A method for monitoring and warning abnormal motion status of autonomous driving mining trucks

Abstract

The present invention relates to a method for monitoring and warning abnormal motion states of autonomous mining trucks, which belongs to the technical field of equipment health management and solves the problem in the prior art of lacking the ability to warn of abnormal states of autonomous mining trucks. The method comprises: step S1, collecting motion state data of the mining truck under normal running conditions; step S2, extracting key information from the motion state data; step S3, performing sliding average processing on factors affecting the motion state in the extracted key information; step S4, processing the smoothed data to obtain 27 different working conditions; step S5, setting two-level abnormality monitoring thresholds for the data of lateral deviation and heading deviation of each type of working condition; step S6, obtaining real-time data of the mining truck running, comparing it with the two-level abnormality detection thresholds, and determining the motion state level of the real-time data as a judgment result; and step S7, taking corresponding countermeasures based on the judgment result.

Description

Technical Field

[0001] The present invention relates to the field of equipment health management, and more specifically to a method for monitoring and providing early warning of abnormal motion states of autonomous driving mining trucks. Background Art

[0002] With the advancement of science and technology, smart mines have become an important development direction for the modern mining industry. In open-pit mines with complex and ever-changing environments, the introduction of autonomous driving technology and the realization of unmanned transportation can avoid major casualties and minimize safety risks. Currently, autonomous mining trucks are in a rapid development stage. The harsh and complex working environment in mining areas has a significant impact on the performance and condition of transport vehicles, making safety accidents prone to occur. In current engineering applications, potential problems in the operation of mining trucks are difficult to detect and address in a timely manner. Usually, appropriate measures can only be taken when significant abnormalities appear. There is also a lack of early warning capabilities for abnormal vehicle conditions, which increases maintenance costs and downtime, reduces operating efficiency, and seriously restricts the overall production capacity of mining areas and the development of the industry.

[0003] In practical applications in mining areas, effective methods for monitoring the motion of autonomous trucks remain lacking. Existing methods, such as widely used threshold monitoring systems based on manual experience, provide a basic framework for assessing motion status. However, these systems struggle to accurately capture the complex and changing motion characteristics of trucks under varying operating conditions, leading to frequent false positives and missed positives. False positives can lead to unnecessary downtime, impacting production efficiency, and potentially causing operator fatigue and distrust. Missed positives, on the other hand, can mean potential faults go undetected, potentially escalating into serious safety incidents.

[0004] Due to a lack of effective monitoring and early warning methods, mining truck maintenance relies heavily on empirical judgment rather than scientific analysis. This situation not only reduces maintenance efficiency but can also lead to unnecessary maintenance activities or overlook genuine potential failures. While this experience-based maintenance model can address routine issues to a certain extent, it struggles to cope with complex and diverse failure modes and potential risks, easily leading to one-sided and delayed maintenance decisions. This can lead to wasted resources due to excessive maintenance, while also creating safety risks by ignoring genuine potential failures. Summary of the Invention

[0005] In view of this, the present invention provides a method for monitoring and warning the abnormal motion state of an autonomous driving mining truck. In view of the problem that potential abnormal problems in the operation of mining trucks are difficult to be discovered and handled in a timely manner, a multi-level abnormal state monitoring and warning method that takes into account the operating conditions is proposed.

[0006] According to an embodiment of the present invention, a method for monitoring and warning abnormal motion status of an autonomous driving mining truck is provided, comprising the following steps:

[0007] Step S1, collecting motion state data of the mining truck under normal running conditions through laser radar, camera, GPS system or sensor;

[0008] Step S2, extracting key information from the motion state data of a normal sports car, the key information including factors affecting the motion state and manifestations of the motion state, the factors affecting the motion state including slope, road curvature, and vehicle speed, and the manifestations of the motion state including lateral deviation and heading deviation;

[0009] Step S3, performing sliding average processing on the factors affecting the motion state in the extracted key information to obtain smoothed slope, road curvature and vehicle speed data as the smoothed key information;

