Abnormal diagnosis method, device and equipment for driving force of unmanned mine vehicle and medium

Abstract

The present disclosure relates to a mine unmanned vehicle driving force abnormality diagnosis method, device, equipment and medium. The mine unmanned vehicle driving force abnormality diagnosis method comprises: obtaining real-time target data and historical target data of the mine unmanned vehicle; calculating actual net acceleration and expected net acceleration based on the real-time target data and the historical target data; updating a count value in a case where it is judged that the actual net acceleration and the expected net acceleration meet a preset starting fault counter condition; and reporting a corresponding driving fault type fault code in a case where the count value exceeds a preset safety threshold. According to the embodiment of the present disclosure, the accuracy of driving force abnormality fault detection can be improved, the false positive and false negative probabilities are greatly reduced, and the safety and reliability of the mine unmanned vehicle operation are effectively improved.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology in mining, and in particular to a method, device, equipment and medium for diagnosing abnormal driving force of unmanned mining vehicles. Background Technology

[0002] Given that unmanned mining applications often involve harsh operating conditions in high-altitude, extremely cold mining areas, prolonged operation in such environments poses a significant challenge to the driver's physical capabilities and demands exceptional driving skills. Therefore, to improve the efficiency of mining operations and effectively reduce labor costs, the market demand for highly safe, efficient, and reliable unmanned mining dump trucks is growing rapidly.

[0003] Given that unmanned mining dump trucks often operate for extended periods under conditions of high dust, high altitude, high humidity, and heavy impact loads, their drive systems (such as motors, gearboxes, and drive shafts) are subjected to extreme stress over long periods, accelerating component fatigue failure. Studies show that drive system failures account for over 40% of mine vehicle downtime, and accurate diagnosis can reduce unplanned downtime by 30%. Furthermore, mine digitalization requires real-time data analysis to predict faults and achieve "predictive maintenance," thereby preventing the escalation of accident chains and the resulting safety incidents. Summary of the Invention

[0004] To address the aforementioned technical issues, this disclosure provides a method, apparatus, equipment, and medium for diagnosing abnormal driving force of unmanned mining vehicles.

[0005] Firstly, this disclosure provides a method for diagnosing abnormal driving force of unmanned mining trucks, including:

[0006] Acquire real-time and historical target data of unmanned mining vehicles;

[0007] Calculate the actual net acceleration and the expected net acceleration based on the real-time target data and the historical target data;

[0008] If the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions, update the counter value;

[0009] If the count value exceeds a preset safety threshold, the corresponding drive fault type fault code will be reported.

[0010] Secondly, this disclosure provides a diagnostic device for abnormal driving force of an unmanned mining vehicle, including:

[0011] The data acquisition module is used to acquire real-time and historical target data of the unmanned mining vehicle;

[0012] The velocity calculation module is used to calculate the actual net acceleration and the expected net acceleration based on the real-time target data and the historical target data;

[0013] The numerical update module is used to update the count value when it is determined that the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions;

[0014] The fault reporting module is used to report the corresponding drive fault type fault code when the count value exceeds a preset safety threshold.

[0015] Thirdly, this disclosure provides a diagnostic device for abnormal driving force of unmanned mining vehicles, including:

[0016] processor;

[0017] Memory, used to store executable instructions;

[0018] The processor is used to read executable instructions from memory and execute the executable instructions to implement the first aspect of the unmanned mining vehicle driving force anomaly diagnosis method.

[0019] Fourthly, this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the method for diagnosing abnormal driving force of unmanned mining vehicles according to the first aspect.

