Mine unmanned vehicle dispatching self-checking method and system

Abstract

The present application relates to the technical field of vehicle self-checking, and provides a mine unmanned vehicle starting self-checking method and system, the method comprising: determining a self-checking level according to a vehicle working condition in a starting stage, obtaining corresponding first vehicle parameters, and comparing the first vehicle parameters with a vehicle standard driving capability curve to determine a driving system self-checking result; obtaining second vehicle parameters by decelerating through rear pressing braking operation parameters, and comparing the second vehicle parameters with a vehicle standard braking capability curve to determine a braking system self-checking result; obtaining third vehicle parameters by steering through rear pressing steering operation parameters, and comparing the third vehicle parameters with a vehicle standard steering capability curve to determine a steering system self-checking result; and executing a production task. The present application verifies the actual motion capability of the vehicle in the starting stage through time sequence comparison of the standard capability curve, and improves the safety of the mine unmanned vehicle.

Description

Technical Field

[0001] This invention relates to the field of vehicle self-inspection technology, and in particular to a method and system for self-inspection of unmanned mining vehicles before departure. Background Technology

[0002] Unmanned transportation in mining areas is a crucial component of smart mine construction. Mining areas typically feature undulating terrain, long uphill climbs with maximum gradients of around 8%, harsh weather conditions, and the need for continuous operation even in rain and snow. Vehicles operate 24 / 7 under high intensity and harsh environments, requiring complete automation. As unmanned wide-body mining vehicles, their primary functions include driving, braking, and steering. Any malfunction in these systems can lead to loss of control, and since the vehicle is unmanned, such failures cannot be detected promptly, resulting in further damage and compromised safety.

[0003] In traditional solutions, there are various implementation methods for the detection of unmanned mining vehicles. One type of solution focuses on the status detection of components when the vehicle is stationary. For example, the controller sends test commands to actuators such as motors and sensors to detect the electrical connection status, command response delay, and consistency of feedback signals. This type of solution can verify the continuity and response of the control loop before the vehicle starts, but it cannot detect whether the output capacity of each actuator meets the operating conditions during actual vehicle movement. Mining roads have complex operating conditions such as long slopes and slippery surfaces. Components that respond normally under static conditions may experience problems such as insufficient torque, brake fade, and steering lag when under load. Static detection is insufficient to identify such potential problems.

[0004] Another type of solution involves functional detection during the dynamic operation of the vehicle, such as comparing the actual action result with a preset target value after controlling the vehicle to perform a specific action. However, in the complex working conditions of mining areas, preset target values ​​are difficult to adapt to performance fluctuations under different working conditions, and the detection dimension is usually limited to a single parameter, without considering the different requirements of vehicle performance for different working scenarios in mining areas.

[0005] In the practical application of the above-mentioned traditional solutions during the launch phase of unmanned vehicles in mining areas, there is a technical problem of how to accurately verify the actual motion capabilities of the three major systems of drive, braking, and steering under complex working conditions. Summary of the Invention

[0006] The purpose of this invention is to solve at least one technical problem in the background art and to provide a method and system for self-inspection of unmanned mining vehicles before departure.

[0007] To achieve the above objectives, the present invention provides a self-inspection method for starting an unmanned mining vehicle, comprising: During the departure phase of the unmanned mining vehicle, the self-inspection level of the unmanned mining vehicle is determined according to the vehicle's operating conditions. The self-inspection level is used to determine the parameter type of the first vehicle parameter collected during the self-inspection process of the drive system. The first vehicle parameter of the unmanned mining vehicle under the self-inspection level is obtained, and the first vehicle parameter is compared with the standard drive capability curve of the vehicle. The drive system self-inspection result is determined based on the drive capability comparison result. The drive system self-test result is that it passes the self-test. The unmanned mining vehicle is controlled to decelerate according to the braking operation parameters to obtain the second vehicle parameters. The second vehicle parameters are compared with the standard braking capacity curve of the vehicle. The braking system self-test result is determined based on the braking capacity comparison result. The braking system self-test result is that it passes the self-test. The unmanned mining vehicle is controlled to steer according to the steering operation parameters to obtain the third vehicle parameters. The third vehicle parameters are compared with the standard steering capability curve of the vehicle. The steering system self-test result is determined based on the steering capability comparison result. When the steering system passes the self-test, the unmanned mining vehicle is controlled to perform production tasks.

[0008] According to one aspect of the present invention, the first vehicle parameters include: a first vehicle operating speed; The first vehicle parameters are compared with the vehicle's standard driving capability curve. Based on the driving capability comparison results, the self-test results of the drive system are determined, including: Get the speed of the first vehicle at the first timestamp; The first standard vehicle operating speed at the first timestamp is obtained from the vehicle's standard driving capability curve; The speed difference between the first vehicle's operating speed and the first standard vehicle's operating speed is determined, and the self-test result of the drive system is determined based on the proportion by which the speed difference exceeds a speed threshold.

[0009] According to one aspect of the present invention, the self-test level is a deep self-test; the first vehicle parameters include at least: the first vehicle operating speed, the first position information, and other parameters; The first vehicle parameters are compared with the vehicle's standard driving capability curve. Based on the driving capability comparison results, the self-test results of the drive system are determined, including: Obtain the first vehicle's speed and first location information at the first timestamp; The first standard vehicle operating speed and the first standard position information at the first timestamp are obtained from the vehicle's standard driving capability curve. Determine the speed difference between the first vehicle's operating speed and the first standard vehicle's operating speed. When the proportion of the speed difference exceeding the speed threshold is greater than or equal to a first preset proportion, determine the mining environment where the unmanned mining vehicle is located based on the first location information, and obtain scene correction parameters corresponding to the mining environment. Using the scenario correction parameters, the threshold parameters corresponding to the other parameters are corrected to obtain the correction threshold; the other parameters include at least one of the following: actual motor torque, motor temperature, wheel speed difference, and road surface adhesion coefficient. The other parameters collected are compared with their corresponding correction thresholds, and the self-test result of the drive system is determined based on the comparison results.

