WO2018233074A1 - 一种停车导航路径的推荐系统及方法 - Google Patents

一种停车导航路径的推荐系统及方法 Download PDF

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Publication number
WO2018233074A1
WO2018233074A1 PCT/CN2017/100388 CN2017100388W WO2018233074A1 WO 2018233074 A1 WO2018233074 A1 WO 2018233074A1 CN 2017100388 W CN2017100388 W CN 2017100388W WO 2018233074 A1 WO2018233074 A1 WO 2018233074A1
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wireless terminal
vehicle
big data
data analysis
analysis system
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PCT/CN2017/100388
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English (en)
French (fr)
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杜光东
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深圳市盛路物联通讯技术有限公司
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Publication of WO2018233074A1 publication Critical patent/WO2018233074A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/352—Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/681—Wristwatch-type devices
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
    • G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
    • G08G1/096805—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route
    • G08G1/096811—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route where the route is computed offboard
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
    • G08G1/141—Traffic control systems for road vehicles indicating individual free spaces in parking areas with means giving the indication of available parking spaces
    • G08G1/143—Traffic control systems for road vehicles indicating individual free spaces in parking areas with means giving the indication of available parking spaces inside the vehicles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/15—Biometric patterns based on physiological signals, e.g. heartbeat, blood flow

Definitions

  • the present invention relates to the field of Internet of Things technologies, and in particular, to a system and method for recommending a parking navigation path.
  • the embodiment of the invention discloses a recommendation system and method for parking navigation path, which can enable the driver to quickly find a parking space suitable for the vehicle type to stop when the mood is unstable, and also enable the driver to predict the parking space in advance.
  • the weather information of the preset duration so that the vehicle protection preparation during parking can be prepared in advance to prevent the vehicle from being damaged by bad weather.
  • a first aspect of the embodiments of the present invention discloses a method for recommending a parking navigation path, including:
  • the wireless terminal identifies whether the emotion of the driver of the vehicle where the wireless terminal is located is stable, and if the emotion is unstable, reports the current vehicle position of the vehicle where the wireless terminal is located to the big data analysis system;
  • the big data analysis system identifies a vehicle type of the vehicle and identifies a type of parking space to which the vehicle type is applicable based on the vehicle type;
  • the big data analysis system finds all the free parking spaces corresponding to the parking space type within its own jurisdiction, and selects the free parking space closest to the current vehicle position from all the found idle parking spaces as the target parking space. ;
  • the big data analysis system queries the weather information of the preset duration corresponding to the target parking space and sends the weather information to the wireless terminal, so that the driver of the vehicle where the wireless terminal is located determines whether the parking needs to be generated according to the weather information.
  • Navigation path
  • the indication information reported by the big data analysis system on the wireless terminal indicates that a parking guide needs to be generated.
  • a parking navigation path between the target parking space and the current vehicle location is generated, and the parking navigation route is sent to the wireless terminal for parking navigation.
  • the method when the wireless terminal identifies that the emotion of the driver of the vehicle where the wireless terminal is located is stable, the method further includes:
  • the wireless terminal collects a frame of the driver's face image at a specified time, and determines a human eye positioning rectangle from the face image, calculates an area of the human eye positioning rectangle, and determines the Determining the degree of eyelidness of the driver's eyes, and evaluating the degree of eye fatigue according to the degree of closure of the driver's eyes, determining whether the driver's eyes are fatigued based on the degree of human eye fatigue, and if fatigue, Performing the reporting of the current vehicle location of the vehicle in which the wireless terminal is located to the big data analysis system.
  • the wireless terminal identifies whether the emotion of the driver of the vehicle where the wireless terminal is located is stable, including:
  • the wireless terminal detects electrocardiogram data of the driver sent by a wearable device worn by a driver of a vehicle where the wireless terminal is located;
  • the wireless terminal performs denoising processing on the electrocardiogram data
  • the wireless terminal extracts an R wave peak in the degaussed ECG data by using an electrocardiogram R wave extraction algorithm, and calculates an RR interval between adjacent R waves in the denoised processed electrocardiogram data;
  • the wireless terminal calculates a frequency domain indicator, a time domain indicator, and a non-linear indicator of the RR interval; wherein the frequency domain indicator includes a parasympathetic nerve activity indicator, and the time domain indicator includes a short-range heart rate variability indicator;
  • the heart rate variability index is calculated by obtaining a root mean square of the sum of the squares of the RR gap differences; the parasympathetic nerve activity index is calculated by a fast Fourier transform; and the nonlinear index is calculated by a fractal dimension calculation method;
  • the wireless terminal analyzes the vitality value of the user's emotion according to the frequency domain indicator, the time domain indicator, and the non-linear indicator; the vitality value is established according to the time domain indicator, the frequency domain indicator, and the nonlinear indicator. a value calculated by a multiple linear regression equation;
  • the wireless terminal identifies, based on the vitality value, whether the driver's mood is unstable.
  • the wireless terminal reports the current vehicle location of the vehicle where the wireless terminal is located to the big data analysis system, including:
  • the wireless terminal scans whether a routing node is preset in the surrounding environment, and if the routing node is preset, detecting whether the routing node is configured with an open access period, if the routing node is configured with the open connection a period of time, identifying whether a current system time of the wireless terminal is located in the open access period in which the routing node is configured;
  • the routing node If the current system time of the wireless terminal is located in the open access period in which the routing node is configured, detecting whether the number of terminals currently accessed by the routing node exceeds the maximum number of terminal access specified by the routing node ;
  • the wireless terminal establishes a wireless connection with the routing node, and the vehicle where the wireless terminal is located The current vehicle location is sent to the routing node, and the routing node transmits the current vehicle location of the vehicle in which the wireless terminal is located to the big data analysis system.
  • the big data analysis system queries the weather information of the preset duration corresponding to the target parking space and sends the weather information to the wireless terminal, including:
  • the big data analysis system determines whether a current workload of the big data analysis system exceeds a workload specified by the big data analysis system
  • the big data analysis system initiates the inclusion of the weather service platform corresponding to the weather information query port through the weather information query port. a weather information inquiry request for the target parking space;
  • the big data analysis system receives the weather information of the preset duration corresponding to the target parking space returned by the weather service platform through the weather information inquiry port;
  • the big data analysis system sends the weather information of the preset duration corresponding to the target parking space to the wireless terminal.
  • a second aspect of the embodiments of the present invention discloses a recommendation system for a parking navigation path, including a wireless terminal and a big data analysis system, wherein:
  • the wireless terminal is configured to identify whether the emotion of the driver of the vehicle where the wireless terminal is located is stable, and if the emotion is unstable, report the current vehicle position of the vehicle where the wireless terminal is located to the big data analysis system;
  • the big data analysis system is configured to identify a vehicle type of the vehicle, and identify a parking space type applicable to the vehicle type according to the vehicle type;
  • the big data analysis system is further configured to find all free parking spaces corresponding to the parking space type within its own jurisdiction, and select the free parking spaces of the current vehicle location that are the most connected from all the idle parking spaces found. As a target parking space;
  • the big data analysis system is further configured to query the weather information of the preset duration corresponding to the target parking space and send the weather information to the wireless terminal, so that the driver of the vehicle where the wireless terminal is located is determined according to the weather information. Whether it is necessary to generate a parking navigation path;
  • the big data analysis system is further configured to: when the indication information reported by the wireless terminal indicates that a parking navigation path needs to be generated, generate a parking navigation path between the target parking space and the current vehicle location, and A parking navigation path is sent to the wireless terminal for parking navigation.
  • the wireless terminal is further configured to: when identifying the emotion of the driver of the vehicle where the wireless terminal is located, collect a frame of the driver's face image at a specified time, and determine from the face image An eye positioning rectangle is calculated, an area of the human eye positioning rectangle is calculated, a degree of closure of the driver's eyes is determined according to a threshold value, and a degree of eye fatigue is evaluated according to a degree of closure of the driver's eyes. Determining whether the driver's eyes are fatigued based on the human eye fatigue level value, and if fatigue, performing the reporting of the current vehicle position of the vehicle in which the wireless terminal is located to the big data analysis system.
  • the manner in which the wireless terminal identifies whether the emotion of the driver of the vehicle where the wireless terminal is located is stable is:
  • the wireless terminal is configured to detect a wearable device worn by a driver of a vehicle where the wireless terminal is located Sending the electrocardiogram data of the driver; performing denoising processing on the electrocardiogram data; extracting an R wave peak in the degaussed ECG data by using an electrocardiogram R wave extraction algorithm, and calculating the denoised processed electrocardiogram
  • the RR spacing between adjacent R waves in the data calculating a frequency domain index, a time domain index, and a nonlinear index of the RR spacing; wherein the frequency domain indicator includes a parasympathetic nerve activity index, and the time domain indicator includes a short range heart rate a variability index; the short-term heart rate variability index is calculated by obtaining a root mean square of a sum of squares of the RR gap differences; the parasympathetic nerve activity index is calculated by a fast Fourier transform; the nonlinear index is obtained by a fractal dimension Calculating a number calculation method; analyzing the vitality value of the user's emotion according to the frequency domain indicator,
  • the manner in which the wireless terminal reports the current vehicle location of the vehicle where the wireless terminal is located to the big data analysis system is specifically:
  • the wireless terminal is configured to scan whether a routing node is preset in the surrounding environment, and if the routing node is preset, detecting whether the routing node is configured with an open access period, if the routing node is configured Determining, according to an open access period, whether a current system time of the wireless terminal is located in the open access period in which the routing node is configured; if a current system time of the wireless terminal is located in a location where the routing node is configured During the open access period, detecting whether the number of terminals currently accessed by the routing node exceeds the maximum number of terminal accesses specified by the routing node; if the number of terminals currently accessed by the routing node does not exceed the route a maximum number of terminal accesses designated by the node, establishing a wireless connection with the routing node, and transmitting a current vehicle location of the vehicle in which the wireless terminal is located to the routing node, the routing terminal being the wireless terminal
  • the current vehicle location of the vehicle in which it is located is sent to the big data
  • the manner in which the big data analysis system queries the weather information of the preset duration corresponding to the target parking space and delivers the weather information to the wireless terminal is specifically:
  • the big data analysis system configured to determine whether a current workload of the big data analysis system exceeds a workload specified by the big data analysis system, if a current workload of the big data analysis system does not exceed the big data And analyzing, by the weather information query port, the weather service platform corresponding to the weather information query port to initiate a weather information query request including the target parking space; and receiving the weather service platform to query the port through the weather information Returning the weather information of the preset duration corresponding to the target parking space; and transmitting the weather information of the preset duration corresponding to the target parking space to the wireless terminal;
  • the method further includes:
  • the big data analysis system is configured to identify whether the target parking space belongs to a parking space rented according to a preset time period, and if the target parking space does not belong to a parking space rented according to a preset time period, corresponding to the target parking space
  • the normal charging rule and the actual parking time are calculated corresponding to the parking fee
  • the ordinary charging rule includes a first charging amount corresponding to the unit time; if the target parking space belongs to the parking space rented according to the preset time period, determining the Whether the actual parking length of the vehicle at the target parking space exceeds the a preset time period, if the preset time period is not exceeded, calculating a corresponding parking fee according to the normal charging rule corresponding to the target parking space and the actual parking time; if the preset time period is exceeded, according to the target parking space Corresponding high-paying rules and the actual parking time are calculated corresponding to the parking fee, the high charging rule includes a second charging amount corresponding to the unit time, and the second charging amount is equal to the multiple of
  • the embodiment of the invention has the following beneficial effects:
  • the driver not only can the driver quickly find a parking space suitable for the vehicle type to stop when the mood is unstable, but also enable the driver to predict the weather information of the preset duration corresponding to the parking space in advance, thereby Prepare for vehicle protection when parking in advance to prevent damage caused by bad weather.