[0020] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0021] The unmanned mining truck drive force anomaly diagnosis method of this disclosure can acquire real-time target data and historical target data of the unmanned mining truck, then calculate the actual net acceleration and expected net acceleration based on the real-time target data and the historical target data, then update the count value if the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions, and finally report the corresponding drive fault type fault code if the count value exceeds the preset safety threshold. This allows for timely and accurate diagnosis when the drive system is abnormal, thereby improving the accuracy of drive force anomaly fault detection, greatly reducing the probability of false alarms and missed alarms, and effectively improving the safety and reliability of unmanned mining truck operation. Attached Figure Description

[0022] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0023] Figure 1A flowchart illustrating a method for diagnosing abnormal driving force of an unmanned mining vehicle provided in an embodiment of this disclosure;

[0024] Figure 2 A flowchart illustrating another method for diagnosing abnormal driving force of an unmanned mining vehicle provided in this embodiment of the present disclosure;

[0025] Figure 3 A flowchart illustrating a method for setting preset fault type definition rules, provided in an embodiment of this disclosure;

[0026] Figure 4 This is a schematic diagram of the structure of an abnormal driving force diagnostic device for an unmanned mining vehicle provided in an embodiment of the present disclosure;

[0027] Figure 5 This is a schematic diagram of the structure of an abnormal driving force diagnostic device for an unmanned mining vehicle provided in an embodiment of this disclosure. Detailed Implementation

[0028] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0029] It should be understood that the various steps described in the method implementation of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method implementation may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0030] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0031] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0032] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0033] The names of messages or information exchanged between multiple devices in this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0034] To address the aforementioned problems, this disclosure provides a method, apparatus, equipment, and medium for diagnosing abnormal driving force in unmanned mining vehicles. The following is in conjunction with… Figures 1 to 3 The present disclosure provides a detailed description of the method for diagnosing abnormal driving force of unmanned mining vehicles.

[0035] Figure 1 A flowchart illustrating a method for diagnosing abnormal driving force of an unmanned mining vehicle provided in an embodiment of this disclosure is shown.

[0036] In this embodiment of the disclosure, the method for diagnosing abnormal driving force of the unmanned mining vehicle can be executed by an electronic device. This electronic device may include, but is not limited to, devices such as computer equipment, cloud servers, or cloud server clusters.

[0037] like Figure 1 As shown, the method for diagnosing abnormal driving force of the unmanned mining vehicle may include the following steps.

[0038] S110: Obtain real-time and historical target data of unmanned mining vehicles.

[0039] In the embodiments disclosed herein, the electronic device can acquire real-time target data and historical target data of the unmanned mining vehicle.

[0040] Optionally, real-time target data may include actual vehicle speed, actual acceleration / deceleration, road gradient, braking command, throttle command, gear position, and parking status.

[0041] Optionally, the historical target data can be an array of historically stored throttle commands and road gradients.

[0042] Specifically, the electronic equipment can acquire real-time target data and historical target data of the unmanned mining vehicle.

[0043] S120. Calculate the actual net acceleration and the expected net acceleration based on the real-time target data and the historical target data.

[0044] In this embodiment of the disclosure, the electronic device can calculate the actual net acceleration and the expected net acceleration based on the real-time target data and the historical target data.

[0045] Alternatively, the actual net acceleration can be a physical quantity that describes how fast the vehicle's speed changes during actual driving.

[0046] Alternatively, the desired net acceleration can be a physical quantity that describes the rate of change of speed of the vehicle during operation.

[0047] Specifically, after obtaining the real-time target data and the historical target data, the electronic device can calculate the actual net acceleration and the expected net acceleration based on the real-time target data and the historical target data.

[0048] S130. If the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions, update the counter value.

[0049] In this embodiment of the disclosure, the electronic device can update the count value when it is determined that the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions.

[0050] Optionally, the preset conditions for starting the fault counter can be pre-set conditions used to determine whether the fault counter needs to be started.

[0051] Optionally, the count value can be the number of times a fault was detected in the recorded vehicle.

[0052] Specifically, after obtaining the actual net acceleration and the expected net acceleration, if the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions, the electronic device can update the counter value.

[0053] S140. If the count value exceeds the preset safety threshold, report the corresponding drive fault type fault code.

