A method and related equipment for determining the shifting pattern of an intelligent driving mining truck

Abstract

This application provides a method and related equipment for determining the shifting pattern of an intelligent driving mining truck, relating to the technical fields of unmanned driving, autonomous driving, and unmanned vehicles. The method includes: acquiring real-vehicle operation data; extracting gear condition data based on the real-vehicle operation data, the condition data including multiple condition points under each gear, each condition point including an accelerator pedal value and a corresponding vehicle speed value; setting a numerical range for the accelerator pedal value, and within this range, sequentially selecting multiple shift acceleration pedal values; for each shift acceleration pedal value, selecting at least a portion of the corresponding condition points, and calculating the upshift or downshift speed for each shift acceleration pedal value based on the selected at least a portion of the condition points; and forming the upshift or downshift pattern information for that gear based on the upshift or downshift speed for each shift acceleration pedal value. Implementing the technical solution of this application can improve the accuracy of determining the shifting pattern.

Description

Technical Field

[0001] This application relates to the field of autonomous driving, and in particular to a method and related equipment for determining the shifting pattern of an intelligent driving mining truck. Background Technology

[0002] In the context of autonomous driving in mining, the power and economy of intelligent driving mining trucks have attracted much attention, and the shifting pattern (shifting curve) directly affects the power and economy of the vehicle.

[0003] Existing technologies determine shift patterns using methods based on theoretical calculations or vehicle calibration, without considering the influence of actual vehicle operating scenarios and conditions. This leads to a certain error between the determined shift patterns and the actual vehicle shift patterns. In particular, in mining autonomous driving scenarios, the error between the actual shift patterns and the shift patterns determined based on theoretical values ​​is relatively large due to the influence of operating scenarios and conditions. Therefore, how to accurately determine the vehicle shift patterns is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] To address at least one technical problem in the prior art, this application provides a method and related equipment for determining the shifting pattern of an intelligent driving mining truck.

[0005] According to a first aspect of this application, a method for determining the shifting pattern of an intelligent driving mining truck is provided, comprising:

[0006] Obtain real-world vehicle operation data;

[0007] Based on the actual vehicle operation data, the working condition data of the gear is extracted. The working condition data includes multiple working condition points under the gear. The working condition points include the accelerator pedal value and the corresponding vehicle speed value.

[0008] Set the range of values ​​for the accelerator pedal, and within this range, select multiple shift acceleration pedal values ​​in sequence;

[0009] For each of the shift acceleration pedal values, select at least some corresponding operating points, and calculate the upshift speed or downshift speed for each of the shift acceleration pedal values ​​based on the selected at least some operating points.

[0010] Based on the upshift or downshift speed of each shift acceleration pedal value, the upshift or downshift pattern information for that gear is formed.

[0011] Optionally, the step of selecting at least some corresponding operating points includes: obtaining specific operating points where the acceleration pedal values ​​are all near the shift acceleration pedal values, the difference between the maximum and minimum vehicle speeds is less than or equal to Δh, and the number is greater than or equal to m, where Δh and m are both optimization parameters.

[0012] Optionally, the step of selecting at least some of the corresponding operating points includes: obtaining specific operating points obtained by clustering, where all acceleration pedal values ​​are near the shift acceleration pedal values.

[0013] Optionally, the range near the shift acceleration pedal value includes the interval range of Δd, where Δd is an optimization parameter.

[0014] Optionally, the step of calculating the upshift or downshift speed for each of the shift acceleration pedal values ​​includes: calculating the average speed of multiple specific operating points.

[0015] Optionally, the step of selecting multiple shift acceleration pedal values ​​includes: selecting multiple shift acceleration pedal values ​​with equal arithmetic differences ΔAccPedal, where ΔAccPedal is an optimization parameter.

[0016] Optionally, the upshift or downshift pattern information of the gear position is used to establish a shift model with the upshift or downshift pattern information of other gear positions.

[0017] Optionally, the step of extracting gear condition data based on the actual vehicle operation data includes: extracting the gear, accelerator pedal value and vehicle speed value at a preset time interval, and extracting gear condition data based on the gear, accelerator pedal value and vehicle speed value at the corresponding time.

