Crowd-sourcing road repairing curing method based on intelligent vehicle-mounted box

A vehicle-mounted box and crowdsourcing mode technology, which is applied in the field of road maintenance and maintenance in crowdsourcing mode based on intelligent vehicle-mounted boxes, can solve the problems of simple data analysis model, no consideration of road potholes, difficulty in re-detecting road surface smoothness, etc., and saves money. Human and material resources and the effect of enriching data sources

Inactive Publication Date: 2019-04-23
广州通易科技有限公司
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AI-Extracted Technical Summary

Problems solved by technology

[0003] However, after the road is opened, it is very difficult to re-inspect the road surface flatness
The road maintenance department often needs to spend a lot of manpower and material resources on the investigation of the location of the road damage, and there is no way to send personnel to repa...
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Method used

(3) after the road data of construction is carried out coordinate transformation, calculate vertical displacement vector, adopt the displacement vector that statistical distribution analysis extracts, this amount compares with the displacement vector average value of this road hist...
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Abstract

The invention discloses a crowd-sourcing road repairing curing method based on an intelligent vehicle-mounted box. The crowd-sourcing road repairing curing method includes the following steps that S1,intelligent vehicle-mounted box data of vehicles driving through the same road are collected, the intelligent vehicle-mounted box is installed on an OBD interface of a vehicle, and a six-axis gyroscope and an accelerometer are integrated in the intelligent vehicle-mounted box; S2, the filtered data are extracted and analyzed, and three-dimensional space acceleration data based on a linear reference frame are constructed; S3, after constructed road data are subjected to coordinate transformation, a vertical displacement vector is calculated to be compared with a historical displacement vectormean value of the road to remove disturbance of fixed road facilities; S4, the vector after comparison is compared with a straight road displacement vector mean value, and results are matched with different road conditions to generate road condition labels; and S5, the road condition labels and location time information are combined to generate a road curing demand map. According to the crowd-sourcing road repairing curing method based on the intelligent vehicle-mounted box, a crowd-sourcing mode is adopted, additional data collected by the vehicle are used, and the new method is provided fora road curing department to evaluate the road conditions.

Application Domain

Technology Topic

Crowd sourcingVertical displacement +9

Image

  • Crowd-sourcing road repairing curing method based on intelligent vehicle-mounted box

Examples

  • Experimental program(1)

Example Embodiment

[0040] Example
[0041] Crowd-sourcing is defined as: "A company or organization outsources tasks previously performed by employees to a non-specific (and usually large) mass network in a free and voluntary manner. Crowd-sourcing. The task of packaging is usually undertaken by individuals, but if it involves tasks that need to be completed by multiple people, it may also appear in the form of individual production relying on open source. "The concept of "crowdsourcing" is actually derived from the innovative model of the enterprise Reflection.
[0042] The crowdsourcing model is actually an organization model that transfers the work traditionally undertaken by the internal employees of the enterprise to the mass groups outside the enterprise in a free and voluntary manner through the Internet. In this process, companies only need to pay contributors a small amount of compensation, and sometimes this kind of contribution is even free.
[0043] Such as figure 1 As shown, in this embodiment, a crowdsourced road maintenance method based on a smart car box specifically includes the following steps:
[0044] First, carry out the preparatory work before data collection, specifically:
[0045] (01) A smart car box integrated with a six-axis gyroscope and accelerometer is installed on the vehicle OBD interface;
[0046] (02) Calculate the measured data to obtain the vertical displacement vector of the straight road, and obtain the average value of the straight vector after statistical distribution;
[0047] (03) After calculating the measurement data, the vertical displacement vectors of different roads are obtained, and the average value of the historical vector of different roads is obtained through statistical distribution.
[0048] (1) Data collection and release to car owners through crowdsourcing mode to collect data on smart car boxes of vehicles driving on the same road. The smart car box is installed on the vehicle's OBD interface and integrates a six-axis gyroscope and accelerometer;
[0049] (1.1) Collect data collected by the smart car box of the driving vehicle; the smart car box is installed on the OBD interface of the vehicle, connected to the car CAN bus, and read and send the power system, safety system, body system, and electronics connected to the CAN bus. Module data of system and comfort system;
[0050] (1.2) Upload the data read by the smart car device to the cloud server.
[0051] (2) Filter the collected vehicle equipment data, extract and analyze the filtered vehicle equipment data, extract six-axis acceleration, speed, direction and position data, and construct three-dimensional spatial acceleration data based on the linear reference system;
[0052] (2.1) Filter out GPS module, six-axis gyroscope and accelerometer according to the attributes of vehicle data;
[0053] (2.2) Extract the continuous six-axis acceleration, speed, direction and position data;
[0054] (2.3) Classify according to its location and time. From GPS data and time data, get the six-axis acceleration, speed, and direction data of different roads at different times, project the three-dimensional data to the vertical direction, and finally match the road number to construct the road The historical road attribute database.
[0055] (3) After the coordinate conversion of the constructed road data, the vertical displacement vector is calculated, and the displacement vector extracted by statistical distribution analysis is used to compare this quantity with the average value of the historical displacement vector of the road to eliminate the interference of fixed road facilities;
[0056] (3.1) Process different road data according to (a i Is the vertical acceleration of time slice i, t i Is i time slice duration), the vertical displacement vector is obtained, and the average value of the displacement vector data is obtained through statistical distribution;
[0057] (3.2) Since the fixed road facilities also have vertical vectors, the mean value of the historical road vector and the mean value of the straight road vector will also be different, so that the interference data can be known;
[0058] (3.3) Compare the average value of the current road vector with the average value of the historical road displacement vector, use the difference between the two to eliminate the interference of fixed facilities, and obtain the optimized average value of the displacement vector.
[0059] (4) The compared quantity is then compared with the average value of the straight road displacement vector, the output of the statistical distribution model is collected, and the result is matched with different road conditions to generate road surface condition labels.
[0060] Step (4) is specifically:
[0061] (4.1) Compare the average value of the optimized displacement vector with the average value of the straight road vector to get the vector difference;
[0062] (4.2) After statistically distributing the vector difference, output the result. If it meets the range of the pavement pothole setting, match the result to the pavement pothole and set it as the pavement pothole road label. Similarly, you can set the pavement speed bump and road raised label .
[0063] (5) Combine the road surface condition label with the location and time information to generate a municipal road maintenance demand map, which is provided to the maintenance decision maker as a decision-making reference.
[0064] (5.1) The set road surface label corresponds to the time and location information to generate a dynamic road maintenance demand map;
[0065] (5.2) According to the set road surface label, match the appropriate maintenance plan as the basis for the maintenance decision of the road maintenance department.
[0066] The following uses a specific application case to further elaborate the technical solution of the present invention:
[0067] First, the vehicle installs the Youjia smart box, and then drives normally, the data is transmitted to the Youjia APP via Bluetooth, and finally uploaded to the cloud server. Youjia cloud server reads data information from the database, filters vehicle data, extracts key information including time, position, vertical six-axis acceleration, direction, etc., and constructs three-dimensional spatial acceleration data based on a linear reference system.
[0068] Secondly, submit the data to the road condition data model for analysis to match the road surface conditions: potholes, road speed bumps and bumps. Add appropriate road surface condition labels, combine time and location information, and match appropriate maintenance programs from the maintenance program database.
[0069] Finally, the data tag is sent to the map service provider to update the map and display real-time road images. At the same time, the data label and maintenance plan are sent to the maintenance and maintenance department to provide decision support for its road maintenance.
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