[0054] In this embodiment of the disclosure, when the count value exceeds a preset safety threshold, the electronic device can report the corresponding drive fault type fault code.

[0055] Optionally, the preset security threshold can be a pre-set threshold.

[0056] Optionally, the drive fault type fault code can be a fault code corresponding to a pre-set drive fault type.

[0057] Specifically, after obtaining the count value, if the count value exceeds a preset safety threshold, the electronic device can report the corresponding drive fault type fault code.

[0058] Therefore, in this embodiment, real-time and historical target data of the unmanned mining vehicle can be acquired. Then, based on the real-time and historical target data, the actual net acceleration and expected net acceleration are calculated. If the actual and expected net accelerations meet the preset fault counter conditions, the counter value is updated. Finally, if the counter value exceeds a preset safety threshold, the corresponding drive fault type fault code is reported. This allows for timely and accurate diagnosis when the drive system malfunctions, improving the accuracy of drive force anomaly fault detection, significantly reducing false alarms and missed alarms, and effectively enhancing the safety and reliability of the unmanned mining vehicle during operation.

[0059] Optionally, S110 may specifically include: setting a sliding window of a preset length; updating the cached throttle commands and road slope in real time based on the sliding window to obtain the historical target data of the unmanned mining truck.

[0060] In this embodiment of the disclosure, the electronic device can set a sliding window of a preset length.

[0061] Specifically, the driving pressure response delay period of the unmanned mining truck is about 600ms. The preset length of the sliding window for the throttle command is set to twice that delay period, and the preset length of the sliding window for the road slope is also set to twice that delay period. The preset length of the corresponding sliding window can be set according to different vehicle models.

[0062] Furthermore, the electronic device can update the cached throttle commands and road gradients in real time based on the sliding window to obtain the historical target data of the unmanned mining truck.

[0063] Specifically, after determining the sliding window corresponding to the throttle command and the sliding window for the road slope, the historically stored throttle commands and road slope data are updated through the sliding windows respectively. For example, when the historically stored throttle commands and road slope data exceed the size of the sliding window, the initially cached data is cleared and new data is added after the data.

[0064] Optionally, S120 may specifically include: calculating the acceleration generated by rolling resistance and the acceleration generated by ramp resistance based on the real-time target data and the historical target data; and calculating the actual net acceleration based on the acceleration generated by rolling resistance and the acceleration generated by ramp resistance.

[0065] In this embodiment of the disclosure, the electronic device can calculate the acceleration generated by rolling resistance and the acceleration generated by ramp resistance based on the real-time target data and the historical target data.

[0066] Optionally, rolling resistance can be the resistance that a pneumatic tire experiences when it rolls in a straight line on an ideal road surface (usually a flat, dry, hard road surface), with its outer edge center symmetry plane aligned with the rolling direction of the wheel, and the resistance that is opposite to the rolling direction.

[0067] Alternatively, ramp resistance can be the gravitational component that an object must overcome when moving on an inclined surface, and its value is proportional to the slope angle and the mass of the object.

[0068] Specifically, the electronic device calculates the adaptive rolling resistance coefficient: f_gain = f_base * (1.0 + 0.15 * tan(v / v_max)), where f_base is the base value of rolling resistance, designed to be 0.025 here, v is the actual vehicle speed, and v_max is the maximum driving speed. Next, based on the real-time target data and the historical target data, the acceleration generated by rolling resistance and the acceleration generated by slope resistance are calculated respectively. For example, the formula for calculating the acceleration generated by rolling resistance is: g_roll = f_gain * g * cos(slope), where f_gain is the adaptive rolling resistance coefficient with vehicle speed, g is the acceleration due to gravity, and slope is the road gradient (positive for uphill, negative for downhill). Note that rolling resistance is always positive and opposite to the direction of travel. For example, the formula for calculating the acceleration generated by slope resistance is: g_grade=g*sin(slope). When going uphill, sin(slope)>0, the slope resistance is positive (opposite to the direction of travel, resistance); when going downhill, sin(slope)<0, the slope resistance is negative (same as the direction of travel, assist).