  • FIG. 1 is a schematic flow chart of a method for recommending a parking navigation path according to an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of a method for a wireless terminal to recognize whether a driver's emotion is stable according to an embodiment of the present invention
  • FIG. 3 is a schematic flow chart of another method for recommending a parking navigation path according to an embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of a recommendation system for a parking navigation path disclosed in an embodiment of the present invention.
  • the embodiment of the invention discloses a recommendation system and method for parking navigation path, which can enable the driver to quickly find a parking space suitable for the vehicle type to stop when the mood is unstable, and also enable the driver to predict the parking space in advance.
  • the weather information of the preset duration so that the vehicle protection preparation during parking can be prepared in advance to prevent the vehicle from being damaged by bad weather. The details are described below separately.
  • FIG. 1 is a schematic flowchart diagram of a method for recommending a parking navigation path according to an embodiment of the present invention.
  • the recommended method of the parking navigation path may include the following steps:
  • the wireless terminal identifies whether the emotion of the driver of the vehicle where the wireless terminal is located is stable. If the emotion is unstable, the current vehicle position of the vehicle where the wireless terminal is located is reported to the big data analysis system.
  • the wireless communication module built in the wireless terminal can input the upper frequency point 470MHz and the lower frequency point 510MHz during production, so that the wireless communication module can automatically define the communication frequency band as 470MHz ⁇ 510MHz, in line with the Chinese SRRC standard; or, you can input the upper frequency point 868MHz, the lower frequency point 908MHz, so that the wireless communication module can automatically define the communication frequency band as 868MHz ⁇ 908MHz, in line with the European ETSI standard; Alternatively, the upper frequency point is 918 MHz and the lower frequency point is 928 MHz, so that the wireless communication module can automatically define the communication frequency band as 918 MHz to 928 MHz to comply with the US FCC standard; or, the communication frequency band of the wireless communication module can also be defined as conforming
  • the provisions of the Japanese ARIB standard or the Canadian IC standard are not limited in the embodiment of the present invention.
  • the wireless terminal may use Frequency Division Multiple Access (FDMA), Frequency-Hopping Spread Spectrum (FHSS), Dynamic Time Division Multiple Access (DTDMA), and backoff.
  • FDMA Frequency Division Multiple Access
  • FHSS Frequency-Hopping Spread Spectrum
  • DTDMA Dynamic Time Division Multiple Access
  • CSMA multiplexing
  • the wireless terminal when the wireless terminal recognizes that the emotion of the driver of the vehicle where the wireless terminal is located is stable, the wireless terminal may further perform the following steps:
  • the wireless terminal collects a frame image of the driver's face every specified time (for example, 120 milliseconds), and determines a human eye positioning rectangle from the face image, calculates an area of the human eye positioning rectangle, and determines the driver's eyes according to the threshold value.
  • the degree of squatting, and the degree of eye fatigue according to the degree of suffocation of the driver's eyes, the driver's eyes are judged to be fatigued based on the degree of human eye fatigue, and if fatigue, the current vehicle position of the vehicle where the wireless terminal is located is reported to Big data analysis system.
  • the manner in which the wireless terminal collects the current vehicle location of the vehicle where the wireless terminal is located may be:
  • the wireless terminal acquires at least two different positioning interfaces configured by the wireless terminal; for example, at least two different positioning interfaces may include a nlpservice positioning interface of Baidu, a nlpservice positioning interface of Gaode, a nlpservice positioning interface of Google, etc., the present invention
  • the embodiment is not limited; and the wireless terminal may send the location request to the at least two different positioning interfaces to trigger each positioning interface to separately send the received positioning request to the corresponding positioning server; and acquire at least one Locating the location information sent by the location server corresponding to the interface, and obtaining the response time from the first moment to the second moment, where the first moment is the moment when the positioning request is sent by each positioning interface, and the second moment is the location received by each positioning interface.
  • the time of the information and comparing the response time corresponding to each positioning interface with the response threshold, and extracting the location information with the highest positioning accuracy from the location information received by the positioning interface whose response time does not exceed the response threshold as the wireless terminal.
  • the implementation of the foregoing embodiment can accurately acquire the parking position and improve the positioning accuracy.
  • the method for the wireless terminal to identify whether the emotion of the driver of the vehicle where the wireless terminal is located may be stable, as shown in FIG. 2, including the following steps:
  • the wireless terminal detects the electrocardiogram data of the driver sent by the wearing device (such as a wristband) worn by the driver of the vehicle where the wireless terminal is located.
  • the wearing device such as a wristband
  • the wireless terminal can detect whether the running time of the vehicle where the wireless terminal is located exceeds a preset duration, and if the preset duration is exceeded, the wireless terminal can detect whether the wireless terminal is located with the vehicle where the wireless terminal is located.
  • a wearable device (such as a wristband) worn by the driver establishes a communication connection, and if so, the wireless terminal can notify the wearable device worn by the driver to transmit the driver's electrocardiogram data to the wireless terminal.
  • the wireless terminal may perform denoising processing on the electrocardiogram data, and extract an R wave peak in the degaussed ECG data by using an electrocardiogram R wave extraction algorithm, and calculate between adjacent R waves in the degaussed ECG data.
  • RR spacing and, calculating the frequency domain index, time domain index and non-linear index of RR spacing; wherein the frequency domain indicator includes parasympathetic nerve activity index, the time domain index includes short-range heart rate variability index; the short-range heart rate variability index obtains RR
  • the root mean square of the sum of squared differences is calculated; the parasympathetic activity index is calculated by fast Fourier transform; the nonlinear index is calculated by the fractal dimension calculation method.
  • the wireless terminal may analyze the emotional activity value of the user according to the frequency domain indicator, the time domain indicator, and the non-linear indicator; wherein the vitality value is a multiple linear regression equation established according to the time domain index, the frequency domain index, and the nonlinear index. Calculating the value; and, based on the vitality value, identifying whether the user's mood is unstable.
  • the wireless terminal reports the current vehicle location of the vehicle where the wireless terminal is located to the big data analysis system, including:
  • the wireless terminal scans whether the routing node is preset in the surrounding environment. If the routing node is preset, whether the routing node is configured with an open access period, and if the routing node is configured with an open access period, the current system time of the wireless terminal is identified. Whether it is located in the open access period in which the routing node is configured;
  • the routing node If the current system time of the wireless terminal is located in the open access period in which the routing node is configured, detecting whether the number of terminals currently accessed by the routing node exceeds the maximum terminal access number specified by the routing node;
  • the wireless terminal If the number of terminals currently accessed by the routing node does not exceed the maximum number of terminal access specified by the routing node, the wireless terminal establishes a wireless connection with the routing node, and sends the current vehicle location of the vehicle where the wireless terminal is located to the routing node, The routing node reports the current vehicle location of the vehicle in which the wireless terminal is located to the big data analysis system.
  • the routing node reports the current vehicle location of the vehicle where the wireless terminal is located to the big data analysis system, so that the wireless terminal can directly establish a long-distance communication connection with the big data analysis system, thereby avoiding direct and large wireless terminals.
  • Data analysis system communication brings greater power consumption.
  • the big data analysis system identifies the vehicle type of the vehicle and identifies the type of parking space to which the vehicle type is applicable based on the vehicle type.
  • the vehicle type may include a car, a truck, a passenger car, a trailer, a motorcycle, etc., which are not limited in the embodiment of the present invention.
  • the big data analysis system may identify the vehicle identifier (such as a license plate) of the vehicle where the wireless terminal is located according to the identity of the wireless terminal, and may identify the vehicle type of the vehicle according to the vehicle identifier of the vehicle where the wireless terminal is located.
  • the type of parking space to which the vehicle type is applicable is identified based on the type of vehicle.
  • the big data analysis system finds all the sizes corresponding to the parking space type within its own jurisdiction.
  • the free parking space selects the free parking space closest to the current vehicle position from among all the idle parking spaces found as the target parking space.
  • the location identifier of the free parking space may include the number of the free parking space and the parking lot name or street name where the free parking space is located.
  • the big data analysis system queries the weather information of the preset duration corresponding to the target parking space and sends the weather information to the wireless terminal, so that the driver of the vehicle where the wireless terminal is located determines whether the parking navigation path needs to be generated according to the weather information.
  • the indication information reported by the big data analysis system on the wireless terminal indicates that when the parking navigation path needs to be generated, a parking navigation path between the target parking space and the current vehicle location is generated, and the parking navigation path is sent to the wireless terminal for parking navigation.
