Unmanned mine car driving state detection method and device, electronic equipment and storage medium

Abstract

The application provides a mine unmanned vehicle driving state detection method and device, electronic equipment and storage medium. The method comprises the following steps: determining a target driving road condition of the mine unmanned vehicle; in the case that the target driving road condition is a slope road condition, performing a first abnormality detection task based on vehicle state data of the mine unmanned vehicle to obtain a first detection result; and / or in the case that the target driving road condition is a muddy road condition, performing a second abnormality detection task based on the vehicle state data of the mine unmanned vehicle to obtain a second detection result; the first abnormality detection task comprises a coasting detection subtask and / or an overspeed detection subtask; and the second abnormality detection task comprises a skidding detection subtask and / or a deviation detection subtask. By adopting a detection mode suitable for different road conditions, the detection result can represent whether the mine unmanned vehicle has an abnormal risk and is in an abnormal state under the corresponding road condition, thereby effectively ensuring the safety of the mine unmanned vehicle driving.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, device, electronic device, and storage medium for detecting the driving status of an unmanned mining vehicle. Background Technology

[0002] In related technologies, the anomaly detection methods for unmanned mining trucks generally involve recording driving data, reporting driving data, and visualizing driving data, or detecting a single abnormal state.

[0003] However, the abnormal states that unmanned mining trucks may encounter vary under different operating conditions. Methods that simply record, report, and display driving data cannot achieve real-time anomaly detection, and methods that only detect single abnormal states have poor adaptability to various operating conditions. Therefore, the anomaly detection methods in related technologies cannot effectively guarantee the safety of unmanned mining truck operation. How to effectively ensure the safety of unmanned mining truck operation is a problem that urgently needs to be solved by the industry. Summary of the Invention

[0004] To address the problems existing in the prior art, embodiments of the present invention provide a method, device, electronic device, and storage medium for detecting the driving status of unmanned mining vehicles.

[0005] In a first aspect, the present invention provides a method for detecting the driving status of an unmanned mining truck, comprising:

[0006] Determine the target road conditions where the unmanned mining truck is located;

[0007] When the target driving road condition is a slope, a first anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a first detection result. The first detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the slope condition.

[0008] And / or, if the target driving road condition is a muddy road condition, a second anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a second detection result. The second detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the muddy road condition.

[0009] The first anomaly detection task includes a runaway detection subtask and / or an overspeed detection subtask; the second anomaly detection task includes a skid detection subtask and / or a deviation detection subtask.

[0010] Optionally, according to the unmanned mining truck driving status detection method provided by the present invention, when the first anomaly detection task includes a runaway detection subtask, the vehicle status data includes the handbrake status, throttle opening, brake opening and gear status fed back by the chassis module of the unmanned mining truck, and the historical path point sequence fed back by the positioning module of the unmanned mining truck; the first detection result includes runaway risk detection result and runaway status detection result.

[0011] The first anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a first detection result, including:

[0012] Based on the handbrake status, throttle opening, brake opening and gear status, analyze whether the unmanned mining vehicle continuously meets the preset slippage risk diagnosis conditions within a first preset time period, and obtain the slippage risk detection result;

[0013] Based on the historical path point sequence, analyze whether the unmanned mining vehicle continuously meets the preset slippage state diagnosis conditions within the second preset time period, and obtain the slippage state detection result.

[0014] The preset vehicle slippage risk diagnosis conditions include: the handbrake is in the released state, the throttle opening is less than a preset throttle opening threshold, the brake opening is less than a preset brake opening threshold, and the gear is in drive or neutral.

[0015] The preset diagnostic condition for the slippage state is that the distance between the first and last path points in the current cycle is less than the distance between the second and last path points in the previous cycle.

[0016] The first distance between the first and last path points is obtained by selecting path points belonging to the current period and calculating the distance between the first and last path points based on the historical path point sequence; the second distance between the first and last path points is obtained by selecting path points belonging to the previous period and calculating the distance between the first and last path points based on the historical path point sequence.

[0017] Optionally, according to the unmanned mining truck driving status detection method provided by the present invention, when the first anomaly detection task includes an overspeed detection subtask, the vehicle status data includes the first vehicle speed fed back by the chassis module of the unmanned mining truck, the acceleration fed back by the positioning module of the unmanned mining truck, and the target vehicle speed fed back by the planning module of the unmanned mining truck; the first detection result includes an overspeed risk detection result and an overspeed status detection result.

[0018] The first anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a first detection result, including:

[0019] Based on the first vehicle speed, the acceleration and the target vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset overspeed risk diagnosis conditions within a third preset time period, and obtain the overspeed risk detection result.

[0020] Based on the first vehicle speed and the target vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset overspeed state diagnosis conditions within the fourth preset time period, and obtain the overspeed state detection result.

[0021] The preset overspeed risk diagnosis conditions include: the first vehicle speed is greater than the target vehicle speed and the acceleration is greater than a preset acceleration threshold.

[0022] The preset overspeed condition diagnosis condition is that the difference between the first vehicle speed and the target vehicle speed is greater than a preset speed error threshold.

[0023] Optionally, according to the unmanned mining truck driving status detection method provided by the present invention, when the second anomaly detection task includes a slippage detection subtask, the vehicle status data includes a first vehicle speed fed back by the chassis module of the unmanned mining truck and a second vehicle speed fed back by the positioning module of the unmanned mining truck; the second detection result includes a slippage risk detection result and a slippage status detection result.

[0024] The second anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a second detection result, including:

[0025] Based on the first vehicle speed and the second vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset slippage risk diagnosis conditions within the fifth preset time period, and obtain the slippage risk detection result;

[0026] Based on the first vehicle speed and the second vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset slippage state diagnosis conditions within a sixth preset time period, and obtain the slippage state detection result.

[0027] The preset slip risk diagnosis condition is that the absolute value of the difference between the first cumulative mileage and the second cumulative mileage is greater than the first preset cumulative mileage error threshold. The preset slip state diagnosis condition is that the absolute value of the difference between the first cumulative mileage and the second cumulative mileage is greater than the second preset cumulative mileage error threshold, and the second preset cumulative mileage error threshold is greater than the first preset cumulative mileage error threshold.

