Overtake control method and control system for an open pit mine

Abstract

The present application provides an open-pit mine overtaking control method and a control system, wherein the control method comprises: acquiring multi-source driving data, the multi-source driving data comprising preset first self-vehicle data and first other-vehicle data and real-time collected second self-vehicle data and second other-vehicle data; identifying the type of other vehicles based on the multi-source driving data to obtain a vehicle type identification result; identifying the overtaking intention of other vehicles based on the second self-vehicle data and the second other-vehicle data; when there is an overtaking intention, executing a corresponding overtaking control strategy based on the vehicle type identification result, wherein when the other vehicle and the self-vehicle are the same type of vehicle, a prohibition overtaking control strategy is triggered; when the other vehicle and the self-vehicle are different types of vehicle, two-way interactive overtaking permission verification is performed based on the second self-vehicle data and the second other-vehicle data, the overtaking permission of the other vehicle is granted and controlled, and a collision accident is prevented to ensure production safety and transportation efficiency.

Description

Technical Field

[0001] This invention relates to open-pit mining technology, and more particularly to overtaking control methods and control systems in open-pit mines. Background Technology

[0002] Open-pit mine transportation is a core part of mining production. Engineering vehicles such as mining dump trucks have characteristics such as heavy load capacity of hundreds of tons, large inertia, long braking distance, and large blind spots. In addition, mine roads are temporary and simple roads with many slopes and curves, dust and strong light interference, no standardized traffic facilities, and multiple vehicles mixed together, including a large number of overloaded special vehicles. When driving on open-pit mine roads, improper driving may lead to the risk of vehicle collisions and reduce production and transportation efficiency. In particular, overtaking behavior on the road may lead to serious production safety problems if not properly controlled.

[0003] Therefore, how to effectively control overtaking behavior in open-pit mines to ensure production safety and transportation efficiency has become an urgent technical issue. Summary of the Invention

[0004] This invention provides a method and control system for overtaking in open-pit mines, which can solve the problem in the prior art of how to effectively control overtaking behavior in open-pit mines to ensure production safety and transportation efficiency.

[0005] A first aspect of the present invention provides a method for controlling overtaking in an open-pit mine, comprising: acquiring multi-source driving data, wherein the multi-source driving data includes preset first vehicle data and first other vehicle data, as well as real-time collected second vehicle data, second other vehicle data, and environmental dynamic data;

[0006] Based on the multi-source driving data, the vehicle type is identified to obtain the vehicle model identification result;

[0007] Based on the second vehicle data, the second other vehicle data, and environmental dynamic parameters, multi-dimensional identification is performed to confirm the overtaking intention of other vehicles;

[0008] When the overtaking intention exists, the corresponding overtaking control strategy is executed based on the vehicle type identification result. Specifically, when the other vehicle and the vehicle are of the same type, the overtaking prohibition control strategy is triggered; when the other vehicle and the vehicle are of different types, the multi-source driving data is input into a dynamic finite state machine model to execute a two-way interactive overtaking permission management process. The dynamic finite state machine model includes multiple predefined states and basic transition parameters corresponding to the predefined states. Each predefined state corresponds one-to-one with multiple pre-set overtaking permission management sub-processes in the overtaking permission management process.

[0009] During the overtaking permission management process, after transitioning to the current predefined state, a current dynamic threshold of the basic transition parameters corresponding to the current predefined state is constructed based on the multi-source driving data; the current transition judgment parameter value is calculated in real time based on the multi-source driving data; when the current transition judgment parameter value is greater than the current dynamic threshold, the process transitions to the next predefined state until the overtaking permission management process terminates.

[0010] Optionally, based on the second vehicle data, the second other vehicle data, and environmental dynamic parameters, multi-dimensional identification is performed to confirm the other vehicle's overtaking intention, including:

[0011] Determine whether the environmental dynamic parameters meet the conditions for overtaking;

[0012] When the overtaking conditions are met, the longitudinal distance, lateral distance, and relative speed between the vehicle and the other vehicle are calculated based on the second vehicle data and the second other vehicle data.

[0013] When the longitudinal distance is less than a preset longitudinal distance, the lateral distance is less than a preset lateral distance, and the relative speed is greater than a preset relative speed, it is confirmed that the other vehicle has the intention to overtake.