  • the method described in FIG. 1 may further include the following steps:
  • the big data analysis system deducts the corresponding parking fee from the electronic account bound to the wireless terminal according to the actual parking time of the vehicle at the target parking space.
  • the big data analysis system may deduct the corresponding parking fee from the electronic account bound to the wireless terminal according to the actual parking duration of the vehicle in the target parking space:
  • the big data analysis system can distinguish whether the target parking space belongs to the parking space rented according to the preset time period. If the target parking space does not belong to the parking space rented according to the preset time period, the general parking fee corresponding to the target parking space and the actual parking time can be calculated. The corresponding parking fee, wherein the ordinary charging rule includes the first charging amount corresponding to the unit time;
  • the target parking space belongs to the parking space rented according to the preset time period, it is determined whether the actual parking time of the vehicle in the target parking space exceeds the preset time period, and if the preset time period is not exceeded, the ordinary charging rule corresponding to the target parking space and the actual parking time period are Calculate the corresponding parking fee;
  • the corresponding parking fee may be calculated according to the high charging rule corresponding to the target parking space and the actual parking time, wherein the high charging rule includes a second charging amount corresponding to the unit time, and the second charging amount is equal to several The first charge amount of the multiple; and the corresponding parking fee is deducted from the electronic account bound to the wireless terminal.
  • the implementation of such an optional embodiment can encourage the driver to drive the vehicle away from the target parking position in time before the vehicle is parked for longer than the preset rental period of the target parking space, so as not to cause a huge parking fee.
  • each of the free parking spaces can be rented according to a preset time period, that is, each idle parking space can be preset with a rental time (for example, 08:30 to 18:00).
  • a rental time for example, 08:30 to 18:00.
  • the owner can rent out his free parking space, and the owner can report to the big data analysis system the rental duration preset by the owner for his free parking space (eg 08:30 ⁇ 18:00).
  • implementing the method described in FIG. 1 can enable the driver to quickly find a parking space suitable for his vehicle type to stop when the mood is unstable, and can also enable the driver to predict in advance the weather information of the preset duration corresponding to the parking space. Therefore, the vehicle protection preparation during parking can be prepared in advance to prevent the vehicle from being damaged by bad weather.
  • FIG. 3 is a schematic flowchart diagram of another method for recommending a parking navigation path according to an embodiment of the present invention.
  • the recommended method of the parking navigation path may include the following steps:
  • the wireless terminal identifies whether the emotion of the driver of the vehicle where the wireless terminal is located is stable. If the emotion is unstable, the current vehicle position of the vehicle where the wireless terminal is located is reported to the big data analysis system.
  • the wireless terminal when the wireless terminal recognizes that the emotion of the driver of the vehicle where the wireless terminal is located is stable, the wireless terminal may further perform the following steps:
  • the wireless terminal collects a frame image of the driver's face every specified time (for example, 120 milliseconds), and determines a human eye positioning rectangle from the face image, calculates an area of the human eye positioning rectangle, and determines the driver's eyes according to the threshold value.
  • the degree of squatting, and the degree of eye fatigue according to the degree of suffocation of the driver's eyes, the driver's eyes are judged to be fatigued based on the degree of human eye fatigue, and if fatigue, the current vehicle position of the vehicle where the wireless terminal is located is reported to Big data analysis system.
  • the manner in which the wireless terminal collects the current vehicle location of the vehicle where the wireless terminal is located may be:
  • the wireless terminal acquires at least two different positioning interfaces configured by the wireless terminal; for example, at least two different positioning interfaces may include a nlpservice positioning interface of Baidu, a nlpservice positioning interface of Gaode, a nlpservice positioning interface of Google, etc., the present invention
  • the embodiment is not limited; and the wireless terminal may send the location request to the at least two different positioning interfaces to trigger each positioning interface to separately send the received positioning request to the corresponding positioning server; and acquire at least one Locating the location information sent by the location server corresponding to the interface, and obtaining the response time from the first moment to the second moment, where the first moment is the moment when the positioning request is sent by each positioning interface, and the second moment is the location received by each positioning interface.
  • the time of the information and comparing the response time corresponding to each positioning interface with the response threshold, and extracting the location information with the highest positioning accuracy from the location information received by the positioning interface whose response time does not exceed the response threshold as the wireless terminal.
  • the implementation of the foregoing embodiment can accurately acquire the parking position and improve the positioning accuracy.
  • the manner in which the wireless terminal identifies whether the emotion of the driver of the vehicle where the wireless terminal is located is stable:
  • the wireless terminal detects the electrocardiogram data of the driver transmitted by the wearable device (such as a wristband) worn by the driver of the vehicle where the wireless terminal is located;
  • the wireless terminal can perform denoising processing on the electrocardiogram data, and extracts from the R wave of the electrocardiogram
  • the method extracts the R wave peak value in the degaussed ECG data, and calculates the RR interval between adjacent R waves in the degaussed ECG data; and calculates the frequency domain index, time domain index and nonlinearity of the RR interval Indicators; among them, the frequency domain indicators include parasympathetic nerve activity indicators, the time domain indicators include short-range heart rate variability indicators; the short-range heart rate variability indicators are calculated by obtaining the root mean square of the sum of the squared differences of RR intervals; the parasympathetic nerve activity index passes the fast Fuli Leaf transformation to calculate; nonlinear index is calculated by fractal dimension calculation method;
  • the wireless terminal can analyze the emotional value of the user's emotion according to the frequency domain index, the time domain index and the non-linear index; wherein the vitality value is a multiple linear regression equation established according to the time domain index, the frequency domain index and the nonlinear index. Calculating the value; and, based on the vitality value, identifying whether the user's mood is unstable.
  • the implementation of the above embodiment can accurately identify whether the driver's emotion is stable.
  • the big data analysis system identifies the vehicle type of the vehicle, and identifies the type of parking space applicable to the vehicle type according to the vehicle type.
  • the vehicle type may include a car, a truck, a passenger car, a trailer, a motorcycle, etc., which are not limited in the embodiment of the present invention.
  • the big data analysis system searches for all the free parking spaces corresponding to the parking space type in its own jurisdiction, and selects the free parking space closest to the current vehicle position as the target parking space from all the idle parking spaces found.
  • the big data analysis system determines whether the current workload of the big data analysis system exceeds the workload specified by the big data analysis system. If not, the weather information inquiry port initiates the target parking space to the weather service platform corresponding to the weather information inquiry port. The weather information inquiry request, and receiving the weather information of the preset time period corresponding to the target parking space returned by the weather service platform through the weather information inquiry port.
  • the big data analysis system sends the weather information of the preset duration corresponding to the target parking space to the wireless terminal, so that the driver of the vehicle where the wireless terminal is located determines whether the parking navigation path needs to be generated according to the weather information.
  • the indication information reported by the big data analysis system on the wireless terminal indicates that when the parking navigation path needs to be generated, a parking navigation path between the target parking space and the current vehicle location is generated, and the parking navigation path is sent to the wireless terminal for parking navigation.
  • the big data analysis system deducts the corresponding parking fee from the electronic account bound to the wireless terminal according to the actual parking time of the vehicle in the target parking space.
  • the big data analysis system may deduct the corresponding parking fee from the electronic account bound to the wireless terminal according to the actual parking duration of the vehicle in the target parking space:
  • the big data analysis system can distinguish whether the target parking space belongs to the parking space rented according to the preset time period. If the target parking space does not belong to the parking space rented according to the preset time period, the general parking fee corresponding to the target parking space and the actual parking time can be calculated. The corresponding parking fee, wherein the ordinary charging rule includes the first charging amount corresponding to the unit time;
  • the target parking space belongs to a parking space that is rented according to the preset time period, it is determined that the vehicle is in the target parking space. Whether the duration of the parking time exceeds the preset time period, if the preset time period is not exceeded, the corresponding parking fee is calculated according to the ordinary charging rule corresponding to the target parking space and the actual parking time;
  • the corresponding parking fee may be calculated according to the high charging rule corresponding to the target parking space and the actual parking time, wherein the high charging rule includes a second charging amount corresponding to the unit time, and the second charging amount is equal to several The first charge amount of the multiple; and the corresponding parking fee is deducted from the electronic account bound to the wireless terminal.
  • the implementation of such an optional embodiment can encourage the driver to drive the vehicle away from the target parking position in time before the vehicle is parked for longer than the preset rental period of the target parking space, so as not to cause a huge parking fee.
  • each of the free parking spaces can be rented according to a preset time period, that is, each idle parking space can be preset with a rental time (for example, 08:30 to 18:00).
  • a rental time for example, 08:30 to 18:00.
  • the owner can rent out his free parking space, and the owner can report to the big data analysis system the rental duration preset by the owner for his free parking space (eg 08:30 ⁇ 18:00).
  • FIG. 4 is a schematic structural diagram of a recommendation system for a parking navigation path according to an embodiment of the present invention. As shown in Figure 4, the system can include:
  • Wireless terminal 401 and big data analysis system 402 wherein:
  • the wireless terminal 401 is configured to identify whether the emotion of the driver of the vehicle where the wireless terminal 401 is located is stable, and if the emotion is unstable, report the current vehicle position of the vehicle where the wireless terminal 401 is located to the big data analysis system 402;
  • a big data analysis system 402 configured to identify a vehicle type of the vehicle, and identify a parking space type applicable to the vehicle type according to the vehicle type;
  • the big data analysis system 402 is further configured to find all the free parking spaces corresponding to the size of the parking space type in its own jurisdiction, and select the free parking space closest to the current vehicle position as the target parking space from all the idle parking spaces found;
  • the big data analysis system 402 is further configured to query weather information of a preset duration corresponding to the target parking space and Is sent to the wireless terminal 401, so that the driver of the vehicle where the wireless terminal 401 is located determines whether it is necessary to generate a parking navigation path according to weather information;
  • the big data analysis system 402 is further configured to: when the indication information reported by the wireless terminal 401 indicates that the parking navigation path needs to be generated, generate a parking navigation path between the target parking space and the current vehicle location, and send the parking navigation path to the wireless terminal 401. Parking navigation.