[0010] Step S4: Divide the obtained smoothed slope, road curvature, and vehicle speed data into three intervals according to their size, record the boundary value of each interval, and permutate and combine the three intervals of the three factors to obtain a total of 27 different working conditions;

[0011] Step S5: Classify the motion state data collected in step S1 under the normal sports car condition according to the intervals divided in step S4. For each of the 27 different working conditions, collect statistics on the lateral deviation and heading deviation data. Use a box plot method to set two levels of abnormal monitoring thresholds for the lateral deviation and heading deviation data of each working condition.

[0012] Step S6, obtaining real-time data of the mining truck running by laser radar, camera, GPS system or sensor, extracting the lateral deviation and heading deviation data in the real-time data, and comparing them with the two-level abnormality detection threshold to determine the motion state level of the real-time data as the judgment result;

[0013] Step S7: Take appropriate countermeasures based on the judgment result obtained in step S6. If the judgment result is normal, no action is required; if it is a potential abnormal state, an abnormality reminder is issued; if it is a significant abnormal state, an abnormality alarm is issued, the fault is reported, and the vehicle is stopped.

[0014] Optionally, step S4 specifically includes the following steps:

[0015] Step S4.1, setting the obtained smoothed slope, road curvature and vehicle speed data as slope s, road curvature c and vehicle speed v respectively;

[0016] Step S4.2: Divide the data ranges of slope, road curvature, and vehicle speed into three consecutive sub-intervals. Set the high-end interval of slope to s1, the middle interval of slope to s2, and the initial interval of slope to s3; the high-end interval of road curvature to c1, the middle interval of road curvature to c2, and the initial interval of road curvature to c3; the high-end interval of vehicle speed to v1, the middle interval of vehicle speed to v2, and the initial interval of vehicle speed to v3.

[0017] In step S4.3, the sub-intervals of slope, road curvature and vehicle speed after the intervals are divided are arranged and combined to obtain 27 different working conditions: (s1, v1, c1), (s2, v1, c1), (s3, v1, c1), (s1, v2, c1), (s2, v2, c1), (s3, v2, c1), (s1, v3, c1), (s2, v3, c1), (s3, v3, c1), (s1, v1, c2), (s2, v1, c2), (s3 ,v1,c2), (s1,v2,c2), (s2,v2,c2), (s3,v2,c2), (s1,v3,c2), (s2,v3,c2), (s3,v3,c2), (s1,v1,c3), ( s2,v1,c3), (s3,v1,c3), (s1,v2,c3), (s2,v2,c3), (s3,v2,c3), (s1,v3,c3), (s2,v3,c3), (s3,v3,c3).

[0018] Optionally, in step S5, the two-level abnormality monitoring threshold includes a first-level threshold and a second-level threshold that is higher than the first-level threshold; wherein, a threshold lower than the first-level threshold represents that the mining truck movement state is normal, a threshold exceeding the first-level threshold but not reaching the second-level threshold represents a potential abnormal state, and a threshold exceeding the second-level threshold represents a significant abnormal state.

[0019] Optionally, step S6 specifically includes the following steps:

[0020] Step S6.1, obtaining real-time data of the mining truck running by using a laser radar, camera, GPS system or sensor;

[0021] Step S6.2, comparing the real-time data with the 27 different working conditions obtained in step S4 to determine the working condition category;

[0022] Step S6.3, according to the working condition category, select the two-level abnormality monitoring threshold corresponding to the category from the results obtained in step S5;

[0023] Step S6.4, extract the data of lateral deviation and heading deviation in the real-time data, and compare them with the two-level abnormal monitoring thresholds of the corresponding category to obtain the motion state level as the judgment result. Among them, if the data of lateral deviation and heading deviation are lower than the first-level threshold, it means that the mining truck is in a normal state; if the data of lateral deviation and heading deviation exceed the first-level threshold but do not reach the second-level threshold, it means that the mining truck is in a potential abnormal state; if the data of lateral deviation and heading deviation exceed the second-level threshold, it means that the mining truck is in a significant abnormal state.