[0014] Optionally, the step of constructing the current dynamic threshold of the basic transition parameters corresponding to the current predefined state based on the multi-source driving data includes:

[0015] Confirm the predefined state of the target currently being executed;

[0016] Extract target data corresponding to the predefined target state from the multi-source driving data;

[0017] The current dynamic threshold is calculated based on the target data.

[0018] Optionally, the predefined states include: a pending overtaking permission state, an overtaking permission requesting state, an overtaking permission granted state, an overtaking execution state, an overtaking completed state, and an overtaking failed state, which are sequentially transitioned.

[0019] Optionally, the basic transfer parameters include:

[0020] The first basic transition parameter for the transition from the pending overtaking permission state to the overtaking permission state request state includes whether the longitudinal distance between the other vehicle and the current vehicle is less than a preset longitudinal distance and whether the lateral distance is less than a preset lateral distance.

[0021] The second basic transition parameter for the state transition from the overtaking permission request to the state of granting overtaking permission includes whether there is a confirmation of overtaking request within a first preset time period;

[0022] The third basic transition parameter for the transition from the overtaking permission state to the overtaking execution state includes whether there is an overtaking behavior within the second preset time period;

[0023] The fourth basic transition parameter for the transition from the overtaking execution state to the overtaking completed state includes whether the overtaking has been completed;

[0024] The fifth basic transfer parameter for the transition from the granted overtaking permission state to the overtaking failure state includes whether there is a confirmed overtaking request within a first preset time period or whether the overtaking is completed within a third preset time period.

[0025] Optionally, the dynamic environmental parameters include: road slope data, road slip rate, and dust concentration data;

[0026] When entering the state of requesting overtaking permission, the road slope data, the road slip ratio and the dust concentration data are extracted from the dynamic environment parameters;

[0027] Based on the preset longitudinal distance, the preset longitudinal distance is corrected using the road slope data, the road slip ratio, and the dust concentration data as correction coefficients to obtain a longitudinal dynamic threshold. The road slope data and the road slip ratio are positively correlated with the longitudinal dynamic threshold, while the dust concentration data is negatively correlated with the longitudinal dynamic threshold.

[0028] Optionally, when entering the overtaking permission request state, communication latency data and communication packet loss rate are determined based on the second vehicle data and the second other vehicle data, and the road slope data, radius of curvature and dust concentration data are extracted from the dynamic environment parameters;

[0029] A communication quality coefficient is determined based on the communication latency data and the communication packet loss rate; an environmental visibility coefficient is determined based on the dust concentration data; a vehicle-to-vehicle situation coefficient is determined based on the longitudinal distance; and a road condition coefficient is determined based on the road slope data and the radius of curvature.

[0030] Based on the first preset duration, and using the communication quality coefficient, the environmental visibility coefficient, the vehicle situation coefficient, and the road condition coefficient as correction coefficients, the first preset duration is corrected according to the following formula to obtain the dynamic request duration threshold;

[0031] T1=max(T 10 ,min(T 11 ,T base ×K comm ×K env ×K pos ×K road ))

[0032] Where T1 is the dynamic request duration threshold, T 10 The duration T is the lower limit constraint for the dynamic request duration threshold. 11 The duration T is a dynamic request duration threshold limit constraint. base K is the first preset duration. comm K is the communication quality coefficient. env K represents the environmental visibility factor. pos K represents the inter-vehicle situation coefficient. road This refers to the road condition coefficient.

[0033] Optionally, when entering the state of granting overtaking permission, the road slope data and the road slip ratio are extracted from the dynamic environment parameters; the vehicle type difference coefficient between the vehicle and the other vehicle is determined based on the first vehicle data and the first other vehicle data, the vehicle type difference coefficient representing the ratio of the volume of the other vehicle to the load of the vehicle;

[0034] Based on the third preset duration, the road slope data, the road slip rate, and the vehicle model difference coefficient are used as correction coefficients to correct the third preset duration, thereby obtaining a dynamic authorization duration threshold. The road slope data, the road slip rate, and the vehicle model difference coefficient are positively correlated with the dynamic authorization duration threshold.

[0035] A second aspect of the present invention provides an overtaking control system for an open-pit mine, comprising a device layer, a communication layer, and an edge computing layer deployed on-board, and a cloud management layer communicatively connected to the device layer. The device layer is used to store data of a first vehicle and collect data of a second vehicle. The communication layer is used to communicate with other vehicles to receive data from the first and second other vehicles. The edge computing layer is used to execute the control method described in any one of the first aspects above.