[0018] Optionally, the method further includes removing abnormal data from the operating condition data, wherein the abnormal data is data with abnormal changes in vehicle speed value in the operating condition data, or, wherein the abnormal data is data in the operating condition data that is restricted by the highest gear.

[0019] Optionally, the step of setting the numerical range of the accelerator pedal value includes: obtaining the corresponding maximum accelerator pedal value and minimum accelerator pedal value based on the operating point of the gear and the operating points of other gears, and using the maximum accelerator pedal value and minimum accelerator pedal value to limit the numerical range.

[0020] According to a second aspect of this application, a device for determining the shifting pattern of an intelligent driving mining truck is provided, comprising:

[0021] The acquisition module is used to acquire real-vehicle operation data;

[0022] The extraction module is used to extract the operating condition data of the gear based on the actual vehicle operation data. The operating condition data includes multiple operating condition points under the gear, and the operating condition points include the accelerator pedal value and the corresponding vehicle speed value.

[0023] The selection module is used to set the numerical range of the accelerator pedal value, and within this numerical range, multiple shift acceleration pedal values ​​are selected sequentially.

[0024] The calculation module is used to select at least a portion of the corresponding operating conditions for each shift acceleration pedal value, and calculate the upshift speed or downshift speed for each shift acceleration pedal value based on the selected at least a portion of the operating conditions.

[0025] The pattern information forming module is used to form upshift or downshift pattern information for a given gear based on the upshift or downshift speed of each shift acceleration pedal value.

[0026] According to a third aspect of this application, an electronic device is provided, comprising:

[0027] Processor; and

[0028] Stored program memory,

[0029] The program includes instructions that, when executed by the processor, cause the processor to perform the method according to any one of the first aspects of this application.

[0030] According to a fourth aspect of this application, a non-transitory computer-readable storage medium is provided storing computer instructions for causing the computer to perform the method according to any one of the first aspects.

[0031] The one or more technical solutions provided in this application, compared to theoretical or calibration data directly obtained from component manufacturers, provide shifting pattern information based on real vehicle operating data that more closely reflects actual vehicle conditions, resulting in more accurate shifting patterns. This information can be directly used for simulation calculations of vehicle fuel consumption, evaluating overall vehicle operating efficiency and costs; it can also be used for MIL (Model in loop, verifying whether the control algorithm model accurately implements functional requirements) / HIL (Hardware in loop, verifying whether the functions implemented by the code on the ECU / EPP / the entire system are consistent with the defined requirements) simulation tests of intelligent driving control algorithms, improving the realism of the simulation and assisting in the performance optimization of the control algorithm. Therefore, the method described in this application can yield more accurate shifting patterns, which is beneficial for clarifying the optimization direction of intelligent driving algorithms, achieving the goal of reducing fuel consumption costs and improving operating efficiency.

[0032] One or more technical solutions provided in this application determine the downshift or upshift speed at a corresponding shift accelerator pedal value based on the average vehicle speed values ​​at certain operating points. This reduces the negative impact of abnormal data on the determination of downshift and upshift speeds, further improving the accuracy of shift pattern determination. Attached Figure Description

[0033] The accompanying drawings illustrate exemplary embodiments of the present application and, together with the description thereof, serve to explain the principles of the present application. These drawings are included to provide a further understanding of the present application and are incorporated in and constitute a part of this specification.

[0034] Figure 1 A flowchart is shown of a method for determining the shifting pattern of an intelligent driving mining truck according to an exemplary embodiment of this application;

[0035] Figure 2 A schematic diagram of accelerator pedal data according to an exemplary embodiment of this application is shown;

[0036] Figure 3 A schematic diagram of another accelerator pedal data is shown according to an exemplary embodiment of this application;

[0037] Figure 4 A schematic diagram of simulated gear positions and actual vehicle gear positions according to an exemplary embodiment of this application is shown;

[0038] Figure 5 A schematic block diagram of an intelligent driving mining truck gear shifting pattern determination device according to an exemplary embodiment of this application is shown;

[0039] Figure 6 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of this application is shown. Detailed Implementation

[0040] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application 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 application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0041] It should be understood that the steps described in the method embodiments of this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.

