that can only perceive illumination, etc., and the sensing device cannot be effectively linked, resulting in data. And the loss of the relevance of the event; the user only installs one type of sensing device that cannot meet their needs, and often needs to install several or more sensing devices to satisfy the full intelligent experience, and these sensing devices often need to pass several or more
  • the control of the data platform not only violates the original intention of intelligence, but also causes trouble for users, and also causes waste of resources.
  • today's intelligent sensing products generally have problems of limited transmission distance, low reliability, and complicated configuration.
  • sensing devices based on short-distance communication methods such as 433MHz wireless technology and Zigbee technology want to connect data directly to the Internet. (Internet), usually need to synchronize the data to the total controller and then communicate with the external Internet through the total controller.
  • Internet usually need to synchronize the data to the total controller and then communicate with the external Internet through the total controller.
  • the communication mode has narrow bandwidth and is not suitable for transmission.
  • Frequency or larger capacity data such as images, sounds, etc.
  • existing sensing devices consume a lot of power and power, so external power supply has to be widely used, resulting in limitations and inflexibility in installation and use.
  • the present invention addresses the above problems and develops an intelligent sensing device.
  • An intelligent sensing device includes: a sensor unit having a plurality of sensors, a wireless communication module connected to the data platform, a storage module, and a processing module; the processing module is coupled to the sensor unit, the wireless communication module, and the storage module connection;
  • the sensor unit includes at least two of a temperature sensor, a humidity sensor, an ambient light sensor, a magnetic field sensor, an acceleration sensor, and a shock sensor;
  • the wireless communication module includes at least a WIFI chip;
  • the processing module learns the motion of the user according to the detection result of the acceleration sensor and uses a preset detection algorithm; the sensing device has multiple modes, and different modes correspond to different preset detection algorithms, and the user sets the mode by using the sensing device. Determining the selection of the preset detection algorithm;
  • the sensor unit further includes: at least one of a wind speed sensor, a pH value sensor, an illuminance sensor, a dissolved oxygen sensor, a carbon dioxide sensor, an air quality sensor, a door magnetic sensor, and a noise sensor;
  • the sensing device further includes a USB serial port conversion module, a mode switching switch, a voltage conversion module, a voltage stabilizing module and a clock module connected to the processing module;
  • the processing module stores the sensor data output by the received sensor unit through the storage structure; each storage structure includes a plurality of data separated by a separator, each piece of data has sensor data, and corresponding The sensor data receives the timestamp information and the sensor type information; the processing module sequentially arranges the storage structures in the order of creation to form a data stream, and uploads the data stream to the data platform according to a preset uploading period; The processing module further uploads the corresponding sensor data directly to the data platform according to the received preset interrupt information;
  • the processing module performs CRC check on the sensor data, stores sensor data that passes the CRC check, and fails to pass the CRC.
  • the verified sensor data reads the CRC check error value
  • the processing module deletes the corresponding data stored in the sensing device
  • the WIFI connection is performed through the wireless communication module.
  • the processing module performs a data stream or sensor data read operation to be uploaded, if the read operation Successfully performing a connection between the sensing device and the data platform, and the processing module executes a watchdog monitoring program;
  • the data platform can send an instruction to the sensing device, and the sensing device sends the operation information of deleting all the instruction queues to the data platform to implement the receiving of the new instruction;
  • the clock of the sensing device is synchronized with the clock of the data platform
  • the processing module has a built-in real-time operating system RTOS;
  • the processing module adopts a processor internally integrated with an AD sampling circuit;
  • the AD sampling circuit includes a first voltage dividing resistor and a second voltage dividing resistor connected in series; the second voltage dividing resistor is performed in the AD sampling circuit One end of the sampling is grounded, and one end of the second voltage dividing resistor is not grounded when the AD sampling circuit is not sampling;
  • processing module operates and sleeps according to the intelligent scheduling interval sleep algorithm
  • the smart scheduling interval sleep algorithm includes the following processes:
  • the system enters a sleep state, and executes 5;
  • the preset detection algorithm includes at least a peak detection algorithm and a dynamic threshold detection algorithm
  • the peak detection algorithm includes the following processes:
  • the acceleration data having an x-axis acceleration, a y-axis acceleration, and a z-axis acceleration, performing 2;
  • f(t) represents the amplitude value of the t-th acceleration data in the s time period
  • x(t) represents the x-axis acceleration corresponding to the t-th acceleration data
  • y(t) represents the y corresponding to the t-th acceleration data.
  • the axial acceleration, z(t) represents the z-axis acceleration corresponding to the t-th acceleration data
  • x(t-1) represents the x-axis acceleration corresponding to the t-1th acceleration data
  • y(t-1) represents the t-1th
  • z(t-1) represents the z-axis acceleration corresponding to the t-1th acceleration data
  • t represents the order of the acceleration data in the s period, and step 5 is performed;
  • f(t) represents the amplitude value of the t-th acceleration data in the s time period
  • x(t) represents the x-axis acceleration corresponding to the t-th acceleration data
  • y(t) represents the y corresponding to the t-th acceleration data.
  • the axial acceleration, z(t) represents the z-axis acceleration corresponding to the t-th acceleration data
  • x(t-2) represents the x-axis acceleration corresponding to the t-2th acceleration data
  • y(t-2) represents the t-2th
  • the y-axis acceleration corresponding to the strip acceleration data, z(t-2) represents the z-axis acceleration corresponding to the t-2th acceleration data
  • t represents the order of the acceleration data in the s period, and step 5 is performed;
  • T represents the amount of acceleration data in the s time period
  • the dynamic threshold detection algorithm includes the following processes:
  • acceleration data output by the acceleration sensor the acceleration data having an x-axis acceleration, a y-axis acceleration, and a z-axis acceleration, performing II;
  • the processing module directly stores the original sampling data output by the sensor as sensor data, or stores the data obtained by processing the original sampling data output by the sensor by using a preset processing manner as sensor data;
  • the processing mode includes at least a first processing mode, a second processing mode, and a third processing mode; the different processing modes correspond to different modes of the sensing device, and the user selects the preset processing mode by performing mode setting on the sensing device;
  • the first processing mode is: obtaining ..., Wherein, x N represents the original sampled data obtained by the Nth sampling of the sensor, x 2N represents the original sampled data obtained by the 2Nth sampling of the sensor, and x NN represents the original sampled data obtained by the NNth sampling of the sensor;
  • the second processing mode is: obtaining ..., Where x N represents the original sampled data obtained by the Nth sampling of the sensor, x 2N represents the original sampled data obtained by the 2Nth sampling of the sensor, x NN represents the original sampled data obtained by the NNth sampling of the sensor, and x max1 represents the original of the sensor The maximum value of the sampled data x 1 , x 2 , x 3 , ... x N , x min1 represents the raw sample data x 1 , x 2 , x 3 , ... x N of the sensor The minimum value, x max2 represents the maximum value of the sensor's raw sample data x N+1 , x N+2 , x N+3 , ...
  • x 2N and x min2 represents the raw sample data of the sensor x N +1 , x N+2 , x N+3 , ... the minimum value in 2 2N
  • x maxN represents the raw sample data of the sensor x (N-1)N , x (N-1)N+ 1 ... the maximum value in x NN
  • x minN represents the minimum of the sensor's original sampled data x (N-1)N , x (N-1)N+1 ......x NN value;
  • the third processing manner is: 1 performing average value and variance calculation for N sensor original sampling data; 2 calculating statistics for the N sensor original sampling data in sequence If a sensor corresponding to the raw sample data x i corresponds to T i ⁇ T ⁇ , n , then x i is discarded, and then N sensors are initially accumulated and returned to 1 until the original sample data of each sensor is calculated. among them, Means the average value of the N sensor raw sample data, S represents the variance of the N sensor raw sample data, x i represents the i-th sensor original sample data, T ⁇ , and n represents the critical value obtained after querying the Grubbs table;
  • the data obtained by the raw sampling data of the sensor and the raw sampling data of the sensor processed by different preset processing methods are outputted by using a data structure form containing data itself and data type information, and different sensors are used to distinguish whether the sensor is a sensor.
  • Raw sample data and different preset processing methods
  • the sensing device has a voiceprint recognition device; the user can broadcast the WIFI configuration information through a voiceprint form, and the voiceprint recognition device converts and recognizes the voiceprint to a corresponding WIFI configuration information;
  • the user of the pattern recognition device can realize the control of one or more sensing devices by sound;
  • the wireless communication module can work in an AP mode and an STA mode, and the configuration process of the AP mode includes the following steps:
  • A1 Enable AP mode and execute A2.
  • A2 Waiting for IP, execute A3;
  • A3 Create a TCP connection, execute A4;
  • A4 Track the TCP connection and execute A5;
  • A5 accept the TCP command, execute A6;
  • A6 Determine the TCP command type and execute A7;
  • A7 If the TCP command type is an exit command or a configuration command, execute A8 after accepting the TCP command; if the TCP command type is the read-aware device information command, the read sensor information command, or the read error information command, the TCP command is accepted. Return to A6;
  • A8 Send AP mode configuration result, execute A9;
  • A9 Close the TCP connection and execute A10.
  • A10 Configure STA mode and execute A11.
  • A11 Exit the configuration process of the AP mode.
  • the sensing device can be connected to the user terminal; the user can implement the configuration process of the AP mode through the user terminal, and view the AP mode configuration information, the read sensing device information, and the read sensor information through the user terminal. And/or read error messages;
  • the sensing device includes at least a USB interface, a microusb interface, and/or a miniUSB interface; the sensing device is connected to the user terminal through the USB interface; the user terminal is a mobile phone, a tablet computer or a PC; The data stored by the device can be imported into the user terminal through a USB interface.
  • a perception system that includes:
  • a data platform connected to multiple sensing devices.
  • the smart sensing device and the sensing system provided by the present invention have low energy consumption, can realize interconnection with a data platform, and support access of multiple sensors, and have stability and sensitivity.
  • High, storage capacity is strong;
  • the sensing system uses a data platform to interconnect with multiple sensing devices, and the sensing device uses WIFI wireless transmission mode to connect with the data platform, that is, the sensing data can be directly synchronized to the background Internet of Things through the wireless WIFI through the Internet.
  • users can use a smart phone or a computer to access a web browser in any part of the world to know the data transmitted by the sensing device in real time.
  • FIG. 1 is a schematic structural view of a sensing system according to the present invention.
  • FIG. 2 is a block diagram showing an example structure of a sensing system according to the present invention.
  • FIG. 3 is a schematic diagram of application of the sensing system of the present invention.
  • FIG. 4 is a flowchart of performing AP mode configuration by the sensing device according to the present invention.