[0028] The first cumulative mileage is obtained based on the vehicle's cumulative mileage within a unit time at the first vehicle speed, and the second cumulative mileage is obtained based on the vehicle's cumulative mileage within a unit time at the second vehicle speed.

[0029] Optionally, according to the unmanned mining truck driving status detection method provided by the present invention, when the second anomaly detection task includes a deviation detection subtask, the vehicle status data includes: the target path information fed back by the planning module of the unmanned mining truck and the vehicle position fed back by the positioning module of the unmanned mining truck; the second detection result includes deviation risk detection result and deviation status detection result;

[0030] The second anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a second detection result, including:

[0031] Based on the target path information and the vehicle location, analyze whether the unmanned mining vehicle continuously meets the preset deviation risk diagnosis conditions within the seventh preset time period, and obtain the deviation risk detection result.

[0032] Based on the target path information and the vehicle position, analyze whether the unmanned mining vehicle continuously meets the preset deviation state diagnosis conditions within the eighth preset time period, and obtain the deviation state detection result.

[0033] The preset deviation risk diagnosis condition is that the minimum position distance is greater than the first preset distance error threshold, and the preset deviation state diagnosis condition is that the minimum position distance is greater than the second preset distance error threshold, and the second preset distance error threshold is greater than the first preset distance error threshold.

[0034] The target path information includes multiple target locations, and the minimum location distance is determined by comparing the distance between the vehicle position and each target location.

[0035] Optionally, according to the unmanned mining truck driving status detection method provided by the present invention, when the target driving road condition is a slope, after performing a first anomaly detection task based on the vehicle status data of the unmanned mining truck and obtaining a first detection result, the method further includes:

[0036] Based on the first detection result, determine whether the unmanned mining vehicle has any abnormal risks under the slope conditions and whether it is already in an abnormal state.

[0037] If it is determined that the unmanned mining vehicle poses an abnormal risk under the slope conditions, a first warning will be issued;

[0038] Alternatively, if it is determined that the unmanned mining vehicle is in an abnormal state under the slope conditions, a first alarm will be issued;

[0039] When the target road condition is a muddy road, after performing the second anomaly detection task based on the vehicle status data of the unmanned mining truck and obtaining the second detection result, the method further includes:

[0040] Based on the second detection result, it is determined whether the unmanned mining vehicle has any abnormal risks under the muddy road conditions and whether it is already in an abnormal state.

[0041] If it is determined that the unmanned mining vehicle poses an abnormal risk under the muddy road conditions, a second early warning will be issued;

[0042] Alternatively, if it is determined that the unmanned mining vehicle is in an abnormal state under the muddy road conditions, a second alarm will be issued.

[0043] Secondly, the present invention also provides an unmanned mining truck driving status detection device, comprising:

[0044] The determination module is used to determine the target road conditions where the unmanned mining vehicle is located;

[0045] The acquisition module is used to perform a first anomaly detection task based on the vehicle status data of the unmanned mining vehicle when the target driving road condition is a slope condition, and to obtain a first detection result. The first detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the slope condition.

[0046] And / or, if the target driving road condition is a muddy road condition, a second anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a second detection result. The second detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the muddy road condition.

[0047] The first anomaly detection task includes a runaway detection subtask and / or an overspeed detection subtask; the second anomaly detection task includes a skid detection subtask and / or a deviation detection subtask.

[0048] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the unmanned mining vehicle driving status detection method as described above.

[0049] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the unmanned mining vehicle driving status detection method as described above.

[0050] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the unmanned mining vehicle driving status detection method as described above.

[0051] The unmanned mining truck driving status detection method, device, electronic equipment, and storage medium provided by this invention, by determining the target driving road condition of the unmanned mining truck, can perform slippage detection and / or speeding detection when the target driving road condition is a slope, and can perform skidding detection and / or deviation detection when the target driving road condition is a muddy road. It can adopt detection methods adapted to different road conditions, and the obtained detection results can indicate whether there are abnormal risks or whether the unmanned mining truck is in an abnormal state under the corresponding road conditions, thereby effectively ensuring the safety of unmanned mining truck driving. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0053] Figure 1 This is one of the flowcharts of the unmanned mining truck driving status detection method provided by the present invention;

[0054] Figure 2 This is the second flowchart of the unmanned mining truck driving status detection method provided by the present invention;

[0055] Figure 3 This is the third flowchart of the unmanned mining truck driving status detection method provided by the present invention;

[0056] Figure 4 This is a schematic diagram of the unmanned mining truck driving status detection device provided by the present invention;

[0057] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0059] Figure 1 This is one of the flowcharts illustrating the unmanned mining truck driving status detection method provided by the present invention, such as... Figure 1As shown, the executing entity of the unmanned mining vehicle driving status detection method can be an electronic device, such as an on-board host. The method includes:

[0060] Step 101: Determine the target road conditions where the unmanned mining vehicle is located.

[0061] Specifically, in order to effectively ensure the safety of unmanned mining trucks, the target road conditions in which the unmanned mining trucks are located can be determined, and then a detection method adapted to the specific road conditions can be adopted.

[0062] Optionally, the target road segment where the unmanned mining truck is located can be determined by vehicle positioning. Then, based on a preset road segment road condition information database, the road condition information corresponding to the target road segment can be queried to determine the target driving road condition where the unmanned mining truck is located. The preset road segment road condition information database stores the road condition information corresponding to each road segment.

[0063] Optionally, images of the road section where the unmanned mining truck is located can be collected, and then image recognition can be performed on the road section images to determine the target driving road conditions where the unmanned mining truck is located.

[0064] Optionally, the target driving conditions can be sloping roads and / or muddy roads.

[0065] Step 102: When the target driving road condition is a slope, based on the vehicle status data of the unmanned mining truck, a first anomaly detection task is performed to obtain a first detection result. The first detection result is used to indicate whether the unmanned mining truck has any abnormal risks and whether it is already in an abnormal state under the slope condition.

[0066] And / or, if the target driving road condition is a muddy road condition, a second anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a second detection result. The second detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the muddy road condition.

[0067] The first anomaly detection task includes a runaway detection subtask and / or an overspeed detection subtask; the second anomaly detection task includes a skid detection subtask and / or a deviation detection subtask.