[0036] Optionally, the backup layer includes a positioning module, a wireless communication module, and an on-board control unit deployed in the vehicle. The positioning module is used to locate the vehicle. The on-board control unit is used to calculate the vehicle's position and speed information based on the positioning information. The on-board control unit is communicatively connected to the cloud management layer.

[0037] The beneficial effects of this application are as follows:

[0038] The system acquires multi-source driving data, including preset first vehicle data and first other vehicle data, and real-time collected second vehicle data and second other vehicle data. Based on this multi-source driving data, it identifies the type of other vehicles to obtain a vehicle model identification result. It then identifies the overtaking intention of other vehicles based on the second vehicle data and the second other vehicle data. When an overtaking intention is detected, it executes a corresponding overtaking control strategy based on the vehicle model identification result. When the other vehicle is the same type as the vehicle, an overtaking prohibition control strategy is triggered. When the other vehicle is a different type than the vehicle, a two-way interactive overtaking permission verification is performed based on the second vehicle data and the second other vehicle data, granting and controlling overtaking permissions for the other vehicle. Completely different control strategies are applied to vehicles of the same type and vehicles of different types. For vehicles of the same type, a rigid overtaking prohibition control strategy is implemented; for vehicles of different types, a flexible authorization and full-process overtaking control strategy is implemented to achieve a balance between safety and efficiency. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the overtaking control method in an open-pit mine according to an embodiment of the present invention.

[0040] Figure 2 This is a schematic diagram of the overtaking control system for open-pit mines according to an embodiment of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0043] This invention provides a method for overtaking control in open-pit mines, applicable to overtaking control systems, such as... Figure 1As shown, the overtaking control system includes a device layer, a communication layer, an edge computing layer, and a cloud management layer. The device layer includes a positioning module, a wireless communication module, and an onboard control unit deployed with the vehicle. In this embodiment, the positioning module can use satellite positioning modules such as GPS or BeiDou, or an RTK positioning module for centimeter-level vehicle positioning. The wireless communication module can use a 433MHz wireless communication module, adopting the dPMR digital protocol standard, supporting point-to-point and point-to-multipoint communication modes, with a transmission rate of 1.2-250kbps, strong penetration, and suitability for the complex environment of open-pit mines. The device layer is connected to the communication layer, which is responsible for data transmission between vehicles. The overtaking control method in open-pit mines is executed through the edge computing layer deployed on the vehicle. Figure 2 As shown, the overtaking control method in this open-pit mine may include the following steps:

[0044] S10. Acquire multi-source driving data. The multi-source driving data includes preset first vehicle data and first other vehicle data, and real-time collected second vehicle data and second other vehicle data. The "vehicle" can be any vehicle in the open-pit mine, and the "other vehicles" can be one or more other vehicles located behind the vehicle. In this embodiment, each vehicle stores its own information, such as vehicle ID, vehicle type information, load information, and other vehicle attribute information as preset first vehicle data. The positioning module collects real-time vehicle status data such as real-time coordinates, speed, direction angle, and data collection timestamps as real-time collected second vehicle data.

[0045] Vehicle-to-vehicle data interaction is achieved through a wireless communication module, broadcasting preset vehicle data and real-time collected vehicle data to surrounding vehicles. After receiving data from other vehicles, the vehicle's wireless communication module obtains preset first other vehicle data and real-time collected second other vehicle data.

[0046] In an optional embodiment, the device layer deployed with the vehicle may further include a detection module, such as an in-vehicle camera or in-vehicle radar. The detection module can detect the surrounding environment data of the vehicle in real time. For example, the in-vehicle camera and in-vehicle radar can collect the vehicle model outlines of the following vehicles and surrounding vehicles, as well as the lateral / longitudinal relative distance and relative speed between the vehicle and surrounding vehicles, as real-time collected second vehicle data.

[0047] In addition, communication quality data such as communication delay duration and packet loss rate can be collected in real time through the wireless communication module; environmental interference data such as dust concentration and visibility, as well as road condition data such as road width, slope, curvature, and slipperiness can be collected through the detection module as dynamic environmental data.

[0048] After obtaining the second set of data for this vehicle and the data for the other vehicles, the relative distances between this vehicle and all other vehicles can be calculated based on the second set of data for this vehicle and the data for the other vehicles, thereby determining the relative positional relationship between this vehicle and all other vehicles. By combining this data with the first set of data for the other vehicles, the vehicle ID is bound to the relative positional relationship, thus clarifying the relative positional relationship between each other vehicle and this vehicle.