[0010] According to one aspect of the present invention, comparing the second vehicle parameters with a standard vehicle braking capacity curve, and determining the braking system self-test result based on the braking capacity comparison result, includes: The unmanned mining vehicle is controlled to decelerate according to braking parameters, and the second vehicle speed and second position information with a second timestamp are obtained. The displacement information is determined based on the second position information. The second standard vehicle operating speed and standard displacement information at the second timestamp are obtained from the vehicle standard braking capability curve. The speed difference between the second vehicle's operating speed and the second standard vehicle's operating speed is determined. If the proportion of the speed difference that meets the speed threshold is greater than or equal to a second preset proportion, the displacement difference between the displacement information and the standard displacement information is determined. The self-test result of the braking system is determined based on the displacement difference.

[0011] According to one aspect of the present invention, comparing the third vehicle parameter with a standard vehicle steering capability curve, and determining the steering system self-test result based on the steering capability comparison result, includes: Obtain the vehicle's steering angle and third position information using the third timestamp; The standard steering angle and third standard position information of the third timestamp are obtained from the vehicle's standard steering capability curve. The angle difference between the vehicle steering angle and the standard steering angle is determined, and the self-test result of the steering system is determined by combining the angle difference and the position difference between the third position information and the third standard position information.

[0012] According to one aspect of the invention, it further includes: When the self-test results of the drive system, the braking system, or the steering system are all negative, some functions of the mining unmanned vehicle are locked. Locking down some functions of the aforementioned unmanned mining vehicle, including: The unmanned mining vehicle is subject to graded function locking based on the fault level. The fault levels include safety-related faults, power-related faults, and non-system faults; If the fault is safety-related, lock the vehicle drive function, high voltage power-on function, automatic operation function, and remote dispatch function. If the fault is related to power, limit the vehicle's output power and maximum speed. If the problem is not a system malfunction, lock the automatic departure function and platooning function.

[0013] According to one aspect of the invention, it further includes: When the self-test result of the drive system, the self-test result of the braking system, or the self-test result of the steering system is a failed self-test, the vehicle parameters of the failed self-test process are obtained, and abnormal data is filtered from the vehicle parameters; The mining environment in which the unmanned mining vehicle is located is determined based on the location information of the unmanned mining vehicle, and the abnormal data is verified based on the scene correction parameters of the mining environment to determine the verification data. The abnormal data is compared with the verification data and the corresponding standard values ​​in the vehicle's standard driving capability curve, the vehicle's standard braking capability curve, or the vehicle's standard steering capability curve. The fault type is determined based on the comparison results, and the cause of the fault is located based on the fault type.

[0014] According to one aspect of the present invention, the vehicle standard driving capability curve, the vehicle standard braking capability curve, and the vehicle standard steering capability curve are respectively constructed in the following manner: The unmanned mining vehicle is controlled to move according to the set operating parameters. Time information, speed information and position information are collected. The time information is processed into relative time and stored as the corresponding vehicle standard driving capability curve, vehicle standard braking capability curve and vehicle standard steering capability curve according to the relative time. The set operating parameters corresponding to the standard driving capability curve of the vehicle include: the set pedal opening, and the corresponding motion includes: accelerating from zero speed to the set speed; The set operating parameters corresponding to the vehicle's standard braking capability curve include: constant braking deceleration, and the corresponding motion includes: decelerating from a set speed to zero speed; The set operating parameters corresponding to the vehicle's standard steering capability curve include: a fixed vehicle speed and a sinusoidal steering command, and the corresponding motion includes: performing steering motion according to the sinusoidal steering command.

[0015] To achieve the above objectives, the present invention also provides a self-inspection system for the departure of unmanned mining vehicles, comprising: The drive system self-test result determination module determines the self-test level of the unmanned mining vehicle according to its operating conditions during the vehicle departure stage. The self-test level is used to determine the parameter type of the first vehicle parameter collected during the drive system self-test. The module obtains the first vehicle parameter of the unmanned mining vehicle under the self-test level, compares the first vehicle parameter with the standard drive capability curve of the vehicle, and determines the drive system self-test result based on the drive capability comparison result. The braking system self-test result determination module determines the self-test result of the drive system if the self-test result is passed. It controls the unmanned mining vehicle to decelerate according to the braking operation parameters to obtain the second vehicle parameters. The second vehicle parameters are compared with the standard braking capacity curve of the vehicle. The self-test result of the braking system is determined based on the braking capacity comparison result. The steering system self-test result determination module determines the third vehicle parameter by controlling the unmanned mining vehicle to steer according to the steering operation parameters after the braking system self-test result is passed. The third vehicle parameter is then compared with the standard steering capability curve of the vehicle, and the steering system self-test result is determined based on the steering capability comparison result. The production task execution module controls the unmanned mining vehicle to execute production tasks when the steering system self-test result is a pass.

[0016] To achieve the above objectives, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the self-testing method for starting an unmanned mining vehicle as described above.

[0017] To achieve the above objectives, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the self-testing method for starting an unmanned mining vehicle as described above.

[0018] According to the present invention, a self-inspection method and system for the departure of unmanned mining vehicles is provided. During the departure stage before the vehicle enters the production phase, the self-inspection level is determined based on the vehicle's operating conditions. Dynamic operational comparison tests based on standard capability curves are performed sequentially on the drive system, braking system, and steering system: the drive system is judged by the speed difference ratio; under deep self-inspection, it is further compared after correcting multiple parameter thresholds such as motor torque, temperature, wheel speed difference, and road adhesion coefficient by incorporating correction parameters for the mining environment scenario; the braking system is judged by a combined assessment of speed and displacement differences; and the steering system is judged by a combined assessment of angle and position differences. Production tasks can only be executed after all three systems pass the self-inspection; otherwise, certain functions are locked and fault diagnosis is performed to locate the cause. This method, through pre-built multi-condition standard capability curves and a time-series comparison mechanism, can accurately verify the actual movement capability of the vehicle during the departure stage under complex mining conditions, identify system performance degradation and potential operating condition compatibility issues in advance, prevent faulty vehicles from participating in production, and ensure the safety and reliability of unmanned mining vehicles in unmanned operating environments. Attached Figure Description

[0019] Figure 1 A flowchart illustrating a self-inspection method for starting an unmanned mining vehicle according to an embodiment of the present invention; Figure 2 This schematic diagram illustrates a self-test flowchart of a braking system according to an embodiment of the present invention. Detailed Implementation

[0020] The invention will now be discussed with reference to exemplary embodiments. It should be understood that the described embodiments are merely intended to enable those skilled in the art to better understand and thus implement the invention, and are not intended to imply any limitation on the scope of the invention.

