A processing system and method for dynamic detection and real-time flattening of mine site roads

Abstract

The application discloses a kind of mine area road dynamic detection and real-time processing system of flattening, comprising: detection unit, the detection unit is used to detect road flatness information;Control unit, the control unit is used to collect the road flatness information detected by detection unit and sends the work instruction of flattening operation to execution unit;Execution unit, the execution unit is used to receive the work instruction of control unit and carries out road flattening operation.Compared with the traditional passive road inspection maintenance mode, the application can achieve active feedback and report road flatness condition, mine area historical road state and maintenance data archive and trace, timely and efficiently active maintenance of road, improve work efficiency, ensure mine area road driving safety.

Description

A system and method for dynamic detection and real-time leveling of roads in mining areas. Technical Field

[0001] This invention relates to a processing system and method for dynamic detection and real-time leveling of roads in mining areas, belonging to the field of engineering machinery technology. Background Technology

[0002] Mining dump trucks are essential equipment in open-pit mining and large-scale earthwork construction, primarily used for transporting various loose materials. Tire wear on mining dump trucks is a significant contributor to vehicle maintenance costs. Therefore, ensuring the smoothness of mining roads not only reduces tire damage but also guarantees safe vehicle operation. Currently, most mines dispatch graders periodically to all mining roads for inspection and maintenance, a passive approach that suffers from poor timeliness and low operational efficiency. Summary of the Invention

[0003] To address the problems existing in the prior art, the present invention provides a processing system and method for dynamic detection and real-time leveling of roads in mining areas.

[0004] To achieve the above objectives, the present invention employs a dynamic detection and real-time leveling system for mining area roads, comprising:

[0005] The detection unit is used to detect road smoothness information;

[0006] The control unit is used to collect road smoothness information detected by the detection unit and send work instructions for smoothing operations to the execution unit;

[0007] An execution unit is used to receive working instructions from the control unit and perform road leveling operations.

[0008] As an improvement, the detection unit includes several mining dump trucks;

[0009] The mining dump truck is equipped with a suspension pressure sensor, tilt angle detector, load system and GPS. The mining dump truck collects suspension information, tilt angle information, load information and position information, and uses the peak-valley vector determination method and the front-to-back comparison method to calculate the smoothness of the mining area road on which the mining dump truck travels.

[0010] As an improvement, the mining dump truck calculates the smoothness of the current road surface based on the collected suspension information and using the unloaded fluctuation vector variance algorithm and the fully loaded single-axle vector variance algorithm.

[0011] As an improvement, the control unit includes a platform server;

[0012] The detection unit aggregates the suspension data, road smoothness data, and current coordinate position information of the mining dump truck to the platform server via the mining area wireless communication network, and the platform server issues working instructions.

[0013] As an improvement, the execution unit includes several graders;

[0014] The control unit sends work instructions to the grader closest to the work site, which then performs the leveling operation.

[0015] In addition, the present invention also provides a method for dynamic detection and real-time leveling of mining area roads, using the aforementioned mining area road dynamic detection and real-time leveling system, the specific steps of which are as follows:

[0016] S1. The mining dump truck learns the current transportation status through its own load system and runs the corresponding empty-load fluctuation vector variance algorithm and full-load single-bridge vector variance algorithm. This algorithm is a preprocessing of the suspension data to obtain the mapping data between the suspension status and the road status.

[0017] S2. Then, using the peak-valley vector determination method and the front-to-back comparison method, the smoothness of the current driving road is calculated. Each mining dump truck transmits its calculated road data and coordinate position information to the platform server, so that the real-time status of the roads in the entire mining area can be known, and targeted work instructions can be issued to the grader.