[0042] The term "comprising" and its variations as used herein are open-ended, 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 following description. It should be noted that the concepts of "first", "second", etc., mentioned in this application are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0043] It should be noted that the terms "a" and "a plurality of" used in this application 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".

[0044] The names of the messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0045] The step numbers in the embodiments of this application are only for the convenience of explaining specific embodiments and are not intended to limit the order in which the steps are performed.

[0046] The following description of the scheme of this application refers to the accompanying drawings. Figure 1 A method for determining the shifting pattern of an intelligent driving mining truck, comprising:

[0047] S101, obtain real vehicle operation data.

[0048] It is understood that the real-vehicle operation data in this application embodiment is data generated by the vehicle in a real-world operating scenario. Real-vehicle operation data includes gear position, vehicle speed, and accelerator pedal position. It is also known that real-vehicle operation data may include other data, such as time of day and maximum gear limit.

[0049] The method for determining the shifting pattern in this application embodiment can be applied to intelligent driving mining trucks. Intelligent driving mining trucks can be unmanned mining trucks, unmanned transport vehicles, ordinary vehicles with unmanned driving functions, etc. The method for determining the shifting pattern in this application embodiment can also be applied to other vehicles that can use this method.

[0050] S102 extracts the operating condition data of each gear based on real vehicle operation data. The operating condition data includes multiple operating points under each gear, including accelerator pedal value and corresponding vehicle speed value.

[0051] Understandably, the gear condition data extracted in this step is the operating condition data for the gears whose shift patterns need to be estimated. This operating condition data is used to calculate the shift patterns of the corresponding gears. For example, if the gears whose shift patterns need to be estimated are 1st, 2nd, ..., nth (where n is the highest gear in the transmission), the operating condition data for 1st gear can be extracted to calculate the shift pattern for 1st gear; the operating condition data for 2nd gear can be extracted to calculate the shift pattern for 2nd gear; and so on, the operating condition data for nth gear can also be extracted to calculate the shift pattern for nth gear. The number of operating condition points for each gear can be determined based on the data content of the corresponding real-vehicle operating data and the actual accuracy requirements, and this application does not limit it.

[0052] Specifically, based on real-vehicle operating data, the gear position, accelerator pedal value, and vehicle speed can be extracted at preset time intervals. Based on these values, multiple operating points for each gear are extracted to obtain the gear's operating condition data. The preset time interval can be set according to actual needs, such as 0.02 seconds, 0.05 seconds, or 0.1 seconds. This preset time interval can be used as an optimization parameter, adjusted according to the accuracy of the determined shifting pattern.

[0053] Understandably, when the operating point includes the time, corresponding gear, corresponding accelerator pedal value, corresponding vehicle speed value, and corresponding maximum limit gear, step S102 can extract the time, gear, accelerator pedal value, vehicle speed value, and maximum limit gear at preset time intervals based on real vehicle operation data, and obtain multiple operating points under each gear according to the time, gear, accelerator pedal value, vehicle speed value, and maximum limit gear.

[0054] For example, based on real vehicle operation data, the gear position, accelerator pedal value, vehicle speed value, and maximum gear limit value are extracted at fixed preset time intervals of 0.05 according to the timestamp, resulting in the following state matrix sequence:

[0055]

[0056] Wherein, [t] is defined i CurtGear i AccPedal i Veh i TopGearLmt i [A] represents a working condition point.

[0057] Where t1 represents the time when operations begin, t i Indicates operating time, t n Indicates the end time of operation, t i -t i-1 =0.05s; CurtGeari It is t i The current gear position of the vehicle is fed back by the TCU (Transmission Control Unit); ACcPedal i It is t i The actual accelerator pedal value at any given moment (accelerator pedal value pressed by the driver / accelerator pedal value issued by the intelligent driving controller); Veh i It is t i The vehicle speed value fed back by the VCU (Vehicle Controller) at any given time; TopGearLmt i It is t i The highest limit gear that the TCU (Transmission Control Unit) is feeding back at any given time.