[0069] Furthermore, the electronic device can calculate the actual net acceleration based on the acceleration generated by the rolling resistance and the acceleration generated by the ramp resistance.

[0070] Specifically, after calculating the acceleration generated by rolling resistance and the acceleration generated by ramp resistance, the actual net acceleration can be calculated based on the acceleration generated by rolling resistance and the acceleration generated by ramp resistance. For example, the formula for calculating the actual net acceleration is: net_accel_act = acc_actual - g_roll - g_grade, where acc_actual is the actual acceleration of the vehicle.

[0071] Optionally, S120 may specifically include: performing an index lookup based on the real-time target data and the historical target data to obtain a speed index and a throttle opening index, and applying boundary constraints to the speed index and the throttle opening index; calculating a first weight and a second weight corresponding to the speed index and the throttle opening index; and performing interpolation calculations based on the speed index, the throttle opening index, the first weight, and the second weight using a bilinear interpolation algorithm to obtain the desired net acceleration.

[0072] In this embodiment of the disclosure, the electronic device can perform an index lookup based on the real-time target data and the historical target data to obtain a speed index and a throttle opening index, and apply boundary constraints to the speed index and the throttle opening index.

[0073] Specifically, the electronic device can perform index lookup based on the real-time target data and the historical target data. For example, based on the current actual vehicle speed and the cached throttle opening, it can search for the corresponding interval in the speed list and the throttle opening list respectively to obtain the speed index and the throttle opening index. Boundary constraints are applied to the searched speed index and throttle opening index to prevent array boundary overflow.

[0074] Furthermore, the electronic device can calculate the first weight and the second weight corresponding to the speed index and the throttle opening index.

[0075] Specifically, the electronic device can calculate the first weight of the current vehicle speed in the speed list interval based on the speed index, and calculate the second weight of the throttle opening index in the throttle opening list interval based on the throttle opening index.

[0076] Furthermore, the electronic device can perform interpolation calculations based on the speed index, the throttle opening index, the first weight, and the second weight using a bilinear interpolation algorithm to obtain the desired net acceleration.

[0077] Specifically, after obtaining the speed index, the throttle opening index, the first weight, and the second weight, interpolation calculation is performed using a bilinear interpolation algorithm. For example, the bilinear interpolation algorithm can be as follows: Assuming there is a two-dimensional dataset, and the values ​​of the four adjacent pixels with the top-left pixel (x0, y0) and the bottom-right pixel (x1, y1) are known to be f(x0, y0), f(x1, y0), f(x0, y1), and f(x1, y1), respectively, the value of pixel (x, y) can be estimated using the bilinear interpolation formula. The mathematical expression for bilinear interpolation is as follows: f(x, y) = (1-Δx)*(1-Δy)*f(x0, y0) + Δx*(1-Δy)*f(x1, y0) + (1-Δx)*Δy*f(x0, y1) + Δx*Δy*f(x1, y1). Here, Δx and Δy are the relative position weights of the target point (x,y) relative to the top left corner point (x0,y0), that is: Δx=(x-x0) / (x1-x0)Δy=(y-y0) / (y1-y0), thus obtaining the desired net acceleration.

[0078] Optionally, S130 may specifically include: if the real-time target data meets the preset abnormality diagnosis conditions, determining whether the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions; if the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions, updating the count value.

[0079] In this embodiment of the disclosure, if the real-time target data is determined to meet the preset abnormality diagnosis conditions, the electronic device can determine whether the actual net acceleration and the expected net acceleration meet the preset fault counter activation conditions.

[0080] Optionally, the preset abnormal diagnosis conditions can be pre-set conditions used to determine whether abnormal diagnosis is needed.