[0018] Compared to traditional passive road inspection and maintenance methods, this invention can proactively report and update road surface condition, archive and trace historical road conditions and maintenance data in the mining area, and perform timely and efficient proactive maintenance of roads, thereby improving work efficiency and ensuring safe driving on mining roads. Attached Figure Description

[0019] Figure 1 is a system block diagram of the present invention;

[0020] Figure 2 is a block diagram of the no-load fluctuation vector variance algorithm of the present invention;

[0021] Figure 3 is a block diagram of the fully loaded single-bridge vector variance algorithm of the present invention;

[0022] Figure 4 is a schematic diagram of the peak-valley vector determination method of the present invention;

[0023] Figure 5 is a schematic diagram of the preceding and following comparison method of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below. However, it should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of the invention.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0026] As shown in Figure 1, a dynamic detection and real-time leveling system for mining area roads includes:

[0027] The detection unit is used to detect road smoothness information;

[0028] The control unit is used to collect road smoothness information detected by the detection unit and send work instructions for smoothing operations to the execution unit;

[0029] An execution unit is used to receive working instructions from the control unit and perform road leveling operations.

[0030] This invention uses mining dump trucks as the detection unit for road smoothness. More specifically, it uses the front and rear suspension pressure sensors, tilt detectors, load data, and GPS of the mining dump trucks as the main data acquisition methods, and employs the peak-valley vector judgment method and the front-rear following comparison method to calculate the smoothness. Each mining dump truck in the system has a control algorithm for acquiring suspension pressure data and generating smoothness data. In particular, based on the unloaded fluctuation vector variance algorithm and the fully loaded single-axle vector variance algorithm, the smoothness of the currently traveling road can be accurately calculated.

[0031] The system aggregates the suspension data, flatness data, and current coordinates of all mining dump trucks to the platform server via the mining area's wireless communication network. The platform server executes the scheduling algorithm and issues work instructions, serving as a crucial information processing hub for the system. The system then issues work instructions to the grader closest to the work site, achieving precise and efficient road leveling. The grader is the final execution unit of the system.

[0032] Mining dump trucks (fleets) utilize their own suspension pressure sensors, load data, tilt detectors, GPS, and other data sources to run specific algorithms. They then transmit road data and location information to a platform server via a wireless network. The platform server, using time periods and road segments as key references, combines the vehicle-reported smoothness data and coordinates to generate a final assessment of the road segment using a spatiotemporal road segment algorithm. Finally, it issues road leveling instructions to the grader (unit) via the wireless network. Subsequent mining trucks passing through this segment update the road smoothness status, forming a closed-loop system of "detection – feedback – dispatch – leveling – re-detection".

[0033] Example 1

[0034] A method for dynamic detection and real-time leveling of roads in mining areas, employing the aforementioned dynamic detection and real-time leveling system for mining roads, includes the following specific steps:

[0035] S1. The mining dump truck learns the current transportation status through its own load system and runs the corresponding empty-load fluctuation vector variance algorithm and full-load single-bridge vector variance algorithm. This algorithm is a preprocessing of the suspension data to obtain the mapping data between the suspension status and the road status.

[0036] Specifically, the algorithm for the variance of the unloaded fluctuation vector is as follows:

[0037]

[0038] In the formula, This represents the variance of the front left hanging vector, and it can be negative.

[0039] This represents the variance of the front right suspension vector, and it can be negative.

[0040] This represents the variance of the rear left hanging vector, and it can be negative.

[0041] This represents the variance of the rear right suspension vector, and it can be negative.

[0042] P a This is the pressure value input for the front left suspension;

[0043] P b This is the pressure value input for the front right suspension;

[0044] P c This is the pressure value input for the rear left suspension;

[0045] P d This is the pressure value input for the rear right suspension;

[0046] M is the number of samples in the data collection, and it is dynamically updated (data is first-in, first-out).

[0047] As shown in Figure 2, the core of the no-load fluctuation vector variance algorithm is to sample the values ​​of the four suspension pressure sensors in M ​​sets and calculate the average value of the data in each set. At the same time, the vector variance is calculated by comparing the most recent sampled values ​​of the four suspension pressure sensors with the average value of the M sets of data. This variance is a signed variance value, that is, the variance value greater than the average value is marked with "+", and the variance value less than the average value is marked with "-". The obtained variance data is used to calculate the deviation value by running the peak-valley vector determination method and the front-rear following comparison method. After deviation correction with vehicle speed, the corresponding smoothness is obtained.

