CN112309132A - Big data snow environment information management system - Google Patents
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Abstract
The invention relates to a big data snow environment information management system, which comprises: the big data server is managed by a navigation data operator, is connected with the grade receiving equipment through a network and is used for storing the latest road section congestion grade of each road section; the grade receiving equipment is used for determining a current road section corresponding to GPS data based on the GPS data of vehicle driving and downloading a road section congestion grade corresponding to the current road section from a big data server based on the current road section; and the speed estimation device is connected with the grade receiving device and is used for receiving the local real-time thickness and calculating the estimated speed of the vehicle passing through the current road section based on the local real-time thickness and the road section congestion grade. The big data snow environment information management system is compact in design and reliable in data. The navigation data of the current road section is calculated by introducing the local snow thickness parameter when the current road section is determined to be the snow road section, so that the error of the navigation data is effectively reduced.
Description
Technical Field
The invention relates to the field of big data management, in particular to a big data snow environment information management system.
Background
Big data (big data), an IT industry term, refers to a data set that cannot be captured, managed, and processed with a conventional software tool within a certain time range, and is a massive, high-growth-rate, diversified information asset that needs a new processing mode to have stronger decision-making power, insight discovery power, and process optimization capability.
In the "big data era" written by vkto, mel, schenberger and kenius, cusk, the big data means that analysis processing is performed using all data without using a shortcut such as a random analysis method (sampling survey). 5V characteristics of big data (proposed by IBM): volume (bulk), Velocity (high speed), Variety (multiple), Value (low Value density), Veracity (authenticity).
The scale of the current city is continuously expanding, the number of urban residents and the number of vehicles are also increasing day by day, and for managers and developers who implement traffic management, traffic monitoring and traffic auxiliary data supply, how to fully utilize the advanced mechanism of big data to provide more efficient and accurate management mechanism and data supply mechanism is one of the problems which are urgently needed to be solved at present. Application domains specific to sub-categories, e.g. the impact of special weather, such as snow-day conditions, on navigation data, require targeted big data application patterns.
Disclosure of Invention
The invention has at least the following three important points:
(1) in view of the characteristics that the thickness of the accumulated snow is higher and the running speed of a slow vehicle is lower, an estimation mechanism of the vehicle passing speed and the vehicle passing duration of a current road section is introduced into a vehicle navigation system, wherein the estimation mechanism is cooperatively controlled by the thickness of the snow of the current road section and the congestion level of the current road section, and the validity of navigation data is ensured;
(2) the method adopts a customized local measurement mode of the snow thickness of the current road section to ensure the reliability of the measurement result of the snow thickness of the current road section;
(3) and only when the current road section is in the snow road section, starting a traffic data estimation mechanism based on the snow thickness of the current road section and the congestion level of the current road section, and improving the self-adaptive level of navigation control.
According to an aspect of the present invention, there is provided a big data snow environment information management system, the system including:
the big data server is managed by a navigation data operator, is connected with the grade receiving equipment through a network and is used for storing the latest road section congestion grade of each road section;
the grade receiving equipment is used for determining a current road section corresponding to GPS data based on the GPS data of vehicle driving and downloading a road section congestion grade corresponding to the current road section from a big data server based on the current road section;
the speed estimation device is connected with the grade receiving device and used for receiving the local real-time thickness and calculating the estimated speed of the vehicle passing through the current road section based on the local real-time thickness and the road section congestion grade;
the road section marking equipment is connected with the grade receiving equipment and used for receiving the local real-time thickness and calculating the time consumed for passing the current road section based on the local real-time thickness and the road section congestion grade to serve as estimated time length to be output;
the signal reporting device is respectively connected with the speed estimation device and the road section marking device and is used for wirelessly reporting the estimated speed and the estimated duration to a big data server of a navigation data operator;
the embedded acquisition equipment is arranged in the center of the front end of the vehicle and is used for executing image data acquisition action on the environment in front of the vehicle so as to obtain an instant acquisition image;
the direction filtering equipment is connected with the embedded acquisition equipment and is used for executing direction filtering processing on the received instant acquisition image so as to obtain and output a corresponding data filtering image;
the parameter identification equipment is connected with the direction filtering equipment and used for acquiring the depth of field value of the central position of the data filtering image to be output as current depth of field data;
the thickness estimation device is connected with the parameter identification device and used for calculating the local real-time thickness as the snow thickness of the current road section where the vehicle runs based on the difference value that the current depth of field data is lower than the reference depth of field data when the current depth of field data is smaller than the reference depth of field data;
the reference depth of field data is a depth of field value of a central position of an image acquired by an embedded acquisition device in a front ground snow-free state through direction filtering.