  • Figure 5 is a graph showing the curve of the output data of the acceleration sensor of the present invention as a function of time for an object that suddenly strikes;
  • Figure 6 is a graph showing the curve of the output data of the acceleration sensor of the present invention as a function of time for a slowly moving object
  • FIG. 7 is a schematic flowchart of a wireless configuration operation of a sensing device by a user of the present invention through a mobile phone or a PC;
  • FIG. 8 is a schematic flow chart showing an example of a process in which the sensing device of the present invention is turned on and after a main task is created;
  • FIG. 9 is a schematic diagram of an application example of a real-time operating system RTOS of the present invention.
  • sensing device 2, user access.
  • An intelligent sensing device as shown in FIG. 1 , FIG. 2 , FIG. 3 , FIG. 4 , FIG. 5 and FIG. 6 includes: a sensor unit having a plurality of sensors, a wireless communication module connected to the data platform, and a storage module.
  • the processing module is coupled to the sensor unit, the wireless communication module, and the storage module;
  • the sensor unit includes: at least one of a temperature sensor, a humidity sensor, an ambient light sensor, a magnetic field sensor, an acceleration sensor, and a shock sensor
  • the wireless communication module includes at least a WIFI chip;
  • the processing module learns the motion of the user according to the detection result of the acceleration sensor and uses a preset detection algorithm;
  • the sensing device 1 has multiple modes, and different modes correspond to different The preset detection algorithm, the user selects the preset detection algorithm by performing mode setting on the sensing device 1;
  • the sensor unit further includes: an air speed sensor, a pH value sensor, an illuminance sensor, and a dissolved oxygen sensor , carbon dioxide sensor, air quality sensor, door magnetic sensor At least one of the noise sensors;
  • the sensing device 1 further includes a USB serial port conversion module, a mode conversion switch, a voltage conversion module, a voltage stabilization module, and a clock module connected to the processing module; further, the processing
  • the interrupt information directly uploads the corresponding sensor data to the data platform; further, the processing module performs CRC check on the sensor data before storing the sensor data output by the received sensor unit, and passes the CRC Verify the sensor data for storage, and Reading, by the CRC check sensor data, a CRC check error value; further, after the data stream or sensor data is uploaded to the data platform, the processing module deletes the corresponding data stored in the sensing device 1; When the processing module needs to upload the data stream or the sensor data to the data platform, the WIFI connection is first performed through the wireless communication module.
  • the processing module When the WIFI connection is successful, the processing module performs the reading operation of the data stream or the sensor data to be uploaded, and if the reading is successful, Performing a connection between the sensing device 1 and a data platform, while the processing module executes a watchdog monitoring program; the data platform can send an instruction to the sensing device 1, and the sensing device 1 deletes all by sending to the data platform
  • the operation information of the instruction queue is implemented to prevent the reception of the new instruction; further, the clock of the sensing device 1 is kept synchronized with the clock of the data platform;
  • the processing module has a built-in real-time operating system RTOS; and the processing module is internally integrated a processor of an AD sampling circuit;
  • the AD sampling circuit includes a first partial voltage connected in series When the second end of the ground voltage dividing resistor in the AD sampling circuit samples, when the sampling circuit is not AD sampling the second voltage dividing resistor ungrounded end; and a second resistance voltage dividing resistor;
  • processing module operates and sleeps according to the intelligent scheduling interval sleep algorithm
  • the smart scheduling interval sleep algorithm includes the following processes:
  • the system enters a sleep state, and executes 5;
  • the task that is run next time, that is, the next scheduled running time is equal to the task of the next running time of the system, when the current time of the system reaches the next running time T′ of the system;
  • the preset detection algorithm includes at least a peak detection algorithm and a dynamic threshold detection algorithm
  • the peak detection algorithm includes the following processes:
  • the acceleration data having an x-axis acceleration, a y-axis acceleration, and a z-axis acceleration, performing 2;
  • f(t) represents the amplitude value of the t-th acceleration data in the s time period
  • x(t) represents the x-axis acceleration corresponding to the t-th acceleration data
  • y(t) represents the y corresponding to the t-th acceleration data.
  • the axial acceleration, z(t) represents the z-axis acceleration corresponding to the t-th acceleration data
  • x(t-1) represents the x-axis acceleration corresponding to the t-1th acceleration data
  • y(t-1) represents the t-1th
  • z(t-1) represents the z-axis acceleration corresponding to the t-1th acceleration data
  • t represents the order of the acceleration data in the s period, and step 5 is performed;
  • f(t) represents the amplitude value of the t-th acceleration data in the s time period
  • x(t) represents the x-axis acceleration corresponding to the t-th acceleration data
  • y(t) represents the y corresponding to the t-th acceleration data.
  • the axial acceleration, z(t) represents the z-axis acceleration corresponding to the t-th acceleration data
  • x(t-2) represents the x-axis acceleration corresponding to the t-2th acceleration data
  • y(t-2) represents the t-2th
  • the y-axis acceleration corresponding to the strip acceleration data, z(t-2) represents the z-axis acceleration corresponding to the t-2th acceleration data
  • t represents the order of the acceleration data in the s period, and step 5 is performed;
  • T represents the amount of acceleration data in the s time period
  • the dynamic threshold detection algorithm includes the following processes:
  • acceleration data output by the acceleration sensor the acceleration data having an x-axis acceleration, a y-axis acceleration, and a z-axis acceleration, performing II;
  • the processing module directly stores the original sampling data output by the sensor as sensor data, or stores the data obtained by processing the original sampling data output by the sensor by using a preset processing manner as sensor data;
  • the processing mode includes at least a first processing mode, a second processing mode, and a third processing mode.
  • the different processing modes correspond to different modes of the sensing device 1, and the user performs mode setting on the sensing device 1 to perform the preset processing mode.
  • the first processing mode is: obtaining ..., Wherein, x N represents the original sampled data obtained by the Nth sampling of the sensor, x 2N represents the original sampled data obtained by the 2Nth sampling of the sensor, and x NN represents the original sampled data obtained by the NNth sampling of the sensor; the second processing mode For: get ..., Where x N represents the original sampled data obtained by the Nth sampling of the sensor, x 2N represents the original sampled data obtained by the 2Nth sampling of the sensor, x NN represents the original sampled data obtained by the NNth sampling of the sensor, and x max1 represents the original of the sensor The maximum value of the sampled data x 1 , x 2 , x 3 , ...
  • x N , x min1 represents the raw sample data x 1 , x 2 , x 3 , ... x N of the sensor
  • the minimum value, x max2 represents the maximum value of the sensor's raw sample data x N+1 , x N+2 , x N+3 , ... x 2N
  • x min2 represents the raw sample data of the sensor x N +1 , x N+2 , x N+3 , ... the minimum value in 2 2N
  • x maxN represents the raw sample data of the sensor x (N-1)N , x (N-1)N+ 1 ...
  • x minN represents the minimum of the sensor's original sampled data x (N - 1)N , x (N-1)N+1 ... x NN
  • the third processing mode is: 1 performing average value and variance calculation for N sensor original sampling data; 2 calculating statistics for the N sensor original sampling data in sequence If a sensor corresponding to the raw sample data x i corresponds to T i ⁇ T ⁇ , n , then x i is discarded, and then N sensors are initially accumulated and returned to 1 until the original sample data of each sensor is calculated.
  • the sensing device 1 has a voiceprint identifying device; the user can broadcast the WIFI configuration information through the voiceprint form, and the voiceprint identifying device converts and recognizes the voiceprint and then converts Configuring information for the corresponding WIFI; the user of the voiceprint recognition device can realize the control of one or more sensing devices 1 by sound;
  • the wireless communication module can work in an AP mode and an STA mode, and the configuration process of the AP mode includes the following steps:
  • A1 Enable AP mode and execute A2.
  • A2 Waiting for IP, execute A3;
  • A3 Create a TCP connection, execute A4;
  • A4 Track the TCP connection and execute A5;
  • A5 accept the TCP command, execute A6;
  • A6 Determine the TCP command type and execute A7;
  • A7 If the TCP command type is an exit command or a configuration command, execute A8 after accepting the TCP command; if the TCP command type is the read-aware device 1 information command, the read sensor information command, or the read error information command, accept the TCP command. After returning to A6;
  • A8 Send AP mode configuration result, execute A9;
  • A9 Close the TCP connection and execute A10.
  • A10 Configure STA mode and execute A11.
  • A11 Exit the configuration process of the AP mode.
  • the sensing device 1 can be connected to the user terminal; the user can implement the configuration process of the AP mode through the user terminal, and view the AP mode configuration information, the read sensing device 1 information, and the read by the user terminal. Sensor information, and/or read error information; further, the sensing device 1 includes at least a USB interface, a microusb interface, and/or a miniUSB interface; the sensing device 1 is connected to the user terminal through the USB interface; The user terminal is a mobile phone, a tablet or a PC; the data stored by the sensing device 1 can be imported into the user terminal through a USB interface.
  • a sensing system comprising: the intelligent sensing device according to any one of the above items; and a data platform connected to the plurality of sensing devices 1.
  • the sensing device 1 of the invention has extremely low energy consumption, can realize interconnection with the data platform, and supports access of multiple sensors, has high stability and sensitivity, and has strong storage capability; the sensing system of the present invention uses one data.
  • the platform is interconnected with the plurality of sensing devices 1.
  • the sensing device 1 is connected to the data platform by using WIFI wireless transmission mode, that is, the sensing data can be directly synchronized to the data platform of the background Internet of Things through the wireless WIFI through the wireless WIFI, so that the user is anywhere in the world.
  • the smart phone and the computer can be used to access the web browser to know the data transmitted by the sensing device 1 in real time; the invention can minimize the power consumption while satisfying the maximum transmission distance and function use; and the intelligent scheduling interval sleep algorithm
  • the implementation can effectively reduce power consumption and improve data collection efficiency, as well as ensure the reliability and accuracy of the data.
  • the storage module of the present invention can select a storage chip with a built-in 16MB space. In actual application, in combination with the unique storage method of the present invention, 285,000 pieces of data can be stored, and sensor data of up to one year can be stored, without WIFI network coverage.
  • each storage structure includes a plurality of data separated by a separator, and each of the data has The sensor data and the corresponding sensor data receive time stamp information and sensor type information; the configuration of the time stamp information received by the sensor data can effectively trace each sensor data; the processing module has a built-in real-time operating system RTOS, which can implement the sensor The behaviors of data acquisition, storage, calculation, and synchronization are performed in real time without interference.
  • the clock of the sensing device 1 is synchronized with the clock of the data platform to ensure time accuracy.