[0048] Furthermore, the fully loaded single-bridge vector variance algorithm is specifically as follows:

[0049]

[0050] In the formula, This represents the variance of the front axle suspension vector, and it can be negative. This represents the vector variance value of the rear axle suspension, and it can be negative. Referring to Figure 3, the core of the fully loaded single-axle vector variance algorithm is to perform N sets of sampling on the sum of the two suspension pressure sensor values ​​of the front axle and calculate the average of these sets of data. Similarly, N sets of sampling on the sum of the two suspension pressure sensor values ​​of the rear axle are performed, and the average of these sets of data is calculated. Simultaneously, the vector variance is calculated by comparing the sum of the two suspension pressure sensor values ​​of the front axle at the most recent moment with the average of the N sets of front axle data, and the vector variance is calculated by comparing the sum of the two suspension pressure sensor values ​​of the rear axle at the most recent moment with the average of the N sets of rear axle data. This variance is a signed variance value; variance values ​​greater than the average are marked "+", and variance values ​​less than the average are marked "-". The obtained variance data is corrected against the slope value from the tilt meter, and then the peak-valley vector judgment method and the front-rear following comparison method are used to calculate the deviation value. After deviation correction with vehicle speed and load, the corresponding smoothness is obtained.

[0051] S2. Then, using the peak-valley vector determination method and the front-to-back comparison method, the smoothness of the current driving road is calculated. Each mining dump truck transmits its calculated road data and coordinate position information to the platform server, so that the real-time status of the roads in the entire mining area can be known, and work instructions can be issued to the grader in a targeted manner.

[0052] The peak-valley vector determination method is specifically as follows:

[0053] Peak Algorithm

[0054] Valley Algorithm

[0055] In the formula, input[i] and input[j] are the filtered results of the input data, which can be directly sampled data or the output results of other algorithms; n is the number of sampled data, which is dynamically updated (data first-in-first-out); and k is the sampling step size. As shown in Figure 4, the pressure fluctuation of any suspension can be detected and determined by the peak-valley vector judgment method. Based on the vector variance value, not only can the magnitude of the fluctuation be calculated, but also whether the road is convex or concave.

[0056] The aforementioned preceding and following comparison method is specifically as follows:

[0057] Let the peak value of the left-hand suspended wave be input_top[a];

[0058] The peak value of the right-hand suspended wave is input_top[b];

[0059] The peak value of the left dangling wave is input_top[c].

[0060] The peak value of the right dangling wave is input_top[d].

[0061] The value of the left hanging trough is input_bottom[a].

[0062] The value of the right hanging trough is input_bottom[b];

[0063] The value of the left drooping trough is input_bottom[c].

[0064] The value of the right hanging trough is input_bottom[d].

[0065] The peak value of the front axle suspension is input_top[ab];

[0066] The peak value of the rear axle suspension is input_top[cd].

[0067] The front axle suspension trough value is input_bottom[ab]; the rear axle suspension trough value is input_bottom[cd]; then...

[0068]

[0069]

[0070]

[0071] In the above formula, if or This satisfies the ideal criteria for judging bumpy road conditions. However, there are also cases where only one tire has traveled on a bumpy road surface. Therefore, a more general criterion is:

[0072]

[0073] If the condition of a pothole-prone road surface is met, then the final smoothness is obtained by adjusting the λ, δ, k, and η obtained from the above algorithm with the current vehicle speed and current load. A smoothness of 1 is defined as a smooth road surface; a smoothness greater than 1.2 or less than 0.8 indicates that the road surface needs to be smoothed. Referring to Figure 5, the pressure fluctuation of the front and rear axle suspensions can be detected and determined using the front-rear following comparison method. Based on the vector variance value, not only can the magnitude of the fluctuation be calculated, but also whether the road surface is convex or concave.