The big data snow environment information management system is compact in design and reliable in data. The navigation data of the current road section is calculated by introducing the local snow thickness parameter when the current road section is determined to be the snow road section, so that the error of the navigation data is effectively reduced.
Drawings
Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
fig. 1 is a schematic view of a driving environment of a passing vehicle in a current driving section according to an embodiment of the present invention.
Fig. 2 is a block diagram showing the configuration of a big data snow environment information management system according to a first embodiment of the present invention.
Fig. 3 is a block diagram showing the configuration of a big data snow environment information management system according to a second embodiment of the present invention.
Detailed Description
Embodiments of a big data snow environment information management system of the present invention will be described in detail with reference to the accompanying drawings.
In the prior art, when a navigation data operator provides navigation data of a current driving road section of a vehicle, special scenes such as snowing are generally not considered, the time length consumed for passing the current driving road section is calculated only based on the number of passing vehicles or historical data of the current driving road section, obviously, the thickness of snow is different, the delay degree of the passing time length of the vehicles is different, and the calculation of the navigation data of the current driving road section is not scientific without considering the thickness factor of the snow.
The running environment of the passing vehicle on the current running road section with the snow thickness reaching a certain threshold value is specifically shown in fig. 1.
In order to overcome the defects, the invention builds a big data snow environment information management system, and can effectively solve the corresponding technical problems.
Two different embodiments are specifically given below for the user to apply and operate according to the needs of the user.
< first embodiment >
Fig. 2 is a block diagram showing a configuration of a big data snow environment information management system according to a first embodiment of the present invention, the system including:
the big data server is managed by a navigation data operator, is connected with the grade receiving equipment through a network and is used for storing the latest road section congestion grade of each road section;
the grade receiving equipment is used for determining a current road section corresponding to GPS data based on the GPS data of vehicle driving and downloading a road section congestion grade corresponding to the current road section from a big data server based on the current road section;
the speed estimation device is connected with the grade receiving device and used for receiving the local real-time thickness and calculating the estimated speed of the vehicle passing through the current road section based on the local real-time thickness and the road section congestion grade;
the road section marking equipment is connected with the grade receiving equipment and used for receiving the local real-time thickness and calculating the time consumed for passing the current road section based on the local real-time thickness and the road section congestion grade to serve as estimated time length to be output;
the signal reporting device is respectively connected with the speed estimation device and the road section marking device and is used for wirelessly reporting the estimated speed and the estimated duration to a big data server of a navigation data operator;
the embedded acquisition equipment is arranged in the center of the front end of the vehicle and is used for executing image data acquisition action on the environment in front of the vehicle so as to obtain an instant acquisition image;
the direction filtering equipment is connected with the embedded acquisition equipment and is used for executing direction filtering processing on the received instant acquisition image so as to obtain and output a corresponding data filtering image;
the parameter identification equipment is connected with the direction filtering equipment and used for acquiring the depth of field value of the central position of the data filtering image to be output as current depth of field data;
the thickness estimation device is connected with the parameter identification device and used for calculating the local real-time thickness as the snow thickness of the current road section where the vehicle runs based on the difference value that the current depth of field data is lower than the reference depth of field data when the current depth of field data is smaller than the reference depth of field data;
the reference depth of field data is a depth of field value of a central position of an image acquired by an embedded acquisition device in a front ground snow-free state through direction filtering.