[0049] S20. Based on the multi-source driving data, identify the vehicle type to obtain the vehicle model identification result.

[0050] In this embodiment, after the vehicle receives data from a first other vehicle via its wireless communication module, it parses the vehicle ID, model information, load information, and other vehicle attribute information from the first other vehicle data to identify the other vehicle's type, thus obtaining the other vehicle's model identification result. Based on the verification result, the vehicle is marked as similar / different type. In this embodiment, the other vehicle can be the vehicle following behind the vehicle.

[0051] In an optional embodiment, to more accurately determine the vehicle model of another vehicle, multiple verifications can be performed based on the second vehicle data. After determining the vehicle model recognition result using the first vehicle data, verification is performed using the second vehicle data. For example, the vehicle model outline captured by the vehicle camera is compared with the vehicle model recognition result to verify whether the vehicle model appearance is consistent; the vehicle model size parameters such as the width, height, and length of the other vehicle are detected by the vehicle radar and compared with the vehicle model recognition result to verify whether the vehicle model size parameters are consistent.

[0052] If the vehicle models have the same appearance and the same size parameters, then the vehicle model recognition result is confirmed to be correct.

[0053] S30. Based on the second vehicle data, the second other vehicle data, and environmental dynamic parameters, perform multi-dimensional identification to confirm the other vehicle's overtaking intention. The second vehicle data may include the vehicle's coordinates, speed, and steering angle data. The second other vehicle data may include the other vehicle's coordinates, speed, and steering angle data. Calculate the longitudinal and lateral distances between the two vehicles using the vehicle's and other vehicle coordinates. Calculate the relative speed between the two vehicles using the vehicle's and other vehicle speeds.

[0054] The system determines whether the lateral distance is less than a first preset value. If the lateral distance is less than the first preset value, it confirms that the vehicle and another vehicle are in the same lane. It also determines whether the longitudinal distance is less than a second preset value. If the longitudinal distance is less than the second preset value, it confirms that another vehicle is approaching the vehicle. Finally, it determines whether the relative speed is greater than a preset speed difference. If the relative speed is greater than the preset speed difference, it confirms that the other vehicle has the speed to overtake. Therefore, if all three conditions are met simultaneously—that the vehicle and another vehicle are in the same lane, that the other vehicle has the speed to overtake—it is confirmed that the other vehicle intends to overtake. If any of these conditions are not met, it is confirmed that the vehicle does not intend to overtake.

[0055] To more accurately determine whether another vehicle intends to overtake, this embodiment can also perform multi-dimensional judgments based on the dynamic relationship between the second vehicle's data and the data of the other vehicle, as well as environmental dynamic data. Specifically, the second vehicle's data and the data of the other vehicle can be used to predict whether the driving trajectories of the two vehicles intersect. For example, the spatiotemporal trajectory, such as position, time, and driving range, for a preset period of time can be predicted using the vehicle's current coordinate data, speed data, and direction angle data. Then, spatial intersection verification, time synchronization verification, and conflict threshold quantification can be used to determine whether the trajectories intersect.

[0056] For example, real-time acquisition of the vehicle's current coordinates , The road resistance correction factor K is determined based on vehicle type and road condition data. 阻 ;

[0057] Predict the position coordinates in the next t seconds using the following formula:

[0058]

[0059] in, This corrects for the speed decay of mining vehicles under heavy loads, adapting to the characteristic that mining vehicles with high inertia cannot accelerate / decelerate quickly.

[0060] Determine the location coordinates corresponding to each time point ( Xt , Yt After that, the trajectories of this vehicle and the other vehicle are generated within a preset time period of n seconds, where the trajectory of the other vehicle is... T 他 ={(X 他1 ,Y 他1 ,t 他1 ), ...(X 他n ,Y 他n ,t 他n The trajectory of this vehicle is... T 本 ={(X 本1 ,Y本1 ,t 本1 ), ...(X 本n ,Y 本n ,t 本n )}.

[0061] Based on the vehicle model and other vehicle models, the trajectory of this vehicle and the trajectory of other vehicles are extended respectively. For example, if the width of this vehicle is 6 meters, then the trajectory of this vehicle is extended along... T 本 Extend ±3 meters in each direction.