  • the sensing device 1 of the present invention has multiple modes and different modes.
  • the mode can correspond to different pre- The detection algorithm and the processing method of different sensor original sampling data, so that the sensing device 1 can more closely match the user requirements and usage scenarios, and improve the user experience; by performing CRC check on the sensor data, the correctness of the data transmission can be ensured.
  • Integrity when the processing module needs to upload data stream or sensor data to the data platform, the WIFI connection is first performed through the wireless communication module. When the WIFI connection is successful, the processing module performs a data stream or sensor data read operation to be uploaded. If the reading is successful, the connection between the sensing device 1 and the data platform is performed, and the processing module executes the watchdog monitoring program; the watchdog monitoring program is used to monitor the operating state of the system in real time to prevent electromagnetic fields and artificial persons from the outside.
  • the disturbance of the program causes the program to run away, avoiding the intrusion loop causing the entire system to stagnate;
  • the sensing device 1 includes at least a USB interface, a microusb interface and/or a miniUSB interface; the sensing device 1 can communicate with the user terminal through the USB interface Connected;
  • the user terminal is a mobile phone, flat
  • the user or the PC can implement the configuration process of the AP mode through the user terminal, for example, sending the WIFI SSID and the password to the sensing device 1, changing the device serial number and other related setting operations, and viewing the AP mode configuration information and reading through the user terminal.
  • the sensing device 1 information, the read sensor information, and/or the read error information are taken; the data stored by the sensing device 1 can also be directly imported into the user terminal from the USB interface, and an Excel spreadsheet is generated and stored in the corresponding path.
  • the sensing device 1 of the present invention has a voiceprint recognition device; the user can broadcast the WIFI configuration information through the voiceprint form, and the voiceprint recognition device converts and recognizes the voiceprint to the corresponding WIFI configuration information through the analysis;
  • the user of the pattern recognition device can realize the control of one or more sensing devices 1 by sound, which greatly improves the security and convenience of the data, and the realization of the group broadcast further improves the convenience of data and saves the configuration time. Users bring convenience. FIG.
  • FIG. 3 is a schematic diagram of an application of the sensing system of the present invention.
  • the data platform can be connected to multiple user access terminals 2, and the user accesses the data platform through the user access terminal 2.
  • the user can use the smart phone or the computer to access the web browser in any part of the world to know the data transmitted by the sensing device 1 in real time.
  • the sensing device 1 of the present invention can not only transmit complete original sampling data, but also provide various data processing calculation modes and data output forms, which is beneficial to save network data transmission amount, reduce equipment power consumption, and improve calculation efficiency.
  • TIME represents a storage structure creation time
  • time 1 , time 2 , and time 3 represent sensor data reception time stamp information, for example, "2015-12-17T18:16:22Z "
  • SensorName represents sensor type information
  • data 1 , data 2 , and data 3 represent sensor data
  • the processing module of the invention integrates a real-time operating system RTOS, thereby realizing the instruction of the system through the real-time operating system interrupt flag, real-time switching between multiple tasks, and jumping directly to the task program without going through a lengthy and cumbersome intermediate useless program.
  • the sensitivity of the device is greatly improved; the specific application of the real-time operating system RTOS is exemplified below, and FIG. 9 is a schematic diagram showing an application example of the real-time operating system RTOS of the present invention.
  • FIG. 2 is a block diagram showing an example structure of the sensing system of the present invention.
  • the processing module may employ a processor
  • the storage module adopts a storage circuit
  • the mode selection switch is used for setting the mode of the sensing device 1 by the user.
  • the voltage conversion module, the USB serial port conversion module, the clock module and the voltage regulator module respectively correspond to a voltage conversion circuit, a USB serial port conversion circuit, a clock circuit and a voltage stabilization circuit;
  • the AD sampling circuit of the invention comprises a first voltage divider resistor and a series connected in series a second voltage dividing resistor; the second voltage dividing resistor is grounded at one end when the AD sampling circuit performs sampling, and the second voltage dividing resistor is not grounded at the end when the AD sampling circuit is not sampling, the first voltage dividing resistor One end is connected to the positive pole of the power supply, the other end is connected to the second voltage dividing resistor, and the other end of the second voltage dividing resistor is connected to the IO port of the processor, and the other end of the second voltage dividing resistor is controlled by controlling whether the IO port of the processor is grounded. Whether it is grounded, so that it only grounds when sampling is needed. After the sampling is completed, it is suspended or pulled high, thus avoiding the direct connection of the voltage dividing resistor.
  • FIG. 5 is a schematic diagram showing the curve of the output data of the acceleration sensor of the present invention as a function of time for an object that suddenly strikes;
  • FIG. 6 is a graph showing the curve of the output data of the acceleration sensor of the present invention as a function of time for a slowly moving object.
  • the wireless communication module of the present invention includes a WIFI chip, that is, Directly adopt chip-level solution research and development to solve the traditional WIFI module, there are many peripheral function circuits around the main chip, and most of the functional circuits are useless to the sensing device 1, only a small part of the circuit actually serves the sensing device 1 but the power consumption is still Failure to meet product requirements can create technical problems with unnecessary power consumption.
  • the WIFI connection is first performed by the wireless communication module.
  • the processing module performs a data stream or sensor data read operation to be uploaded. If the reading is successful, the connection between the sensing device 1 and the data platform is performed, and the processing module executes the watchdog monitoring program.
  • the following is a corresponding implementation process that can be operated in actual application:
  • the sensing device 1 of the present invention supports the user to perform wireless configuration operations through a user terminal such as a mobile phone or a PC, and can view the current configuration information, sensor reading information, error information, etc. of the sensing device 1 through the configuration interface of the mobile phone or the PC, FIG. A flow chart of the wireless configuration operation of the sensing device 1 by the user of the present invention through a mobile phone or a PC is shown. The specific process is as follows:
  • the adding device mode includes scanning a QR code and inputting a serial number;
  • B11 The configuration is successful. If the configuration succeeds, the green light flashes to indicate that the configuration is successful. If the configuration is unsuccessful, the red light is always on to prompt the WIFI password error, and the red light flashes to prompt the network to disconnect.
  • FIG. 8 is a schematic flowchart showing an example of a process in which the sensing device 1 of the present invention is turned on and after the main task is created. As shown in FIG. 8 , the actual process may be as follows: ;
  • C6 Identify the button status. If the button continues for n1 seconds, perform the device serial number reading operation and execute C7. If the button continues for n2 seconds, enter the wireless AP mode and execute it. If the button lasts for n3 seconds, the factory setting is restored and C is executed.
  • C10 Query setting activation status
  • C11 Determine whether to activate, if yes, execute C12, otherwise execute C12 after activating the device;
  • the default running time T1, T2, T3, ..., Tm of the current non-running tasks 1, 2, ..., m of the present invention may be changed, so the intelligent scheduling interval sleep algorithm is executed when the system runs the task; Comparing the mean value F(s) of all acceleration data amplitude values in the s time period with a preset value, and determining whether the user is currently in a falling state according to the comparison result, and determining the user if the comparison result is that F(s) is greater than a preset value.
  • the plurality of the invention refers to two or more; the data obtained by the first processing mode of the sensor for processing the original sampling data of the sensor is ..., x 1 represents the original sampled data obtained by the first sampling of the sensor, x 2 represents the original sampled data obtained by the second sampling of the sensor, x 3 represents the original sampled data obtained by the third sampling of the sensor, and x N+1 represents the sensor N
  • x N+2 represents the original sampled data obtained by the N+2th sampling of the sensor, and x N+3 represents the original sampled data obtained by the N+3th sampling of the sensor, x (N- 1) N represents the original sampled data obtained by the (N-1)Nth sampling
  • the raw sample data of ⁇ , n sensor, T ⁇ , n represents the critical value obtained after querying the Grubbs table, and the actual application is based on the preset critical condition and the size of N to obtain the T ⁇ , n value;
  • Device 1's clock is synchronized with the data platform's clock
  • the clock of the sensing device 1 is synchronized with the clock of the data platform according to a preset synchronization period, or the clock synchronization operation is performed when the sensing device 1 uploads data to the data platform;
  • the processing module also directly uploads corresponding sensor data to the data platform according to the received preset interrupt information; the preset interrupt information is generated according to the data output of some specific sensors, for example, when the vibration sensor outputs data, a corresponding interrupt is generated.
  • the information processing module uploads the vibration sensor output data to the data platform according to the interrupt information.
  • the sensing device 1 of the invention has extremely low energy consumption, extremely long waiting time and high sensitivity; in actual application, the sensing device 1 can be designed as a small and light product that can be attached to almost any object, and further, the sensing device 1
  • the specific state data of the attached object or the surrounding environment of the object can be directly synchronized to the data platform through the wireless WIFI through the Internet, so that any object can easily access the Internet; the multi-dimensional data combination can be widely used in industry, Animal husbandry, agriculture, mechanized production, medical and logistics industries to achieve environmental monitoring, judgment, analysis, control, early warning and prompts.
  • the development of the environment-aware technology has also made people have more expectations for the medical field while promoting the advancement of the wearable device.
  • the human body can be performed through the smart sensing device.
  • the physical data collection is transmitted to the patient's family and medical staff through wireless WIFI to form a complete medical monitoring ecosystem.
  • the patient's body temperature and rest conditions such as sleep and noise can be detected in real time. , ambient temperature and humidity and light conditions, the use of personalized abnormality determination of the collected data, and the corresponding data analysis and processing, and the data is synchronized to the data platform, so that the on-duty doctors and nurses can always pay attention to all from the computer or tablet
  • the patient's health status can also alert the health care provider when the data changes beyond the set threshold or range.
  • the sensing device of the present invention is applied. 1 It can record some data on the body in time, and can be transmitted to the medical related professionals for analysis in time with the Internet application, so that people can find early treatment for hidden diseases without delaying normal life;
  • the sensing device 1 of the invention can also be applied to the logistics industry.
  • the customer cannot understand whether the temperature in the transportation process reaches the standard all the way, and there are serious bumps and shocks in the transportation process, for the logistics.
  • the service provider's service level SLA cannot be accurately measured; usually, it can only achieve the verification effect by unpacking the goods after reaching the destination.
  • the sensing device 1 of the present invention By using the sensing device 1 of the present invention to deploy into the goods, the goods can be transported. Temperature, humidity, and brightness at every point in the whole process Accurately capture environmental changes such as fluctuations.
  • Such applications help logistics service providers to monitor service levels.
  • they are convenient for logistics customers to test and measure the loss of goods. Moreover, they are effective for possible disputes.