[0068] Specifically, after determining the target road conditions where the unmanned mining truck is located, if the target road conditions are sloped, a slippage detection subtask (i.e., slippage detection) and / or speeding detection subtask (i.e., speeding detection) can be executed based on the vehicle status data of the unmanned mining truck. If the target road conditions are muddy, a slippage detection subtask (i.e., slippage detection) and / or deviation detection subtask (i.e., deviation detection) can be executed. It can adopt detection methods adapted to different road conditions, and the obtained detection results can indicate whether there are abnormal risks or whether the unmanned mining truck is in an abnormal state under the corresponding road conditions.

[0069] It is understandable that the existence of abnormal risks can mean that the unmanned mining vehicle has not yet experienced the specified abnormal situation (such as slippage, overspeed, skidding, or deviation), but there is a high probability that the specified abnormal situation will occur; being in an abnormal state can mean that the unmanned mining vehicle has already experienced the specified abnormal situation.

[0070] Understandably, when the target road condition is an incline, unmanned mining trucks may experience runaway or speeding anomalies. Anomaly detection tasks adapted to incline conditions could include runaway detection subtasks and / or speeding detection subtasks. The runaway detection subtask is used to detect whether the unmanned mining truck has a runaway anomaly risk and whether it is already in a runaway anomaly state; the speeding detection subtask is used to detect whether the unmanned mining truck has a speeding anomaly risk and whether it is already in a speeding anomaly state.

[0071] Understandably, when the target road condition is muddy, unmanned mining trucks may experience slippage and deviation anomalies. Anomaly detection tasks adapted to muddy road conditions can include slippage detection subtasks and / or deviation detection subtasks. The slippage detection subtask is used to detect whether the unmanned mining truck has a slippage anomaly risk and whether it is already in a slippage anomaly state; the deviation detection subtask is used to detect whether the unmanned mining truck has a deviation anomaly risk (i.e., the risk of deviating from the specified path) and whether it is already in a deviation anomaly state (i.e., the abnormal state of deviating from the specified path).

[0072] Optionally, the unmanned mining truck may include a chassis module, a positioning module, and a planning module. The chassis module can be used to control the components in the mining truck chassis and to provide feedback on the vehicle's driving status data and the status data of each component in the chassis. The positioning module can be used to locate the vehicle, record the vehicle's trajectory, and provide feedback on the vehicle's driving status data. The planning module can be used to plan the vehicle's target path and target driving status.

[0073] Optionally, the vehicle status data of the unmanned mining truck may include real-time feedback from the chassis module regarding the handbrake status, throttle opening, brake opening, gear status, and vehicle speed; real-time feedback from the positioning module regarding historical path point sequences, vehicle acceleration, vehicle speed, and vehicle position; and target speed and target path information from the planning module. Based on the aforementioned vehicle status data of the unmanned mining truck, anomaly detection tasks adapted to the target road conditions can be performed to obtain detection results.

[0074] Optionally, the security and real-time performance of the unmanned mining vehicle driving status detection program can be ensured by enabling a separate thread to run.

[0075] The unmanned mining truck driving status detection method provided by this invention determines the target driving road condition of the unmanned mining truck. When the target driving road condition is a slope, it can perform slippage detection and / or speeding detection. When the target driving road condition is a muddy road condition, it can perform slippage detection and / or deviation detection. It can adopt a detection method adapted to different road conditions. The detection results can indicate whether there are abnormal risks or whether the unmanned mining truck is in an abnormal state under the corresponding road conditions, thus effectively ensuring the safety of unmanned mining truck driving.

[0076] Optionally, according to the unmanned mining truck driving status detection method provided by the present invention, when the first anomaly detection task includes a runaway detection subtask, the vehicle status data includes the handbrake status, throttle opening, brake opening and gear status fed back by the chassis module of the unmanned mining truck, and the historical path point sequence fed back by the positioning module of the unmanned mining truck; the first detection result includes runaway risk detection result and runaway status detection result.

[0077] The first anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a first detection result, including:

[0078] Based on the handbrake status, throttle opening, brake opening and gear status, analyze whether the unmanned mining vehicle continuously meets the preset slippage risk diagnosis conditions within a first preset time period, and obtain the slippage risk detection result;

[0079] Based on the historical path point sequence, analyze whether the unmanned mining vehicle continuously meets the preset slippage state diagnosis conditions within the second preset time period, and obtain the slippage state detection result.

[0080] The preset vehicle slippage risk diagnosis conditions include: the handbrake is in the released state, the throttle opening is less than the preset throttle opening threshold, the brake opening is less than the preset brake opening threshold, and the gear is either drive (D) or neutral (N).

[0081] The preset diagnostic condition for the slippage state is that the distance between the first and last path points in the current cycle is less than the distance between the second and last path points in the previous cycle.

[0082] The first distance between the first and last path points is obtained by selecting path points belonging to the current period and calculating the distance between the first and last path points based on the historical path point sequence; the second distance between the first and last path points is obtained by selecting path points belonging to the previous period and calculating the distance between the first and last path points based on the historical path point sequence.

[0083] Specifically, when the target driving condition is a slope, the unmanned mining truck may experience runaway anomalies. Anomaly detection tasks adapted to slope conditions can include a runaway detection subtask. Based on the handbrake status, throttle opening, brake opening, and gear status, the system analyzes whether the unmanned mining truck continuously meets preset runaway risk diagnosis conditions within a first preset time period. If the unmanned mining truck continuously meets the preset runaway risk diagnosis conditions within the first preset time period, it is determined that the unmanned mining truck has a runaway risk; if the unmanned mining truck cannot continuously meet the preset runaway risk diagnosis conditions within the first preset time period, it is determined that the unmanned mining truck does not have a runaway risk.

[0084] Specifically, based on the historical path point sequence, it can be analyzed whether the unmanned mining truck continuously meets the preset slippage state diagnosis conditions within the second preset time period. If the unmanned mining truck continuously meets the preset slippage state diagnosis conditions within the second preset time period, it is determined that the unmanned mining truck is in the slippage state. If the unmanned mining truck cannot continuously meet the preset slippage state diagnosis conditions within the second preset time period, it is determined that the unmanned mining truck is not in the slippage state.