[0058] Based on the data from different gear positions, we obtain the accelerator pedal value, maximum gear limit, and vehicle speed at various operating points for gears 1, 2, ..., n (where n is the highest gear in the transmission). For example, the data for gear 2 is as follows:

[0059]

[0060] Among them, t a The moment when the TCU (Transmission Control Unit) reports the vehicle's current gear as 2 for the first time, and t k The time when the vehicle's current gear is 2 for the second time, as reported by the TCU (Transmission Control Unit), and so on, t j The last time the vehicle's current gear was 2, as fed back by the TCU (Transmission Control Unit); similarly, AccPedal... i Veh i TopGeqrLmt i For t i The actual accelerator pedal position, vehicle speed, and maximum gear limit at any given time.

[0061] Before executing S103, this embodiment of the application can preprocess the gear condition data. By analyzing the condition data, it can identify the condition points restricted by the highest gear and the condition points with abnormal speed changes, and then perform data rejection processing. Through data rejection processing, the impact of abnormal data on the determination of shift patterns can be reduced, thereby improving the accuracy of shift pattern determination.

[0062] S103, set the value range of the accelerator pedal, and select multiple shift acceleration pedal values ​​in sequence within the value range.

[0063] In this step, the range of accelerator pedal values ​​can be set according to actual needs. For example, the maximum and minimum accelerator pedal values ​​can be obtained based on the operating points of the gear and other gears, and the range can be defined using the maximum and minimum accelerator pedal values. Alternatively, the range of accelerator pedal values ​​can be directly set based on vehicle parameters.

[0064] In this step, multiple shift acceleration pedal values ​​can be selected sequentially according to actual needs. For example, based on a set numerical range, the accelerator pedal values ​​can be evenly divided with equal differences ΔAccPedal, and multiple acceleration pedal values ​​with equal differences ΔAccPedal can be selected, where ΔAccPedal is an optimization parameter. In this embodiment, reducing the equal difference ΔAccPedal can refine the accelerator pedal division, increase data points, and improve accuracy, but it will also bring more computational load. Therefore, the equal difference ΔAccPedal can be set according to the requirements of accuracy and computational load.

[0065] S104: For each shift acceleration pedal value, select at least some corresponding operating points, and calculate the upshift speed or downshift speed for each shift acceleration pedal value based on the selected at least some operating points.

[0066] In this step, the upshift or downshift speed is calculated based on at least some operating points, which can eliminate the influence of some data anomalies and improve the accuracy of the upshift or downshift speed.

[0067] In this step, various methods can be used to select at least a portion of the corresponding operating conditions. For example, specific operating conditions can be obtained where all acceleration pedal values ​​are near the shift acceleration pedal value, the difference between the maximum and minimum vehicle speeds is less than or equal to Δh, and the number of such points is greater than or equal to m. Here, Δh and m are optimization parameters. These specific operating conditions for the shift acceleration pedal value are at least a portion of the operating conditions corresponding to that shift acceleration pedal value. For example, specific operating conditions obtained by clustering can be obtained where all acceleration pedal values ​​are near the shift acceleration pedal value. Specifically, operating conditions where all acceleration pedal values ​​are near the shift acceleration pedal value can be obtained, and the vehicle speed values ​​of the corresponding operating conditions can be clustered to obtain the corresponding specific operating conditions. More specifically, the k-means clustering algorithm can be used to cluster the vehicle speed values ​​of the corresponding operating conditions. It is known that other clustering algorithms can also be used to cluster the vehicle speed values ​​of the corresponding operating conditions.

[0068] The vicinity of the shift acceleration pedal value can be a corresponding range of the shift acceleration pedal value. For example, the range of the vicinity of the shift acceleration pedal value can be Δd, where Δd is the optimization parameter. Specifically, the shift acceleration pedal value of [shift acceleration pedal value - Δd, shift acceleration pedal value + Δd] can be used as the shift acceleration pedal value near the shift acceleration pedal value. It is known that the optimization parameter Δd can be adjusted according to the accuracy of the determined shift pattern or the corresponding computational load. Depending on the actual situation, other methods can also be used, setting the range of the vicinity of the shift acceleration pedal value with the shift acceleration pedal value as the base point and the parameter Δd as the range.