  • the wireless terminal 401 is further configured to: when identifying the emotional stability of the driver of the vehicle where the wireless terminal is located, collect a frame image of the driver's face at a specified time, and determine a human eye positioning rectangle from the face image, and calculate The area of the human eye positioning rectangle determines the degree of closure of the driver's eyes based on the threshold value, and evaluates the degree of eye fatigue according to the degree of closure of the driver's eyes, and determines whether the driver's eyes are fatigued based on the degree of human eye fatigue. If fatigue occurs, the current vehicle location of the vehicle in which the wireless terminal is located is reported to the big data analysis system 402.
  • the manner in which the wireless terminal 401 identifies whether the emotion of the driver of the vehicle in which the wireless terminal 401 is located is stable:
  • the wireless terminal 401 is configured to detect electrocardiogram data of the driver sent by the wearable device worn by the driver of the vehicle where the wireless terminal is located; perform denoising processing on the electrocardiogram data; and extract the electrocardiogram data in the denoised processed ECG data by using an electrocardiogram R wave extraction algorithm R wave peak value, and calculating the RR spacing between adjacent R waves in the degaussed ECG data; calculating the frequency domain index, time domain index and nonlinear index of the RR interval; wherein the frequency domain index includes the parasympathetic nerve activity index,
  • the time domain indicator includes the short-range heart rate variability index; the short-range heart rate variability index is calculated by obtaining the root mean square of the sum of the squares of the RR spacing differences; the parasympathetic nerve activity index is calculated by the fast Fourier transform; the nonlinear index is calculated by the fractal dimension
  • the method calculates the vitality value of the user's emotion according to the frequency domain index, the time domain index and the non-linear index; the vitality
  • the manner in which the wireless terminal 401 reports the current vehicle location of the vehicle where the wireless terminal is located to the big data analysis system is specifically:
  • the wireless terminal 401 is configured to scan whether a routing node is preset in the surrounding environment. If a routing node is preset, detecting whether the routing node is configured with an open access period, if the routing node is configured with an open access period, identifying the wireless terminal Whether the current system time is located in the open access period in which the routing node is configured; if the current system time of the wireless terminal is located in the open access period in which the routing node is configured, detecting whether the number of terminals currently accessed by the routing node exceeds the routing node The specified maximum number of terminal accesses; if the number of terminals currently accessed by the routing node does not exceed the maximum number of terminal accesses specified by the routing node, establish a wireless connection with the routing node, and set the current vehicle location of the vehicle where the wireless terminal is located Sended to the routing node, the routing node reports the current vehicle location of the vehicle where the wireless terminal is located to the big data analysis system.
  • the manner in which the big data analysis system 402 queries the weather information of the preset duration corresponding to the target parking space and delivers the weather information to the wireless terminal is specifically:
  • the big data analysis system 402 is configured to determine whether the current workload of the big data analysis system exceeds the workload specified by the big data analysis system. If the current workload of the big data analysis system does not exceed the workload specified by the big data analysis system, the weather information is passed.
  • the query port initiates a weather information query request including the target parking space to the weather service platform corresponding to the weather information inquiry port; and receives the weather information of the preset time period corresponding to the target parking space returned by the weather service inquiry port; and the target parking space
  • the weather information corresponding to the preset duration is sent to the wireless terminal.
  • the big data analysis system 402 is also used to:
  • the charging rule includes the first charging amount corresponding to the unit time; if the target parking space belongs to the parking space rented according to the preset time period, it is determined whether the actual parking time of the vehicle in the target parking space exceeds the preset time period, if the preset time period is not exceeded, according to The corresponding parking fee corresponding to the target parking space and the actual parking time are calculated.