[0024] Compared with the existing technology, the method for abnormal monitoring and early warning of the motion state of an autonomous driving mining truck provided by the implementation of the present invention can effectively monitor the abnormal motion state of the mining truck, greatly reducing the false alarm or missed alarm of the threshold monitoring system set based on manual experience. The set multi-level thresholds can realize the timely discovery and processing of potential problems, have the ability to warn of abnormal conditions, and improve the efficiency of operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. By referring to the drawings, the features and advantages of the present invention can be more clearly understood. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] Figure 1 The present invention provides a flowchart of a method for monitoring and warning abnormal motion status of an autonomous driving mining truck according to an embodiment of the present invention.

[0027] Figure 2 This is a diagram showing the effect of dividing the slope, curvature and vehicle speed in an embodiment of the method for monitoring and warning the abnormal motion state of an autonomous driving mining truck provided by an embodiment of the present invention.

[0028] Figure 3 A schematic diagram of calculating two-level thresholds for lateral deviation and heading deviation based on a box plot method in an embodiment of a method for monitoring and warning abnormal motion status of an autonomous driving mining truck provided by an embodiment of the present invention.

[0029] Figure 4 A flowchart of an embodiment of a method for monitoring and warning abnormal motion status of an autonomous driving mining truck provided by an embodiment of the present invention.

[0030] Figure 5 A schematic diagram of a normal situation in which the abnormality level is determined based on the excess size in an embodiment of a method for monitoring and warning the abnormal motion state of an autonomous driving mining truck provided by an embodiment of the present invention.

[0031] Figure 6 A schematic diagram of an abnormality alert situation in which the abnormality level is determined based on the excess size in an embodiment of the method for monitoring and warning the abnormal motion state of an autonomous driving mining truck provided by an embodiment of the present invention.

[0032] Figure 7 A schematic diagram of an abnormal alarm situation in which the abnormal level is determined based on the excess size in an embodiment of the method for monitoring and warning the abnormal motion state of an autonomous driving mining truck provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features therein can be combined with each other without conflict.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0035] The following describes in detail a method for monitoring and warning abnormal motion status of an autonomous driving mining truck according to an embodiment of the present invention with reference to the accompanying drawings.

[0036] like Figure 1 As shown, according to an embodiment of the present invention, a method for abnormal motion state monitoring and early warning of an autonomous driving mining truck is provided, which includes the following steps.

[0037] Step S1, collects the motion state data of the mining truck under normal running conditions through data collection methods such as laser radar, camera, GPS system, and sensor.

[0038] Step S2 extracts key information from the motion state data of a normal sports car. This key information includes factors affecting the motion state and the manifestation of the motion state. Factors affecting the motion state may include slope, road curvature, vehicle speed, etc.; manifestations of the motion state may include lateral deviation, heading deviation, etc.

[0039] The slope is the ratio of the vertical height h of the mining truck on the road, i.e. the slope surface, to its horizontal width l, i.e. the tangent value of the slope angle (tan∠a value, where ∠a is the angle between the slope and the horizontal plane). The slope has a significant impact on the driving force, braking force, and driving stability of the mining truck. The road curvature describes the sharpness of the road bend, and through a differential definition, it shows the rate of change of the tangent direction angle at a point on the curve with the arc length. The magnitude of the curvature is directly related to the control difficulty and stability requirements of the mining truck when driving on a curve. The speed refers to the speed of the mining truck while driving on the road, and is a physical quantity that measures the speed of the mining truck.

[0040] Lateral deviation refers to the difference between a truck's actual and planned route when traveling along a prescribed route. It reflects the degree of deviation between the truck's actual and planned route and is an important indicator for evaluating a truck's trajectory tracking capabilities. Heading deviation refers to the deviation between the model-predicted heading and the desired trajectory heading within the prediction time domain, reflecting the truck's directional control accuracy.