[0021] As used herein, the term "comprising" and its variations are to be interpreted as open-ended terms meaning "including but not limited to". The term "based on" is to be interpreted as "at least partially based on". The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment".

[0022] Figure 1 This is a schematic flowchart illustrating a self-inspection method for starting an unmanned mining vehicle according to an embodiment of the present invention. Figure 1 As shown, in this embodiment, the self-inspection method for starting an unmanned mining vehicle includes: During the departure phase of the unmanned mining vehicle, the self-inspection level of the unmanned mining vehicle is determined according to the vehicle's operating conditions. The self-inspection level is used to determine the parameter type of the first vehicle parameter collected during the drive system self-inspection process. The first vehicle parameter of the unmanned mining vehicle under the self-inspection level is obtained, and the first vehicle parameter is compared with the vehicle's standard drive capability curve. The drive system self-inspection result is determined based on the drive capability comparison result. The self-test result of the drive system is that it passes the self-test. The control of the unmanned mining vehicle decelerates according to the braking operation parameters to obtain the second vehicle parameters. The second vehicle parameters are compared with the standard braking capacity curve of the vehicle. The self-test result of the braking system is determined based on the braking capacity comparison result. The braking system self-test result is that it passes the self-test. The third vehicle parameter is obtained by controlling the unmanned mining vehicle to steer according to the steering operation parameter. The third vehicle parameter is compared with the standard steering capability curve of the vehicle. The steering system self-test result is determined based on the steering capability comparison result. When the steering system self-test result is passed, the unmanned mining vehicle is controlled to perform production tasks.

[0023] In this embodiment, the unmanned mining vehicle can be a wide-body mining vehicle. The departure stage refers to the stage before the unmanned mining vehicle enters the production site to perform production. To ensure vehicle safety, a self-inspection area can be set up at the exit of the departure parking lot. The self-inspection area may include a drive system inspection area, a braking system inspection area, and a steering system inspection area.

[0024] The vehicle's operating condition is determined based on the autonomous mining vehicle's operational status during the departure phase and the state of the mining environment in which the vehicle is located. This may include low-speed flat road conditions, low-speed slope conditions, or low-speed slippery road conditions. In this embodiment, the mining environment is determined based on vehicle location information, road slope, road type, and load status. The vehicle's operating condition is then determined based on this environment. The drive system's self-inspection level is determined based on the correspondence between the vehicle's operating condition and its self-inspection level. The self-inspection level includes basic self-inspection or deep self-inspection.

[0025] By performing a self-check on the drive system during the vehicle departure phase, the status of the drive system can be identified in advance before the vehicle enters the production phase, eliminating potential vehicle operation hazards and improving vehicle safety.

[0026] After the drive system passes its self-test, the unmanned mining vehicle is controlled to enter the braking system self-test area at a set speed to perform the braking system self-test. Following the drive system's successful self-test, the braking system self-test is performed sequentially, forming a series of mandatory testing procedures. This prevents further testing from proceeding if any core system has a potential hazard, thus improving the safety and reliability of the testing.

[0027] After the braking system self-test is passed, the unmanned mining vehicle can be further controlled to enter the steering system self-test area to perform a steering system self-test. The steering system self-test is performed after the drive and braking systems have passed their self-tests, fully covering the three core functions of vehicle driving, braking, and steering, ensuring that all actuators of the vehicle are in normal working order.

[0028] Upon successful self-test of the steering system, a notification message indicating the completion of the self-test can be provided, and the system will automatically exit the self-test system, allowing the production system to take over the unmanned mining vehicle. The notification message could be, for example, "Vehicle XXX has passed self-test and can participate in production." The unmanned mining vehicle can then enter the production system to perform any type of production task within the mine.

[0029] Vehicles can only participate in production after all three systems have passed self-inspection, which avoids vehicles with serious malfunctions from entering the operation stage, prevents further damage to vehicles and production losses, and improves the safety of mining production.

[0030] Furthermore, according to one embodiment of the present invention, the self-test level is a basic self-test, and the first vehicle parameter includes: the first vehicle operating speed.

[0031] The first vehicle parameters are compared with the vehicle's standard driving capability curve. Based on the driving capability comparison results, the self-test results of the drive system are determined, including: Get the speed of the first vehicle at the first timestamp; The first standard vehicle operating speed is obtained from the first time stamp of the vehicle's standard driving capability curve; Determine the speed difference between the first vehicle's operating speed and the first standard vehicle's operating speed, and determine the drive system's self-test result based on the proportion of the speed difference exceeding a speed threshold.

[0032] The basic self-test is used to detect fundamental parameters such as vehicle speed. The self-test system controls the unmanned mining vehicle to enter the drive system detection area at a set speed threshold. Starting from the set speed threshold, the vehicle will drive according to the first operating parameters, acquiring the first vehicle parameters in real time. The first operating parameters may include constant torque request or drive pedal opening.

[0033] Furthermore, the first vehicle speed can be compared with the vehicle's standard driving capability curve by aligning relative timestamps, thus determining the speed difference between the first vehicle speed reached at the first timestamp and the first standard vehicle speed determined based on the vehicle's standard driving capability curve. Specifically, using the vehicle's start time (0 seconds) as the time base, the first vehicle speed and the first standard vehicle speed are aligned with the same timestamp.