  • the corresponding parking fee is calculated according to the high charging rule corresponding to the target parking space and the actual parking time.
  • the high charging rule includes the unit.
  • the second charge amount corresponding to the time, and the second charge amount is equal to a plurality of multiples of the first charge amount; and the corresponding parking fee is deducted from the electronic account bound to the wireless terminal.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • PROM Programmable Read-Only Memory
  • EPROM Erasable Programmable Read Only Memory
  • OTPROM One-time Programmable Read-Only Memory
  • EEPROM Electronically Erasable Read-Write Memory
  • CD-ROM Compact Disc Read-Only Memory

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Abstract

一种停车导航路径的推荐系统及方法，包括：无线终端（401）识别出其所在车辆的驾驶员的情绪不稳定时将车辆的当前车辆位置上报给大数据分析系统（402）（101）；识别出该车辆的车辆类型适用的车位类型（102）；查找出该车位类型对应大小的所有空闲停车位，从查找出的所有空闲停车中选择最接近当前车辆位置的空闲停车位作为目标停车位（103）；查询目标停车位对应的预设时长的天气信息并下发给无线终端（401）；在无线终端（401）上报的指示信息表示需要生成停车导航路径时，生成目标停车位与当前车辆位置之间的停车导航路径并发送给无线终端（401）进行停车导航（105）。可使驾驶员在情绪不稳时快速的查找到适合其车辆类型的停车位进行停车，可使驾驶员提前预知停车位对应的预设时长的天气信息。

Description

一种停车导航路径的推荐系统及方法 技术领域
本发明涉及物联网技术领域，尤其涉及一种停车导航路径的推荐系统及方法。
背景技术
当前，随着群众生活水平的不断提升，我国的汽车刚性需求保持旺盛，汽车保有量保持迅猛增长趋势，2016年新注册登记的汽车达2752万辆，保有量净增2212万辆，均为历史最高水平。全国有49个城市的汽车保有量超过100万辆，18个城市的汽车保有量超200万辆，6个城市的汽车保有量超300万辆。其中，汽车保有量超过200万辆的18个城市依次是北京、成都、重庆、上海、深圳、苏州、天津、郑州、西安、杭州、武汉、广州、石家庄、东莞、南京、青岛、宁波、佛山。
在汽车保有量保持迅猛增长的过程中，为了便于群众停车，越来越多的停车位被逐渐的开发出来。在实践中发现，很多时候驾驶员在情绪不稳定时很难快速的查找到适合其车辆类型的停车位进行停车，从而不利于减少因驾驶员情绪不稳而造成的交通事故；而且，对于户外的停车位，驾驶员往往无法提前预知停车位对应的预设时长的天气信息，从而无法提前做好停车时的车辆防护准备，使得车辆容易被恶劣天气造成损伤。
发明内容
本发明实施例公开了一种停车导航路径的推荐系统及方法，能够使驾驶员在情绪不稳时快速的查找到适合其车辆类型的停车位进行停车，还可以使驾驶员提前预知停车位对应的预设时长的天气信息，从而可以提前做好停车时的车辆防护准备，防止车辆被恶劣天气造成损伤。
本发明实施例第一方面公开一种停车导航路径的推荐方法，包括：
无线终端识别所述无线终端所在车辆的驾驶员的情绪是否稳定，如果情绪不稳定，将所述无线终端所在车辆的当前车辆位置上报给大数据分析系统；
所述大数据分析系统识别出所述车辆的车辆类型，并根据所述车辆类型辨别出所述车辆类型适用的车位类型；
所述大数据分析系统在自身管辖范围内查找出所述车位类型对应大小的所有空闲停车位，从查找出的所有空闲停车中选择最接近所述的当前车辆位置的空闲停车位作为目标停车位；
所述大数据分析系统查询所述目标停车位对应的预设时长的天气信息并下发给所述无线终端，以使所述无线终端所在车辆的驾驶员根据所述天气信息决定是否需要生成停车导航路径；
所述大数据分析系统在所述无线终端上报的指示信息表示需要生成停车导 航路径时，生成所述目标停车位与所述当前车辆位置之间的停车导航路径，并将所述停车导航路径发送给所述无线终端进行停车导航。
作为一种可选的实施方式，在本发明实施例第一方面中，所述无线终端识别出所述无线终端所在车辆的驾驶员的情绪稳定时，所述方法还包括：
所述无线终端每隔指定时间采集一帧所述驾驶员的人脸图像，并从所述人脸图像中确定出人眼定位矩形，计算所述人眼定位矩形的面积，根据阈值判断所述驾驶员的眼睛的睁闭程度，以及根据所述驾驶员的眼睛的睁闭程度评价出人眼疲劳程度值，基于所述人眼疲劳程度值判断所述驾驶员的眼睛是否疲劳，如果疲劳，执行所述的将所述无线终端所在车辆的当前车辆位置上报给大数据分析系统。
作为一种可选的实施方式，在本发明实施例第一方面中，所述无线终端识别所述无线终端所在车辆的驾驶员的情绪是否稳定，包括：
所述无线终端检测所述无线终端所在车辆的驾驶员佩戴的穿戴设备发送的所述驾驶员的心电图数据；
所述无线终端对所述心电图数据进行去噪处理；
所述无线终端采用心电图R波提取算法提取经过去噪处理的心电图数据中的R波峰值，以及计算所述经过去噪处理的心电图数据中相邻R波之间RR间距；
所述无线终端计算所述RR间距的频域指标、时域指标及非线性指标；其中，所述频域指标包括副交感神经活性指标，所述时域指标包括短程心率变动性指标；所述短程心率变动性指标通过获取所述RR间距差值平方和的均方根来计算；所述副交感神经活性指标通过快速傅里叶变换来计算；所述非线性指标通过分形维数计算方法来计算；
所述无线终端根据所述频域指标、时域指标及非线性指标，分析所述用户的情绪的活力值；所述活力值为根据所述时域指标、频域指标及非线性指标建立的多元线性回归方程计算得到的值；
所述无线终端根据所述活力值识别所述驾驶员的情绪是否不稳定。
作为一种可选的实施方式，在本发明实施例第一方面中，所述无线终端将所述无线终端所在车辆的当前车辆位置上报给大数据分析系统，包括：
所述无线终端扫描周围环境中是否预先设置有路由节点，如果预先设置有所述路由节点，检测所述路由节点是否被配置有开放接入时段，如果所述路由节点被配置有所述开放接入时段，识别所述无线终端的当前系统时间是否位于所述路由节点被配置的所述开放接入时段内；
如果所述无线终端的当前系统时间位于所述路由节点被配置的所述开放接入时段内，检测所述路由节点的当前接入的终端数量是否超过所述路由节点指定的最大终端接入数量；
如果所述路由节点的当前接入的终端数量未超过所述路由节点指定的最大终端接入数量，所述无线终端建立与所述路由节点之间的无线连接，并且将所述无线终端所在车辆的当前车辆位置发送给所述路由节点，由所述路由节点将所述无线终端所在车辆的当前车辆位置发送给所述大数据分析系统。
作为一种可选的实施方式，在本发明实施例第一方面中，所述大数据分析系统查询所述目标停车位对应的预设时长的天气信息并下发给所述无线终端，包括:
所述大数据分析系统判断所述大数据分析系统的当前工作负荷是否超过所述大数据分析系统指定的工作负荷；
如果所述大数据分析系统的当前工作负荷未超过所述大数据分析系统指定的工作负荷，所述大数据分析系统通过天气信息查询端口向所述天气信息查询端口对应的天气服务平台发起包括所述目标停车位的天气信息查询请求；
以及，所述大数据分析系统接收所述天气服务平台通过所述天气信息查询端口返回的所述目标停车位对应的预设时长的天气信息；
以及，所述大数据分析系统将所述目标停车位对应的预设时长的天气信息下发给所述无线终端。
本发明实施例第二方面公开一种停车导航路径的推荐系统，包括无线终端和大数据分析系统，其中：
所述无线终端，用于识别所述无线终端所在车辆的驾驶员的情绪是否稳定，如果情绪不稳定，将所述无线终端所在车辆的当前车辆位置上报给所述大数据分析系统；
所述大数据分析系统，用于识别出所述车辆的车辆类型，并根据所述车辆类型辨别出所述车辆类型适用的车位类型；
所述大数据分析系统，还用于在自身管辖范围内查找出所述车位类型对应大小的所有空闲停车位，从查找出的所有空闲停车中选择最接近所述的当前车辆位置的空闲停车位作为目标停车位；
所述大数据分析系统，还用于查询所述目标停车位对应的预设时长的天气信息并下发给所述无线终端，以使所述无线终端所在车辆的驾驶员根据所述天气信息决定是否需要生成停车导航路径；
所述大数据分析系统，还用于在所述无线终端上报的指示信息表示需要生成停车导航路径时，生成所述目标停车位与所述当前车辆位置之间的停车导航路径，并将所述停车导航路径发送给所述无线终端进行停车导航。
作为一种可选的实施方式，在本发明实施例第二方面中：
所述无线终端，还用于在识别出所述无线终端所在车辆的驾驶员的情绪稳定时，每隔指定时间采集一帧所述驾驶员的人脸图像，并从所述人脸图像中确定出人眼定位矩形，计算所述人眼定位矩形的面积，根据阈值判断所述驾驶员的眼睛的睁闭程度，以及根据所述驾驶员的眼睛的睁闭程度评价出人眼疲劳程度值，基于所述人眼疲劳程度值判断所述驾驶员的眼睛是否疲劳，如果疲劳，执行所述的将所述无线终端所在车辆的当前车辆位置上报给大数据分析系统。
作为一种可选的实施方式，在本发明实施例第二方面中：
所述无线终端识别所述无线终端所在车辆的驾驶员的情绪是否稳定的方式具体为：
所述无线终端，用于检测所述无线终端所在车辆的驾驶员佩戴的穿戴设备发 送的所述驾驶员的心电图数据；对所述心电图数据进行去噪处理；采用心电图R波提取算法提取经过去噪处理的心电图数据中的R波峰值，以及计算所述经过去噪处理的心电图数据中相邻R波之间RR间距；计算所述RR间距的频域指标、时域指标及非线性指标；其中，所述频域指标包括副交感神经活性指标，所述时域指标包括短程心率变动性指标；所述短程心率变动性指标通过获取所述RR间距差值平方和的均方根来计算；所述副交感神经活性指标通过快速傅里叶变换来计算；所述非线性指标通过分形维数计算方法来计算；根据所述频域指标、时域指标及非线性指标，分析所述用户的情绪的活力值；所述活力值为根据所述时域指标、频域指标及非线性指标建立的多元线性回归方程计算得到的值；根据所述活力值识别所述驾驶员的情绪是否不稳定。