[0081] Specifically, the electronic device can determine whether the real-time target data meets preset anomaly diagnosis conditions, such as whether it is in neutral, whether the parking brake is engaged, or whether the throttle opening is below a set threshold. If the preset anomaly diagnosis conditions are not met, no anomaly diagnosis is performed, avoiding unnecessary calculations that increase the risk of misjudgment and time consumption. When the preset anomaly diagnosis conditions are met, the electronic device can determine whether the actual net acceleration and the expected net acceleration meet preset start-up fault counter conditions, such as by determining whether the actual net acceleration, the expected net acceleration, and the proportion of the actual net acceleration in the expected net acceleration meet the preset start-up fault counter conditions.

[0082] Furthermore, if the actual net acceleration and the expected net acceleration satisfy the preset start-up fault counter conditions, the count value is updated.

[0083] Specifically, when the electronic device determines that the actual net acceleration and the expected net acceleration meet the preset conditions for activating the fault counter, it activates the fault counter to update the count value.

[0084] Optionally, S140 may specifically include: reporting a true fault flag when the count value exceeds a preset safety threshold; and reporting the corresponding drive fault type fault code based on the true fault flag and according to a preset fault type definition rule.

[0085] In this embodiment of the disclosure, if the count value exceeds a preset safety threshold, the electronic device can report a true fault flag.

[0086] The counter counting mechanism is as follows: Count increment mechanism: The timing speed is dynamically adjusted based on the proportion of the actual net acceleration in the expected net acceleration; Count decay mechanism: When the conditions for starting the counter are not met or there is no fault, the counter needs to be decayed slowly to avoid invalid counting and increase the risk of misjudgment.

[0087] Specifically, after the electronic device starts the fault counter to update the count value, if the count value exceeds the preset safety threshold, the electronic device can report a true fault.

[0088] Furthermore, electronic devices can report the corresponding drive fault type fault code based on a true fault flag and according to preset fault type definition rules.

[0089] Specifically, after a genuine fault is reported, the electronic equipment can report the corresponding drive fault type fault code according to preset fault type definition rules. For example, preset fault type definition rules might include: Insufficient drive force: When the drive throttle opening exceeds 50% under no-load conditions and 70% under heavy-load conditions, and remains so for 5 seconds or more (considering the driving force response delay of mining vehicles), and the torque achievement rate (actual torque / requested torque) is <85%, it is determined to be insufficient drive force. Conditions such as road surface adhesion coefficient less than 0.3, gradient change >5%, and power threshold dynamic adjustment at altitudes exceeding 3000m must be excluded. Drive force failure: When the drive throttle opening exceeds 50% under no-load conditions and 70% under heavy-load conditions, and remains so for 3 seconds or more (considering the driving force response delay of mining vehicles), and the wheel-end torque is below 10Nm, or the drive shaft speed is almost close to 0, it is determined to be drive force failure. Conditions such as gear shifting, kinetic energy recovery, and forced power cut-off by the protection system must be excluded. Abnormal driving force: When the throttle opening exceeds 50% under no-load conditions and 70% under heavy-load conditions, and the torque response time from the accelerator pedal to the wheel end is greater than 120% of the baseline value (greater than 600ms for gasoline vehicles and greater than 300ms for electric vehicles), it is determined to be an abnormal driving force, and the condition of the system without protection mode activation must be excluded.

[0090] Optionally, in this embodiment of the disclosure, when preset working conditions are met, abnormal diagnosis and reporting are not performed, such as when the road surface adhesion coefficient is less than 0.3; the road slope change rate exceeds the set safety threshold; the flag is activated during the gear shifting process; the flag is activated during kinetic energy recovery.

[0091] Figure 2 A flowchart illustrating another method for diagnosing abnormal driving force of unmanned mining vehicles provided in an embodiment of this disclosure is shown.