[0074] The amount of suspension fluctuation is related not only to the load of the mine car but also to the speed of the car. Therefore, the above-mentioned related algorithms need to be corrected for deviation from the curves of each parameter. The deviation curve table can be drawn by statistical analysis of a large amount of data. As an important algorithm of this invention, the method of obtaining this table should also be protected.

[0075] This invention can proactively report and update road surface condition, archive and trace historical road conditions and maintenance data in the mining area, and perform timely and efficient proactive maintenance of roads, thereby improving work efficiency and ensuring safe driving on mining roads.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions or improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic detection and real-time leveling of roads in mining areas, characterized in that, A dynamic detection and real-time leveling system for mining roads is adopted. This system includes: a detection unit for detecting road smoothness information; a control unit for collecting the road smoothness information detected by the detection unit and sending leveling operation instructions to the execution unit; and an execution unit for receiving the work instructions from the control unit and performing road leveling operations. The specific steps are as follows: S1, the mining dump truck obtains its current transportation status through its own load system and runs the corresponding empty-load fluctuation vector variance algorithm and full-load single-axle vector variance algorithm. These algorithms preprocess the suspension data to obtain mapping data between the suspension status and the road status; S2, the peak-valley vector determination method and the front-rear following comparison method are then used to calculate the smoothness of the current driving road. Each mining dump truck transmits its calculated road data and coordinate position information to the platform server, thus obtaining the real-time road status of the entire mining area and issuing targeted work instructions to the grader; the peak-valley vector determination method specifically refers to the peak algorithm. Valley algorithm In the formula, input[i] and input[j] are the filtered results of the input data; n is the number of sampled data, which is dynamically updated, and k is the sampling step size; the aforementioned front-to-back comparison method is as follows: Let the peak value of the front left suspension be input_top[a]; the peak value of the front right suspension be input_top[b]; the peak value of the rear left suspension be input_top[c]; the peak value of the rear right suspension be input_top[d]; the trough value of the front left suspension be input_bottom[a]; the trough value of the front right suspension be input_bottom[b]; the trough value of the rear left suspension be input_bottom[c]; the trough value of the rear right suspension be input_bottom[d]; the peak value of the front axle suspension be input_top[ab]; the peak value of the rear axle suspension be input_top[cd]; the trough value of the front axle suspension be input_bottom[ab]; the trough value of the rear axle suspension be input_bottom[cd]; then ， ， ， In the above formula, P a The pressure value P input for the front left suspension b The pressure value input for the front right suspension, P c The pressure value P input for the rear left suspension d This is the pressure value input for the rear right suspension; if ,or If so, the ideal conditions for judging bumpy road conditions are met. For cases where only one tire has traveled on a bumpy road surface, a more general condition is: Then the condition of a pothole-prone road surface is considered to be met; the results obtained from the above algorithm 、 、 、 After deviation correction based on the current vehicle speed and current load, the final flatness is obtained.

2. The method for dynamic detection and real-time leveling of mining area roads according to claim 1, characterized in that, The detection unit includes several mining dump trucks; each mining dump truck is equipped with a suspension pressure sensor, tilt angle detector, load system and GPS. The mining dump truck collects suspension information, tilt angle information, load information and position information, and uses the peak-valley vector determination method and the front-to-back comparison method to calculate the smoothness of the mining area road on which the mining dump truck travels.

3. The method for dynamic detection and real-time leveling of mining area roads according to claim 2, characterized in that, The control unit includes a platform server; the detection unit collects the suspension data, road smoothness data and current coordinate position information of the mining dump truck to the platform server through the mining area wireless communication network, and the platform server issues working instructions.

4. The method for dynamic detection and real-time leveling of mining area roads according to claim 3, characterized in that, The execution unit includes several graders; the control unit issues work instructions to the grader closest to the work site, and the grader performs the leveling operation.

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