[0062] Obtain the road grid coordinate system of the mine. This road grid coordinate system can be based on the RTK base station coordinate system of the mine, overlaid with the mine's electronic map, and decompose all transportation roads into an m×m coordinate grid.

[0063] Extract all grid sets of other vehicles covered by the safe range of other vehicles' trajectories and the grid set of this vehicle covered by the safe range of this vehicle's trajectory.

[0064] Calculate the intersection of the grid set of other vehicles and the grid set of this vehicle. If there is no intersection, then there is no trajectory overlap; if there is an intersection, then perform time synchronization verification on the trajectory of this vehicle and the trajectory of other vehicles.

[0065] The time synchronization verification includes: calculating the time difference between the arrival of other vehicles and the vehicle itself at each intersecting grid, determining whether it is within the risk time window; for each intersecting grid, extracting the arrival time of other vehicles and the arrival time of the vehicle itself, and calculating the time difference; determining whether the time difference is within the set mine risk time window. If it is within the window, there is a trajectory intersection in time and space, that is, other vehicles have the intention to overtake, and the overtaking intention determined by the lateral spacing, longitudinal spacing and vehicle speed is reinforced.

[0066] Furthermore, after trajectory prediction is completed, if there is an intention to overtake, the target road condition data between the current position and the trajectory intersection coordinates is obtained. Based on the vehicle type, the other vehicle type, and the target road condition data, it is determined whether overtaking conditions are met. For example, if the road width in the target road condition data is less than or equal to the sum of the widths of the current vehicle and the other vehicle, then overtaking conditions are not met, indicating that the following driver has no intention to overtake. Similarly, if the curvature in the target road condition data is greater than a preset curvature, then overtaking conditions are not met, and the following driver has no intention to overtake. If overtaking conditions are met, the existence of an overtaking intention is further confirmed. If an overtaking intention exists, proceed to step S40; otherwise, return to step S10.

[0067] S40. Execute the corresponding overtaking control strategy based on the vehicle type recognition result. In previous vehicle path planning and collision avoidance management in mines, vehicle operation and travel paths were often planned in advance. Vehicles traveled according to the customized work locations and path plans, and when overtaking or yielding was required, it was often the driver's autonomous decision. However, mining areas often have heavy-duty, high-inertia mining vehicles with long braking distances and large blind spots. Furthermore, mining areas have harsh environments such as closed roads, narrow roads, many slopes and curves, and dust / strong light. When overtaking or yielding, collisions may occur due to factors such as braking distance and blind spots. However, for vehicles of the same type, blind spots highly overlap, the nature of operations is the same, the load is basically the same, and there is no differentiated work priority. Therefore, the overall benefit of overtaking is not high. In addition, the blind spots highly overlap, the speed limit is the same, and the vehicle size is similar. Overtaking is not only difficult, but also has a high probability of collision. Therefore, given the characteristics of mining vehicles in open-pit mines and the characteristics of the mining environment, it is necessary to strictly control overtaking of vehicles of the same type and prohibit overtaking of vehicles of the same type.

[0068] For vehicles of different shapes and sizes, their dimensions and speeds differ significantly. For example, a dump truck is 6 meters wide, while a water truck is 3.5 meters wide; a dump truck with a heavy load has a speed of 10-20 km / h, while a water truck with an empty load has a speed of 30-40 km / h. Not only is there lateral clearance, but overtaking can be completed without excessive acceleration. The operational requirements differ greatly, therefore, overtaking can improve operational efficiency. Furthermore, because large vehicles have large blind spots and generate a lot of dust, smaller vehicles face a greater risk of collision when driving near large vehicles. Therefore, it is necessary to allow vehicles of different shapes and sizes to overtake.

[0069] In this embodiment, when the other vehicle is the same type as the vehicle itself, a no-overtaking control strategy is triggered; when the other vehicle is a different type, a two-way interactive overtaking permission verification is performed based on the second vehicle data and the second other vehicle data, granting and controlling overtaking permission to the other vehicle. In this embodiment, completely different control strategies are implemented for vehicles of the same type and vehicles of different types. Specifically, a rigid no-overtaking control strategy is implemented for vehicles of the same type, while a flexible authorization and full-process overtaking control strategy is implemented for vehicles of different types, achieving a balance between safety and efficiency.