  • the data is supported as evidence, and can reduce the liability risk of the shipper's transportation and distribution; the invention adopts the intelligent dispatch interval sleep mode, and only uses two 7th or 5th batteries to sustain power supply for more than 6 months, completely Meet the requirements of long-distance transportation.

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Abstract

本发明公开了一种智能感知设备及感知系统，所述感知设备包括：具备多个传感器的传感器单元、与数据平台相连接的无线通信模块、存储模块、以及处理模块；所述处理模块与所述传感器单元、无线通信模块和存储模块相连接；所述处理模块根据加速度传感器的检测结果并利用预设检测算法来获知用户的运动情况；所述感知设备具有多种模式，不同模式对应不同的预设检测算法，用户通过对感知设备进行模式设定来对所述预设检测算法进行选择；本发明所述感知设备能耗极低，能够实现与数据平台的互联，且支持多种传感器的接入，稳定性和灵敏度均较高，存储能力强。

Description

智能感知设备及感知系统 技术领域
本发明涉及一种智能感知设备及感知系统。
背景技术
目前，信息化社会对通信的要求已经不仅仅限于人与人之间的通信，传感技术和网络技术的发展使人与物品、物品与物品之间的通信成为可能，智能感知设备在这样的大环境下应运而生。
现有技术中的智能感知设备存在如下缺陷：碎片化严重、感知参数单一，比如市场上的只能感知温度的感知设备、只能感知光照的感知设备等，感知设备不能有效联动起来，造成数据以及事件的关联性的丧失；用户只安装一种感知设备无法满足其需求，往往需要安装几个甚至更多的感知设备才能满足全智能体验，同时这些感知设备往往需要通过几个甚至更多的数据平台进行控制，既违背了智能化的初衷给用户带来麻烦，也造成了资源的浪费。另外，现在的智能感知产品普遍存在传输距离有限、可靠性低和配置繁琐的问题，例如以433MHz无线技术、Zigbee技术等短距离通讯方式为基础的很多感知设备若想将数据直接连入到互联网(Internet)，通常需要统一将数据同步到总控制器上再通过总控制器与外部互联网通讯，当设备数量增大后通讯过程中容易出现瓶颈等问题，通讯方式带宽较窄，不适合传输高频率或较大容量的数据(例如图像，声音等)；还有，现有的感知设备耗电和功耗偏高，因此不得不普遍采用外部供电，导致安装和使用具有局限性，不灵活。
发明内容
本发明针对以上问题的提出，而研制一种智能感知设备。
本发明的技术手段如下：
一种智能感知设备，包括：具备多个传感器的传感器单元、与数据平台相连接的无线通信模块、存储模块、以及处理模块；所述处理模块与所述传感器单元、无线通信模块和存储模块相连接；
所述传感器单元包括：温度传感器、湿度传感器、环境光传感器、磁场传感器、加速度传感器和震动传感器中的至少两个；所述无线通信模块至少包括WIFI芯片；
所述处理模块根据加速度传感器的检测结果并利用预设检测算法来获知用 户的运动情况；所述感知设备具有多种模式，不同模式对应不同的预设检测算法，用户通过对感知设备进行模式设定来对所述预设检测算法进行选择；
进一步地，
所述传感器单元还包括：风速传感器、pH值传感器、光照度传感器、溶解氧传感器、二氧化碳传感器、空气质量传感器、门磁传感器、噪声传感器中的至少一个；
所述感知设备还包括与处理模块相连接的USB串口转换模块、模式转换开关、电压转换模块、稳压模块和时钟模块；
进一步地，所述处理模块通过存储结构体对接收到的传感器单元输出的传感器数据进行存储；每一存储结构体包括多条采用分隔符进行分隔的数据，每条数据中具有传感器数据、以及相应的传感器数据接收时间戳信息和传感器类型信息；所述处理模块将各存储结构体按照创建顺序依次排列后形成数据流，并根据预设上传周期将所述数据流上传至所述数据平台；所述处理模块还根据接收到的预设中断信息将相应的传感器数据直接上传至数据平台；
进一步地，所述处理模块在对接收到的传感器单元输出的传感器数据进行存储之前，先对所述传感器数据进行CRC校验，并对通过CRC校验的传感器数据进行存储，以及对未通过CRC校验的传感器数据读取CRC校验错误值；
进一步地，
在所述数据流或传感器数据上传至数据平台后，所述处理模块将感知设备中存储的对应数据删除；
当所述处理模块需要向数据平台上传数据流或传感器数据时，首先通过无线通信模块进行WIFI连接，当WIFI连接成功时，处理模块进行待上传的数据流或传感器数据的读取操作，若读取成功则进行所述感知设备与数据平台之间的连接，同时处理模块执行看门狗监视程序；
所述数据平台能够向所述感知设备发送指令，所述感知设备通过向数据平台发送删除所有指令队列的操作信息以实现不进行新指令的接收；
进一步地，
所述感知设备的时钟与数据平台的时钟保持同步；
所述处理模块内置实时操作系统RTOS；
所述处理模块采用内部集成有AD采样电路的处理器；所述AD采样电路包括相互串联的第一分压电阻和第二分压电阻；所述第二分压电阻在所述AD采 样电路进行采样时一端接地，当所述AD采样电路不进行采样时所述第二分压电阻一端不接地；
进一步地，所述处理模块按照智能调度间隔式休眠算法进行运行和休眠；
其中，所述智能调度间隔式休眠算法包括如下流程：
①获知系统当前时间Ts和当前未运行任务1、2、…、m下次的预设运行时间T1、T2、T3、…、Tm，执行②；
②依次计算出当前未运行任务1、2、…、m的执行频率F1、F2、F3、…、Fm，其中，F1＝T1-Ts、F2＝T2-Ts、F3＝T3-Ts、…、Fm＝Tm-Ts，执行③；
③确定任务1、2、…、m的执行频率F1、F2、F3、…、Fm中的最小值Fs，根据T′＝Ts+Fs确定下次运行的任务并得出系统下次运行时间T′，执行④；
④系统进入休眠状态，执行⑤；
⑤当系统当前时间达到系统下次运行时间T′，系统进入唤醒状态并运行相应任务，返回①。
进一步地，所述预设检测算法至少包括峰值检测算法和动态阈值检测算法；
所述峰值检测算法包括如下流程：
①获得所述加速度传感器在s时间段内依次输出的加速度数据；所述加速度数据具有x轴加速度、y轴加速度和z轴加速度，执行②；
②判断加速度传感器的采样频率是否高于预设采样频率，是则执行④，否则执行③；
③计算
Figure PCTCN2018084201-appb-000001
其中，f(t)表示在s时间段内第t条加速度数据的幅度值、x(t)表示第t条加速度数据对应的x轴加速度、y(t)表示第t条加速度数据对应的y轴加速度、z(t)表示第t条加速度数据对应的z轴加速度、x(t-1)表示第t-1条加速度数据对应的x轴加速度、y(t-1)表示第t-1条加速度数据对应的y轴加速度、z(t-1)表示第t-1条加速度数据对应的z轴加速度、t表示在s时间段内的加速度数据排列顺序，执行步骤⑤；
④计算
Figure PCTCN2018084201-appb-000002
其中，f(t)表示在s时间段内第t条加速度数据的幅度值、x(t)表示第t条加速度数据对应的x轴加速度、y(t)表示第t条加速度数据对应的y轴加速度、z(t)表 示第t条加速度数据对应的z轴加速度、x(t-2)表示第t-2条加速度数据对应的x轴加速度、y(t-2)表示第t-2条加速度数据对应的y轴加速度、z(t-2)表示第t-2条加速度数据对应的z轴加速度、t表示在s时间段内的加速度数据排列顺序，执行步骤⑤；
⑤获取在s时间段内所有加速度数据幅度值的均值
Figure PCTCN2018084201-appb-000003
其中，T表示在s时间段内的加速度数据数量，执行⑥；
⑥将s时间段内所有加速度数据幅度值的均值F(s)与预设值进行比较，并根据比较结果确定用户当前是否处于跌倒状态；
所述动态阈值检测算法包括如下流程：
Ⅰ：获得所述加速度传感器输出的加速度数据；所述加速度数据具有x轴加速度、y轴加速度和z轴加速度，执行Ⅱ；
Ⅱ：当所述加速度数据达到N个后，计算N个加速度数据的均值作为动态阈值，执行Ⅲ；
Ⅲ：将计算出动态阈值后每次获得的加速度数据与该动态阈值进行比较，并根据比较结果确定用户是否迈出步伐，执行Ⅳ；
Ⅳ：当计算出动态阈值后获得的加速度数据再次达到N个后，重新计算N个加速度数据的均值并更新动态阈值，返回步骤Ⅲ；
进一步地，所述处理模块将传感器输出的原始采样数据直接作为传感器数据进行存储，或者将通过预设处理方式对传感器输出的原始采样数据进行处理后得到的数据作为传感器数据进行存储；所述预设处理方式至少包括第一处理方式、第二处理方式和第三处理方式；不同处理方式对应感知设备的不同模式，用户通过对感知设备进行模式设定来对所述预设处理方式进行选择；
所述第一处理方式为：获得
Figure PCTCN2018084201-appb-000004
Figure PCTCN2018084201-appb-000005
…、
Figure PCTCN2018084201-appb-000006
其中，x N表示传感器第N次采样获得的原始采样数据、x 2N表示传感器第2N次采样获得的原始采样数据、x NN表示传感器第NN次采样获得的原始采样数据；
所述第二处理方式为：获得
Figure PCTCN2018084201-appb-000007
Figure PCTCN2018084201-appb-000008
…、
Figure PCTCN2018084201-appb-000009
其中，x N表示传感器第N次采样获得的原始采样数据、x 2N表示传感器第2N次采样获得的原始采样数据、x NN表示传感器第NN次采样获得的原始采样数据，x max1表示传感器的原始采样数据x 1,x 2,x 3,......x N中的最大值，x min1表示传感器的原始采样数据x 1,x 2,x 3,......x N中的最小值，x max2表示传感器的原始采样数据x N+1,x N+2,x N+3,......x 2N中的最大值，x min2表示传感器的原始采样数据x N+1,x N+2,x N+3,......x 2N中的最小值，x maxN表示传感器的原始采样数据x (N-1)N,x (N-1)N+1......x NN中的最大值，x minN表示传感器的原始采样数据x (N-1)N,x (N-1)N+1......x NN中的最小值；