[0034] If the percentage of speed difference exceeding the speed threshold is greater than or equal to a first preset percentage, the drive system self-test result is "failed". If the percentage of speed difference exceeding the speed threshold is less than the first preset percentage, the drive system self-test result is "passed". A speed threshold can be set for each timepoint on the curve; the speed threshold for each timepoint can be the same or different. The first timepoint can be each timepoint or a subset of sampled timepoints. The first preset percentage can be 40%, 30%, etc.

[0035] The basic self-test can quickly identify performance deviations of the drive system under normal operating conditions by comparing the speed parameters with the time sequence of the standard curve. The test process is simple and efficient, and is suitable for rapid screening of low-risk operating conditions.

[0036] Furthermore, according to one embodiment of the present invention, the self-test level is a deep self-test, and the first vehicle parameters include at least: the first vehicle operating speed, the first position information, and other parameters.

[0037] The first vehicle parameters are compared with the vehicle's standard driving capability curve. Based on the driving capability comparison results, the self-test results of the drive system are determined, including: Obtain the first vehicle's speed and first location information at the first timestamp; The first standard vehicle operating speed and first standard position information with the first time stamp are obtained from the vehicle's standard driving capability curve; Determine the speed difference between the first vehicle's operating speed and the first standard vehicle's operating speed. When the proportion of the speed difference exceeding the speed threshold is greater than or equal to the first preset proportion, determine the mining environment where the unmanned mining vehicle is located based on the first location information, and obtain the scene correction parameters corresponding to the mining environment. By using scenario correction parameters, the threshold parameters corresponding to other parameters are corrected to obtain the correction threshold; other parameters include at least one of the following: actual motor torque, motor temperature, wheel speed difference, and road surface adhesion coefficient. The other collected parameters are compared with their corresponding correction thresholds, and the self-test results of the drive system are determined based on the comparison results.

[0038] When the self-inspection level is deep self-inspection, the first vehicle parameters include at least the first vehicle running speed, the first position information and other parameters, including at least one of the following: actual motor torque, motor temperature, wheel speed difference and road surface adhesion coefficient.

[0039] Based on the initial location information, combined with map information, slope information, and road surface type, the mining environment in which the unmanned mining vehicle is located can be determined. The mining environment includes at least one of the following: open-air ramp, underground flat road, underground ramp, slippery road surface, and gravel road surface. After determining the mining environment, scene correction parameters corresponding to that environment can be obtained. Using these scene correction parameters, threshold parameters corresponding to the actual motor torque, motor temperature, wheel speed difference, and road surface adhesion coefficient are corrected to obtain the correction thresholds for each parameter.

[0040] The scene correction parameters are determined based on at least one of the following: road surface type, slope, and historical correction data of the scene in which the vehicle is located. They can also be dynamically determined based on the actual working conditions of the vehicle.

[0041] In this embodiment, for example, it is determined whether the vehicle changes from a flat road to a sloping road surface based on the first location information. If it is determined to be on a sloping road surface, the vehicle slope scene correction coefficient is obtained. The slope scene correction coefficient is obtained by comparing the slope acceleration obtained from the slope value of the vehicle. The slope acceleration is compared with the acceleration obtained from the standard driving capability curve of the vehicle. The slope scene correction coefficient of the vehicle is used as the scene correction parameter (that is, when the vehicle enters the slope from a flat road, the relevant parameters corresponding to the flat road are not applicable, so it is necessary to correct the scene correction parameter based on the flat road parameters). Then, the threshold parameters corresponding to other parameters are corrected using the scene correction parameter to obtain the standard capability curve under the corrected slope scene. The other collected parameters are compared with the corresponding corrected standard capability curves for the ramp scenario, and the self-test results of the drive system are determined based on the comparison results.

[0042] It is worth noting that the above-mentioned processes of obtaining in-depth self-checking and scene correction parameters, threshold correction, and comparison are also applicable to braking system self-checking.

[0043] The actual motor torque, motor temperature, wheel speed difference, and road surface adhesion coefficient collected at the first timestamp are compared with their corresponding correction thresholds. If the comparison results show that the actual motor torque, motor temperature, wheel speed difference, and road surface adhesion coefficient all meet the correction thresholds, the drive system self-test result is considered passed. If the comparison results show that any one of the actual motor torque, motor temperature, wheel speed difference, or road surface adhesion coefficient does not meet the correction threshold, the drive system self-test result is considered failed.

[0044] In this embodiment, the depth self-test, based on the basic speed detection, introduces location information to determine the mining environment and dynamically adjusts the thresholds of multiple parameters through scene correction parameters. This can adapt to performance fluctuations under complex working conditions in the mining area (such as slopes and slippery roads), reduce the false judgment rate, and improve the accuracy and scene adaptability of the detection.

[0045] Furthermore, according to one embodiment of the present invention, comparing the second vehicle parameters with the vehicle's standard braking capacity curve, and determining the braking system self-test result based on the braking capacity comparison result, includes: The unmanned mining vehicle is controlled to decelerate according to the braking operation parameters, the second vehicle running speed and second position information with the second timestamp are obtained, and the displacement information is determined based on the second position information. The second standard vehicle operating speed and standard displacement information with the second timestamp are obtained from the vehicle's standard braking capability curve; The speed difference between the second vehicle's operating speed and the second standard vehicle's operating speed is determined. If the proportion of the speed difference that meets the speed threshold is greater than or equal to the second preset proportion, the displacement difference between the displacement information and the standard displacement information is determined. The self-test result of the braking system is determined based on the displacement difference.

[0046] In this embodiment, the second preset ratio can be 20% or 30%, etc., and the specific ratio is determined according to actual needs. Braking operation parameters include constant braking request or negative torque request.