[0070] Specifically, for vehicles of the same type, if another vehicle shows an intention to overtake, it is confirmed as an illegal overtaking maneuver. An alarm signal is then sent to the corresponding vehicle via a wireless communication module. This alarm signal includes information about the overtaking intention and vehicle identification information, such as the vehicle ID. Upon receiving the wireless alarm signal, the other vehicle analyzes it using its onboard control unit. If the analyzed vehicle identification information matches that of the vehicle in question, the onboard control unit sends an alarm signal to the driver. Optionally, the onboard control unit can also limit vehicle speed increases via the CAN bus to enforce a safe following distance. Simultaneously, the alarm information and the illegal overtaking event can be uploaded to the cloud management layer. For example, the vehicle ID, timestamp, real-time coordinates, and trajectory data of the vehicle with overtaking intention can be uploaded to the cloud management layer.

[0071] In one embodiment, for vehicles of different shapes, overtaking permission needs to be granted through bidirectional interaction between the vehicle and other vehicles. In this embodiment, a bidirectional interactive overtaking permission management process can be executed based on a pre-built dynamic overtaking permission state machine model. The overtaking permission management process includes multiple pre-defined overtaking permission management sub-processes, each of which serves as a predefined state of the overtaking permission state machine model.

[0072] The predefined states include the following states that transition sequentially: pending overtaking permission, overtaking permission requesting, granted overtaking permission, overtaking in progress, completed overtaking, and overtaking failed.

[0073] The basic transfer parameters include:

[0074] The first basic transition parameters for the transition from the pending overtaking permission state to the overtaking permission request state include whether the longitudinal distance between the other vehicle and the current vehicle is less than a preset longitudinal distance and whether the lateral distance is less than a preset lateral distance.

[0075] The second basic transition parameter for the state transition from the overtaking permission status request to the granted overtaking permission status includes whether there is a confirmation overtaking request within a first preset time period.

[0076] The third basic transfer parameter for the transition from the overtaking permission state to the overtaking execution state includes whether an overtaking behavior has occurred within a second preset time period.

[0077] The fourth basic transition parameter for the transition from the overtaking execution state to the overtaking completed state includes whether the overtaking has been completed.

[0078] The fifth basic transfer parameter for the transition from the granted overtaking permission state to the overtaking failure state includes whether there is a confirmed overtaking request within a first preset time period or whether the overtaking is completed within a third preset time period.

[0079] Open-pit mines present complex scenarios such as heavy loads, numerous slopes, and high dust concentrations. The transition conditions between states in a finite state machine model are often fixed. If a heavily loaded mining truck's braking distance increases when traveling on a slope, using the same timeout period as on a flat road could easily lead to overtaking failure before authorization is completed. Furthermore, increased communication latency due to dust obstruction could cause the fixed interaction time to be misjudged as invalid authorization. Therefore, in this embodiment, basic transition parameters can be set for the transition conditions between each predefined state in the dynamic overtaking authorization state machine model. During the overtaking authorization management process, dynamic thresholds for these basic transition parameters are constructed based on real-time multi-source driving data to address errors in the overtaking authorization management process caused by environmental changes such as vehicle load, road conditions, and communication quality.

[0080] In this embodiment, during the overtaking permission management process, after transitioning to the current predefined state, a current dynamic threshold of the basic transition parameters corresponding to the current predefined state is constructed based on the multi-source driving data; the current transition judgment parameter value is calculated in real time based on the multi-source driving data; when the current transition judgment parameter value is greater than the current dynamic threshold, the process transitions to the next predefined state until the overtaking permission management process terminates.

[0081] Specifically, when the overtaking intention exists, the overtaking permission management process is initiated. After being transferred to the current predefined state, the current dynamic threshold of the basic transfer parameters corresponding to the current predefined state is constructed based on the multi-source driving data. The current transfer judgment parameter value is calculated in real time based on the multi-source driving data. When the current transfer judgment parameter value is greater than the current dynamic threshold, the process is transferred to the next predefined state until the overtaking permission management process is terminated.

[0082] Specifically, upon entering a new predefined state, the currently executed target predefined state is confirmed; target data corresponding to the target predefined state is extracted from the multi-source driving data; and the current dynamic threshold is calculated based on the target data.