[0085] Understandably, the distance between the first and last path points can be calculated periodically. In the current period, based on the historical path point sequence (which includes multiple historical path points), multiple path points belonging to the current period can be selected, and the distance between the first and last path points (the distance between the first and last path points among the multiple path points) can be calculated for these multiple path points. This yields the first and last path point distance for the current period, i.e., the first first and last path point distance. Similarly, the second first and last path point distance can be the first and last path point distance calculated in the previous period.

[0086] Optionally, when the vehicle stops, the historical path points recorded in the above historical path point sequence can be cleared.

[0087] Understandably, the runaway risk detection result can indicate whether there is a risk of runaway for the unmanned mining truck, and the runaway status detection result can indicate whether the unmanned mining truck is already in a runaway state.

[0088] Optionally, the first preset duration can be 1 to 2 seconds, the second preset duration can be 1 to 2 seconds, the preset throttle opening threshold can be 3% to 5%, and the preset brake opening threshold can be 3% to 5%.

[0089] Therefore, when the target driving condition is a slope, by executing the slippage detection sub-task, it is possible to analyze whether the unmanned mining truck has a slippage risk and whether it is already in a slippage state. The detection results can assist the unmanned mining truck in making early warning or alarm processing for slippage risks or slippage states, which can improve the safety of unmanned vehicles driving on slope conditions.

[0090] Optionally, according to the unmanned mining truck driving status detection method provided by the present invention, when the first anomaly detection task includes an overspeed detection subtask, the vehicle status data includes the first vehicle speed fed back by the chassis module of the unmanned mining truck, the acceleration fed back by the positioning module of the unmanned mining truck, and the target vehicle speed fed back by the planning module of the unmanned mining truck; the first detection result includes an overspeed risk detection result and an overspeed status detection result.

[0091] The first anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a first detection result, including:

[0092] Based on the first vehicle speed, the acceleration and the target vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset overspeed risk diagnosis conditions within a third preset time period, and obtain the overspeed risk detection result.

[0093] Based on the first vehicle speed and the target vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset overspeed state diagnosis conditions within the fourth preset time period, and obtain the overspeed state detection result.

[0094] The preset overspeed risk diagnosis conditions include: the first vehicle speed is greater than the target vehicle speed and the acceleration is greater than a preset acceleration threshold.

[0095] The preset overspeed condition diagnosis condition is that the difference between the first vehicle speed and the target vehicle speed is greater than a preset speed error threshold.

[0096] Specifically, when the target driving condition is a slope, the unmanned mining truck may exhibit speeding anomalies. Anomaly detection tasks adapted to slope conditions can include a speeding detection subtask. Based on the first vehicle speed, acceleration, and target vehicle speed, it can be analyzed whether the unmanned mining truck continuously meets preset speeding risk diagnosis conditions within a third preset time period. If the unmanned mining truck continuously meets the preset speeding risk diagnosis conditions within the third preset time period, it is determined that the unmanned mining truck has a speeding risk; if the unmanned mining truck cannot continuously meet the preset speeding risk diagnosis conditions within the third preset time period, it is determined that the unmanned mining truck does not have a speeding risk.

[0097] Specifically, based on the first vehicle speed and the target vehicle speed, it can be analyzed whether the unmanned mining vehicle continuously meets the preset overspeed state diagnosis conditions within the fourth preset time period. If the unmanned mining vehicle continuously meets the preset overspeed state diagnosis conditions within the fourth preset time period, it is determined that the unmanned mining vehicle is in an overspeed state. If the unmanned mining vehicle cannot continuously meet the preset overspeed state diagnosis conditions within the fourth preset time period, it is determined that the unmanned mining vehicle is not in an overspeed state.

[0098] Understandably, the speeding risk detection result can indicate whether the unmanned mining truck is at risk of speeding, and the speeding status detection result can indicate whether the unmanned mining truck is already in a speeding state.

[0099] Optionally, the third preset duration can be in the range of 1 to 2 seconds, the fourth preset duration can be in the range of 1 to 2 seconds, and the preset acceleration threshold can be in the range of 0.8 m / s². 2 ~1.2m / s 2 The preset speed error threshold can be set in the range of 1 m / s to 1.5 m / s.

[0100] Therefore, when the target driving road condition is a slope, by executing the overspeed detection sub-task, it is possible to analyze whether the unmanned mining truck has an overspeed risk and whether it is already in an overspeed state. The detection results can assist the unmanned mining truck in making early warning or alarm processing for overspeed risks or overspeed states, which can improve the safety of unmanned vehicles driving on slope conditions.

[0101] Optionally, according to the unmanned mining truck driving status detection method provided by the present invention, when the second anomaly detection task includes a slippage detection subtask, the vehicle status data includes a first vehicle speed fed back by the chassis module of the unmanned mining truck and a second vehicle speed fed back by the positioning module of the unmanned mining truck; the second detection result includes a slippage risk detection result and a slippage status detection result.

[0102] The second anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a second detection result, including:

[0103] Based on the first vehicle speed and the second vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset slippage risk diagnosis conditions within the fifth preset time period, and obtain the slippage risk detection result;

[0104] Based on the first vehicle speed and the second vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset slippage state diagnosis conditions within a sixth preset time period, and obtain the slippage state detection result.

[0105] The preset slip risk diagnosis condition is that the absolute value of the difference between the first cumulative mileage and the second cumulative mileage is greater than the first preset cumulative mileage error threshold. The preset slip state diagnosis condition is that the absolute value of the difference between the first cumulative mileage and the second cumulative mileage is greater than the second preset cumulative mileage error threshold, and the second preset cumulative mileage error threshold is greater than the first preset cumulative mileage error threshold.

[0106] The first cumulative mileage is obtained based on the vehicle's cumulative mileage within a unit time at the first vehicle speed, and the second cumulative mileage is obtained based on the vehicle's cumulative mileage within a unit time at the second vehicle speed.