[0047] like Figure 2 As shown, the braking system self-test mainly includes the following steps: Step S210, control the unmanned mining vehicle to decelerate according to the braking operation parameters, obtain the second vehicle running speed and second position information at the second time stamp, and determine the displacement information based on the second position information. Step S220, obtain the second standard vehicle running speed and standard displacement information at the second time stamp from the vehicle's standard braking capacity curve. Step S230, determine the speed difference between the second vehicle running speed and the second standard vehicle running speed, and judge whether the proportion of the speed difference that meets the speed threshold is greater than the second preset proportion. If yes, proceed to step S240 (self-test fails); if no, proceed to step S250. Step S250, displacement comparison. Step S260, judge whether the displacement difference between the displacement information and the standard displacement information meets the requirements. If yes, proceed to step S240; if no, proceed to step S270. Step S270, braking system self-test passes.

[0048] The vehicle decelerates according to a constant braking request or a negative torque request, acquiring real-time information on the second vehicle's speed and displacement, and aligning these timestamps. The second vehicle's speed and displacement information at the second timestamp are then compared with the second standard vehicle speed and displacement information determined based on the vehicle's standard braking capability curve. If the proportion of speed differences meeting a speed threshold is greater than or equal to a second preset proportion, the braking system's capability is insufficient, and the braking system self-test fails. If the proportion of speed differences meeting a speed threshold is less than the second preset proportion, a displacement comparison is performed over relative time. If the proportion of displacement differences exceeding a displacement threshold is greater than or equal to the second preset proportion, braking stability is insufficient, and the braking self-test fails. When the proportion of displacement differences exceeding a displacement threshold is less than the second preset proportion, the braking system self-test result is considered passed. After the vehicle's speed reaches 0 and the braking system passes the self-test, the self-test system controls the unmanned mining vehicle to resume travel at the set speed.

[0049] In this embodiment, the braking system self-test uses a two-dimensional joint judgment of speed difference and displacement difference to simultaneously detect the braking force output capability and braking stability of the braking system, avoiding missed detections caused by single parameter detection and improving the comprehensiveness and reliability of braking system detection.

[0050] Furthermore, according to one embodiment of the present invention, comparing the third vehicle parameters with the vehicle's standard steering capability curve, and determining the steering system self-test result based on the steering capability comparison result, includes: Obtain the vehicle's steering angle and third position information using the third timestamp; Obtain the standard steering angle and third standard position information of the third timestamp from the vehicle's standard steering capability curve; Determine the angle difference between the vehicle steering angle and the standard steering angle, and combine the angle difference with the position difference between the third position information and the third standard position information to determine the steering system self-test result.

[0051] The process begins by determining the angular difference between the vehicle's steering angle and the standard steering angle. If the angular difference is less than a threshold, the steering system self-test is considered successful. If the angular difference is greater than or equal to the threshold, the positional difference between the third position information and the third standard position information is determined, and the steering system self-test result is determined based on the positional difference.

[0052] The system controls the unmanned mining vehicle to request steering according to a sinusoidal curve at a fixed rate. It acquires the vehicle's steering angle and third position information in real time, comparing these with the vehicle's standard steering capability curve. By using relative time, it obtains the standard steering angle and the third standard vehicle position under the standard steering capability curve. When the angle difference is greater than or equal to an angle threshold, it compares the position difference between the third position information and the third standard position information. If the proportion of the position difference exceeding the position threshold is greater than or equal to a third preset proportion, the steering system self-test fails. If the proportion of the position difference exceeding the position threshold is less than the third preset proportion, the steering system self-test passes.

[0053] In this embodiment, the steering system self-test uses a two-dimensional joint judgment of steering angle and position information to detect the steering system's angle response accuracy and position tracking capability, comprehensively evaluate the dynamic performance of the steering system during actual movement, and avoid problems such as steering lag or drift that cannot be identified by single-angle detection.

[0054] Furthermore, according to one embodiment of the present invention, the method of the present invention further includes: When the self-test results of the drive system, braking system, or steering system are all negative, some functions of the unmanned mining vehicle are locked.

[0055] Locking in some functions of unmanned mining vehicles, including: The unmanned mining vehicles are subject to graded function locking based on the fault level. Fault levels include safety-related faults, power-related faults, and non-system faults; If the fault is safety-related, lock the vehicle drive function, high voltage power-on function, automatic operation function, and remote dispatch function. If the fault is related to power, limit the vehicle's output power and maximum speed. If the problem is not a system malfunction, lock the automatic departure function and platooning function.

[0056] In this implementation, based on the self-test failure results and the determined fault type and level, a tiered function lock is applied to the unmanned mining vehicle to restrict certain functions. For Level 1 safety faults, such as excessively high motor temperature, abnormal high voltage, transmission jamming, or brake failure, the vehicle's drive function, high-voltage power-on function, automatic operation function, and remote dispatch function are locked, prohibiting the vehicle from driving and operating. For Level 2 power faults, such as insufficient torque or unstable voltage, the full-speed driving function, heavy-load operation function, and slope driving function are locked, limiting the vehicle's output power and maximum speed. For Level 3 non-system faults, the automatic departure function and platooning function are locked, allowing manual reselection of the departure area or adjustment of operating conditions followed by another self-test.

[0057] In this embodiment, after determining the fault level, maintenance alarm information can be sent to the mining vehicle management platform. Upon receiving the maintenance instruction from the mining vehicle management platform, the location information of the maintenance area can be obtained, and further path planning can be performed for the unmanned mining vehicle to reach the maintenance area to determine the maintenance path. Specifically, taking the location of the unmanned mining vehicle as the starting point and the location information of the maintenance area as the ending point, combined with the current mining environment of the unmanned mining vehicle, map data, real-time road condition information, and scene constraint parameters of the mining environment are retrieved, and path planning constraints are determined according to the fault type. Real-time road condition information includes at least road congestion, construction sections, and obstacle locations. Scene constraint parameters include at least slope gradient, road surface adhesion coefficient, underground tunnel width, and speed limit requirements. Based on the starting point, ending point, and path planning constraints, a path planning algorithm is used to generate multiple initial maintenance paths. From the multiple initial maintenance paths, the path with the shortest distance, shortest estimated travel time, and highest constraint satisfaction is selected as the maintenance path. The system outputs the starting coordinates, ending coordinates, nodes along the route, road segment type, speed limit, estimated travel time, and fault status of the maintenance path to the vehicle control system and mining vehicle management platform. This ensures that vehicles travel along the maintenance path to the maintenance area, enabling timely vehicle maintenance and improving vehicle safety.