[0083] For example, when there is an intention to overtake, the vehicle enters the "awaiting overtaking permission" state. This state indicates that no other vehicle has initiated an overtaking request, and the vehicle monitors the status of surrounding vehicles. Specifically, it monitors the relative positions of different types of vehicles. If the longitudinal distance between the other vehicle and the vehicle is less than a preset longitudinal distance, and the lateral distance is less than a preset lateral distance, and the road segment is a permitted overtaking area, the vehicle enters the "overtaking permission requesting" state, and the driver of the other vehicle can initiate an overtaking request.

[0084] Because open-pit mine roads are unpaved, with slopes, dust, and mud, and mining vehicles are often heavily loaded, road conditions and dust concentrations significantly impact visibility, affecting vehicle movement. The longitudinal distance between entering the "pending overtaking permission request" state and the point where another vehicle can initiate an overtaking request varies. Therefore, in this embodiment, when entering the "pending overtaking permission request" state, road slope data, road slip ratio, and dust concentration data are extracted from the dynamic environmental parameters. The road slope data and road slip ratio affect the acceleration, travel efficiency, and braking distance of other vehicles. A steeper slope and a higher slip ratio result in lower acceleration and travel efficiency for other vehicles; a higher slip ratio also results in a longer braking distance. Therefore, a steeper slope and a higher slip ratio correspond to a longer distance at which an overtaking request can be initiated. Higher dust concentrations have a greater impact on the driver's visibility, and a shorter distance at which an overtaking request can be initiated.

[0085] Specifically, based on the preset longitudinal distance, the preset longitudinal distance is corrected using the road slope data, the road slip ratio, and the dust concentration data as correction coefficients to obtain a longitudinal dynamic threshold. The road slope data and the road slip ratio are positively correlated with the longitudinal dynamic threshold, while the dust concentration data is negatively correlated with the longitudinal dynamic threshold.

[0086] For example, the vertical dynamic threshold can be dynamically calculated using the following formula:

[0087] D1=D0×(1+α)×(1+β)×(1-γ / 100);

[0088] Where D1 is the longitudinal dynamic threshold, D0 is the preset longitudinal threshold, α is the road slope data, β is the road slip ratio, and γ is the dust concentration data (mg / m³). 3 ).

[0089] When entering the overtaking permission request state, the communication latency data and communication packet loss rate are determined based on the data of the second vehicle and the data of the other vehicle. The road slope data, radius of curvature and dust concentration data are extracted from the dynamic environment parameters.

[0090] The communication quality coefficient is determined based on the communication latency data and the communication packet loss rate; the environmental visibility coefficient is determined based on the dust concentration data; the inter-vehicle situation coefficient is determined based on the longitudinal distance; and the road condition coefficient is determined based on the road slope data and the radius of curvature.

[0091] Wherein, the communication quality coefficient is K comm =1+0.05×(T 延 / 100)+0.02×P 丢 Among them, K comm T is the communication quality factor.延 For communication delay data, P 丢 The packet loss rate is used for communication; the inter-vehicle situation coefficient is K. env =1+0.01×(γ / 100), where K env The environmental visibility coefficient is γ, and the dust concentration data is K; the vehicle-to-vehicle situation coefficient is K. pos =1+(D 纵 / 500), where K pos D is the inter-vehicle situation coefficient. 纵 The actual longitudinal distance between this vehicle and other vehicles; the road condition coefficient is K. road =1+0.01×|α|+(500-min(R,500)) / 1000, where K road Here, α represents the road condition coefficient, α represents the road slope data, and R represents the radius of curvature.

[0092] Based on the first preset duration, and using the communication quality coefficient, the environmental visibility coefficient, the vehicle situation coefficient, and the road condition coefficient as correction coefficients, the first preset duration is corrected according to the following formula to obtain the dynamic request duration threshold;

[0093] T1=max(T 10 ,min(T 11 ,T base ×K comm ×K env ×K pos ×K road ))

[0094] Where T1 is the dynamic request duration threshold, T 10 The duration T is the lower limit constraint for the dynamic request duration threshold. 11 The duration T is a dynamic request duration threshold limit constraint. base K is the first preset duration. comm K is the communication quality coefficient. env K represents the environmental visibility factor. pos K represents the inter-vehicle situation coefficient. road This refers to the road condition coefficient.

[0095] When entering the state of granting overtaking permission, the road slope data and the road slip ratio are extracted from the dynamic environment parameters; the vehicle type difference coefficient between the vehicle and the other vehicle is determined based on the first vehicle data and the first other vehicle data, and the vehicle type difference coefficient represents the ratio of the volume or the load of the other vehicle to the vehicle.