[0041] By comprehensively considering factors such as slope, road curvature, vehicle speed, lateral deviation and heading deviation, the motion control performance of the mining truck can be fully evaluated, and a strong guarantee can be provided for its safe, stable and accurate driving under different working conditions.

[0042] Step S3 performs a sliding average on the extracted key information, including factors influencing motion, such as slope, road curvature, and vehicle speed, to obtain smoothed key information. To ensure data accuracy and prevent interference from erroneous data, a sliding average is performed on the key information. A sliding window size of 20 is used, meaning that the data at a given moment and the following 20 data points are grouped together, and the average is calculated as the data value at that moment. This smoothed key information includes the smoothed slope, road curvature, and vehicle speed data.

[0043] This implementation uses a moving average method: data points within a specified time window are averaged to smooth the data. The window size significantly affects the smoothing effect; larger windows provide a more pronounced smoothing effect, but this may mask actual data changes. Data smoothing can eliminate noise and outliers, making overall trends and patterns more apparent.

[0044] Data smoothing can effectively eliminate noise and outliers, improve data accuracy, reliability and visualization, and thus enhance the precision of data analysis. It can also more clearly reveal data patterns and trends, providing a reliable foundation for subsequent data analysis and mining. It is conducive to accurately judging abnormal conditions of mining trucks and making corresponding decisions and operations, reducing the occurrence of faults, improving safety levels, and improving the operating efficiency of mining trucks.

[0045] In step S4, the obtained smoothed slope, road curvature, and vehicle speed data are divided into three intervals according to their size, the boundary value of each interval is recorded, and the three intervals of the three factors are arranged and combined to obtain a total of 27 different working conditions. Step S4 specifically includes the following steps.

[0046] Step S4.1, setting the obtained smoothed slope, road curvature and vehicle speed data as slope s, road curvature c and vehicle speed v respectively.

[0047] Step S4.2, set the boundary values ​​of slope, road curvature and vehicle speed, and divide them into three intervals respectively. Set the high-end interval of slope to s1, the middle interval of slope to s2, the initial interval of slope to s3, the high-end interval of road curvature to c1, the middle interval of road curvature to c2, the initial interval of road curvature to c3, the high-end interval of vehicle speed to v1, the middle interval of vehicle speed to v2, and the initial interval of vehicle speed to v3.

[0048] Step S4.3, next, perform permutations and combinations to obtain 27 different working conditions as follows: (s1, v1, c1), (s2, v1, c1), (s3, v1, c1), (s1, v2, c1), (s2, v2, c1), (s3, v2, c1), (s1, v3, c1), (s2, v3, c1), (s3, v3, c1), (s1, v1, c2), (s2, v1, c2), (s3, v1, c2), (s1,v2,c2), (s2,v2,c2), (s3,v2,c2), (s1,v3,c2), (s2,v3,c2), (s3,v3,c2), (s1,v1,c3), (s2,v 1,c3), (s3,v1,c3), (s1,v2,c3), (s2,v2,c3), (s3,v2,c3), (s1,v3,c3), (s2,v3,c3), (s3,v3,c3). See Figure 2 Dividing data into intervals for analysis significantly refines the granularity of analysis compared to overall analysis. By dividing large data sets into smaller intervals, the unique characteristics and changing trends of the data within each interval can be more accurately calculated. This avoids subtle errors that may be overlooked in overall analysis due to data aggregation, providing a more precise basis for decision-making. Because each interval is independent and targeted, it reduces potential errors and interference factors in overall analysis, thereby reaching more accurate and reliable conclusions.

[0049] In step S5, the normal running state data collected in step S1 is classified according to the intervals defined in step S4. For each of the 27 different operating conditions, lateral deviation and heading deviation data are collected. Using a boxplot method, two-level abnormality monitoring thresholds are set for the lateral deviation and heading deviation data for each operating condition. These two-level abnormality monitoring thresholds include a first-level threshold and a second-level threshold that is higher than the first-level threshold. A threshold below the first-level threshold indicates normal mining truck operation; a threshold exceeding the first-level threshold but not reaching the second-level threshold indicates a potential abnormality; and a threshold exceeding the second-level threshold indicates a significant abnormality.