作为一种可选的实施方式，在本发明实施例第二方面中，所述无线终端将所述无线终端所在车辆的当前车辆位置上报给大数据分析系统的方式具体为：
所述无线终端，用于扫描周围环境中是否预先设置有路由节点，如果预先设置有所述路由节点，检测所述路由节点是否被配置有开放接入时段，如果所述路由节点被配置有所述开放接入时段，识别所述无线终端的当前系统时间是否位于所述路由节点被配置的所述开放接入时段内；如果所述无线终端的当前系统时间位于所述路由节点被配置的所述开放接入时段内，检测所述路由节点的当前接入的终端数量是否超过所述路由节点指定的最大终端接入数量；如果所述路由节点的当前接入的终端数量未超过所述路由节点指定的最大终端接入数量，建立与所述路由节点之间的无线连接，并且将所述无线终端所在车辆的当前车辆位置发送给所述路由节点，由所述路由节点将所述无线终端所在车辆的当前车辆位置发送给所述大数据分析系统。
作为一种可选的实施方式，在本发明实施例第二方面中：
所述大数据分析系统查询所述目标停车位对应的预设时长的天气信息并下发给所述无线终端的方式具体为：
所述大数据分析系统，用于判断所述大数据分析系统的当前工作负荷是否超过所述大数据分析系统指定的工作负荷，如果所述大数据分析系统的当前工作负荷未超过所述大数据分析系统指定的工作负荷，通过天气信息查询端口向所述天气信息查询端口对应的天气服务平台发起包括所述目标停车位的天气信息查询请求；接收所述天气服务平台通过所述天气信息查询端口返回的所述目标停车位对应的预设时长的天气信息；将所述目标停车位对应的预设时长的天气信息下发给所述无线终端；
其中，所述方法还包括：
所述大数据分析系统，用于辨别所述目标停车位是否属于按预设时段出租的停车位，如果所述目标停车位不属于按预设时段出租的停车位，按照所述目标停车位对应的普通收费规则以及所述实际停车时长计算相应的停车费用，所述普通收费规则包括单位时间对应的第一收费金额；如果所述目标停车位属于按预设时段出租的停车位，判断所述车辆在所述目标停车位的实际停车时长是否超出所述 预设时段，如果未超出所述预设时段，按照所述目标停车位对应的普通收费规则以及所述实际停车时长计算相应的停车费用；如果超过所述预设时段，按照所述目标停车位对应的高额收费规则以及所述实际停车时长计算相应的停车费用，所述高额收费规则包括单位时间对应的第二收费金额，并且所述第二收费金额等于若干倍数的所述第一收费金额；以及，从无线终端绑定的电子账号中扣除所述相应的停车费用。从而可以实现无人停车收费，提高停车收费效率。
与现有技术相比，本发明实施例具有以下有益效果：
本发明实施例中，不仅可以使驾驶员在情绪不稳时快速的查找到适合其车辆类型的停车位进行停车，还可以使驾驶员提前预知停车位对应的预设时长的天气信息，从而可以提前做好停车时的车辆防护准备，防止车辆被恶劣天气造成损伤。
附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施例公开的一种停车导航路径的推荐方法的流程示意图；
图2是本发明实施例公开的一种无线终端识别驾驶员的情绪是否稳定的方法的流程示意图；
图3是本发明实施例公开的另一种停车导航路径的推荐方法的流程示意图；
图4是本发明实施例公开的一种停车导航路径的推荐系统的结构示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明实施例公开了一种停车导航路径的推荐系统及方法，能够使驾驶员在情绪不稳时快速的查找到适合其车辆类型的停车位进行停车，还可以使驾驶员提前预知停车位对应的预设时长的天气信息，从而可以提前做好停车时的车辆防护准备，防止车辆被恶劣天气造成损伤。以下分别进行详细说明。
实施例一
请参阅图1，图1是本发明实施例公开的一种停车导航路径的推荐方法的流程示意图。如图1所示，该停车导航路径的推荐方法可以包括以下步骤：
101、无线终端识别无线终端所在车辆的驾驶员的情绪是否稳定，如果情绪不稳定，将无线终端所在车辆的当前车辆位置上报给大数据分析系统。
本发明实施例中，无线终端内置的无线通讯模块在生产时，可以输入上频点470MHz，下频点510MHz，这样无线通讯模块可以自动将通讯频段定义为 470MHz～510MHz，以符合中国SRRC标准的规定；或者，也可以输入上频点868MHz，下频点908MHz，这样无线通讯模块可以自动将通讯频段定义为868MHz～908MHz，以符合欧洲ETSI标准的规定；或者，可以输入上频点918MHz，下频点928MHz，这样无线通讯模块可以自动将通讯频段定义为918MHz～928MHz，以符合美国FCC标准的规定；或者，无线通讯模块的通讯频段也可以定义为符合日本ARIB标准或加拿大IC标准的规定，本发明实施例不作限定。
本发明实施例中，无线终端可以采用频分复用(Frequency Division Multiple Access，FDMA)、跳频(Frequency-Hopping Spread Spectrum，FHSS)、动态时分复用(Dynamic Time Division Multiple Access，DTDMA)、退避复用(CSMA)相结合的方法来解决干扰问题，本发明实施例不作限定。
作为一种可选的实施方式，无线终端识别出所述无线终端所在车辆的驾驶员的情绪稳定时，无线终端还可以执行以下步骤：
无线终端每隔指定时间(如120毫秒)采集一帧驾驶员的人脸图像，并从人脸图像中确定出人眼定位矩形，计算人眼定位矩形的面积，根据阈值判断驾驶员的眼睛的睁闭程度，以及根据驾驶员的眼睛的睁闭程度评价出人眼疲劳程度值，基于人眼疲劳程度值判断驾驶员的眼睛是否疲劳，如果疲劳，将无线终端所在车辆的当前车辆位置上报给大数据分析系统。
作为一种可选的实施方式，无线终端采集无线终端所在车辆的当前车辆位置的方式可以为：
无线终端获取无线终端配置的至少两个不同的定位接口；举例来说，至少两个不同的定位接口可以包括百度的nlpservice定位接口、高德的nlpservice定位接口、谷歌的nlpservice定位接口等，本发明实施例不作限定；以及，无线终端可以将定位请求发送至上述至少两个不同的定位接口，以触发每个定位接口分别将接收到的定位请求发送给各自对应的定位服务器；以及，获取至少一个定位接口对应的定位服务器发送的位置信息，并获取从第一时刻到第二时刻的响应时间，第一时刻为每个定位接口发送定位请求的时刻，第二时刻为每个定位接口接收到位置信息的时刻；以及，将与每个定位接口对应的响应时间与响应阈值进行比较，并从响应时间未超过响应阈值的定位接口所接收的位置信息中提取定位精度最高的位置信息作为无线终端所在车辆的当前车辆位置。
本发明实施例中，实施上述实施方式可以精确的获取停车位置，提高定位精确度。
作为一种可选的实施方式，无线终端识别无线终端所在车辆的驾驶员的情绪是否稳定的方法可以如图2所示，包括以下步骤：
S201、无线终端检测无线终端所在车辆的驾驶员佩戴的穿戴设备(如手环)发送的驾驶员的心电图数据。
举例来说，无线终端可以检测无线终端所在车辆的行驶时长是否超过预设时长，如果超过预设时长，无线终端可以检测无线终端是否与无线终端所在车辆的 驾驶员佩戴的穿戴设备(如手环)建立通讯连接，如果是，无线终端可以通知驾驶员佩戴的穿戴设备向无线终端发送驾驶员的心电图数据。
S202、无线终端可以对心电图数据进行去噪处理，并采用心电图R波提取算法提取经过去噪处理的心电图数据中的R波峰值，以及计算经过去噪处理的心电图数据中相邻R波之间RR间距；以及，计算RR间距的频域指标、时域指标及非线性指标；其中，频域指标包括副交感神经活性指标，时域指标包括短程心率变动性指标；短程心率变动性指标通过获取RR间距差值平方和的均方根来计算；副交感神经活性指标通过快速傅里叶变换来计算；非线性指标通过分形维数计算方法来计算。
S203、无线终端可以根据频域指标、时域指标及非线性指标，分析该用户的情绪的活力值；其中，活力值为根据时域指标、频域指标及非线性指标建立的多元线性回归方程计算得到的值；以及，根据活力值识别该用户的情绪是否不稳定。
本发明实施例中，实施上述图2所描述的方法可以精确的识别出驾驶员的情绪是否稳定。
本发明实施例中，无线终端将无线终端所在车辆的当前车辆位置上报给大数据分析系统，包括：
无线终端扫描周围环境中是否预先设置有路由节点，如果预先设置有路由节点，检测路由节点是否被配置有开放接入时段，如果路由节点被配置有开放接入时段，识别无线终端的当前系统时间是否位于路由节点被配置的开放接入时段内；
如果无线终端的当前系统时间位于路由节点被配置的开放接入时段内，检测路由节点的当前接入的终端数量是否超过路由节点指定的最大终端接入数量；
如果路由节点的当前接入的终端数量未超过路由节点指定的最大终端接入数量，无线终端建立与路由节点之间的无线连接，并且将无线终端所在车辆的当前车辆位置发送给路由节点，由路由节点将无线终端所在车辆的当前车辆位置上报给大数据分析系统。
本发明实施例中，由路由节点将无线终端所在车辆的当前车辆位置上报给大数据分析系统可以避免无线终端直接与大数据分析系统建立较长距离的通信连接，从而可以避免无线终端直接与大数据分析系统通信带来的较大功耗。
102、大数据分析系统识别出该车辆的车辆类型，并根据该车辆类型辨别出该车辆类型适用的车位类型。
本发明实施例中，车辆类型可以包括轿车、载货车、客车、挂车、摩托车等，本发明实施例不作限定。
本发明实施例中，大数据分析系统可以根据无线终端的身份标识，识别出无线终端所在车辆的车辆标识(如车牌)，并且可以根据无线终端所在车辆的车辆标识识别出该车辆的车辆类型，并根据该车辆类型辨别出该车辆类型适用的车位类型。
103、大数据分析系统在自身管辖范围内查找出该车位类型对应大小的所有 空闲停车位，从查找出的所有空闲停车中选择最接近该当前车辆位置的空闲停车位作为目标停车位。
本发明实施例中，空闲停车位的位置标识可以包括空闲停车位的编号以及空闲停车位所在的停车场名称或街道名称。
104、大数据分析系统查询目标停车位对应的预设时长的天气信息并下发给无线终端，以使无线终端所在车辆的驾驶员根据天气信息决定是否需要生成停车导航路径。
105、大数据分析系统在无线终端上报的指示信息表示需要生成停车导航路径时，生成目标停车位与当前车辆位置之间的停车导航路径，并将停车导航路径发送给无线终端进行停车导航。
本发明实施例中，图1所描述的方法还可以包括以下步骤：
大数据分析系统根据车辆在目标停车位的实际停车时长，从无线终端绑定的电子账号中扣除相应的停车费用。
作为一种可选的实施方式，大数据分析系统根据车辆在目标停车位的实际停车时长，从无线终端绑定的电子账号中扣除相应的停车费用的方式可以为：
大数据分析系统可以辨别目标停车位是否属于按预设时段出租的停车位，如果目标停车位不属于按预设时段出租的停车位，可以按照目标停车位对应的普通收费规则以及实际停车时长计算相应的停车费用，其中，普通收费规则包括单位时间对应的第一收费金额；
如果目标停车位属于按预设时段出租的停车位，判断车辆在目标停车位的实际停车时长是否超出预设时段，如果未超出预设时段，按照目标停车位对应的普通收费规则以及实际停车时长计算相应的停车费用；
如果超过预设时段，可以按照目标停车位对应的高额收费规则以及实际停车时长计算相应的停车费用，其中，高额收费规则包括单位时间对应的第二收费金额，并且第二收费金额等于若干倍数的第一收费金额；以及，从无线终端绑定的电子账号中扣除相应的停车费用。
其中，实施这种可选的实施方式，可以鼓励驾驶员在其车辆停放时长超出目标停车位预设的出租时段之前，及时将车辆驶离目标停车位置，以免导致巨额的停车费用。