[0092] like Figure 2 As shown, the electronic device can acquire real-time target data and historical target data of the unmanned mining vehicle. The real-time target data includes actual vehicle speed, actual acceleration / acceleration, road slope, braking command, throttle command, gear position, and parking status. By setting a sliding window of a preset length, and updating the cached throttle command and road slope data in real time based on the sliding window, the historical target data of the unmanned mining vehicle is obtained.

[0093] Further, the acceleration generated by rolling resistance and the acceleration generated by ramp resistance are calculated based on the real-time target data and the historical target data; the actual net acceleration is calculated based on the acceleration generated by rolling resistance and the acceleration generated by ramp resistance. An index lookup is performed based on the real-time target data and the historical target data to obtain a speed index and a throttle opening index; a first weight and a second weight corresponding to the speed index and the throttle opening index are calculated; and the desired net acceleration is obtained by interpolation using a bilinear interpolation algorithm based on the speed index, the throttle opening index, the first weight, and the second weight.

[0094] Finally, if the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions, the count value is updated; if the count value exceeds the preset safety threshold, the corresponding drive fault type fault code is reported.

[0095] Figure 3 A flowchart illustrating a method for setting preset fault type definition rules is shown in an embodiment of this disclosure.

[0096] like Figure 3As shown, the electronic equipment can determine preset fault type classification rules, such as insufficient driving force: when the drive throttle opening exceeds 50% under no-load conditions and 70% under heavy load conditions, and lasts for 5 seconds or more (considering the driving force response delay of mining vehicles), and the torque realization rate (actual torque / requested torque) is <85%, it is judged as insufficient driving force. Conditions such as road surface adhesion coefficient less than 0.3, gradient change >5%, and power threshold dynamic adjustment at altitudes exceeding 3000m must be excluded. Driving force failure: when the drive throttle opening exceeds 50% under no-load conditions and 70% under heavy load conditions, and lasts for 3 seconds or more (considering the driving force response delay of mining vehicles), and the wheel end torque is less than 10Nm, or the drive shaft speed is almost close to 0, it is judged as driving force failure. Conditions such as gear shifting, kinetic energy recovery, and forced power cut-off by the protection system must be excluded. Abnormal driving force: When the throttle opening exceeds 50% under no-load conditions and 70% under heavy-load conditions, and the torque response time from the accelerator pedal to the wheel end is greater than 120% of the baseline value (greater than 600ms for gasoline vehicles and greater than 300ms for electric vehicles), it is determined to be an abnormal driving force, and the condition of the system without protection mode activation must be excluded.

[0097] Figure 4 A schematic diagram of the structure of an abnormal driving force diagnostic device for an unmanned mining vehicle provided in an embodiment of this disclosure is shown.

[0098] like Figure 4 As shown, the unmanned mining vehicle driving force abnormality diagnosis device 400 may include a data acquisition module 410, a speed calculation module 420, a value update module 430, and a fault reporting module 440.

[0099] The data acquisition module 410 can be used to acquire real-time target data and historical target data of unmanned mining vehicles;

[0100] The velocity calculation module 420 can be used to calculate the actual net acceleration and the expected net acceleration based on the real-time target data and the historical target data;

[0101] The numerical update module 430 can be used to update the count value when it is determined that the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions;

[0102] The fault reporting module 440 can be used to report the corresponding drive fault type fault code when the count value exceeds a preset safety threshold.

[0103] Therefore, in this embodiment, real-time and historical target data of the unmanned mining vehicle can be acquired. Then, based on the real-time and historical target data, the actual net acceleration and expected net acceleration are calculated. If the actual and expected net accelerations meet the preset fault counter conditions, the counter value is updated. Finally, if the counter value exceeds a preset safety threshold, the corresponding drive fault type fault code is reported. This allows for timely and accurate diagnosis when the drive system malfunctions, improving the accuracy of drive force anomaly fault detection, significantly reducing false alarms and missed alarms, and effectively enhancing the safety and reliability of the unmanned mining vehicle during operation.