[0050] Specifically, if Figure 3As shown in the figure, the two-level anomaly monitoring threshold setting based on the box plot includes the following steps: the box plot consists of a rectangular box and two extended lines (box whiskers), and the two ends of the box represent the first quartile (Q1) and the third quartile (Q3), respectively, which include the middle 50% of the data set. The length between the first quartile (Q1) and the third quartile (Q3) is the interquartile range (IQR). The whiskers extend to the minimum and maximum values ​​of the data (excluding outliers), and their length is usually 1.5 times the interquartile range (IQR). Outliers are marked outside the whiskers as independent points, revealing outliers in the data. Setting 2 times the interquartile range (IQR) as the anomaly reminder point and 3 times the interquartile range (IQR) as the anomaly alarm point can effectively divide the obtained data.

[0051] Step S6: Real-time data of the mining truck's running is acquired through a laser radar, camera, GPS system, or sensor. The lateral deviation and heading deviation data in this real-time data are extracted and compared with the two-level anomaly detection threshold to determine the motion state level of the real-time data as a judgment result. Each piece of real-time data from the mining truck represents its state at a specific moment. Each piece of real-time data includes data on three influencing factors: vehicle speed, curvature, and slope. The lateral deviation and heading deviation data corresponding to these three influencing factors are also present. After the three influencing factors of a piece of real-time data are classified according to the boundary values, the lateral deviation and heading deviation data corresponding to the influencing factors of the real-time data piece are also passively classified.

[0052] The following references Figure 2 The following example illustrates how real-time data from a mining truck can be categorized by interval. Assume that the vehicle speed v ranges from 0 to 34, the curvature c ranges from 0 to 0.26, and the slope s ranges from -1 to 8 (this example is only used to facilitate understanding of this embodiment and does not limit the present invention in any way):

[0053] c=[0,0.09,0.18,0.26];

[0054] v = [0, 11, 23, 34];

[0055] s=[-1,-0.4,0.2,0.8].

[0056] Set the boundary values ​​and the corresponding sub-intervals: c1=[0,0.09), c2=[0.09,0.18), c3=[0.18,0.26]; v1=[0,11), v2=[11,23), v3=[23,34]; s1=[-1,-0.4), s2=[-0.4,0.2), s3=[0.2,0.8]. Then perform permutations and combinations, and divide the obtained sub-intervals into 27 types of working conditions, such as Figure 2As shown schematically, each grid represents a working condition.

[0057] Example of real-time data list of mining card 1

[0058]

[0059]

[0060] Taking the first data in the list of the above example as an example, the vehicle speed is 19, the slope (gradient) is 0.3 in the v2 interval, the curvature is 0.007 in the s3 interval, and the data is judged to be in the 20th category in the c1 interval. It is necessary to compare the lateral deviation -0.056569 and the heading deviation -0.19644 of the data with the previously calculated thresholds of the lateral deviation and heading deviation of the 20th category.

[0061] Specifically, step S6 includes the following steps.

[0062] Step S6.1, obtain real-time data of the mining truck running through laser radar, camera, GPS system or sensor.

[0063] Step S6.2: Compare the real-time data with the 27 different operating conditions obtained in step S4 to determine the operating condition category. Specifically, the real-time data is compared with the 27 different operating conditions obtained in step S4. Specifically, the slope, road curvature, and vehicle speed values ​​in the key information of the real-time data are compared with the boundary values ​​of the 27 different operating conditions to determine the operating condition category.

[0064] Step S6.3: According to the working status category, select the two-level abnormality monitoring threshold value of the corresponding category from the result obtained in step S5.