所述第三处理方式为：①针对N个传感器原始采样数据进行平均值和方差计算；②对所述N个传感器原始采样数据依次计算统计量
Figure PCTCN2018084201-appb-000010
Figure PCTCN2018084201-appb-000011
若某一传感器原始采样数据x i对应的T i≥T α,n，则将x i丢弃，然后重新累计N个传感器原始采样数据后返回①，直至各传感器原始采样数据均计算过统计量，其中，
Figure PCTCN2018084201-appb-000012
表示N个传感器原始采样数据的平均值、S表示N个传感器原始采样数据的方差、x i表示第i个传感器原始采样数据、T α,n表示查询格拉布斯表后获得的临界值；
针对传感器原始采样数据以及传感器原始采样数据经过不同预设处理方式处理后得到的数据，均采用包含数据本身信息和数据类型信息的数据结构形式进行数据输出，通过不同数据类型信息来区分是否是传感器原始采样数据以及不同的预设处理方式；
进一步地，所述感知设备具有声纹识别装置；用户能够将WIFI配置信息通过声纹形式广播出去，所述声纹识别装置通过解析识别声纹后转换为相应的WIFI配置信息；通过所述声纹识别装置用户能够实现通过声音实现一个或多个感知设备的控制；
进一步地，所述无线通信模块能够工作在AP模式和STA模式，所述AP模式的配置流程包括如下步骤：
A1：开启AP模式，执行A2；
A2：等待获取IP，执行A3；
A3：创建TCP连接，执行A4；
A4：跟踪TCP连接，执行A5；
A5：接受TCP命令，执行A6；
A6：判断TCP命令类型，执行A7；
A7：若TCP命令类型为退出命令或配置命令，则接受TCP命令后执行A8；若TCP命令类型为读取感知设备信息命令、读取传感器信息命令或读取错误信息命令，则接受TCP命令后返回A6；
A8：发送AP模式配置结果，执行A9；
A9：关闭TCP连接，执行A10；
A10：配置STA模式，执行A11；
A11：退出AP模式的配置流程；
进一步地，所述感知设备能够与用户终端相连接；用户能够通过用户终端实现所述AP模式的配置流程，以及通过用户终端查看AP模式配置信息、读取的感知设备信息、读取的传感器信息、和/或读取的错误信息；
进一步地，所述感知设备至少包括USB接口、microusb接口和/或miniUSB接口；所述感知设备通过所述USB接口与用户终端相连接；所述用户终端为手机、平板电脑或PC；所述感知设备存储的数据能够通过USB接口导入到所述用户终端中。
一种感知系统，包括：
多个上述任一项所述的智能感知设备；
与多个感知设备相连接的数据平台。
由于采用了上述技术方案，本发明提供的智能感知设备及感知系统，所述感知设备能耗极低，能够实现与数据平台的互联，且支持多种传感器的接入，稳定性和灵敏度均较高，存储能力强；所述感知系统采用一个数据平台与多个感知设备互联，感知设备采用WIFI无线传输方式与数据平台连接，即能够将感知数据通过无线WIFI直接通过互联网同步到后台物联网的数据平台中，使得用户在世界的任何地方均可使用智能手机、电脑访问Web浏览器获知感知设备实时传递出来的数据。
附图说明
图1是本发明所述感知系统的结构示意图；
图2是本发明所述感知系统的示例结构框图；
图3是本发明所述感知系统的应用示意图；
图4是本发明所述感知设备进行AP模式配置的流程图；
图5是针对突然发生撞击的物体，本发明加速度传感器的输出数据随时间变化的曲线示意图；
图6是针对缓慢运动的物体，本发明加速度传感器的输出数据随时间变化的曲线示意图；
图7是本发明用户通过手机或PC进行感知设备无线配置操作的流程示意图；
图8是本发明所述感知设备开机后至创建主任务期间的示例流程示意图；
图9是本发明实时操作系统RTOS的应用示例示意图。
图中：1、感知设备，2、用户访问端。
具体实施方式
如图1、图2、图3、图4、图5和图6所示的一种智能感知设备，包括：具备多个传感器的传感器单元、与数据平台相连接的无线通信模块、存储模块、以及处理模块；所述处理模块与所述传感器单元、无线通信模块和存储模块相连接；所述传感器单元包括：温度传感器、湿度传感器、环境光传感器、磁场传感器、加速度传感器和震动传感器中的至少两个；所述无线通信模块至少包括WIFI芯片；所述处理模块根据加速度传感器的检测结果并利用预设检测算法来获知用户的运动情况；所述感知设备1具有多种模式，不同模式对应不同的预设检测算法，用户通过对感知设备1进行模式设定来对所述预设检测算法进行选择；进一步地，所述传感器单元还包括：风速传感器、pH值传感器、光照度传感器、溶解氧传感器、二氧化碳传感器、空气质量传感器、门磁传感器、噪声传感器中的至少一个；所述感知设备1还包括与处理模块相连接的USB串口转换模块、模式转换开关、电压转换模块、稳压模块和时钟模块；进一步地，所述处理模块通过存储结构体对接收到的传感器单元输出的传感器数据进行存储；每一存储结构体包括多条采用分隔符进行分隔的数据，每条数据中具有传感器数据、以及相应的传感器数据接收时间戳信息和传感器类型信息；所述处理模块将各存储结构体按照创建顺序依次排列后形成数据流，并根据预设上传周期将所述数据流上传至所述数据平台；所述处理模块还根据接收到的预设中断信息将相应的传感器数据直接上传至数据平台；进一步地，所述处理模块在对接收到的传感器单元输出的传感器数据进行存储之前，先对所述传感器数据进行CRC校验，并对通过CRC校验的传感器数据进行存储，以及对未通过CRC 校验的传感器数据读取CRC校验错误值；进一步地，在所述数据流或传感器数据上传至数据平台后，所述处理模块将感知设备1中存储的对应数据删除；当所述处理模块需要向数据平台上传数据流或传感器数据时，首先通过无线通信模块进行WIFI连接，当WIFI连接成功时，处理模块进行待上传的数据流或传感器数据的读取操作，若读取成功则进行所述感知设备1与数据平台之间的连接，同时处理模块执行看门狗监视程序；所述数据平台能够向所述感知设备1发送指令，所述感知设备1通过向数据平台发送删除所有指令队列的操作信息以实现不进行新指令的接收；进一步地，所述感知设备1的时钟与数据平台的时钟保持同步；所述处理模块内置实时操作系统RTOS；所述处理模块采用内部集成有AD采样电路的处理器；所述AD采样电路包括相互串联的第一分压电阻和第二分压电阻；所述第二分压电阻在所述AD采样电路进行采样时一端接地，当所述AD采样电路不进行采样时所述第二分压电阻一端不接地；
进一步地，所述处理模块按照智能调度间隔式休眠算法进行运行和休眠；
其中，所述智能调度间隔式休眠算法包括如下流程：
①获知系统当前时间Ts和当前未运行任务1、2、…、m下次的预设运行时间T1、T2、T3、…、Tm，执行②；
②依次计算出当前未运行任务1、2、…、m的执行频率F1、F2、F3、…、Fm，其中，F1＝T1-Ts、F2＝T2-Ts、F3＝T3-Ts、…、Fm＝Tm-Ts，执行③；
③确定任务1、2、…、m的执行频率F1、F2、F3、…、Fm中的最小值Fs，根据T′＝Ts+Fs确定下次运行的任务并得出系统下次运行时间T′，执行④；
④系统进入休眠状态，执行⑤；
⑤当系统当前时间达到系统下次运行时间T′，系统进入唤醒状态并运行相应任务，返回①。
其中，下次运行的任务，即下次的预设运行时间等于系统下次运行时间的任务，当系统当前时间达到系统下次运行时间T′；
进一步地，所述预设检测算法至少包括峰值检测算法和动态阈值检测算法；
所述峰值检测算法包括如下流程：
①获得所述加速度传感器在s时间段内依次输出的加速度数据；所述加速度数据具有x轴加速度、y轴加速度和z轴加速度，执行②；
②判断加速度传感器的采样频率是否高于预设采样频率，是则执行④，否则执行③；
③计算
Figure PCTCN2018084201-appb-000013
其中，f(t)表示在s时间段内第t条加速度数据的幅度值、x(t)表示第t条加速度数据对应的x轴加速度、y(t)表示第t条加速度数据对应的y轴加速度、z(t)表示第t条加速度数据对应的z轴加速度、x(t-1)表示第t-1条加速度数据对应的x轴加速度、y(t-1)表示第t-1条加速度数据对应的y轴加速度、z(t-1)表示第t-1条加速度数据对应的z轴加速度、t表示在s时间段内的加速度数据排列顺序，执行步骤⑤；
④计算
Figure PCTCN2018084201-appb-000014
其中，f(t)表示在s时间段内第t条加速度数据的幅度值、x(t)表示第t条加速度数据对应的x轴加速度、y(t)表示第t条加速度数据对应的y轴加速度、z(t)表示第t条加速度数据对应的z轴加速度、x(t-2)表示第t-2条加速度数据对应的x轴加速度、y(t-2)表示第t-2条加速度数据对应的y轴加速度、z(t-2)表示第t-2条加速度数据对应的z轴加速度、t表示在s时间段内的加速度数据排列顺序，执行步骤⑤；
⑤获取在s时间段内所有加速度数据幅度值的均值
Figure PCTCN2018084201-appb-000015
其中，T表示在s时间段内的加速度数据数量，执行⑥；
⑥将s时间段内所有加速度数据幅度值的均值F(s)与预设值进行比较，并根据比较结果确定用户当前是否处于跌倒状态；
所述动态阈值检测算法包括如下流程：
Ⅰ：获得所述加速度传感器输出的加速度数据；所述加速度数据具有x轴加速度、y轴加速度和z轴加速度，执行Ⅱ；
Ⅱ：当所述加速度数据达到N个后，计算N个加速度数据的均值作为动态阈值，执行Ⅲ；
Ⅲ：将计算出动态阈值后每次获得的加速度数据与该动态阈值进行比较，并根据比较结果确定用户是否迈出步伐，执行Ⅳ；
Ⅳ：当计算出动态阈值后获得的加速度数据再次达到N个后，重新计算N个加速度数据的均值并更新动态阈值，返回步骤Ⅲ；
进一步地，所述处理模块将传感器输出的原始采样数据直接作为传感器数 据进行存储，或者将通过预设处理方式对传感器输出的原始采样数据进行处理后得到的数据作为传感器数据进行存储；所述预设处理方式至少包括第一处理方式、第二处理方式和第三处理方式；不同处理方式对应感知设备1的不同模式，用户通过对感知设备1进行模式设定来对所述预设处理方式进行选择；所述第一处理方式为：获得
Figure PCTCN2018084201-appb-000016
Figure PCTCN2018084201-appb-000017
…、
Figure PCTCN2018084201-appb-000018
其中，x N表示传感器第N次采样获得的原始采样数据、x 2N表示传感器第2N次采样获得的原始采样数据、x NN表示传感器第NN次采样获得的原始采样数据；所述第二处理方式为：获得