[0058] In this implementation, by using graded function locking, differentiated vehicle control measures are taken according to the severity of the fault. This ensures that safety-related faults are strictly controlled while avoiding excessive restrictions on minor faults, thereby improving the precision and safety of vehicle management.

[0059] Furthermore, according to one embodiment of the present invention, the method of the present invention further includes: When the self-test results of the drive system, braking system, or steering system are all negative, the vehicle parameters of the self-test process that failed are obtained, and abnormal data is filtered out from the vehicle parameters. The location information of the unmanned mining vehicle is used to determine the mining environment in which the unmanned mining vehicle is located, and the abnormal data is verified based on the scene correction parameters of the mining environment to determine the verification data. The abnormal data is compared with the verification data and the corresponding standard values ​​in the vehicle's standard driving capability curve, vehicle's standard braking capability curve, or vehicle's standard steering capability curve. The fault type is determined based on the comparison results, and the cause of the fault is located based on the fault type.

[0060] In this embodiment, when the self-test results of the drive system, braking system, or steering system are all failed, the vehicle parameters from the failed self-test process can be obtained, and abnormal data can be filtered from these parameters. The vehicle parameters can be all parameters obtained during the failed self-test process. The comparison result for each vehicle parameter is determined, and parameters with unqualified comparison results are identified as abnormal data.

[0061] Based on the real-time location of the unmanned mining vehicle, the current mining environment of the vehicle is determined, such as an open-air ramp, an underground flat road, or a slippery surface. The corresponding scene correction parameters for this environment are retrieved, and each abnormal data point is verified to obtain validation data. This verification process involves adjusting the abnormal data according to the scene correction parameters.

[0062] Next, the abnormal data is compared with the validation data and the original standard value. If the comparison result shows that the abnormal data matches the validation data but does not match the original standard value, then it is determined to be a non-system fault. A non-system fault could be, for example, insufficient scenario adaptation. If the comparison result shows that the abnormal data does not match either the validation data or the original standard value, then it is determined to be a system fault.

[0063] When identifying system faults, the fault type can be determined based on the correspondence between abnormal data and multiple preset fault types. Fault types can include, for example, motor overheating, abnormal power supply, etc.

[0064] Further, the fault level is determined based on the importance of the fault module to which the fault type belongs. The cause of the fault is then determined from the fault table associated with the fault type, in descending order of fault level. The fault levels, in descending order, include safety-related faults, power-related faults, and non-system faults. Based on the cause of the fault, maintenance prompts are provided. These prompts may include one or more of the following: the part to be repaired, the repair method, and the timing of the repair.

[0065] In this embodiment, by filtering abnormal data, verifying scenarios, and comparing multiple layers, it is possible to distinguish between system faults and non-system faults (such as insufficient scenario adaptation), accurately locate the cause of the fault, provide clear guidance for vehicle maintenance, shorten the fault diagnosis time, and improve vehicle maintenance efficiency.

[0066] Furthermore, according to one embodiment of the present invention, the standard driving capability curve, the standard braking capability curve, and the standard steering capability curve of the vehicle are constructed in the following manner: the unmanned mining vehicle is controlled to move according to the set operating parameters, time information, speed information, and position information are collected, the time information is processed into relative time, and stored according to the relative time as the corresponding standard driving capability curve, standard braking capability curve, and standard steering capability curve of the vehicle.

[0067] The set operating parameters corresponding to the standard driving capability curve of the vehicle include: the set pedal opening, and the corresponding motion includes: accelerating from zero speed to the set speed.

[0068] For the standard driving capability curve of the vehicle, the mining unmanned vehicle is accelerated from 0 to 30km / h according to the set pedal opening. During the acceleration process, the time, speed and latitude and longitude information of the mining unmanned vehicle are collected. The time, speed and latitude and longitude information are processed into relative time and stored as the standard driving capability curve of the vehicle according to the relative time.

[0069] In this embodiment, the acceleration of the mining unmanned vehicle can be controlled according to the set pedal opening degree. The vehicle's time information, speed information, and latitude and longitude information are collected and processed into relative time. The relative time, speed information, and latitude and longitude information are used as input to construct sample data. The sample data is then subjected to working condition clustering and time series fitting through a machine learning model to generate standard driving curves under various standard working conditions. These curves are stored according to relative time to construct the vehicle's standard driving capability curve.

[0070] The process involves matching high-precision terrain data of the mining area with latitude and longitude information to calculate the road slope value at the corresponding location. Simultaneously, road conditions such as straight roads, uphill roads, and undulating roads are distinguished based on latitude and longitude location distribution. The collected vehicle time, speed, and latitude / longitude information are used to construct a three-dimensional feature vector for each road condition. This vector includes fixed pedal opening, road slope, and road condition type encoding. Speed ​​and relative time are used as the ground truth values ​​for the sample time series, ultimately forming sample data labeled with road conditions. The K-Means clustering algorithm is used to cluster the sample data into standard road condition datasets with the same opening, slope, and road condition. For each standard road condition, a separate time-series regression fitting model is trained to generate standard drive curves for each standard road condition. The time-series regression fitting model uses relative time as the model input feature and the vehicle speed at the corresponding time as the ground truth label for model fitting. The time-series regression fitting model can be a neural network model.

[0071] The standard braking capacity curve of a vehicle corresponds to the following set operating parameters: constant braking deceleration, and the corresponding motion includes: deceleration from a set speed to zero speed.