[0065] Step S6.4, extract the data of lateral deviation and heading deviation from the key information of real-time data, and compare them with the two-level abnormal monitoring thresholds of the corresponding category to obtain the motion state level as the judgment result. Among them, if the data of lateral deviation and heading deviation are lower than the first-level threshold, it means that the mining truck is in a normal state; if the data of lateral deviation and heading deviation exceed the first-level threshold but do not reach the second-level threshold, it means that the mining truck is in a potential abnormal state; if the data of lateral deviation and heading deviation exceed the second-level threshold, it means that the mining truck is in a significant abnormal state.

[0066] Step S7: Take appropriate countermeasures based on the judgment result obtained in step S6. If the judgment result indicates a normal state, no action is required; if the judgment result indicates a potential abnormal state, an abnormality reminder is issued; if the judgment result indicates a significant abnormal state, an abnormality alarm is issued, the fault is reported, and the vehicle is shut down. In the case of a potential abnormal state, immediate action is not necessary; monitoring is maintained. If the abnormality reminder persists, the vehicle is converted to a significant abnormal state and appropriate action is taken. In the case of a significant abnormal state, an abnormality alarm can also be issued and the mining truck can be dispatched to a maintenance area for inspection.

[0067] The following references Figures 4 to 7 , an exemplary embodiment of a method for monitoring and warning abnormal motion status of an autonomous driving mining truck using an embodiment of the present invention is described in detail.

[0068] For ease of understanding, this embodiment uses the data of a mining truck in a certain mining area in a normal running state as the basic data, and the subsequent real-time running data of the mining truck is used as the experimental data. Figure 1 As shown, in this exemplary embodiment, abnormal status monitoring and early warning are performed on the autonomous driving mining truck, including performing the following steps.

[0069] Step S1: Collect motion data of the mining truck under normal operation using data collection methods such as lidar, cameras, GPS systems, and sensors. The collected motion data of the mining truck under normal operation is stored in a log file. The motion data is extracted from the log file as the basic data of the mining truck. To ensure the accuracy and objectivity of data analysis, a large amount of normal operation data can be collected, including data under different weather conditions and different missions.

[0070] Step S2, extracting key information from the motion state data of a normal sports car obtained in step S1, including: factors affecting the motion state: slope, road curvature, vehicle speed, etc.; manifestations of the motion state: lateral deviation, heading deviation, etc.

[0071] Step S3 performs a sliding average on the factors affecting the motion state within the extracted key information to obtain smoothed key information. To ensure data accuracy and prevent interference from erroneous data, a sliding average is performed on the data with a sliding window size of 20. This means that the data at a given moment and the following 20 data points are grouped together, and the average is calculated as the data value at that moment. This smoothed key information includes smoothed slope, road curvature, and vehicle speed data.

[0072] In step S4, the obtained smoothed slope, road curvature, and vehicle speed data are divided into three intervals according to their size, and the boundary value of each interval is recorded, resulting in a total of 27 different working conditions.

[0073] In step S5, the normal running state data collected in step S1 is classified according to the intervals defined in step S4. For each category, lateral deviation and heading deviation data are collected. Using a boxplot method, two levels of abnormality monitoring thresholds are set for the lateral deviation and heading deviation data, respectively. These two levels of abnormality monitoring thresholds include a first-level threshold and a second-level threshold that is higher than the first-level threshold. A threshold below the first-level threshold indicates normal mining truck motion; a threshold exceeding the first-level threshold but not reaching the second-level threshold indicates a potential abnormality; and a threshold exceeding the second-level threshold indicates a significant abnormality.

[0074] Step S6, acquiring real-time data, and determining the motion state level of the real-time data based on the relationship between the lateral deviation and heading deviation in the real-time data and the threshold value, that is, whether it is abnormal and the abnormality level.