[0069] As we know, generally speaking, increasing the parameters m, Δd, and Δh can improve the accuracy of the shifting pattern, but it will lead to an increase in the amount of calculation. Therefore, the optimal parameters m, Δd, and Δh should be determined based on the actual amount of calculation and accuracy requirements.

[0070] The upshift or downshift speed of the shift acceleration pedal value can be obtained by calculating the mean value of the vehicle speed at multiple specific operating points. When the specific operating points are obtained through clustering, they can also be calculated based on the vehicle speed of the cluster center.

[0071] It is known that, generally, the upshift speed of the shift acceleration pedal value is the maximum speed corresponding to that shift acceleration pedal value, and the downshift speed is the minimum speed corresponding to that shift acceleration pedal value. However, due to various reasons such as shift delays and data anomalies, the actual upshift and downshift speeds are often not the corresponding maximum and minimum speeds. This application overcomes these shortcomings by using the average vehicle speed at specific operating points to calculate the corresponding upshift or downshift speed. Specifically, in this embodiment, the average vehicle speed at specific operating points can be calculated. The average maximum speed at the specific operating point corresponding to the shift acceleration pedal value is selected as the upshift speed corresponding to the shift acceleration pedal value of that gear, and the average minimum speed at the specific operating point corresponding to the shift acceleration pedal value is selected as the upshift speed corresponding to the shift acceleration pedal value of that gear. Of course, multiple sets of specific operating conditions can be determined for the acceleration pedal value, and the set of specific operating conditions with the highest vehicle speed and the set of specific operating conditions with the lowest vehicle speed can be selected as the specific operating conditions for calculating the upshift speed and downshift speed respectively.

[0072] For example, see Figure 2 Taking 2nd gear as an example, based on the accelerator pedal value data in the working condition data set of 1st gear, 2nd gear, ..., nth gear (n is the highest gear of the transmission), the maximum and minimum values ​​of the accelerator pedal values ​​in this data set are obtained. Then, the accelerator pedal values ​​are evenly divided between the minimum and maximum values ​​by equal differences ΔAccPedal, resulting in the following data:

[0073] [AccPedal1AccPedal2…AccPedal m-1 AccPedal m ]

[0074] Where AccPedal1 represents the minimum accelerator pedal value under this gear condition, AccPedal m This indicates the maximum accelerator pedal pressure under this gear condition, dccPedal i =AccPedal1+(i-1)*ΔAccPedal represents the value of the i-th accelerator pedal, and so on;

[0075] See Figure 3 For each AccPedal i All are processed as follows: In the x-axis direction, using AccPedal i Centered on the axis, a horizontal interval is selected on both the left and right sides of the width Δd, and the vehicle speed points within this interval are defined as valid data; in the y-axis direction, an interval with a height of Δh is constructed, forming a data window with an area of ​​2Δd×Δh with the horizontal interval.

[0076] Downshift point determination: Move the data window along the y-axis in the direction of increasing vehicle speed. When the number of data points within the window reaches or exceeds m for the first time, calculate the average value of the data within the window as AccPedal. i The downshift speed can be used to eliminate the influence of some data outliers, such as... Figure 3 The circle in the middle.

[0077]

[0078] Where num is the number of data points in the data window.

[0079] Upshift point determination: Similarly, move the data window along the y-axis in the direction of increasing vehicle speed. When the number of data points in the window reaches or exceeds m for the last time, calculate the average value of the data in the window as the upshift speed.

[0080]

[0081] Where num is the number of data points in the data window.

[0082] Using the method described above, from AccPedal i Traverse to AccPedal m After that, you can obtain the upshift and downshift data for that gear.

[0083] By processing the data for other gears in the same way, we can obtain the upshift and downshift data for gears 1, 2, ..., n (where n is the highest gear in the transmission). Among them, ΔAccPedal, Δd, Δh, and m are optimization parameters that can be combined with simulation results to optimize the accelerator pedal interval, data window size, and number of data points.

[0084] S105 generates upshift or downshift pattern information for a given gear based on the upshift or downshift speed of each shift acceleration pedal value.