Figure PCTCN2018084201-appb-000019
Figure PCTCN2018084201-appb-000020
…、
Figure PCTCN2018084201-appb-000021
其中，x N表示传感器第N次采样获得的原始采样数据、x 2N表示传感器第2N次采样获得的原始采样数据、x NN表示传感器第NN次采样获得的原始采样数据，x max1表示传感器的原始采样数据x 1,x 2,x 3,......x N中的最大值，x min1表示传感器的原始采样数据x 1,x 2,x 3,......x N中的最小值，x max2表示传感器的原始采样数据x N+1,x N+2,x N+3,......x 2N中的最大值，x min2表示传感器的原始采样数据x N+1,x N+2,x N+3,......x 2N中的最小值，x maxN表示传感器的原始采样数据x (N-1)N,x (N-1)N+1......x NN中的最大值，x minN表示传感器的原始采样数据x (N- 1)N,x (N-1)N+1......x NN中的最小值；所述第三处理方式为：①针对N个传感器原始采样数据进行平均值和方差计算；②对所述N个传感器原始采样数据依次计算统计量
Figure PCTCN2018084201-appb-000022
若某一传感器原始采样数据x i对应的T i≥T α,n，则将x i丢弃，然后重新累计N个传感器原始采样数据后返回①，直至各传感器原始采样数据均计算过统计量，其中，
Figure PCTCN2018084201-appb-000023
表示N个传感器原始采样数据的平均值、S表示N个传感器原始采样数据的方差、x i表示第i个传感器原始采样数据、T α,n表示查询格拉布斯表后获得的临界值；针对传感器原始采样数据以及传感器原始采样数据经过不同预设处理方式处理后得到的数据，均采用包含数据本身信息和数据类型信息的数据结构形式进行数据输出，通过不同数据类型信息来区 分是否是传感器原始采样数据以及不同的预设处理方式；进一步地，所述感知设备1具有声纹识别装置；用户能够将WIFI配置信息通过声纹形式广播出去，所述声纹识别装置通过解析识别声纹后转换为相应的WIFI配置信息；通过所述声纹识别装置用户能够实现通过声音实现一个或多个感知设备1的控制；
进一步地，所述无线通信模块能够工作在AP模式和STA模式，所述AP模式的配置流程包括如下步骤：
A1：开启AP模式，执行A2；
A2：等待获取IP，执行A3；
A3：创建TCP连接，执行A4；
A4：跟踪TCP连接，执行A5；
A5：接受TCP命令，执行A6；
A6：判断TCP命令类型，执行A7；
A7：若TCP命令类型为退出命令或配置命令，则接受TCP命令后执行A8；若TCP命令类型为读取感知设备1信息命令、读取传感器信息命令或读取错误信息命令，则接受TCP命令后返回A6；
A8：发送AP模式配置结果，执行A9；
A9：关闭TCP连接，执行A10；
A10：配置STA模式，执行A11；
A11：退出AP模式的配置流程；
进一步地，所述感知设备1能够与用户终端相连接；用户能够通过用户终端实现所述AP模式的配置流程，以及通过用户终端查看AP模式配置信息、读取的感知设备1信息、读取的传感器信息、和/或读取的错误信息；进一步地，所述感知设备1至少包括USB接口、microusb接口和/或miniUSB接口；所述感知设备1通过所述USB接口与用户终端相连接；所述用户终端为手机、平板电脑或PC；所述感知设备1存储的数据能够通过USB接口导入到所述用户终端中。
一种感知系统，包括：多个上述任一项所述的智能感知设备；与多个感知设备1相连接的数据平台。
本发明所述感知设备1能耗极低，能够实现与数据平台的互联，且支持多种传感器的接入，稳定性和灵敏度均较高，存储能力强；本发明所述感知系统采用一个数据平台与多个感知设备1互联，感知设备1采用WIFI无线传输方式 与数据平台连接，即能够将感知数据通过无线WIFI直接通过互联网同步到后台物联网的数据平台中，使得用户在世界的任何地方均可使用智能手机、电脑访问Web浏览器获知感知设备1实时传递出来的数据；本发明在满足最大的传输距离以及功能使用的同时，能够最大限度的降低功耗；通过智能调度间隔式休眠算法的执行可以有效地降低功耗和提高数据采集效率，以及保证数据的可靠性和准确性。
市场上很多感知设备1无法缓存大量的传感器数据，当传输网络连接出现问题时，容易造成数据丢失情况。本发明所述存储模块可以选用内置16MB空间的存储芯片，实际应用时结合本发明特有的存储方式可以存储28.5万条数据，即可存储长达一年以上的传感器数据，在没有WIFI网络覆盖的情况下仍然可以长时间使用；所述处理模块通过存储结构体对接收到的传感器单元输出的传感器数据进行存储；每一存储结构体包括多条采用分隔符进行分隔的数据，每条数据中具有传感器数据、以及相应的传感器数据接收时间戳信息和传感器类型信息；通过传感器数据接收时间戳信息的配置可以有效的对每条传感器数据进行溯源；所述处理模块内置实时操作系统RTOS，可以实现传感器数据采集、存储、计算和同步等行为实时执行，互不干扰；所述感知设备1的时钟与数据平台的时钟保持同步，保证了时间的准确性；本发明感知设备1具有多种模式，不同模式可以对应不同的预设检测算法和不同的传感器原始采样数据的处理方式，进而使得感知设备1能够更加匹配用户需求和使用场景，提高用户体验；通过对所述传感器数据进行CRC校验，可以保证数据传输的正确性和完整性；当所述处理模块需要向数据平台上传数据流或传感器数据时，首先通过无线通信模块进行WIFI连接，当WIFI连接成功时，处理模块进行待上传的数据流或传感器数据的读取操作，若读取成功则进行所述感知设备1与数据平台之间的连接，同时处理模块执行看门狗监视程序；通过看门狗监视程序对系统运行状态进行实时监测，防止来自外界电磁场及人为的干扰而导致程序的跑飞，避免陷入死循环造成整个系统陷入停滞状态；所述感知设备1至少包括USB接口、microusb接口和/或miniUSB接口；所述感知设备1可以通过USB接口与用户终端相连接；所述用户终端为手机、平板电脑或PC；用户能够通过用户终端实现所述AP模式的配置流程，例如将WIFI SSID及密码发送给感知设备1，改变设备序列号等相关设置操作，以及通过用户终端查看AP模式配置信息、读取的感知设备1信息、读取的传感器信息、和/或读取的错误信息；还可直接把感知 设备1存储的数据从USB接口导入到用户终端中，生成Excel表格存储到相应的路径。本发明所述感知设备1具有声纹识别装置；用户能够将WIFI配置信息通过声纹形式广播出去，所述声纹识别装置通过解析识别声纹后转换为相应的WIFI配置信息；通过所述声纹识别装置用户能够实现通过声音实现一个或多个感知设备1的控制，很大程度上提高了数据的安全性和便捷性，群广播的实现进一步提高了数据的便捷性，节约配置时间，为用户带来便利。图3示出了本发明所述感知系统的应用示意图，如图3所示，实际应用时，数据平台可以与多个用户访问端2相连接，用户通过用户访问端2对数据平台进行访问，进而使得用户在世界的任何地方均可使用智能手机、电脑访问Web浏览器获知感知设备1实时传递出来的数据。
本发明所述感知设备1不仅可以传输完整的原始采样数据，还可以提供多种数据处理计算方式和数据输出形式，有利于节省网络数据传输量、降低设备耗电和提高计算效率等。
CRC校验数据的实施过程如下：
Data_CheckCrc(uint8_t*data,uint8_t bytes)
1.//data:代表每一个待校验的传感器数值
2.//bytes:代表待校验的数值的字节数
3.uint8_t byte,crc＝0xff；//初始化参数
4.for(byte＝0；byte<bytes；byte++)
5.for(bit＝0；bit<8；bit++)//依次按字节、按位读取校验值
6.crc＝(crc<<1)^polynomial；//最后得到校验码，将校验码写入crc
7.……//更新其他数据操作类型及参数
8.return crc；//校验完成，将校验码返回
本发明所述处理模块对所述传感器数据进行CRC校验的实施过程如下：
SingleShotMeasure(float*data1,float*data2,float*…)
1.*data1＝ERROR_CODE；*data2＝ERROR_CODE；//设置初始值为error状态
2.set recive[]；data1,data2…；//初始化参数
3.//recive[]表示所接收数据
4.//data1,data2表示需要校验的两个值
5.Single_Start()；//数据传输开始；先发高位MSB,后发低位LSB
6.flag＝CheckCrc(&recive[],n)；//对所接收到的recive值进行校验，并将结果储存在flag中
7.if(flag)//对flag进行判断
8.//如果校验没有错误，则改变*data1和*data2的返回值为接收到的值
9.*data1＝ERROR_CODE；*data2＝ERROR_CODE；//如果校验有错误，则返回错误
10.Single_Stop()；//数据传输结束,将所读取的值返回
下面给出本发明所述存储结构体的应用示例，其中TIME表示存储结构体创建时间；time 1、time 2、time 3表示传感器数据接收时间戳信息，例如"2015-12-17T18:16:22Z"；SensorName表示传感器类型信息；data 1、data 2、data 3表示传感器数据；！表示数据分隔符；分隔符的设置使得上传数据流时不用进行数据转换，既提高了效率，又确保精准性。
存储结构体的应用示例：
Figure PCTCN2018084201-appb-000024
本发明所述处理模块内置实时操作系统RTOS，进而实现对系统的指令通过实时操作系统中断标志，在多个任务之间实现实时切换，不经过冗长繁琐的中间无用程序，直接跳到任务程序，很大程度上提高了设备的灵敏度；下面举例说明实时操作系统RTOS的具体应用，其中，图9示出了本发明实时操作系统RTOS的应用示例示意图。
实时操作系统RTOS的应用示例：
Figure PCTCN2018084201-appb-000025
Figure PCTCN2018084201-appb-000026