< second embodiment >
Fig. 3 is a block diagram showing a configuration of a big data snow environment information management system according to a second embodiment of the present invention, the system including:
the big data server is managed by a navigation data operator, is connected with the grade receiving equipment through a network and is used for storing the latest road section congestion grade of each road section;
the grade receiving equipment is used for determining a current road section corresponding to GPS data based on the GPS data of vehicle driving and downloading a road section congestion grade corresponding to the current road section from a big data server based on the current road section;
the speed estimation device is connected with the grade receiving device and used for receiving the local real-time thickness and calculating the estimated speed of the vehicle passing through the current road section based on the local real-time thickness and the road section congestion grade;
the road section marking equipment is connected with the grade receiving equipment and used for receiving the local real-time thickness and calculating the time consumed for passing the current road section based on the local real-time thickness and the road section congestion grade to serve as estimated time length to be output;
the signal reporting device is respectively connected with the speed estimation device and the road section marking device and is used for wirelessly reporting the estimated speed and the estimated duration to a big data server of a navigation data operator;
the embedded acquisition equipment is arranged in the center of the front end of the vehicle and is used for executing image data acquisition action on the environment in front of the vehicle so as to obtain an instant acquisition image;
the direction filtering equipment is connected with the embedded acquisition equipment and is used for executing direction filtering processing on the received instant acquisition image so as to obtain and output a corresponding data filtering image;
the parameter identification equipment is connected with the direction filtering equipment and used for acquiring the depth of field value of the central position of the data filtering image to be output as current depth of field data;
the thickness estimation device is connected with the parameter identification device and used for calculating the local real-time thickness as the snow thickness of the current road section where the vehicle runs based on the difference value that the current depth of field data is lower than the reference depth of field data when the current depth of field data is smaller than the reference depth of field data;
the satellite navigation equipment is arranged on the shell of the vehicle, is connected with the grade receiving equipment and is used for wirelessly receiving GPS data of the running of the vehicle;
the reference depth of field data is a depth of field value of a central position of an image acquired by an embedded acquisition device in a front ground snow-free state through direction filtering.
Further, in the big-data snow-day environment information management system of the above embodiment:
calculating a local real-time thickness as a snow thickness of a current road segment on which the vehicle is traveling based on a difference value in which the current depth of field data is lower than the reference depth of field data includes: the larger the difference, the larger the local real-time thickness.
Further, in the big-data snow-day environment information management system of the above embodiment:
calculating an estimated speed of a vehicle passing through the current road section based on the local real-time thickness and the road section congestion level comprises the following steps: under the condition that the road congestion levels are the same, the larger the local real-time thickness is, the slower the calculated estimated speed of the vehicle passing through the current road section is.
Further, in the big-data snow-day environment information management system of the above embodiment:
calculating the time it takes to pass the current road segment as the estimated length of time based on the local real-time thickness and the road segment congestion level includes: under the condition that the road congestion levels are the same, the larger the local real-time thickness is, the longer the time is needed for calculating to pass through the current road section.
Further, in the big-data snow-day environment information management system of the above embodiment:
calculating an estimated speed of a vehicle passing through the current road section based on the local real-time thickness and the road section congestion level comprises the following steps: under the condition that the local real-time thickness is the same, the lower the road section congestion level is, the higher the calculated estimated speed of passing the vehicles on the current road section is.
Further, in the big-data snow-day environment information management system of the above embodiment:
calculating the time it takes to pass the current road segment as the estimated length of time based on the local real-time thickness and the road segment congestion level includes: the lower the congestion level of the road segment, the shorter the time it takes to calculate to pass the current road segment, given the same local real-time thickness.
Further, in the big-data snow-day environment information management system of the above embodiment:
the direction filtering device, the parameter discriminating device and the thickness estimating device are provided at a center console position of a vehicle;
wherein the directional filtering device, the parameter discrimination device and the thickness estimation device share the same parallel data interface.
Further, in the big data snow environment information management system of the above embodiment, the system further includes:
and the SDRAM storage device is connected with the thickness estimation device and is used for storing the reference depth of field data in advance.
Further, in the big data snow environment information management system of the above embodiment, the system further includes:
a mode control device for starting the parameter discrimination device and the thickness estimation device when the wirelessly received GPS data on which the vehicle runs is on a snow covered section;
wherein the mode control device is further configured to turn off the parameter discriminating device and the thickness estimating device when the wirelessly received GPS data on which the vehicle is traveling is not on a snow covered section.
In addition, in the SDRAM storage device, SDRAM, i.e. Synchronous Dynamic Random Access Memory, synchronizes Dynamic Random Access Memory, where synchronization means that a Synchronous clock is required for Memory operation, and transmission of internal commands and data transmission are based on the SDRAM storage device; dynamic means that the memory array needs to be refreshed continuously to ensure that data is not lost; random means that data are not stored linearly and sequentially, but data are read and written by freely appointing addresses. The clock frequency of the SDR SDRAM is the frequency of data storage. The operating voltage of the SDRAM is 3.3V.
It is to be understood that while the present invention has been described in conjunction with the preferred embodiments thereof, it is not intended to limit the invention to those embodiments. It will be apparent to those skilled in the art from this disclosure that many changes and modifications can be made, or equivalents modified, in the embodiments of the invention without departing from the scope of the invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.