[0107] Specifically, when the target road condition is a muddy, uneven surface, the unmanned mining truck may experience slippage. Anomaly detection tasks adapted to muddy road conditions can include a slippage detection subtask. Based on the first and second vehicle speeds, it can be analyzed whether the unmanned mining truck continuously meets the preset slippage risk diagnosis conditions within a fifth preset time period. If the unmanned mining truck continuously meets the preset slippage risk diagnosis conditions within the fifth preset time period, it is determined that the unmanned mining truck has a slippage risk; if the unmanned mining truck cannot continuously meet the preset slippage risk diagnosis conditions within the fifth preset time period, it is determined that the unmanned mining truck does not have a slippage risk.

[0108] Specifically, based on the first vehicle speed and the second vehicle speed, it can be analyzed whether the unmanned mining truck continuously meets the preset slippage state diagnosis conditions within the sixth preset time period. If the unmanned mining truck continuously meets the preset slippage state diagnosis conditions within the sixth preset time period, it is determined that the unmanned mining truck is in a slippage state. If the unmanned mining truck cannot continuously meet the preset slippage state diagnosis conditions within the sixth preset time period, it is determined that the unmanned mining truck is not in a slippage state.

[0109] Understandably, the slippage risk detection result can indicate whether the unmanned mining truck is at risk of slippage, and the slippage status detection result can indicate whether the unmanned mining truck is already in a slippage state.

[0110] Alternatively, the first and second accumulated mileage can be calculated using the following formula:

[0111]

[0112] Among them, vchassis This indicates the first vehicle speed reported by the chassis module, v imu This represents the second vehicle speed reported by the positioning module, s chassis This represents the first cumulative mileage calculated based on the vehicle speed fed back by the chassis module, s imu This represents the second cumulative mileage calculated based on the vehicle speed fed back by the positioning module, and t represents the preset unit time used to calculate the cumulative mileage.

[0113] Optionally, the fifth preset duration can be 1 to 2 seconds, the sixth preset duration can be 1 to 2 seconds, the first preset cumulative mileage error threshold can be 0.5 to 1.0 m, and the second preset cumulative mileage error threshold can be 1.0 to 2.0 m.

[0114] Therefore, when the target driving road condition is a muddy road, by performing the slip detection sub-task, it is possible to analyze whether the unmanned mining truck has a slip risk and whether it is already in a slip state. The detection results can assist the unmanned mining truck in making early warning or alarm processing for slip risk or slip state, which can improve the safety of unmanned vehicles driving in muddy road conditions.

[0115] Optionally, according to the unmanned mining truck driving status detection method provided by the present invention, when the second anomaly detection task includes a deviation detection subtask, the vehicle status data includes: the target path information fed back by the planning module of the unmanned mining truck and the vehicle position fed back by the positioning module of the unmanned mining truck; the second detection result includes deviation risk detection result and deviation status detection result;

[0116] The second anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a second detection result, including:

[0117] Based on the target path information and the vehicle location, analyze whether the unmanned mining vehicle continuously meets the preset deviation risk diagnosis conditions within the seventh preset time period, and obtain the deviation risk detection result.

[0118] Based on the target path information and the vehicle position, analyze whether the unmanned mining vehicle continuously meets the preset deviation state diagnosis conditions within the eighth preset time period, and obtain the deviation state detection result.

[0119] The preset deviation risk diagnosis condition is that the minimum position distance is greater than the first preset distance error threshold, and the preset deviation state diagnosis condition is that the minimum position distance is greater than the second preset distance error threshold, and the second preset distance error threshold is greater than the first preset distance error threshold.

[0120] The target path information includes multiple target locations, and the minimum location distance is determined by comparing the distance between the vehicle position and each target location.

[0121] Specifically, when the target road condition is muddy, the unmanned mining truck may deviate from its designated path. Anomaly detection tasks adapted to muddy road conditions can include a deviation detection subtask. Based on the target path information and vehicle position, it can be analyzed whether the unmanned mining truck continuously meets the preset deviation risk diagnosis conditions within a seventh preset time period. If the unmanned mining truck continuously meets the preset deviation risk diagnosis conditions within the seventh preset time period, it is determined that the unmanned mining truck has a deviation risk; if the unmanned mining truck cannot continuously meet the preset deviation risk diagnosis conditions within the seventh preset time period, it is determined that the unmanned mining truck does not have a deviation risk.

[0122] Specifically, based on the target path information and vehicle location, it can be analyzed whether the unmanned mining truck continuously meets the preset deviation state diagnosis conditions within the eighth preset time period. If the unmanned mining truck continuously meets the preset deviation state diagnosis conditions within the eighth preset time period, it is determined that the unmanned mining truck is in a deviation state. If the unmanned mining truck cannot continuously meet the preset deviation state diagnosis conditions within the eighth preset time period, it is determined that the unmanned mining truck is not in a deviation state.

[0123] Understandably, deviation risk detection results can indicate whether the unmanned mining truck is at risk of deviation, while deviation state detection results can indicate whether the unmanned mining truck is already in a deviation state.

[0124] Optionally, the seventh preset duration can be 1 to 2 seconds, the eighth preset duration can be 1 to 2 seconds, the first preset distance error threshold can be 0.5 to 1.0 m, and the second preset distance error threshold can be 1.5 to 2.0 m.

[0125] Therefore, when the target driving road condition is a muddy road, by executing the deviation detection sub-task, it is possible to analyze whether the unmanned mining truck has a deviation risk and whether it is already in a deviation state. The detection results can assist the unmanned mining truck in making early warning or alarm processing for deviation risks or deviation states, which can improve the safety of unmanned vehicles driving in muddy road conditions.

[0126] Optionally, according to the unmanned mining truck driving status detection method provided by the present invention, when the target driving road condition is a slope, after performing a first anomaly detection task based on the vehicle status data of the unmanned mining truck and obtaining a first detection result, the method further includes:

[0127] Based on the first detection result, determine whether the unmanned mining vehicle has any abnormal risks under the slope conditions and whether it is already in an abnormal state.

[0128] If it is determined that the unmanned mining vehicle poses an abnormal risk under the slope conditions, a first warning will be issued;

[0129] Alternatively, if it is determined that the unmanned mining vehicle is in an abnormal state under the slope conditions, a first alarm will be issued;

[0130] When the target road condition is a muddy road, after performing the second anomaly detection task based on the vehicle status data of the unmanned mining truck and obtaining the second detection result, the method further includes:

[0131] Based on the second detection result, it is determined whether the unmanned mining vehicle has any abnormal risks under the muddy road conditions and whether it is already in an abnormal state.