图2示出了本发明所述感知系统的示例结构框图，实际应用时，处理模块可以采用处理器，所述存储模块采用存储电路，模式选择开关用于用户对感知设备1的模式进行设定，电压转换模块、USB串口转换模块、时钟模块、稳压模块分别对应电压转换电路、USB串口转换电路、时钟电路和稳压电路；本发明AD采样电路包括相互串联的第一分压电阻和第二分压电阻；所述第二分压电阻在所述AD采样电路进行采样时一端接地，当所述AD采样电路不进行采样时所述第二分压电阻一端不接地，第一分压电阻一端连接供电电源正极，另一端连接第二分压电阻，第二分压电阻另一端与处理器的IO口相连接，通过控制处理器的IO口是否接地，来控制第二分压电阻另一端是否接地，这样只在需要采样时接地，采样完成以后则悬浮或者拉高，进而避免了分压电阻直接接地而造成一直消耗电流的损失，很大程度上降低了功耗。
图5示出了针对突然发生撞击的物体，本发明加速度传感器的输出数据随时间变化的曲线示意图；图6示出了针对缓慢运动的物体，本发明加速度传感器的输出数据随时间变化的曲线示意图；针对突然发生撞击的物体或缓慢运动的物体，采用不同的预设检测算法如峰值检测算法、动态阈值检测算法才能准确检测出物体的运动状态；本发明所述无线通信模块包括WIFI芯片，即直接采用芯片级方案研发，以解决传统的WIFI模块，主芯片周围有很多外围功能电路，而大部分功能电路对感知设备1是无用的，只有少部分电路真正为感知设备1服务然而功耗还不满足产品需求，会产生很多不必要的功耗的技术问题。
本发明当所述处理模块需要向数据平台上传数据流或传感器数据时，首先通过无线通信模块进行WIFI连接，当WIFI连接成功时，处理模块进行待上传 的数据流或传感器数据的读取操作，若读取成功则进行所述感知设备1与数据平台之间的连接，同时处理模块执行看门狗监视程序，下面给出实际应用时可以操作的相应实施过程：
DataPostTask(void*pvParameters)
1.开始
2.Int lRetVal初始化参数
3.WlanConnect()//一旦检测到数据,连接WIFI,并将返回结果存储到lRetVal中
4.if(lRetVal)//读取lRetVal，如果lRetVal＝1，则说明WIFI连接成功
5.//WIFI连接成功时,读取数据长度len,并将返回结果存储到lRetVal中
6.if(lRetVal)//再次读取lRetVal如果lRetVal＝1，则说明数据读取成功
7.ConnectToHTTPServer(&httpClient)//当数据读取成功时,连接到HTTP服务器
8.WatchdogAck()；此时例程执行看门狗，一旦程序跑飞连接失败，系统自动复位
9.HTTPPostMethod()；//向HTTP传送数据
10.HTTPCli_disconnect()；//数据传送完成断开HTTP连接
11.WlanStop()//断开WIFI连接
本发明所述感知设备1支持用户通过用户终端如手机或PC进行无线配置操作，并可以通过手机或PC的配置界面查看感知设备1当前的配置信息、传感器读取信息、错误信息等，图7示出了本发明用户通过手机或PC进行感知设备1无线配置操作的流程示意图，具体流程如下：
B1：感知设备1开机；
B2：初始化感知设备1；
B3：打开手机或PC配置端；
B4：点击添加新设备；
B5：选择添加设备方式；所述添加设备方式包括扫二维码和输入序列号；
B6：点击关联设备；
B7：关联是否成功，是则执行B8，否则返回B4；
B8：查找设备；
B9：连接WIFI；
B10：配置设备；
B11：校验是否配置成功，若配置成功则绿灯闪烁，以提示配置成功，若配置不成功，则通过红灯常亮来提示WIFI密码错误，通过红灯闪烁来提示网络断开。
图8示出了本发明所述感知设备1开机后至创建主任务期间的示例流程示意图，如图8所示，实际应用时，所述感知设备1开机后至创建主任务期间的流程可以如下；
C1：开机；
C2：设备初始化；
C3：读取并判断系统状态；
C4：是否是开机状态，是则执行C5，否则执行C6；
C5：读取设备启动原因，若是上电启动，则更新系统时间并执行C14，若是唤醒启动，则执行C14；
C6：识别按键状态，若按键持续n1秒则进行设备序列号读取操作后执行C7，若按键持续n2秒则进入无线AP模式后执行，若按键持续n3秒则恢复出厂设置后执行C；
C7：是否读到序列号，是则读取WIFI设置信息后执行C8，否则恢复出厂设置后执行C9；
C8：WIFI是否设置成功，是则执行C10，否则执行C9；
C9：进行无线AP模式，执行C10；
C10：查询设置激活状态；
C11：判断是否激活，是则执行C12，否则激活设备后执行C12；
C12：读取系统当前时间；
C13：系统当前时间是否准确，是则执行C14，否则更新系统时间后执行C14；
C14：创建主任务。
本发明当前未运行任务1、2、…、m下次的预设运行时间T1、T2、T3、…、Tm是可能变化的，因此当系统运行任务时均执行一下智能调度间隔式休眠算法；将s时间段内所有加速度数据幅度值的均值F(s)与预设值进行比较，并根据比较结果确定用户当前是否处于跌倒状态，若比较结果为F(s)大于预设值则确定用户当前处于跌倒状态；在计算出动态阈值之后，将每次获得的加速度数据与该动态阈值进行比较，并根据比较结果确定用户是否迈出步伐，若比较结果为该次获得的加速度数据大于动态阈值，则确定用户迈出了步伐；本发明所述多个指两个及两个以上；本发明通过第一处理方式对传感器原始采样数据进行处理后得到的数据为
Figure PCTCN2018084201-appb-000027
Figure PCTCN2018084201-appb-000028
…、
Figure PCTCN2018084201-appb-000029
x 1表示传感器第1次采样获得的原始采样数据、x 2表示传感器第2次采样获得的原始采样数据、x 3表示传感器第3次采样获得的原始采样数据，x N+1表示传感器第N+1次采样获得的原始采样数据、x N+2表示传感器第N+2次采样获得的原始采样数据、x N+3表示传感器第N+3次采样获得的原始采样数据，x (N-1)N表示传感器第(N-1)N次采样获得的原始采样数据、x (N-1)N+1表示传感器第(N-1)N+1次采样获得的原始采样数据；通过第二处理方式对传感器原始采样数据进行处理后得到的数据为K 1、K 2、…、K N；通过第三处理方式对传感器原始采样数据进行处理后得到的数据为对应的统计量均小于T α,n的传感器原始采样数据，T α,n表示查询格拉布斯表后获得的临界值，实际应用时具体根据预设临界条件和N的大小进行查询获得T α,n值；所述感知设备1的时钟与数据平台的时钟保持同步，相应的时钟校准机制为所述感知设备1的时钟按照预设同步周期与数据平台的时钟进行同步，或者当所述感知设备1向数据平台上传数据时进行时钟同步的操作；本发明所述处理模块还根据接收到的预设中断信息将相应的传感器数据直接上传至数据平台；预设中断信息是根据某些特定传感器的数据输出产生的，比如当震动传感器输出数据后则产生相应的中断信息，处理模块根据该中断信息将震动传感器输出数据上传至数据平台。
本发明所述感知设备1能耗极低、待电时间极长、灵敏度高；实际应用时可以将感知设备1设计成体积小巧轻薄能够贴附于几乎任何物体上的产品，进一步地，感知设备1将所贴物体或物体周边环境的特定状态数据通过无线WIFI直接通过互联网同步到数据平台中，以做到让任何物体都能轻松接入互联网；通过多维度的数据组合可广泛运用于工业、畜牧业、农业、机械化生产、医疗和物流等行业，以实现环境的监控、判断、分析、控制、预警和提示等功能。环境感知技术的发展在促进可穿戴设备进步的同时也让人们对医疗领域有了更多的期待；当本发明所述感知设备1应用于医疗行业时，具体地，可以通过智能感知设备进行人体体征数据采集，通过无线WIFI传输给病人家属及医护人员，形成一个完整的医疗监测生态系统，通过多个智能感知设备的部署，能够实时检测病房内病人的体温及休息情况如睡眠情况、噪音情况、环境温湿度及光亮情况等，对采集数据采用个性化异常判定，以及相应的数据分析及处理后，并将数据同步到数据平台中，使值班医生和护士可以从电脑或平板上时刻关注所有病人的健康状态，当数据变化超出设定阈值或范围时，还可发出警报提示医 护人员；对于患者而言，某些慢性病、长期病以及一些隐性疾病想要确诊，往往需要住院观察记录数据，比如长期收集数据汇总后告知主治医生才能进行诊断，病人因此需要承担较高昂的住院费用，并且未确诊前有较大的心理压力，由于某些致命疾病如癌症在恶化之前往往没有特别明显的特征；应用本发明所述感知设备1能够及时的记录身体上的一些数据，并可以结合互联网应用及时传输给医疗相关专业的人士进行分析，使人们在不耽误正常生活的情况下，对于隐性的疾病能够早发现早治疗；本发明所述感知设备1还可以应用于物流行业，具体地，现在物流环节中，尤其是冷链货品，客户无法了解运输途中温度是否全程达到标准，而且运输过程中存在严重颠簸及震荡，对于物流运输提供商的服务水平SLA无法准确衡量；通常只能靠到达目的地后开箱进行逐一货物监测方可达到验证效果，通过使用本发明所述感知设备1部署到货物中，可以将货物在运输全程之中每一时间点的温度、湿度、亮度和震荡幅度等环境变化准确捕捉到，这样的应用一方面帮助物流运输服务商监督服务水平，另一方面也方便于物流客户的检验以及衡量货品的损耗程度，而且，对于可能发生的纠纷提供有效的数据作为证据支持，而且可降低货主的运输和配送等责任风险；本发明对系统采用智能调度间隔式休眠方式，仅用两节7号或5号电池即可持续供电6个月以上，完全满足长途运输的要求。
以上所述，仅为本发明较佳的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，根据本发明的技术方案及其发明构思加以等同替换或改变，都应涵盖在本发明的保护范围之内。

Claims (14)

  1. 一种智能感知设备，其特征在于所述感知设备包括：具备多个传感器的传感器单元、与数据平台相连接的无线通信模块、存储模块、以及处理模块；所述处理模块与所述传感器单元、无线通信模块和存储模块相连接；
    所述传感器单元包括：温度传感器、湿度传感器、环境光传感器、磁场传感器、加速度传感器和震动传感器中的至少两个；所述无线通信模块至少包括WIFI芯片；
    所述处理模块根据加速度传感器的检测结果并利用预设检测算法来获知用户的运动情况；所述感知设备具有多种模式，不同模式对应不同的预设检测算法，用户通过对感知设备进行模式设定来对所述预设检测算法进行选择。
  2. 根据权利要求1所述的智能感知设备，其特征在于，
    所述传感器单元还包括：风速传感器、pH值传感器、光照度传感器、溶解氧传感器、二氧化碳传感器、空气质量传感器、门磁传感器、噪声传感器中的至少一个；
    所述感知设备还包括与处理模块相连接的USB串口转换模块、模式转换开关、电压转换模块、稳压模块和时钟模块。