[0075] Specifically, if Figure 4 As shown, real-time abnormality monitoring and early warning of the motion state of an autonomous mining truck are performed, including the following steps: obtaining key information of the real-time data, including slope, road curvature, vehicle speed, lateral deviation, heading deviation, etc., and determining the current working state of the autonomous mining truck based on the 27 different working conditions obtained in step S4; comparing the lateral deviation and heading deviation in the real-time data with the two-level abnormality monitoring thresholds of the corresponding type obtained in step S5 to determine the motion state level of the real-time data, that is, whether it is abnormal and the abnormality level.

[0076] See the accompanying drawings, wherein Figure 5 FIG. 1 is a schematic diagram showing a normal state of real-time data in this embodiment. Figure 6 This is a schematic diagram of an abnormal reminder of real-time data in this embodiment. Figure 7 This is a schematic diagram of an abnormal alarm in real-time data in this embodiment. Figures 5 to 7 The schematic diagrams include four parts from top to bottom, namely, lateral deviation, abnormality of lateral deviation, heading deviation, and abnormality of heading deviation. Figures 5 to 7 The top part of each diagram is the lateral deviation part, where the five lines from top to bottom are the upper limit of abnormal alarm, the upper limit of abnormal reminder, the real-time lateral deviation data, the lower limit of abnormal reminder, and the lower limit of abnormal alarm. Figure 5 As shown in Figure 2, the real-time lateral deviation data are all within the abnormal warning threshold, and this data group is normal data. Figure 6 As shown in the figure, the lateral deviation data line is between the abnormal reminder data line and the abnormal alarm data line, indicating that the data is abnormal data and an abnormal reminder is required. Figure 7 As shown, the lateral deviation data line is outside the abnormal alarm data line, indicating that the data is particularly abnormal data and an abnormal alarm is required.

[0077] exist Figures 5 to 7The second part of each diagram in the figure is the abnormal situation of lateral deviation. Figure 5 As shown in the figure, the data status judgment of the lateral deviation abnormality diagram is all 0, which means it is normal data. Figure 6 As shown in the figure, if the status judgment of the data displayed in the lateral deviation abnormality diagram is 1, it shows that the data is outside the abnormal reminder threshold and within the abnormal warning threshold. Figure 7 As shown, if the lateral deviation abnormality diagram shows that the status judgment of the data is 2, it means that the data is outside the abnormal alarm threshold.

[0078] Step S7, based on the judgment result of step S6, if the data is normal, output normal as the judgment result; otherwise, judge the data as abnormal, and determine the abnormality level according to the degree of abnormality, and then dispatch the mining truck to the maintenance area for inspection.

[0079] Specifically, if Figure 4 As shown, the response measures taken in this embodiment are as follows: if the judgment result is a normal state, no processing is required; in a potential abnormal state, an abnormal reminder is issued, and no immediate measures need to be taken. If the abnormal reminder continues to appear and appears for more than eight minutes within ten minutes, the fault is reported and the vehicle is stopped; in a significant abnormal state, an abnormal alarm is issued, the fault is immediately reported and the vehicle is stopped, or the mining truck is immediately dispatched to the maintenance area for further inspection to find out the cause of the abnormality.

[0080] All of the above optional technical solutions can be combined in any way to form optional embodiments of the present application, and will not be described in detail here.