本发明实施例中，每一个空闲的停车位可以按预设时段进行出租，也即是说，每一个空闲的停车位可以预设出租时长(例如08：30～18：00)。举例来说，某一车主在早上8点钟开车上班之后，车主可以将其空闲的停车位进行出租，并车主可以向大数据分析系统上报车主为其空闲的停车位预设的出租时长(例如08：30～18：00)。
可见，实施图1所描述的方法，能够使驾驶员在情绪不稳时快速的查找到适合其车辆类型的停车位进行停车，还可以使驾驶员提前预知停车位对应的预设时长的天气信息，从而可以提前做好停车时的车辆防护准备，防止车辆被恶劣天气造成损伤。
可见，实施图1所描述的方法，还可以实现无人停车收费，提高收费管理效率。
可见，实施图1所描述的方法，可以精确的识别出驾驶员的眼睛是否疲劳，以及可以精确的识别出驾驶员的情绪是否稳定。
可见，实施图1所描述的方法，可以鼓励驾驶员在其车辆停放时长超出目标停车位预设的出租时段之前，及时将车辆驶离目标停车位置，以免导致巨额的停车费用。
实施例二
请参阅图3，图3是本发明实施例公开的另一种停车导航路径的推荐方法的流程示意图。如图3所示，该停车导航路径的推荐方法可以包括以下步骤：
301、无线终端识别无线终端所在车辆的驾驶员的情绪是否稳定，如果情绪不稳定，将无线终端所在车辆的当前车辆位置上报给大数据分析系统。
作为一种可选的实施方式，无线终端识别出所述无线终端所在车辆的驾驶员的情绪稳定时，无线终端还可以执行以下步骤：
无线终端每隔指定时间(如120毫秒)采集一帧驾驶员的人脸图像，并从人脸图像中确定出人眼定位矩形，计算人眼定位矩形的面积，根据阈值判断驾驶员的眼睛的睁闭程度，以及根据驾驶员的眼睛的睁闭程度评价出人眼疲劳程度值，基于人眼疲劳程度值判断驾驶员的眼睛是否疲劳，如果疲劳，将无线终端所在车辆的当前车辆位置上报给大数据分析系统。
作为一种可选的实施方式，无线终端采集无线终端所在车辆的当前车辆位置的方式可以为：
无线终端获取无线终端配置的至少两个不同的定位接口；举例来说，至少两个不同的定位接口可以包括百度的nlpservice定位接口、高德的nlpservice定位接口、谷歌的nlpservice定位接口等，本发明实施例不作限定；以及，无线终端可以将定位请求发送至上述至少两个不同的定位接口，以触发每个定位接口分别将接收到的定位请求发送给各自对应的定位服务器；以及，获取至少一个定位接口对应的定位服务器发送的位置信息，并获取从第一时刻到第二时刻的响应时间，第一时刻为每个定位接口发送定位请求的时刻，第二时刻为每个定位接口接收到位置信息的时刻；以及，将与每个定位接口对应的响应时间与响应阈值进行比较，并从响应时间未超过响应阈值的定位接口所接收的位置信息中提取定位精度最高的位置信息作为无线终端所在车辆的当前车辆位置。
本发明实施例中，实施上述实施方式可以精确的获取停车位置，提高定位精确度。
作为一种可选的实施方式，无线终端识别无线终端所在车辆的驾驶员的情绪是否稳定的方式可以为：
无线终端检测无线终端所在车辆的驾驶员佩戴的穿戴设备(如手环)发送的驾驶员的心电图数据；
以及，无线终端可以对心电图数据进行去噪处理，并采用心电图R波提取算 法提取经过去噪处理的心电图数据中的R波峰值，以及计算经过去噪处理的心电图数据中相邻R波之间RR间距；以及，计算RR间距的频域指标、时域指标及非线性指标；其中，频域指标包括副交感神经活性指标，时域指标包括短程心率变动性指标；短程心率变动性指标通过获取RR间距差值平方和的均方根来计算；副交感神经活性指标通过快速傅里叶变换来计算；非线性指标通过分形维数计算方法来计算；
以及，无线终端可以根据频域指标、时域指标及非线性指标，分析该用户的情绪的活力值；其中，活力值为根据时域指标、频域指标及非线性指标建立的多元线性回归方程计算得到的值；以及，根据活力值识别该用户的情绪是否不稳定。
本发明实施例中，实施上述实施方式可以精确的识别出驾驶员的情绪是否稳定。
302、大数据分析系统识别出该车辆的车辆类型，并根据该车辆类型辨别出该车辆类型适用的车位类型。
本发明实施例中，车辆类型可以包括轿车、载货车、客车、挂车、摩托车等，本发明实施例不作限定。
303、大数据分析系统在自身管辖范围内查找出该车位类型对应大小的所有空闲停车位，从查找出的所有空闲停车中选择最接近该当前车辆位置的空闲停车位作为目标停车位。
304、大数据分析系统判断大数据分析系统的当前工作负荷是否超过大数据分析系统指定的工作负荷，如果未超过，通过天气信息查询端口向天气信息查询端口对应的天气服务平台发起包括目标停车位的天气信息查询请求，以及，接收天气服务平台通过天气信息查询端口返回的目标停车位对应的预设时长的天气信息。
305、大数据分析系统将目标停车位对应的预设时长的天气信息下发给无线终端，以使无线终端所在车辆的驾驶员根据天气信息决定是否需要生成停车导航路径。
306、大数据分析系统在无线终端上报的指示信息表示需要生成停车导航路径时，生成目标停车位与当前车辆位置之间的停车导航路径，并将停车导航路径发送给无线终端进行停车导航。
307、大数据分析系统根据车辆在目标停车位的实际停车时长，从无线终端绑定的电子账号中扣除相应的停车费用。
作为一种可选的实施方式，大数据分析系统根据车辆在目标停车位的实际停车时长，从无线终端绑定的电子账号中扣除相应的停车费用的方式可以为：
大数据分析系统可以辨别目标停车位是否属于按预设时段出租的停车位，如果目标停车位不属于按预设时段出租的停车位，可以按照目标停车位对应的普通收费规则以及实际停车时长计算相应的停车费用，其中，普通收费规则包括单位时间对应的第一收费金额；
如果目标停车位属于按预设时段出租的停车位，判断车辆在目标停车位的实 际停车时长是否超出预设时段，如果未超出预设时段，按照目标停车位对应的普通收费规则以及实际停车时长计算相应的停车费用；
如果超过预设时段，可以按照目标停车位对应的高额收费规则以及实际停车时长计算相应的停车费用，其中，高额收费规则包括单位时间对应的第二收费金额，并且第二收费金额等于若干倍数的第一收费金额；以及，从无线终端绑定的电子账号中扣除相应的停车费用。
其中，实施这种可选的实施方式，可以鼓励驾驶员在其车辆停放时长超出目标停车位预设的出租时段之前，及时将车辆驶离目标停车位置，以免导致巨额的停车费用。
本发明实施例中，每一个空闲的停车位可以按预设时段进行出租，也即是说，每一个空闲的停车位可以预设出租时长(例如08：30～18：00)。举例来说，某一车主在早上8点钟开车上班之后，车主可以将其空闲的停车位进行出租，并车主可以向大数据分析系统上报车主为其空闲的停车位预设的出租时长(例如08：30～18：00)。
可见，实施图3所描述的方法，能够使驾驶员在情绪不稳时快速的查找到适合其车辆类型的停车位进行停车，还可以使驾驶员提前预知停车位对应的预设时长的天气信息，从而可以提前做好停车时的车辆防护准备，防止车辆被恶劣天气造成损伤。
可见，实施图3所描述的方法，还可以实现无人停车收费，提高收费管理效率。
可见，实施图3所描述的方法，可以精确的识别出驾驶员的眼睛是否疲劳，以及可以精确的识别出驾驶员的情绪是否稳定。
可见，实施图3所描述的方法，可以鼓励驾驶员在其车辆停放时长超出目标停车位预设的出租时段之前，及时将车辆驶离目标停车位置，以免导致巨额的停车费用。
实施例三
请参阅图4，图4是本发明实施例公开的一种停车导航路径的推荐系统的结构示意图。如图4所示，该系统可以包括：
无线终端401和大数据分析系统402，其中：
无线终端401，用于识别无线终端401所在车辆的驾驶员的情绪是否稳定，如果情绪不稳定，将无线终端401所在车辆的当前车辆位置上报给大数据分析系统402；
大数据分析系统402，用于识别出该车辆的车辆类型，并根据该车辆类型辨别出该车辆类型适用的车位类型；
大数据分析系统402，还用于在自身管辖范围内查找出该车位类型对应大小的所有空闲停车位，从查找出的所有空闲停车中选择最接近当前车辆位置的空闲停车位作为目标停车位；
大数据分析系统402，还用于查询目标停车位对应的预设时长的天气信息并 下发给无线终端401，以使无线终端401所在车辆的驾驶员根据天气信息决定是否需要生成停车导航路径；
大数据分析系统402，还用于在无线终端401上报的指示信息表示需要生成停车导航路径时，生成目标停车位与当前车辆位置之间的停车导航路径，并将停车导航路径发送给无线终端401进行停车导航。
作为一种可选的实施方式，在图4所示的停车导航路径的推荐系统中：
无线终端401，还用于在识别出无线终端所在车辆的驾驶员的情绪稳定时，每隔指定时间采集一帧驾驶员的人脸图像，并从人脸图像中确定出人眼定位矩形，计算人眼定位矩形的面积，根据阈值判断驾驶员的眼睛的睁闭程度，以及根据驾驶员的眼睛的睁闭程度评价出人眼疲劳程度值，基于人眼疲劳程度值判断驾驶员的眼睛是否疲劳，如果疲劳，将无线终端所在车辆的当前车辆位置上报给大数据分析系统402。
作为一种可选的实施方式，在图4所示的停车导航路径的推荐系统中：
无线终端401识别无线终端401所在车辆的驾驶员的情绪是否稳定的方式具体为：
无线终端401，用于检测无线终端所在车辆的驾驶员佩戴的穿戴设备发送的驾驶员的心电图数据；对心电图数据进行去噪处理；采用心电图R波提取算法提取经过去噪处理的心电图数据中的R波峰值，以及计算经过去噪处理的心电图数据中相邻R波之间RR间距；计算RR间距的频域指标、时域指标及非线性指标；其中，频域指标包括副交感神经活性指标，时域指标包括短程心率变动性指标；短程心率变动性指标通过获取RR间距差值平方和的均方根来计算；副交感神经活性指标通过快速傅里叶变换来计算；非线性指标通过分形维数计算方法来计算；根据频域指标、时域指标及非线性指标，分析用户的情绪的活力值；活力值为根据时域指标、频域指标及非线性指标建立的多元线性回归方程计算得到的值；根据活力值识别驾驶员的情绪是否不稳定。
作为一种可选的实施方式，在图4所示的停车导航路径的推荐系统中：
无线终端401将无线终端所在车辆的当前车辆位置上报给大数据分析系统的方式具体为：
无线终端401，用于扫描周围环境中是否预先设置有路由节点，如果预先设置有路由节点，检测路由节点是否被配置有开放接入时段，如果路由节点被配置有开放接入时段，识别无线终端的当前系统时间是否位于路由节点被配置的开放接入时段内；如果无线终端的当前系统时间位于路由节点被配置的开放接入时段内，检测路由节点的当前接入的终端数量是否超过路由节点指定的最大终端接入数量；如果路由节点的当前接入的终端数量未超过路由节点指定的最大终端接入数量，建立与路由节点之间的无线连接，并且将无线终端所在车辆的当前车辆位置发送给路由节点，由路由节点将无线终端所在车辆的当前车辆位置上报给大数据分析系统。
作为一种可选的实施方式，在图4所示的停车导航路径的推荐系统中：
大数据分析系统402查询目标停车位对应的预设时长的天气信息并下发给无线终端的方式具体为：
大数据分析系统402用于判断大数据分析系统的当前工作负荷是否超过大数据分析系统指定的工作负荷，如果大数据分析系统的当前工作负荷未超过大数据分析系统指定的工作负荷，通过天气信息查询端口向天气信息查询端口对应的天气服务平台发起包括目标停车位的天气信息查询请求；接收天气服务平台通过天气信息查询端口返回的目标停车位对应的预设时长的天气信息；将目标停车位对应的预设时长的天气信息下发给无线终端。
其中，大数据分析系统402还用于：