[0072] The process of establishing the vehicle's standard braking capability curve includes: starting from the set speed, issuing a deceleration command, which is used to control the unmanned mining vehicle to maintain a constant speed of -2m / s. 2 The deceleration rate decreases from the set speed to 0. During the deceleration process, the time information, speed information, and latitude and longitude information of the mining unmanned vehicle are collected. This information is processed into relative time and stored as the vehicle's standard braking capacity curve according to the relative time.

[0073] The standard steering capability curve of a vehicle corresponds to the following set operating parameters: fixed vehicle speed and sinusoidal steering command, and the corresponding motion includes: steering motion according to the sinusoidal steering command.

[0074] The process of establishing the standard steering capability curve of the vehicle includes: setting the unmanned mining vehicle to travel at a fixed speed, issuing a sinusoidal steering command with an amplitude of 30 degrees and a period of 20 seconds during the driving process, collecting the time, speed, latitude and longitude information of the unmanned mining vehicle during the steering process, processing the time, speed, latitude and longitude information into relative time, and storing it as the standard steering capability curve of the vehicle according to the relative time.

[0075] In this embodiment, standard capability curves for each system are pre-constructed to provide a quantitative comparison benchmark for self-inspection before departure; a unified time series benchmark based on relative time is adopted to ensure the comparability of actual operating parameters with standard parameters; and a multi-condition curve construction method based on machine learning can cover complex working conditions in the mining area, improving the applicability and detection accuracy of the standard curves.

[0076] Furthermore, to achieve the above objectives, the present invention also provides a self-inspection system for the departure of unmanned mining vehicles, comprising: The drive system self-test result determination module determines the self-test level of the unmanned mining vehicle according to the vehicle's operating conditions during the vehicle departure stage. The self-test level is used to determine the parameter type of the first vehicle parameter collected during the drive system self-test process. The module obtains the first vehicle parameter of the unmanned mining vehicle under the self-test level, compares the first vehicle parameter with the vehicle's standard drive capability curve, and determines the drive system self-test result based on the drive capability comparison result. The braking system self-test result determination module determines the self-test result of the drive system. If the self-test result is passed, the unmanned mining vehicle is controlled to decelerate according to the braking operation parameters to obtain the second vehicle parameters. The second vehicle parameters are compared with the standard braking capacity curve of the vehicle, and the self-test result of the braking system is determined based on the braking capacity comparison result. The steering system self-test result determination module determines the self-test result of the braking system. The self-test result is passed. The unmanned mining vehicle is controlled to steer according to the steering operation parameters to obtain the third vehicle parameters. The third vehicle parameters are compared with the standard steering capability curve of the vehicle. The steering system self-test result is determined based on the steering capability comparison result. The production task execution module controls the unmanned mining vehicle to execute production tasks when the steering system self-check result is passed.

[0077] The above-mentioned mine unmanned vehicle departure self-inspection system according to the present invention can realize the above-mentioned mine unmanned vehicle departure self-inspection method. The specific process steps are as described above and will not be repeated here.

[0078] Furthermore, to achieve the above objectives, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the self-testing method for starting an unmanned mining vehicle as described above.

[0079] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the self-testing method for starting an unmanned mining vehicle as described above.

[0080] Those skilled in the art will recognize that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0081] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and equipment can be referred to the corresponding process in the foregoing method implementation, and will not be repeated here.

[0082] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0083] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the objectives of the embodiments of the present invention, depending on actual needs.

[0084] In addition, the functional modules in the embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0085] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to conventional solutions, or a portion 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 sending / receiving methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0086] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in this application.

[0087] It should be understood that the sequence number of each step in the invention and its embodiments does not absolutely imply 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.

Claims

1. A self-inspection method for starting an unmanned mining vehicle, characterized in that, include: During the departure phase of the unmanned mining vehicle, the self-inspection level of the unmanned mining vehicle is determined according to the vehicle's operating conditions. The self-inspection level is used to determine the parameter type of the first vehicle parameter collected during the self-inspection process of the drive system. The first vehicle parameter of the unmanned mining vehicle under the self-inspection level is obtained, and the first vehicle parameter is compared with the standard drive capability curve of the vehicle. The drive system self-inspection result is determined based on the drive capability comparison result. The drive system self-test result is that it passes the self-test. The unmanned mining vehicle is controlled to decelerate according to the braking operation parameters to obtain the second vehicle parameters. The second vehicle parameters are compared with the standard braking capacity curve of the vehicle. The braking system self-test result is determined based on the braking capacity comparison result. The braking system self-test result is that it passes the self-test. The unmanned mining vehicle is controlled to steer according to the steering operation parameters to obtain the third vehicle parameters. The third vehicle parameters are compared with the standard steering capability curve of the vehicle. The steering system self-test result is determined based on the steering capability comparison result. When the steering system passes the self-test, the unmanned mining vehicle is controlled to perform production tasks.

2. The self-inspection method for starting an unmanned mining vehicle according to claim 1, characterized in that, The first vehicle parameters include: the first vehicle's operating speed; The first vehicle parameters are compared with the vehicle's standard driving capability curve. Based on the driving capability comparison results, the self-test results of the drive system are determined, including: Get the speed of the first vehicle at the first timestamp; The first standard vehicle operating speed at the first timestamp is obtained from the vehicle's standard driving capability curve; The speed difference between the first vehicle's operating speed and the first standard vehicle's operating speed is determined, and the self-test result of the drive system is determined based on the proportion by which the speed difference exceeds a speed threshold.