[0085] Information on upshifting or downshifting patterns can be represented by upshifting or downshifting curves, or by other methods.

[0086] The upshifting or downshifting pattern information of a gear can be used to establish a shifting model by comparing it with the upshifting or downshifting pattern information of other gears. After establishing the shifting model, simulated gear data can be output based on real vehicle operating data. Based on the simulated gear data and the actual gear data, the downshifting and upshifting patterns can be optimized. The optimized parameters can be any one or more of the optimization parameters mentioned above. Specifically, the shifting model is built using extracted operating condition data such as time, accelerator pedal position, and vehicle speed as input, and the obtained shifting patterns are used as parameters. The output gear results are then compared with real vehicle gear data to correct and optimize the shifting patterns. The specific method is as follows:

[0087] Error calculation: Under the same working conditions, the error between the shifting pattern obtained by the above method and the actual vehicle shifting pattern is reflected in the time difference of the shifting point. The shifting time difference is used as an indicator to evaluate the effect of different parameter combinations.

[0088] Parameter optimization: Decreasing ΔAccPedal refines the accelerator pedal division, increases data points, and improves accuracy, but increases computational workload; increasing Δd, Δh, and m all increase the number of data points within the data window, thus improving accuracy; considering both computational workload and computational error, the following error evaluation equation is established:

[0089]

[0090]

[0091] E represents the computational load, C1, C2, C3, and C4 represent the weighting coefficients of each influencing factor in the error index, P1, P2, P3, and P4 represent the weighting coefficients of each influencing factor in the time index, and T represents the computational error. Multiple data combinations of ΔAccPedal, Δd, Δh, and m are selected. Based on the above evaluation equation, combined with the computational load and error, the optimal parameter combination is selected to obtain the shift curve data.

[0092] To verify the effectiveness of the method proposed in this application, extracted real vehicle operating condition data was used as input, and the obtained shifting rules were used as parameters to build a shifting simulation model, outputting simulated gear data under various operating conditions; and compared with real vehicle gear data under the same operating conditions, the results are as follows. Figure 4 As shown (the horizontal axis represents operating time, the vertical axis represents gear position, the dashed line represents the simulated gear position, and the solid line represents the actual vehicle gear position), the simulated gear position and the actual vehicle gear position basically overlap, which shows that the shifting pattern obtained by the method of this application has a good effect.

[0093] In summary, this application provides a method for determining the shifting rules of intelligent driving mining trucks. Taking into account the special operating scenarios and conditions of wide-body mining dump trucks, the shifting rules are obtained based on actual vehicle operating data. These rules can be used for simulation calculations of overall vehicle fuel consumption and for simulation testing of the MIL / HIL of intelligent driving control algorithms. This approach allows for comprehensive consideration from multiple perspectives, including vehicle economy and on-site operating scenarios. By analyzing the simulation results and optimizing the control algorithm, the method aims to reduce fuel consumption costs, improve operating efficiency, and ultimately enhance the economic performance of wide-body mining dump trucks.

[0094] Compared to theoretical or calibration data obtained directly from component manufacturers, the shift curve determined by this application based on a large amount of real-vehicle operating data has a more significant effect. The shift curve data obtained by this method is closer to real-vehicle operating conditions, resulting in more accurate shift patterns. This data can be directly used for simulation calculations of vehicle fuel consumption, evaluating the overall vehicle operating efficiency and cost of the current algorithm; it can also be used for MIL / HIL simulation testing of intelligent driving control algorithms, improving the realism of the simulation and assisting in the performance optimization of the control algorithm. Therefore, the method described in this application can obtain more accurate shift patterns, which is beneficial for clarifying the optimization direction of intelligent driving algorithms, achieving the goal of reducing fuel consumption costs and improving operating efficiency.

[0095] See Figure 5 A device for determining the shifting pattern of an intelligent driving mining truck, comprising:

[0096] Module 501 is used to acquire real vehicle operation data.

[0097] The extraction module 502 is used to extract the operating condition data of the gear based on the actual vehicle operation data. The operating condition data includes multiple operating points under the gear, and the operating point includes the accelerator pedal value and the corresponding vehicle speed value.