辨别目标停车位是否属于按预设时段出租的停车位，如果目标停车位不属于按预设时段出租的停车位，按照目标停车位对应的普通收费规则以及实际停车时长计算相应的停车费用，普通收费规则包括单位时间对应的第一收费金额；如果目标停车位属于按预设时段出租的停车位，判断车辆在目标停车位的实际停车时长是否超出预设时段，如果未超出预设时段，按照目标停车位对应的普通收费规则以及实际停车时长计算相应的停车费用；如果超过预设时段，按照目标停车位对应的高额收费规则以及实际停车时长计算相应的停车费用，高额收费规则包括单位时间对应的第二收费金额，并且第二收费金额等于若干倍数的第一收费金额；以及，从无线终端绑定的电子账号中扣除相应的停车费用。
可见，实施图4所描述的系统，能够使驾驶员在情绪不稳时快速的查找到适合其车辆类型的停车位进行停车，还可以使驾驶员提前预知停车位对应的预设时长的天气信息，从而可以提前做好停车时的车辆防护准备，防止车辆被恶劣天气造成损伤。
可见，实施图4所描述的系统，还可以实现无人停车收费，提高收费管理效率。
可见，实施图4所描述的系统，可以精确的识别出驾驶员的眼睛是否疲劳，以及可以精确的识别出驾驶员的情绪是否稳定。
可见，实施图4所描述的系统，可以鼓励驾驶员在其车辆停放时长超出目标停车位预设的出租时段之前，及时将车辆驶离目标停车位置，以免导致巨额的停车费用。
可见，实施图4所描述的系统，可以避免无线终端直接与大数据分析系统通信带来的较大功耗。
本领域普通技术人员可以理解上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储介质中，存储介质包括只读存储器(Read-Only Memory，ROM)、随机存储器(Random Access Memory，RAM)、可编程只读存储器(Programmable Read-only Memory，PROM)、可擦除可编程只读存储器(Erasable Programmable Read Only Memory，EPROM)、一次可编程只读存储器(One-time Programmable Read-Only Memory，OTPROM)、电子抹除式可复写只读存储器(Electrically-Erasable  Programmable Read-Only Memory，EEPROM)、只读光盘(Compact Disc Read-Only Memory，CD-ROM)或其他光盘存储器、磁盘存储器、磁带存储器、或者能够用于携带或存储数据的计算机可读的任何其他介质。
以上对本发明实施例公开的一种停车导航路径的推荐系统及方法进行了详细介绍，本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本发明的限制。

Claims (10)

  1. 一种停车导航路径的推荐方法，其特征在于，包括：
    无线终端识别所述无线终端所在车辆的驾驶员的情绪是否稳定，如果情绪不稳定，将所述无线终端所在车辆的当前车辆位置上报给大数据分析系统；
    所述大数据分析系统识别出所述车辆的车辆类型，并根据所述车辆类型辨别出所述车辆类型适用的车位类型；
    所述大数据分析系统在自身管辖范围内查找出所述车位类型对应大小的所有空闲停车位，从查找出的所有空闲停车中选择最接近所述当前车辆位置的空闲停车位作为目标停车位；
    所述大数据分析系统查询所述目标停车位对应的预设时长的天气信息并下发给所述无线终端，以使所述无线终端所在车辆的驾驶员根据所述天气信息决定是否需要生成停车导航路径；
    所述大数据分析系统在所述无线终端上报的指示信息表示需要生成停车导航路径时，生成所述目标停车位与所述当前车辆位置之间的停车导航路径，并将所述停车导航路径发送给所述无线终端进行停车导航。
  2. 根据权利要求1所述的停车导航路径的推荐方法，其特征在于，所述无线终端识别出所述无线终端所在车辆的驾驶员的情绪稳定时，所述方法还包括：
    所述无线终端每隔指定时间采集一帧所述驾驶员的人脸图像，并从所述人脸图像中确定出人眼定位矩形，计算所述人眼定位矩形的面积，根据阈值判断所述驾驶员的眼睛的睁闭程度，以及根据所述驾驶员的眼睛的睁闭程度评价出人眼疲劳程度值，基于所述人眼疲劳程度值判断所述驾驶员的眼睛是否疲劳，如果疲劳，执行所述的将所述无线终端所在车辆的当前车辆位置上报给大数据分析系统。
  3. 根据权利要求2所述的停车导航路径的推荐方法，其特征在于，所述无线终端识别所述无线终端所在车辆的驾驶员的情绪是否稳定，包括：
    所述无线终端检测所述无线终端所在车辆的驾驶员佩戴的穿戴设备发送的所述驾驶员的心电图数据；
    所述无线终端对所述心电图数据进行去噪处理；
    所述无线终端采用心电图R波提取算法提取经过去噪处理的心电图数据中的R波峰值，以及计算所述经过去噪处理的心电图数据中相邻R波之间RR间距；
    所述无线终端计算所述RR间距的频域指标、时域指标及非线性指标；其中，所述频域指标包括副交感神经活性指标，所述时域指标包括短程心率变动性指标；所述短程心率变动性指标通过获取所述RR间距差值平方和的均方根来计算；所述副交感神经活性指标通过快速傅里叶变换来计算；所述非线性指标通过分形维数计算方法来计算；
    所述无线终端根据所述频域指标、时域指标及非线性指标，分析所述用户的情绪的活力值；所述活力值为根据所述时域指标、频域指标及非线性指标建立的多元线性回归方程计算得到的值；
    所述无线终端根据所述活力值识别所述驾驶员的情绪是否不稳定。
  4. 根据权利要求1～3任一项所述的停车导航路径的推荐方法，其特征在于，所述无线终端将所述无线终端所在车辆的当前车辆位置上报给大数据分析系统，包括：
    所述无线终端扫描周围环境中是否预先设置有路由节点，如果预先设置有所述路由节点，检测所述路由节点是否被配置有开放接入时段，如果所述路由节点被配置有所述开放接入时段，识别所述无线终端的当前系统时间是否位于所述路由节点被配置的所述开放接入时段内；
    如果所述无线终端的当前系统时间位于所述路由节点被配置的所述开放接入时段内，检测所述路由节点的当前接入的终端数量是否超过所述路由节点指定的最大终端接入数量；
    如果所述路由节点的当前接入的终端数量未超过所述路由节点指定的最大终端接入数量，所述无线终端建立与所述路由节点之间的无线连接，并且将所述无线终端所在车辆的当前车辆位置发送给所述路由节点，由所述路由节点将所述无线终端所在车辆的当前车辆位置发送给所述大数据分析系统。
  5. 根据权利要求1～4所述的停车导航路径的推荐方法，其特征在于，所述大数据分析系统查询所述目标停车位对应的预设时长的天气信息并下发给所述无线终端，包括:
    所述大数据分析系统判断所述大数据分析系统的当前工作负荷是否超过所述大数据分析系统指定的工作负荷；
    如果所述大数据分析系统的当前工作负荷未超过所述大数据分析系统指定的工作负荷，所述大数据分析系统通过天气信息查询端口向所述天气信息查询端口对应的天气服务平台发起包括所述目标停车位的天气信息查询请求；
    以及，所述大数据分析系统接收所述天气服务平台通过所述天气信息查询端口返回的所述目标停车位对应的预设时长的天气信息；
    以及，所述大数据分析系统将所述目标停车位对应的预设时长的天气信息下发给所述无线终端。
  6. 一种停车导航路径的推荐系统，其特征在于，包括无线终端和大数据分析系统，其中：
    所述无线终端，用于识别所述无线终端所在车辆的驾驶员的情绪是否稳定，如果情绪不稳定，将所述无线终端所在车辆的当前车辆位置上报给所述大数据分析系统；
    所述大数据分析系统，用于识别出所述车辆的车辆类型，并根据所述车辆类型辨别出所述车辆类型适用的车位类型；
    所述大数据分析系统，还用于在自身管辖范围内查找出所述车位类型对应大小的所有空闲停车位，从查找出的所有空闲停车中选择最接近所述当前车辆位置的空闲停车位作为目标停车位；
    所述大数据分析系统，还用于查询所述目标停车位对应的预设时长的天气信息并下发给所述无线终端，以使所述无线终端所在车辆的驾驶员根据所述天气信 息决定是否需要生成停车导航路径；
    所述大数据分析系统，还用于在所述无线终端上报的指示信息表示需要生成停车导航路径时，生成所述目标停车位与所述当前车辆位置之间的停车导航路径，并将所述停车导航路径发送给所述无线终端进行停车导航。
  7. 根据权利要求6所述的停车导航路径的推荐系统，其特征在于：
    所述无线终端，还用于在识别出所述无线终端所在车辆的驾驶员的情绪稳定时，每隔指定时间采集一帧所述驾驶员的人脸图像，并从所述人脸图像中确定出人眼定位矩形，计算所述人眼定位矩形的面积，根据阈值判断所述驾驶员的眼睛的睁闭程度，以及根据所述驾驶员的眼睛的睁闭程度评价出人眼疲劳程度值，基于所述人眼疲劳程度值判断所述驾驶员的眼睛是否疲劳，如果疲劳，执行所述的将所述无线终端所在车辆的当前车辆位置上报给大数据分析系统。
  8. 根据权利要求7所述的停车导航路径的推荐系统，其特征在于，所述无线终端识别所述无线终端所在车辆的驾驶员的情绪是否稳定的方式具体为：
    所述无线终端，用于检测所述无线终端所在车辆的驾驶员佩戴的穿戴设备发送的所述驾驶员的心电图数据；对所述心电图数据进行去噪处理；采用心电图R波提取算法提取经过去噪处理的心电图数据中的R波峰值，以及计算所述经过去噪处理的心电图数据中相邻R波之间RR间距；计算所述RR间距的频域指标、时域指标及非线性指标；其中，所述频域指标包括副交感神经活性指标，所述时域指标包括短程心率变动性指标；所述短程心率变动性指标通过获取所述RR间距差值平方和的均方根来计算；所述副交感神经活性指标通过快速傅里叶变换来计算；所述非线性指标通过分形维数计算方法来计算；根据所述频域指标、时域指标及非线性指标，分析所述用户的情绪的活力值；所述活力值为根据所述时域指标、频域指标及非线性指标建立的多元线性回归方程计算得到的值；根据所述活力值识别所述驾驶员的情绪是否不稳定。
  9. 根据权利要求6～8任一项所述的停车导航路径的推荐系统，其特征在于，所述无线终端将所述无线终端所在车辆的当前车辆位置上报给大数据分析系统的方式具体为：
    所述无线终端，用于扫描周围环境中是否预先设置有路由节点，如果预先设置有所述路由节点，检测所述路由节点是否被配置有开放接入时段，如果所述路由节点被配置有所述开放接入时段，识别所述无线终端的当前系统时间是否位于所述路由节点被配置的所述开放接入时段内；如果所述无线终端的当前系统时间位于所述路由节点被配置的所述开放接入时段内，检测所述路由节点的当前接入的终端数量是否超过所述路由节点指定的最大终端接入数量；如果所述路由节点的当前接入的终端数量未超过所述路由节点指定的最大终端接入数量，建立与所述路由节点之间的无线连接，并且将所述无线终端所在车辆的当前车辆位置发送给所述路由节点，由所述路由节点将所述无线终端所在车辆的当前车辆位置发送给所述大数据分析系统。
  10. 根据权利要求6～9所述的停车导航路径的推荐系统，其特征在于，所述 大数据分析系统查询所述目标停车位对应的预设时长的天气信息并下发给所述无线终端的方式具体为：
    所述大数据分析系统，用于判断所述大数据分析系统的当前工作负荷是否超过所述大数据分析系统指定的工作负荷，如果所述大数据分析系统的当前工作负荷未超过所述大数据分析系统指定的工作负荷，通过天气信息查询端口向所述天气信息查询端口对应的天气服务平台发起包括所述目标停车位的天气信息查询请求；接收所述天气服务平台通过所述天气信息查询端口返回的所述目标停车位对应的预设时长的天气信息；将所述目标停车位对应的预设时长的天气信息下发给所述无线终端。
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