[0104] In some embodiments of this disclosure, the real-time target data includes actual vehicle speed, actual acceleration / acceleration, road gradient, braking command, throttle command, gear position, and parking status.

[0105] In some embodiments of this disclosure, the data acquisition module 410 may specifically include:

[0106] The window setting unit can be used to set a sliding window of a preset length;

[0107] The data update unit can be used to update the cached throttle commands and road slopes in real time based on the sliding window to obtain the historical target data of the unmanned mining truck.

[0108] In some embodiments of this disclosure, the speed calculation module 420 may specifically include:

[0109] The first calculation unit can be used to calculate the acceleration generated by rolling resistance and the acceleration generated by ramp resistance based on the real-time target data and the historical target data.

[0110] The second calculation unit can be used to calculate the actual net acceleration based on the acceleration generated by the rolling resistance and the acceleration generated by the ramp resistance.

[0111] In some embodiments of this disclosure, the numerical update module 430 may specifically include:

[0112] The index lookup unit can be used to perform an index lookup based on the real-time target data and the historical target data to obtain the speed index and the throttle opening index;

[0113] The third calculation unit can be used to calculate the first weight and the second weight corresponding to the speed index and the throttle opening index;

[0114] The fourth calculation unit can be used to perform interpolation calculations based on the speed index, the throttle opening index, the first weight, and the second weight using a bilinear interpolation algorithm to obtain the desired net acceleration.

[0115] In some embodiments of this disclosure, the numerical update module 430 may specifically include:

[0116] The condition judgment unit can be used to determine whether the actual net acceleration and the expected net acceleration meet the preset fault counter start condition when the real-time target data meets the preset abnormal diagnosis condition.

[0117] The numerical update unit can be used to update the count value when it is determined that the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions.

[0118] In some embodiments of this disclosure, the fault reporting module 440 may specifically include:

[0119] The first reporting unit can be used to report a fault flag as true when the count value exceeds a preset safety threshold;

[0120] The second reporting unit can be used to report the corresponding drive fault type fault code based on a true fault flag and according to a preset fault type definition rule.

[0121] It should be noted that, Figure 4 The unmanned mining vehicle drive force anomaly diagnostic device 400 shown can perform... Figures 1 to 3 The various steps in the method embodiment shown are implemented. Figures 1 to 3 The processes and effects in the method embodiments shown are not described in detail here.

[0122] Figure 5 A schematic diagram of the structure of a diagnostic device for abnormal driving force of an unmanned mining vehicle provided in an embodiment of this disclosure is shown.

[0123] In some embodiments of this disclosure, Figure 5 The diagnostic device for abnormal driving force of the unmanned mining vehicle shown can be an electronic device. Specifically, the electronic device can include, but is not limited to, devices such as computer equipment, cloud servers, or cloud server clusters.

[0124] like Figure 5 As shown, the unmanned mining vehicle driving force abnormality diagnosis device may include a processor 501 and a memory 502 storing computer program instructions.

[0125] Specifically, the processor 501 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0126] Memory 502 may include a large-capacity storage for information or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to the integrated gateway device. In a particular embodiment, memory 502 is a non-volatile solid-state memory. In a particular embodiment, memory 502 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0127] The processor 501 reads and executes the computer program instructions stored in the memory 502 to perform the steps of the unmanned mining vehicle driving force abnormality diagnosis method provided in the embodiments of this disclosure.

[0128] In one example, the unmanned mining vehicle drive force anomaly diagnostic device may further include a transceiver 503 and a bus 504. Wherein, as Figure 5 As shown, the processor 501, memory 502 and transceiver 503 are connected via bus 504 and communicate with each other.

[0129] Bus 504 may include hardware, software, or both. For example, and not limitingly, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 504 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0130] This disclosure also provides a computer-readable storage medium that can store a computer program. When the computer program is executed by a processor, the processor enables the processor to implement the unmanned mining vehicle driving force abnormality diagnosis method provided in this disclosure.