[0132] If it is determined that the unmanned mining vehicle poses an abnormal risk under the muddy road conditions, a second early warning will be issued;

[0133] Alternatively, if it is determined that the unmanned mining vehicle is in an abnormal state under the muddy road conditions, a second alarm will be issued.

[0134] Specifically, when the target driving condition is a slope, by performing the first anomaly detection task, it is possible to analyze whether the unmanned mining vehicle has specific abnormal risks (such as the risk of slipping or speeding) and whether it is already in a specific abnormal state (such as slipping or speeding). The detection results can assist the unmanned mining vehicle in providing the first warning or alarm for specific abnormal risks or states, thereby improving the safety of unmanned vehicles driving on slopes.

[0135] Specifically, when the target driving road condition is a muddy road condition, by performing a second anomaly detection task, it is possible to analyze whether the unmanned mining vehicle has specific abnormal risks (such as slippage risk or deviation risk) and whether it is already in a specific abnormal state (such as slippage state or deviation state). The detection results can assist the unmanned mining vehicle in providing a second early warning or a second alarm for specific abnormal risks or specific abnormal states, which can improve the safety of unmanned vehicles driving in muddy road conditions.

[0136] Optionally, Figure 2 This is the second flowchart illustrating the unmanned mining truck driving status detection method provided by the present invention, as shown below. Figure 2 As shown, the unmanned mining truck driving status detection method includes steps 201 to 204:

[0137] Step 201: Determine the target road conditions where the unmanned mining vehicle is located;

[0138] Step 202: When the target driving road condition is a slope, based on the handbrake status, throttle opening, brake opening and gear status, analyze whether the unmanned mining vehicle continuously meets the preset slippage risk diagnosis conditions within the first preset time period, and obtain the slippage risk detection results.

[0139] Step 203: Based on the runaway risk detection results, determine whether the unmanned mining truck has a runaway risk on a slope.

[0140] Step 204: If it is determined that there is a risk of the unmanned mining truck slipping on a slope, a slippage warning will be issued.

[0141] Optionally, Figure 3 This is the third flowchart of the unmanned mining truck driving status detection method provided by the present invention, as shown below. Figure 3 As shown, the unmanned mining truck driving status detection method includes steps 301 to 303:

[0142] Step 301: Determine the target road conditions where the unmanned mining vehicle is located;

[0143] Step 302: If the target driving road condition is a slope, execute the slippage detection subtask and the speeding detection subtask to obtain the first detection result; and / or, if the target driving road condition is a muddy road condition, execute the slippage detection subtask and the deviation detection subtask to obtain the second detection result.

[0144] Step 303: If the target driving road condition is a slope, a warning judgment and an alarm judgment are made based on the first detection result; and / or, if the target driving road condition is a muddy road condition, a warning judgment and an alarm judgment are made based on the second detection result.

[0145] The unmanned mining truck driving status detection method provided by this invention determines the target driving road condition of the unmanned mining truck. When the target driving road condition is a slope, it can perform slippage detection and / or speeding detection. When the target driving road condition is a muddy road condition, it can perform slippage detection and / or deviation detection. It can adopt a detection method adapted to different road conditions. The detection results can indicate whether there are abnormal risks or whether the unmanned mining truck is in an abnormal state under the corresponding road conditions, thus effectively ensuring the safety of unmanned mining truck driving.

[0146] The unmanned mining truck driving status detection device provided by the present invention is described below. The unmanned mining truck driving status detection device described below and the unmanned mining truck driving status detection method described above can be referred to in correspondence.

[0147] Figure 4 This is a schematic diagram of the unmanned mining truck driving status detection device provided by the present invention, as shown below. Figure 4As shown, the device includes: a determining module 401 and an acquiring module 402, wherein:

[0148] The determination module 401 is used to determine the target road conditions where the unmanned mining vehicle is located.

[0149] The acquisition module 402 is used to perform a first anomaly detection task and acquire a first detection result based on the vehicle status data of the unmanned mining vehicle when the target driving road condition is a slope condition. The first detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the slope condition.

[0150] And / or, if the target driving road condition is a muddy road condition, a second anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a second detection result. The second detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the muddy road condition.

[0151] The first anomaly detection task includes a runaway detection subtask and / or an overspeed detection subtask; the second anomaly detection task includes a skid detection subtask and / or a deviation detection subtask.

[0152] The unmanned mining truck driving status detection device provided by this invention can detect slippage and / or speeding when the target driving condition is a slope, and can detect skidding and / or deviation when the target driving condition is a muddy road. It can adopt detection methods adapted to different road conditions, and the detection results can indicate whether there are abnormal risks or whether the unmanned mining truck is in an abnormal state under the corresponding road conditions, thus effectively ensuring the safety of unmanned mining truck driving.

[0153] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute an unmanned mining vehicle driving status detection method, which includes:

[0154] Determine the target road conditions where the unmanned mining truck is located;

[0155] When the target driving road condition is a slope, a first anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a first detection result. The first detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the slope condition.

[0156] And / or, if the target driving road condition is a muddy road condition, a second anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a second detection result. The second detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the muddy road condition.

[0157] The first anomaly detection task includes a runaway detection subtask and / or an overspeed detection subtask; the second anomaly detection task includes a skid detection subtask and / or a deviation detection subtask.

[0158] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0159] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the unmanned mining truck driving status detection method provided by the above methods, the method including:

[0160] Determine the target road conditions where the unmanned mining truck is located;

[0161] When the target driving road condition is a slope, a first anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a first detection result. The first detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the slope condition.

[0162] And / or, if the target driving road condition is a muddy road condition, a second anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a second detection result. The second detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the muddy road condition.

[0163] The first anomaly detection task includes a runaway detection subtask and / or an overspeed detection subtask; the second anomaly detection task includes a skid detection subtask and / or a deviation detection subtask.

[0164] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the unmanned mining truck driving state detection method provided by the above methods, the method comprising:

[0165] Determine the target road conditions where the unmanned mining truck is located;

[0166] When the target driving road condition is a slope, a first anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a first detection result. The first detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the slope condition.