[0098] The selection module 503 is used to set the numerical range of the accelerator pedal value, and within this numerical range, multiple shift acceleration pedal values ​​are selected sequentially.

[0099] The calculation module 504 is used to select at least some corresponding operating points for each shift acceleration pedal value, and calculate the upshift speed or downshift speed for each shift acceleration pedal value based on the selected at least some operating points.

[0100] The pattern information forming module 505 is used to form upshift or downshift pattern information for a given gear based on the upshift or downshift speed of each shift acceleration pedal value.

[0101] In one embodiment, when the calculation module 504 selects at least some of the corresponding operating points, it is specifically used to: obtain specific operating points where the acceleration pedal values ​​are all near the shift acceleration pedal values, the difference between the maximum and minimum vehicle speeds is less than or equal to Δh, and the number is greater than or equal to m, where Δh and m are both optimization parameters.

[0102] In one implementation, when the calculation module 504 selects at least some of the corresponding operating points, it is specifically used to: obtain specific operating points obtained by clustering, where all acceleration pedal values ​​are near the shift acceleration pedal values.

[0103] In one implementation, the range near the shift acceleration pedal value includes the interval range of Δd, where Δd is an optimization parameter.

[0104] In one embodiment, when the calculation module 504 calculates the upshift or downshift speed for each shift acceleration pedal value, it is specifically used to: calculate the average speed of multiple specific operating points.

[0105] In one implementation, the selection module 503 is used to select multiple shift acceleration pedal values, specifically to select multiple shift acceleration pedal values ​​with equal differences ΔAccPedal, where ΔAccPedal is an optimization parameter.

[0106] In one implementation, the upshift or downshift pattern information of a gear is used to establish a shift model with the upshift or downshift pattern information of other gears.

[0107] In one implementation, when module 503 is selected to set the numerical range of the accelerator pedal value, it is specifically used to: obtain the corresponding maximum accelerator pedal value and minimum accelerator pedal value based on the operating point of the gear and the operating points of other gears, and use the maximum accelerator pedal value and minimum accelerator pedal value to limit the numerical range.

[0108] In one embodiment, the extraction module 502 is used to extract the gear condition data based on real vehicle operation data. Specifically, it is used to: extract the gear, accelerator pedal value and vehicle speed value at a preset time interval, and extract the gear condition data based on the gear, accelerator pedal value and vehicle speed value at the corresponding time.

[0109] In one embodiment, the device further includes an abnormal data removal module for removing abnormal data from the operating condition data. The abnormal data is data showing abnormal changes in vehicle speed values ​​in the operating condition data, or data in the operating condition data that is restricted by the highest limiting gear.

[0110] An exemplary embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the electronic device to perform a method according to an embodiment of this application.

[0111] An exemplary embodiment of this application also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of this application.

[0112] An exemplary embodiment of this application also provides a computer program product, including a computer program, wherein, when executed by a computer's processor, the computer program is used to cause the computer to perform a method according to an embodiment of this application.

[0113] refer to Figure 6 The present invention describes a structural block diagram of an electronic device 600 that can serve as a server or client of this application, which is an example of a hardware device that can be applied to various aspects of this application. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the application described and / or claimed herein.

[0114] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0115] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, output unit 607, storage unit 608, and communication unit 609. Input unit 606 can be any type of device capable of inputting information to electronic device 600. Input unit 606 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 607 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 604 may include, but is not limited to, disk and optical disk. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0116] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above. For example, in some embodiments, the methods of this application can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. In some embodiments, the computing unit 601 can be configured to perform the methods of this application by any other suitable means (e.g., by means of firmware).