  3. 根据权利要求1所述的智能感知设备，其特征在于所述处理模块通过存储结构体对接收到的传感器单元输出的传感器数据进行存储；每一存储结构体包括多条采用分隔符进行分隔的数据，每条数据中具有传感器数据、以及相应的传感器数据接收时间戳信息和传感器类型信息；所述处理模块将各存储结构体按照创建顺序依次排列后形成数据流，并根据预设上传周期将所述数据流上传至所述数据平台；所述处理模块还根据接收到的预设中断信息将相应的传感器数据直接上传至数据平台。
  4. 根据权利要求3所述的智能感知设备，其特征在于所述处理模块在对接收到的传感器单元输出的传感器数据进行存储之前，先对所述传感器数据进行CRC校验，并对通过CRC校验的传感器数据进行存储，以及对未通过CRC校验的传感器数据读取CRC校验错误值。
  5. 根据权利要求3所述的智能感知设备，其特征在于，
    在所述数据流或传感器数据上传至数据平台后，所述处理模块将感知设备中存储的对应数据删除；
    当所述处理模块需要向数据平台上传数据流或传感器数据时，首先通过无 线通信模块进行WIFI连接，当WIFI连接成功时，处理模块进行待上传的数据流或传感器数据的读取操作，若读取成功则进行所述感知设备与数据平台之间的连接，同时处理模块执行看门狗监视程序；
    所述数据平台能够向所述感知设备发送指令，所述感知设备通过向数据平台发送删除所有指令队列的操作信息以实现不进行新指令的接收。
  6. 根据权利要求1所述的智能感知设备，其特征在于，
    所述感知设备的时钟与数据平台的时钟保持同步；
    所述处理模块内置实时操作系统RTOS；
    所述处理模块采用内部集成有AD采样电路的处理器；所述AD采样电路包括相互串联的第一分压电阻和第二分压电阻；所述第二分压电阻在所述AD采样电路进行采样时一端接地，当所述AD采样电路不进行采样时所述第二分压电阻一端不接地。
  7. 根据权利要求1所述的智能感知设备，其特征在于所述处理模块按照智能调度间隔式休眠算法进行运行和休眠；
    其中，所述智能调度间隔式休眠算法包括如下流程：
    ①获知系统当前时间Ts和当前未运行任务1、2、…、m下次的预设运行时间T1、T2、T3、…、Tm，执行②；
    ②依次计算出当前未运行任务1、2、…、m的执行频率F1、F2、F3、…、Fm，其中，F1＝T1-Ts、F2＝T2-Ts、F3＝T3-Ts、…、Fm＝Tm-Ts，执行③；
    ③确定任务1、2、…、m的执行频率F1、F2、F3、…、Fm中的最小值Fs，根据T′＝Ts+Fs确定下次运行的任务并得出系统下次运行时间T′，执行④；
    ④系统进入休眠状态，执行⑤；
    ⑤当系统当前时间达到系统下次运行时间T′，系统进入唤醒状态并运行相应任务，返回①。
  8. 根据权利要求1所述的智能感知设备，其特征在于所述预设检测算法至少包括峰值检测算法和动态阈值检测算法；
    所述峰值检测算法包括如下流程：
    ①获得所述加速度传感器在s时间段内依次输出的加速度数据；所述加速度数据具有x轴加速度、y轴加速度和z轴加速度，执行②；
    ②判断加速度传感器的采样频率是否高于预设采样频率，是则执行④，否则执行③；
    ③计算
    Figure PCTCN2018084201-appb-100001
    其中，f(t)表示在s时间段内第t条加速度数据的幅度值、x(t)表示第t条加速度数据对应的x轴加速度、y(t)表示第t条加速度数据对应的y轴加速度、z(t)表示第t条加速度数据对应的z轴加速度、x(t-1)表示第t-1条加速度数据对应的x轴加速度、y(t-1)表示第t-1条加速度数据对应的y轴加速度、z(t-1)表示第t-1条加速度数据对应的z轴加速度、t表示在s时间段内的加速度数据排列顺序，执行步骤⑤；
    ④计算
    Figure PCTCN2018084201-appb-100002
    其中，f(t)表示在s时间段内第t条加速度数据的幅度值、x(t)表示第t条加速度数据对应的x轴加速度、y(t)表示第t条加速度数据对应的y轴加速度、z(t)表示第t条加速度数据对应的z轴加速度、x(t-2)表示第t-2条加速度数据对应的x轴加速度、y(t-2)表示第t-2条加速度数据对应的y轴加速度、z(t-2)表示第t-2条加速度数据对应的z轴加速度、t表示在s时间段内的加速度数据排列顺序，执行步骤⑤；
    ⑤获取在s时间段内所有加速度数据幅度值的均值
    Figure PCTCN2018084201-appb-100003
    其中，T表示在s时间段内的加速度数据数量，执行⑥；
    ⑥将s时间段内所有加速度数据幅度值的均值F(s)与预设值进行比较，并根据比较结果确定用户当前是否处于跌倒状态；
    所述动态阈值检测算法包括如下流程：
    Ⅰ：获得所述加速度传感器输出的加速度数据；所述加速度数据具有x轴加速度、y轴加速度和z轴加速度，执行Ⅱ；
    Ⅱ：当所述加速度数据达到N个后，计算N个加速度数据的均值作为动态阈值，执行Ⅲ；
    Ⅲ：将计算出动态阈值后每次获得的加速度数据与该动态阈值进行比较，并根据比较结果确定用户是否迈出步伐，执行Ⅳ；
    Ⅳ：当计算出动态阈值后获得的加速度数据再次达到N个后，重新计算N个加速度数据的均值并更新动态阈值，返回步骤Ⅲ。
  9. 根据权利要求1所述的智能感知设备，其特征在于所述处理模块将传感器输出的原始采样数据直接作为传感器数据进行存储，或者将通过预设处理方 式对传感器输出的原始采样数据进行处理后得到的数据作为传感器数据进行存储；所述预设处理方式至少包括第一处理方式、第二处理方式和第三处理方式；不同处理方式对应感知设备的不同模式，用户通过对感知设备进行模式设定来对所述预设处理方式进行选择；
    所述第一处理方式为：获得
    Figure PCTCN2018084201-appb-100004
    Figure PCTCN2018084201-appb-100005
    …、
    Figure PCTCN2018084201-appb-100006
    其中，x N表示传感器第N次采样获得的原始采样数据、x 2N表示传感器第2N次采样获得的原始采样数据、x NN表示传感器第NN次采样获得的原始采样数据；
    所述第二处理方式为：获得
    Figure PCTCN2018084201-appb-100007
    Figure PCTCN2018084201-appb-100008
    …、
    Figure PCTCN2018084201-appb-100009
    其中，x N表示传感器第N次采样获得的原始采样数据、x 2N表示传感器第2N次采样获得的原始采样数据、x NN表示传感器第NN次采样获得的原始采样数据，x max1表示传感器的原始采样数据x 1,x 2,x 3,......x N中的最大值，x min1表示传感器的原始采样数据x 1,x 2,x 3,......x N中的最小值，x max2表示传感器的原始采样数据x N+1,x N+2,x N+3,......x 2N中的最大值，x min2表示传感器的原始采样数据x N+1,x N+2,x N+3,......x 2N中的最小值，x max N表示传感器的原始采样数据x (N-1)N,x (N-1)N+1......x NN中的最大值，x min N表示传感器的原始采样数据x (N-1)N,x (N-1)N+1......x NN中的最小值；
    所述第三处理方式为：①针对N个传感器原始采样数据进行平均值和方差计算；②对所述N个传感器原始采样数据依次计算统计量
    Figure PCTCN2018084201-appb-100010
    Figure PCTCN2018084201-appb-100011
    若某一传感器原始采样数据x i对应的T i≥T α,n，则将x i丢弃，然后重新累计N个传感器原始采样数据后返回①，直至各传感器原始采样数据均计算过统计量，其中，
    Figure PCTCN2018084201-appb-100012
    表示N个传感器原始采样数据的平均值、S表示N个传感器原始采样数据的方差、x i表示第i个传感器原始采样数据、T α,n表示查询格拉布斯表后获得的临界值；
    针对传感器原始采样数据以及传感器原始采样数据经过不同预设处理方式处理后得到的数据，均采用包含数据本身信息和数据类型信息的数据结构形式 进行数据输出，通过不同数据类型信息来区分是否是传感器原始采样数据以及不同的预设处理方式。
  10. 根据权利要求1所述的智能感知设备，其特征在于所述感知设备具有声纹识别装置；用户能够将WIFI配置信息通过声纹形式广播出去，所述声纹识别装置通过解析识别声纹后转换为相应的WIFI配置信息；通过所述声纹识别装置用户能够实现通过声音实现一个或多个感知设备的控制。
  11. 根据权利要求1所述的智能感知设备，其特征在于所述无线通信模块能够工作在AP模式和STA模式，所述AP模式的配置流程包括如下步骤：
    A1：开启AP模式，执行A2；
    A2：等待获取IP，执行A3；
    A3：创建TCP连接，执行A4；
    A4：跟踪TCP连接，执行A5；
    A5：接受TCP命令，执行A6；
    A6：判断TCP命令类型，执行A7；
    A7：若TCP命令类型为退出命令或配置命令，则接受TCP命令后执行A8；若TCP命令类型为读取感知设备信息命令、读取传感器信息命令或读取错误信息命令，则接受TCP命令后返回A6；
    A8：发送AP模式配置结果，执行A9；
    A9：关闭TCP连接，执行A10；
    A10：配置STA模式，执行A11；
    A11：退出AP模式的配置流程。
  12. 根据权利要求11所述的智能感知设备，其特征在于所述感知设备能够与用户终端相连接；用户能够通过用户终端实现所述AP模式的配置流程，以及通过用户终端查看AP模式配置信息、读取的感知设备信息、读取的传感器信息、和/或读取的错误信息。
  13. 根据权利要求12所述的智能感知设备，其特征在于所述感知设备至少包括USB接口、microusb接口和/或miniUSB接口；所述感知设备通过所述USB接口与用户终端相连接；所述用户终端为手机、平板电脑或PC；所述感知设备存储的数据能够通过USB接口导入到所述用户终端中。
  14. 一种感知系统，其特征在于所述感知系统包括：
    多个权利要求1至13任一项所述的智能感知设备；
    与多个感知设备相连接的数据平台。
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