[0081] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0082] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for monitoring and warning abnormal motion status of an autonomous driving mining truck, characterized in that: The following steps are involved: Step S1, collecting motion state data of the mining truck under normal running conditions through laser radar, camera, GPS system or sensor; Step S2, extracting key information from the motion state data of a normal sports car, the key information including factors affecting the motion state and manifestations of the motion state, the factors affecting the motion state including slope, road curvature, and vehicle speed, and the manifestations of the motion state including lateral deviation and heading deviation; Step S3, performing sliding average processing on the factors affecting the motion state in the extracted key information to obtain smoothed slope, road curvature and vehicle speed data as the smoothed key information; Step S4: Divide the obtained smoothed slope, road curvature, and vehicle speed data into three intervals according to their size, record the boundary value of each interval, and permutate and combine the three intervals of the three factors to obtain a total of 27 different working conditions; Step S5: Classify the motion state data collected in step S1 under the normal sports car condition according to the intervals divided in step S4. For each of the 27 different working conditions, collect statistics on the lateral deviation and heading deviation data. Use a box plot method to set two levels of abnormal monitoring thresholds for the lateral deviation and heading deviation data of each working condition. Step S6: Real-time data of the mining truck's running is obtained through a laser radar, camera, GPS system or sensor, and the lateral deviation and heading deviation data in the real-time data are extracted and compared with the two-level abnormality detection threshold to determine the motion state level of the real-time data as the judgment result; Step S7, take corresponding countermeasures according to the judgment result obtained in step S6. If the judgment result is a normal state, no processing is required; if it is a potential abnormal state, an abnormal reminder is issued; if it is a significant abnormal state, an abnormal alarm is issued, the fault is reported and the vehicle is stopped.

2. The method for abnormal motion state monitoring and early warning of an autonomous driving mining truck according to claim 1 is characterized in that: The step S4 specifically includes the following steps: Step S4.1, setting the obtained smoothed slope, road curvature and vehicle speed data as slope s, road curvature c and vehicle speed v respectively; Step S4.2: Divide the data ranges of slope, road curvature, and vehicle speed into three consecutive sub-intervals. Set the high-end interval of slope to s1, the middle interval of slope to s2, and the initial interval of slope to s3; the high-end interval of road curvature to c1, the middle interval of road curvature to c2, and the initial interval of road curvature to c3; the high-end interval of vehicle speed to v1, the middle interval of vehicle speed to v2, and the initial interval of vehicle speed to v3. In step S4.3, the sub-intervals of slope, road curvature and vehicle speed after the intervals are divided are arranged and combined to obtain 27 different working conditions: (s1, v1, c1), (s2, v1, c1), (s3, v1, c1), (s1, v2, c1), (s2, v2, c1), (s3, v2, c1), (s1, v3, c1), (s2, v3, c1), (s3, v3, c1), (s1, v1, c2), (s2, v1, c2), (s3 ,v1,c2), (s1,v2,c2), (s2,v2,c2), (s3,v2,c2), (s1,v3,c2), (s2,v3,c2), (s3,v3,c2), (s1,v1,c3), ( s2,v1,c3), (s3,v1,c3), (s1,v2,c3), (s2,v2,c3), (s3,v2,c3), (s1,v3,c3), (s2,v3,c3), (s3,v3,c3).

3. The method for abnormal motion state monitoring and early warning of an autonomous driving mining truck according to claim 2 is characterized in that: In the step S5: The two-level abnormality monitoring threshold includes a first-level threshold and a second-level threshold that is higher than the first-level threshold; Among them, below the first level threshold represents that the mining truck movement status is normal, exceeding the first level threshold but not reaching the second level threshold represents a potential abnormal state, and exceeding the second level threshold represents a significant abnormal state.

4. The method for monitoring and warning abnormal motion status of an autonomous driving mining truck according to claim 3 is characterized in that: The step S6 specifically includes the following steps: Step S6.1, obtaining real-time data of the mining truck running by using a laser radar, camera, GPS system or sensor; Step S6.2, comparing the real-time data with the 27 different working conditions obtained in step S4 to determine the working condition category; Step S6.3, according to the working condition category, select the two-level abnormality monitoring threshold corresponding to the category from the results obtained in step S5; Step S6.4, extract the data of lateral deviation and heading deviation in the real-time data, and compare them with the two-level abnormal monitoring thresholds of the corresponding category to obtain the motion state level as the judgment result. Among them, if the data of lateral deviation and heading deviation are lower than the first-level threshold, it means that the mining truck is in a normal state; if the data of lateral deviation and heading deviation exceed the first-level threshold but do not reach the second-level threshold, it means that the mining truck is in a potential abnormal state; if the data of lateral deviation and heading deviation exceed the second-level threshold, it means that the mining truck is in a significant abnormal state.

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