[0117] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0118] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0119] As used in this application, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0120] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0121] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0122] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

Claims

1. A method for determining the shifting pattern of an intelligent driving mining truck, characterized in that, include: Obtain real-world vehicle operation data; Based on the actual vehicle operation data, the working condition data of the gear is extracted. The working condition data includes multiple working condition points under the gear. The working condition points include the accelerator pedal value and the corresponding vehicle speed value. Set the range of values ​​for the accelerator pedal, and within this range, select multiple shift acceleration pedal values ​​in sequence; For each of the shift acceleration pedal values, select at least some corresponding operating points, and calculate the upshift speed or downshift speed for each of the shift acceleration pedal values ​​based on the selected at least some operating points. Based on the upshift or downshift speed of each shift acceleration pedal value, the upshift or downshift pattern information for that gear is formed. The step of extracting gear condition data based on the actual vehicle operation data includes: extracting the gear, accelerator pedal value and vehicle speed value at a preset time interval, and extracting gear condition data based on the gear, accelerator pedal value and vehicle speed value at the corresponding time.

2. The method for determining the shifting pattern of an intelligent driving mining truck according to claim 1, characterized in that, The step of selecting at least some corresponding operating points includes: obtaining specific operating points where the acceleration pedal values ​​are all near the shift acceleration pedal values, the difference between the maximum and minimum vehicle speeds is less than or equal to Δh, and the number is greater than or equal to m, where Δh and m are both optimization parameters.

3. The method for determining the shifting pattern of an intelligent driving mining truck according to claim 1, characterized in that, The step of selecting at least some of the corresponding operating points includes: obtaining specific operating points obtained by clustering, where all acceleration pedal values ​​are near the shift acceleration pedal values.

4. The method for determining the shifting pattern of an intelligent driving mining truck according to claim 2 or 3, characterized in that, The range near the shift acceleration pedal value includes the interval range of Δd, where Δd is an optimization parameter.

5. A method for determining the shifting pattern of an intelligent driving mining truck according to claim 2 or 3, characterized in that, The step of calculating the upshift or downshift speed for each of the shift acceleration pedal values ​​includes: calculating the average speed of multiple specific operating points.

6. The method for determining the shifting pattern of an intelligent driving mining truck according to claim 1, characterized in that, The step of selecting multiple shift acceleration pedal values ​​includes: selecting multiple shift acceleration pedal values ​​with equal arithmetic differences ΔAccPedal, where ΔAccPedal is an optimization parameter.

7. A method for determining the shifting pattern of an intelligent driving mining truck according to any one of claims 1 to 3, characterized in that, The upshift or downshift pattern information of the gear is used to establish a shifting model with the upshift or downshift pattern information of other gears.

8. A method for determining the shifting pattern of an intelligent driving mining truck according to any one of claims 1 to 3, characterized in that, The method further includes removing abnormal data from the operating condition data, wherein the abnormal data is data with abnormal changes in vehicle speed value in the operating condition data, or, wherein the abnormal data is data in the operating condition data that is restricted by the highest gear.

9. A method for determining the shifting pattern of an intelligent driving mining truck according to any one of claims 1 to 3, characterized in that, The step of setting the numerical range of the accelerator pedal value includes: obtaining the corresponding maximum accelerator pedal value and minimum accelerator pedal value based on the operating point of the gear and the operating points of other gears, and using the maximum accelerator pedal value and minimum accelerator pedal value to limit the numerical range.

10. A device for determining the shifting pattern of an intelligent driving mining truck, characterized in that, include: The acquisition module is used to acquire real-vehicle operation data; The extraction module is used to extract the operating condition data of the gear based on the actual vehicle operation data. The operating condition data includes multiple operating condition points under the gear, and the operating condition points include the accelerator pedal value and the corresponding vehicle speed value. The selection module is used to set the numerical range of the accelerator pedal value, and within this numerical range, multiple shift acceleration pedal values ​​are selected sequentially. The calculation module is used to select at least a portion of the corresponding operating conditions for each shift acceleration pedal value, and calculate the upshift speed or downshift speed for each shift acceleration pedal value based on the selected at least a portion of the operating conditions. The pattern information forming module is used to form upshift or downshift pattern information for a given gear based on the upshift or downshift speed of each shift acceleration pedal value. The extraction module is used to extract gear condition data based on real vehicle operation data. Specifically, it is used to: extract the gear, accelerator pedal value and vehicle speed value at a preset time interval, and extract the gear condition data based on the gear, accelerator pedal value and vehicle speed value at the corresponding time.

11. An electronic device, characterized in that, include: processor; as well as Stored program memory, The program includes instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-9.

12. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-9.

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