[0131] The aforementioned storage medium may include, for example, a memory 502 containing computer program instructions, which can be executed by the processor 501 of the unmanned mining truck drive force anomaly diagnosis device to complete the unmanned mining truck drive force anomaly diagnosis method provided in this embodiment. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc ROM (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0132] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0133] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for diagnosing abnormal driving force of an unmanned mining vehicle, characterized in that, include: Acquire real-time and historical target data of unmanned mining vehicles; Calculate the actual net acceleration and the expected net acceleration based on the real-time target data and the historical target data; If the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions, update the counter value; If the count value exceeds a preset safety threshold, the corresponding drive fault type fault code shall be reported. The calculation of the expected net acceleration based on the real-time target data and the historical target data includes: Based on the real-time target data and the historical target data, an index lookup is performed to obtain the speed index and the throttle opening index, and boundary constraints are applied to the speed index and the throttle opening index. Calculate the first weight and the second weight corresponding to the speed index and the throttle opening index; The desired net acceleration is obtained by interpolating using a bilinear interpolation algorithm based on the speed index, the throttle opening index, the first weight, and the second weight.

2. The method according to claim 1, characterized in that, The real-time target data includes actual vehicle speed, actual acceleration / acceleration, road gradient, braking command, throttle command, gear position, and parking status.

3. The method according to claim 1, characterized in that, Obtain historical target data for unmanned mining trucks, including: Set a preset length for the sliding window; Based on the sliding window, the cached throttle commands and road slope are updated in real time to obtain the historical target data of the unmanned mining truck.

4. The method according to claim 1, characterized in that, The calculation of the actual net acceleration based on the real-time target data and the historical target data includes: Calculate the acceleration generated by rolling resistance and the acceleration generated by ramp resistance based on the real-time target data and the historical target data; The actual net acceleration is calculated based on the acceleration generated by the rolling resistance and the acceleration generated by the ramp resistance.

5. The method according to claim 1, characterized in that, The step of updating the count value when determining that the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions includes: If the real-time target data meets the preset abnormal diagnosis conditions, determine whether the actual net acceleration and the expected net acceleration meet the preset fault counter activation conditions. If the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions, the counter value is updated.

6. The method according to claim 1, characterized in that, When the count value exceeds a preset safety threshold, the corresponding drive fault type fault code is reported, including: If the count value exceeds a preset safety threshold, a fault flag is reported as true. Based on the true fault flag, the corresponding drive fault type fault code is reported according to the preset fault type definition rules.

7. A diagnostic device for abnormal driving force of an unmanned mining vehicle, characterized in that, include: The data acquisition module is used to acquire real-time and historical target data of the unmanned mining vehicle; The velocity calculation module is used to calculate the actual net acceleration and the expected net acceleration based on the real-time target data and the historical target data; The numerical update module is used to update the count value when it is determined that the actual net acceleration and the expected net acceleration meet the preset start-up fault counter conditions; The fault reporting module is used to report the corresponding drive fault type fault code when the count value exceeds a preset safety threshold. The calculation of the expected net acceleration based on the real-time target data and the historical target data includes: Based on the real-time target data and the historical target data, an index lookup is performed to obtain the speed index and the throttle opening index, and boundary constraints are applied to the speed index and the throttle opening index. Calculate the first weight and the second weight corresponding to the speed index and the throttle opening index; The desired net acceleration is obtained by interpolating using a bilinear interpolation algorithm based on the speed index, the throttle opening index, the first weight, and the second weight.

8. A diagnostic device for abnormal driving force of an unmanned mining vehicle, characterized in that, include: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the unmanned mining vehicle driving force abnormality diagnosis method according to any one of claims 1-6.

9. A non-volatile computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, causes the processor to implement the method for diagnosing abnormal driving force of unmanned mining vehicles as described in any one of claims 1-6.

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