[0167] And / or, if the target driving road condition is a muddy road condition, a second anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a second detection result. The second detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the muddy road condition.

[0168] The first anomaly detection task includes a runaway detection subtask and / or an overspeed detection subtask; the second anomaly detection task includes a skid detection subtask and / or a deviation detection subtask.

[0169] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0170] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting the driving status of an unmanned mining vehicle, characterized in that, include: Determine the target road conditions where the unmanned mining truck is located; When the target driving road condition is a slope, based on the vehicle status data of the unmanned mining vehicle, a first anomaly detection task is performed to obtain a first detection result. The first detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the slope condition. And / or, if the target driving road condition is a muddy road condition, a second anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a second detection result. The second detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the muddy road condition. The first anomaly detection task includes a runaway detection subtask and / or an overspeed detection subtask; The second anomaly detection task includes a slip detection subtask and / or a deviation detection subtask; When the first anomaly detection task includes a runaway detection subtask, the vehicle status data includes the handbrake status, throttle opening, brake opening, and gear status fed back by the chassis module of the unmanned mining truck, as well as the historical path point sequence fed back by the positioning module of the unmanned mining truck; the first detection result includes the runaway risk detection result and the runaway status detection result. The first anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a first detection result, including: Based on the handbrake status, throttle opening, brake opening and gear status, analyze whether the unmanned mining vehicle continuously meets the preset slippage risk diagnosis conditions within a first preset time period, and obtain the slippage risk detection result; Based on the historical path point sequence, analyze whether the unmanned mining vehicle continuously meets the preset slippage state diagnosis conditions within the second preset time period, and obtain the slippage state detection result. The preset vehicle slippage risk diagnosis conditions include: the handbrake is in the released state, the throttle opening is less than a preset throttle opening threshold, the brake opening is less than a preset brake opening threshold, and the gear is in drive or neutral. The preset diagnostic condition for the slippage state is that the distance between the first and last path points in the current cycle is less than the distance between the second and last path points in the previous cycle. The first distance between the first and last path points is obtained by selecting path points belonging to the current period and calculating the distance between the first and last path points based on the historical path point sequence; the second distance between the first and last path points is obtained by selecting path points belonging to the previous period and calculating the distance between the first and last path points based on the historical path point sequence. When the first anomaly detection task includes an overspeed detection subtask, the vehicle status data includes the first vehicle speed fed back by the chassis module of the unmanned mining truck, the acceleration fed back by the positioning module of the unmanned mining truck, and the target vehicle speed fed back by the planning module of the unmanned mining truck; the first detection result includes the overspeed risk detection result and the overspeed status detection result. The first anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a first detection result, including: Based on the first vehicle speed, the acceleration and the target vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset overspeed risk diagnosis conditions within a third preset time period, and obtain the overspeed risk detection result. Based on the first vehicle speed and the target vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset overspeed state diagnosis conditions within the fourth preset time period, and obtain the overspeed state detection result. The preset overspeed risk diagnosis conditions include: the first vehicle speed is greater than the target vehicle speed and the acceleration is greater than a preset acceleration threshold. The preset overspeed condition diagnosis condition is that the difference between the first vehicle speed and the target vehicle speed is greater than a preset speed error threshold. When the second anomaly detection task includes a slippage detection subtask, the vehicle status data includes the first vehicle speed fed back by the chassis module of the unmanned mining truck and the second vehicle speed fed back by the positioning module of the unmanned mining truck; the second detection result includes slippage risk detection result and slippage state detection result. The second anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a second detection result, including: Based on the first vehicle speed and the second vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset slippage risk diagnosis conditions within the fifth preset time period, and obtain the slippage risk detection result; Based on the first vehicle speed and the second vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset slippage state diagnosis conditions within a sixth preset time period, and obtain the slippage state detection result. The preset slip risk diagnosis condition is that the absolute value of the difference between the first cumulative mileage and the second cumulative mileage is greater than the first preset cumulative mileage error threshold. The preset slip state diagnosis condition is that the absolute value of the difference between the first cumulative mileage and the second cumulative mileage is greater than the second preset cumulative mileage error threshold, and the second preset cumulative mileage error threshold is greater than the first preset cumulative mileage error threshold. The first cumulative mileage is obtained based on the vehicle's cumulative mileage per unit time calculated at the first vehicle speed, and the second cumulative mileage is obtained based on the vehicle's cumulative mileage per unit time calculated at the second vehicle speed. When the second anomaly detection task includes a deviation detection subtask, the vehicle status data includes: the target path information fed back by the planning module of the unmanned mining truck and the vehicle position fed back by the positioning module of the unmanned mining truck; the second detection result includes deviation risk detection result and deviation status detection result; The second anomaly detection task, based on the vehicle status data of the unmanned mining truck, is performed to obtain a second detection result, including: Based on the target path information and the vehicle location, analyze whether the unmanned mining vehicle continuously meets the preset deviation risk diagnosis conditions within the seventh preset time period, and obtain the deviation risk detection result. Based on the target path information and the vehicle position, analyze whether the unmanned mining vehicle continuously meets the preset deviation state diagnosis conditions within the eighth preset time period, and obtain the deviation state detection result. The preset deviation risk diagnosis condition is that the minimum position distance is greater than the first preset distance error threshold, and the preset deviation state diagnosis condition is that the minimum position distance is greater than the second preset distance error threshold, and the second preset distance error threshold is greater than the first preset distance error threshold. The target path information includes multiple target locations, and the minimum location distance is determined by comparing the distance between the vehicle position and each target location.

2. The method for detecting the driving status of an unmanned mining vehicle according to claim 1, characterized in that, When the target road condition is a slope, after performing the first anomaly detection task based on the vehicle status data of the unmanned mining truck and obtaining the first detection result, the method further includes: Based on the first detection result, determine whether the unmanned mining vehicle has any abnormal risks under the slope conditions and whether it is already in an abnormal state. If it is determined that the unmanned mining vehicle poses an abnormal risk under the slope conditions, a first warning will be issued; Alternatively, if it is determined that the unmanned mining vehicle is in an abnormal state under the slope conditions, a first alarm will be issued; When the target road condition is a muddy road, after performing the second anomaly detection task based on the vehicle status data of the unmanned mining truck and obtaining the second detection result, the method further includes: Based on the second detection result, it is determined whether the unmanned mining vehicle has any abnormal risks under the muddy road conditions and whether it is already in an abnormal state. If it is determined that the unmanned mining vehicle poses an abnormal risk under the muddy road conditions, a second early warning will be issued; Alternatively, if it is determined that the unmanned mining vehicle is in an abnormal state under the muddy road conditions, a second alarm will be issued.