Claims (10)
1. A big data snow day environmental information management system, characterized by comprising:
the big data server is managed by a navigation data operator, is connected with the grade receiving equipment through a network and is used for storing the latest road section congestion grade of each road section;
the grade receiving equipment is used for determining a current road section corresponding to GPS data based on the GPS data of vehicle driving and downloading a road section congestion grade corresponding to the current road section from a big data server based on the current road section;
the speed estimation device is connected with the grade receiving device and used for receiving the local real-time thickness and calculating the estimated speed of the vehicle passing through the current road section based on the local real-time thickness and the road section congestion grade;
the road section marking equipment is connected with the grade receiving equipment and used for receiving the local real-time thickness and calculating the time consumed for passing the current road section based on the local real-time thickness and the road section congestion grade to serve as estimated time length to be output;
the signal reporting device is respectively connected with the speed estimation device and the road section marking device and is used for wirelessly reporting the estimated speed and the estimated duration to a big data server of a navigation data operator;
the embedded acquisition equipment is arranged in the center of the front end of the vehicle and is used for executing image data acquisition action on the environment in front of the vehicle so as to obtain an instant acquisition image;
the direction filtering equipment is connected with the embedded acquisition equipment and is used for executing direction filtering processing on the received instant acquisition image so as to obtain and output a corresponding data filtering image;
the parameter identification equipment is connected with the direction filtering equipment and used for acquiring the depth of field value of the central position of the data filtering image to be output as current depth of field data;
the thickness estimation device is connected with the parameter identification device and used for calculating the local real-time thickness as the snow thickness of the current road section where the vehicle runs based on the difference value that the current depth of field data is lower than the reference depth of field data when the current depth of field data is smaller than the reference depth of field data;
the reference depth of field data is a depth of field value of a central position of an image acquired by an embedded acquisition device in a front ground snow-free state through direction filtering.
2. The big data snow environment information management system according to claim 1, wherein:
calculating a local real-time thickness as a snow thickness of a current road segment on which the vehicle is traveling based on a difference value in which the current depth of field data is lower than the reference depth of field data includes: the larger the difference, the larger the local real-time thickness.
3. The big data snow environment information management system according to claim 2, wherein:
calculating an estimated speed of a vehicle passing through the current road section based on the local real-time thickness and the road section congestion level comprises the following steps: under the condition that the road congestion levels are the same, the larger the local real-time thickness is, the slower the calculated estimated speed of the vehicle passing through the current road section is.
4. The big data snow environment information management system according to claim 3, wherein:
calculating the time it takes to pass the current road segment as the estimated length of time based on the local real-time thickness and the road segment congestion level includes: under the condition that the road congestion levels are the same, the larger the local real-time thickness is, the longer the time is needed for calculating to pass through the current road section.
5. The big data snow environment information management system according to claim 4, wherein:
calculating an estimated speed of a vehicle passing through the current road section based on the local real-time thickness and the road section congestion level comprises the following steps: under the condition that the local real-time thickness is the same, the lower the road section congestion level is, the higher the calculated estimated speed of passing the vehicles on the current road section is.
6. The big data snow environment information management system according to claim 5, wherein:
calculating the time it takes to pass the current road segment as the estimated length of time based on the local real-time thickness and the road segment congestion level includes: the lower the congestion level of the road segment, the shorter the time it takes to calculate to pass the current road segment, given the same local real-time thickness.
7. The big data snow day environmental information management system of claim 6, further comprising:
and the satellite navigation equipment is arranged on the shell of the vehicle, is connected with the grade receiving equipment and is used for wirelessly receiving GPS data of the running of the vehicle.
8. The big data snow day environmental information management system of claim 7, wherein:
the direction filtering device, the parameter discriminating device and the thickness estimating device are provided at a center console position of a vehicle;
wherein the directional filtering device, the parameter discrimination device and the thickness estimation device share the same parallel data interface.
9. The big data snow environment information management system of claim 8, further comprising:
and the SDRAM storage device is connected with the thickness estimation device and is used for storing the reference depth of field data in advance.
10. The big data snow day environmental information management system of claim 9, wherein the system further comprises:
a mode control device for starting the parameter discrimination device and the thickness estimation device when the wirelessly received GPS data on which the vehicle runs is on a snow covered section;
wherein the mode control device is further configured to turn off the parameter discriminating device and the thickness estimating device when the wirelessly received GPS data on which the vehicle is traveling is not on a snow covered section.
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| CN113688579A (en) * | 2021-07-15 | 2021-11-23 | 北京交通大学 | Ecological conditioning method for reducing accumulated snow on roads |
| CN113688579B (en) * | 2021-07-15 | 2024-02-09 | 北京交通大学 | Ecological regulating method for reducing snow on highway |
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