3. The self-inspection method for starting an unmanned mining vehicle according to claim 1, characterized in that, The self-inspection level is a deep self-inspection; the first vehicle parameters include at least: the first vehicle operating speed, the first location information, and other parameters; The first vehicle parameters are compared with the vehicle's standard driving capability curve. Based on the driving capability comparison results, the self-test results of the drive system are determined, including: Obtain the first vehicle's speed and first location information at the first timestamp; The first standard vehicle operating speed and the first standard position information at the first timestamp are obtained from the vehicle's standard driving capability curve. Determine the speed difference between the first vehicle's operating speed and the first standard vehicle's operating speed. When the proportion of the speed difference exceeding the speed threshold is greater than or equal to a first preset proportion, determine the mining environment where the unmanned mining vehicle is located based on the first location information, and obtain scene correction parameters corresponding to the mining environment. Using the scenario correction parameters, the threshold parameters corresponding to the other parameters are corrected to obtain the correction threshold; the other parameters include at least one of the following: actual motor torque, motor temperature, wheel speed difference, and road surface adhesion coefficient. The other parameters collected are compared with their corresponding correction thresholds, and the self-test result of the drive system is determined based on the comparison results.

4. The self-inspection method for starting an unmanned mining vehicle according to claim 1, characterized in that, The second vehicle parameter is compared with the vehicle's standard braking capacity curve. Based on the braking capacity comparison result, the braking system self-test result is determined, including: The unmanned mining vehicle is controlled to decelerate according to braking parameters, and the second vehicle speed and second position information with a second timestamp are obtained. The displacement information is determined based on the second position information. The second standard vehicle operating speed and standard displacement information at the second timestamp are obtained from the vehicle standard braking capability curve. The speed difference between the second vehicle's operating speed and the second standard vehicle's operating speed is determined. If the proportion of the speed difference that meets the speed threshold is greater than or equal to a second preset proportion, the displacement difference between the displacement information and the standard displacement information is determined. The self-test result of the braking system is determined based on the displacement difference.

5. The self-inspection method for starting an unmanned mining vehicle according to claim 1, characterized in that, The third vehicle parameter is compared with the vehicle's standard steering capability curve. Based on the steering capability comparison result, the steering system self-test result is determined, including: Obtain the vehicle's steering angle and third position information using the third timestamp; The standard steering angle and third standard position information of the third timestamp are obtained from the vehicle's standard steering capability curve. The angle difference between the vehicle steering angle and the standard steering angle is determined, and the self-test result of the steering system is determined by combining the angle difference and the position difference between the third position information and the third standard position information.

6. The self-inspection method for starting an unmanned mining vehicle according to claim 1, characterized in that, Also includes: When the self-test results of the drive system, the braking system, or the steering system are all negative, some functions of the mining unmanned vehicle are locked. Locking down some functions of the aforementioned unmanned mining vehicle, including: The unmanned mining vehicle is subject to graded function locking based on the fault level. The fault levels include safety-related faults, power-related faults, and non-system faults; If the fault is safety-related, lock the vehicle drive function, high voltage power-on function, automatic operation function, and remote dispatch function. If the fault is related to power, limit the vehicle's output power and maximum speed. If the problem is not a system malfunction, lock the automatic departure function and platooning function.

7. The self-inspection method for starting an unmanned mining vehicle according to claim 3, characterized in that, Also includes: When the self-test result of the drive system, the self-test result of the braking system, or the self-test result of the steering system is a failed self-test, the vehicle parameters of the failed self-test process are obtained, and abnormal data is filtered from the vehicle parameters; The mining environment in which the unmanned mining vehicle is located is determined based on the location information of the unmanned mining vehicle, and the abnormal data is verified based on the scene correction parameters of the mining environment to determine the verification data. The abnormal data is compared with the verification data and the corresponding standard values ​​in the vehicle's standard driving capability curve, the vehicle's standard braking capability curve, or the vehicle's standard steering capability curve. The fault type is determined based on the comparison results, and the cause of the fault is located based on the fault type.

8. The self-inspection method for starting an unmanned mining vehicle according to claim 1, characterized in that, The vehicle standard driving capability curve, the vehicle standard braking capability curve, and the vehicle standard steering capability curve are constructed in the following ways: The unmanned mining vehicle is controlled to move according to the set operating parameters. Time information, speed information and position information are collected. The time information is processed into relative time and stored as the corresponding vehicle standard driving capability curve, vehicle standard braking capability curve and vehicle standard steering capability curve according to the relative time. The set operating parameters corresponding to the standard driving capability curve of the vehicle include: the set pedal opening, and the corresponding motion includes: accelerating from zero speed to the set speed; The set operating parameters corresponding to the vehicle's standard braking capability curve include: constant braking deceleration, and the corresponding motion includes: decelerating from a set speed to zero speed; The set operating parameters corresponding to the vehicle's standard steering capability curve include: a fixed vehicle speed and a sinusoidal steering command, and the corresponding motion includes: performing steering motion according to the sinusoidal steering command.

9. A self-inspection system for starting unmanned mining vehicles, characterized in that: include: The drive system self-test result determination module determines the self-test level of the unmanned mining vehicle according to its operating conditions during the vehicle departure stage. The self-test level is used to determine the parameter type of the first vehicle parameter collected during the drive system self-test. The module obtains the first vehicle parameter of the unmanned mining vehicle under the self-test level, compares the first vehicle parameter with the standard drive capability curve of the vehicle, and determines the drive system self-test result based on the drive capability comparison result. The braking system self-test result determination module determines the self-test result of the drive system if the self-test result is passed. It controls the unmanned mining vehicle to decelerate according to the braking operation parameters to obtain the second vehicle parameters. The second vehicle parameters are compared with the standard braking capacity curve of the vehicle. The self-test result of the braking system is determined based on the braking capacity comparison result. The steering system self-test result determination module determines the third vehicle parameter by controlling the unmanned mining vehicle to steer according to the steering operation parameters after the braking system self-test result is passed. The third vehicle parameter is then compared with the standard steering capability curve of the vehicle, and the steering system self-test result is determined based on the steering capability comparison result. The production task execution module controls the unmanned mining vehicle to execute production tasks when the steering system self-test result is a pass.

10. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the self-test method for starting an unmanned mining vehicle as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the self-test method for starting an unmanned mining vehicle as described in any one of claims 1-8.

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