3. A device for detecting the driving status of an unmanned mining truck, characterized in that, include: The determination module is used to determine the target road conditions where the unmanned mining vehicle is located; The acquisition module is used to perform a first anomaly detection task based on the vehicle status data of the unmanned mining vehicle when the target driving road condition is a slope condition, and to obtain a first detection result. The first detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the slope condition. And / or, if the target driving road condition is a muddy road condition, a second anomaly detection task is performed based on the vehicle status data of the unmanned mining vehicle to obtain a second detection result. The second detection result is used to indicate whether the unmanned mining vehicle has any abnormal risks and whether it is already in an abnormal state under the muddy road condition. The first anomaly detection task includes a runaway detection subtask and / or an overspeed detection subtask; The second anomaly detection task includes a slip detection subtask and / or a deviation detection subtask; When the first anomaly detection task includes a runaway detection subtask, the vehicle status data includes the handbrake status, throttle opening, brake opening, and gear status fed back by the chassis module of the unmanned mining truck, as well as the historical path point sequence fed back by the positioning module of the unmanned mining truck; the first detection result includes the runaway risk detection result and the runaway status detection result. The acquisition module is specifically used for: Based on the handbrake status, throttle opening, brake opening and gear status, analyze whether the unmanned mining vehicle continuously meets the preset slippage risk diagnosis conditions within a first preset time period, and obtain the slippage risk detection result; Based on the historical path point sequence, analyze whether the unmanned mining vehicle continuously meets the preset slippage state diagnosis conditions within the second preset time period, and obtain the slippage state detection result. The preset vehicle slippage risk diagnosis conditions include: the handbrake is in the released state, the throttle opening is less than a preset throttle opening threshold, the brake opening is less than a preset brake opening threshold, and the gear is in drive or neutral. The preset diagnostic condition for the slippage state is that the distance between the first and last path points in the current cycle is less than the distance between the second and last path points in the previous cycle. The first distance between the first and last path points is obtained by selecting path points belonging to the current period and calculating the distance between the first and last path points based on the historical path point sequence; the second distance between the first and last path points is obtained by selecting path points belonging to the previous period and calculating the distance between the first and last path points based on the historical path point sequence. When the first anomaly detection task includes an overspeed detection subtask, the vehicle status data includes the first vehicle speed fed back by the chassis module of the unmanned mining truck, the acceleration fed back by the positioning module of the unmanned mining truck, and the target vehicle speed fed back by the planning module of the unmanned mining truck; the first detection result includes the overspeed risk detection result and the overspeed status detection result. The acquisition module is specifically used for: Based on the first vehicle speed, the acceleration and the target vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset overspeed risk diagnosis conditions within a third preset time period, and obtain the overspeed risk detection result. Based on the first vehicle speed and the target vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset overspeed state diagnosis conditions within the fourth preset time period, and obtain the overspeed state detection result. The preset overspeed risk diagnosis conditions include: the first vehicle speed is greater than the target vehicle speed and the acceleration is greater than a preset acceleration threshold. The preset overspeed condition diagnosis condition is that the difference between the first vehicle speed and the target vehicle speed is greater than a preset speed error threshold. When the second anomaly detection task includes a slippage detection subtask, the vehicle status data includes the first vehicle speed fed back by the chassis module of the unmanned mining truck and the second vehicle speed fed back by the positioning module of the unmanned mining truck; the second detection result includes slippage risk detection result and slippage state detection result. The acquisition module is specifically used for: Based on the first vehicle speed and the second vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset slippage risk diagnosis conditions within the fifth preset time period, and obtain the slippage risk detection result; Based on the first vehicle speed and the second vehicle speed, analyze whether the unmanned mining vehicle continuously meets the preset slippage state diagnosis conditions within a sixth preset time period, and obtain the slippage state detection result. The preset slip risk diagnosis condition is that the absolute value of the difference between the first cumulative mileage and the second cumulative mileage is greater than the first preset cumulative mileage error threshold. The preset slip state diagnosis condition is that the absolute value of the difference between the first cumulative mileage and the second cumulative mileage is greater than the second preset cumulative mileage error threshold, and the second preset cumulative mileage error threshold is greater than the first preset cumulative mileage error threshold. The first cumulative mileage is obtained based on the vehicle's cumulative mileage per unit time calculated at the first vehicle speed, and the second cumulative mileage is obtained based on the vehicle's cumulative mileage per unit time calculated at the second vehicle speed. When the second anomaly detection task includes a deviation detection subtask, the vehicle status data includes: the target path information fed back by the planning module of the unmanned mining truck and the vehicle position fed back by the positioning module of the unmanned mining truck; the second detection result includes deviation risk detection result and deviation status detection result; The acquisition module is specifically used for: Based on the target path information and the vehicle location, analyze whether the unmanned mining vehicle continuously meets the preset deviation risk diagnosis conditions within the seventh preset time period, and obtain the deviation risk detection result. Based on the target path information and the vehicle position, analyze whether the unmanned mining vehicle continuously meets the preset deviation state diagnosis conditions within the eighth preset time period, and obtain the deviation state detection result. The preset deviation risk diagnosis condition is that the minimum position distance is greater than the first preset distance error threshold, and the preset deviation state diagnosis condition is that the minimum position distance is greater than the second preset distance error threshold, and the second preset distance error threshold is greater than the first preset distance error threshold. The target path information includes multiple target locations, and the minimum location distance is determined by comparing the distance between the vehicle position and each target location.

4. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the unmanned mining vehicle driving status detection method as described in any one of claims 1 to 2.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the unmanned mining vehicle driving status detection method as described in any one of claims 1 to 2.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the unmanned mining vehicle driving status detection method as described in any one of claims 1 to 2.

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