[0096] Based on the third preset duration, the road slope data, road slip rate, and vehicle model difference coefficient are used as correction coefficients to adjust the third preset duration, resulting in a dynamic authorization duration threshold. The road slope data, road slip rate, and vehicle model difference coefficient are positively correlated with the dynamic authorization duration threshold. In this embodiment, mining vehicles, especially heavy mining trucks, experience reduced power, longer braking distances, and significantly longer overtaking times when driving on slopes. A fixed duration would cause overtaking to be interrupted before completion, leading to a risk of collisions. Therefore, the steeper the slope, the longer the authorization duration should be dynamically extended to ensure safe overtaking. Simultaneously, mining roads are often muddy, slippery, and contain a lot of gravel, resulting in poor road surface adhesion and reduced vehicle handling stability. During overtaking, vehicle speed decreases and trajectory deviates. A fixed duration would cause overtaking to exceed the time limit or fail. Therefore, the slipperier the road surface, the longer the authorization duration should be dynamically extended to improve the stability and success rate of overtaking on low-adhesion surfaces. Furthermore, mining operations involve a wide variety of vehicle types with vastly different load capacities (dump trucks / water trucks / engineering vehicles, empty / fully loaded), resulting in significant variations in overtaking speeds and driving performance. A fixed authorization duration cannot be adequately adapted, causing smaller vehicles to be unable to overtake larger vehicles, and empty vehicles to be unable to overtake fully loaded vehicles. Dynamically adapting the authorization duration based on vehicle size and load differences ensures safe overtaking for all vehicle types and loads. For example, the larger / heavier the following vehicle, the greater the difference in vehicle type between it and the following vehicle; therefore, a longer dynamic authorization duration is required to guarantee safe overtaking for all vehicle types and loads.

[0097] In one embodiment, the overtaking request initiated by the following vehicle can be verified via real-time intercom. Specifically, the real-time intercom verification process includes: the driver of the following vehicle presses the overtaking request button, and the system sends an overtaking request frame containing the target vehicle ID through a 433MHz module. After receiving the request, the terminal of the driver of the preceding vehicle alerts the driver of the preceding vehicle through voice prompts and flashing lights. The driver of the preceding vehicle must respond with confirmation via the intercom module within 10 seconds, and the onboard control unit records the timestamp and content of the voice confirmation. After receiving the authorization confirmation, the driver of the following vehicle sets, for example, a 30-second validity period for the overtaking permission, during which overtaking is permitted. If the overtaking is not completed within the time limit, the system automatically triggers a re-evaluation process, and the driver of the following vehicle must initiate the request again.

[0098] When multiple vehicles request to overtake simultaneously, the system uses a priority queue to manage the conflict. The priority determination rules are as follows: load priority, for non-standard vehicles, the greater the load, the higher the priority; time priority: the vehicle that initiates the request first will be given priority to overtake; route priority: vehicles traveling near dangerous road sections (such as slopes and sharp bends) will be given priority to overtake.

[0099] This application embodiment also provides an overtaking control system for an open-pit mine, including a device layer, a communication layer, and an edge computing layer deployed on-board, as well as a cloud management layer communicatively connected to the device layer. The device layer is used to store data of the first vehicle and collect data of the second vehicle. The communication layer is used to communicate with other vehicles to receive data of the first and second other vehicles. The edge computing layer is used to execute the control method described in any one of the above embodiments.

[0100] Specifically, the device layer includes a vehicle-mounted positioning module, a wireless communication module, and an onboard control unit. In this embodiment, the positioning module can be a GPS positioning module, a Beidou positioning module, or an RTK positioning module for centimeter-level vehicle positioning. The wireless communication module can be a 433MHz wireless communication module using the dPMR digital protocol standard, supporting point-to-point and point-to-multipoint communication modes, with a transmission rate of 1.2-250kbps, strong penetration, and suitability for the complex environment of open-pit mines. The device layer is connected to the communication layer, which is responsible for data transmission between vehicles. The overtaking control method in open-pit mines is executed through an edge computing layer deployed on the vehicle.

[0101] The edge computing layer provided in this application includes a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other through the communication bus. The memory is used to store computer programs. The processor is used to execute the methods in any of the above embodiments by running the computer programs stored in the memory.

[0102] Optionally, in this embodiment, the aforementioned communication bus may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0103] The communication interface is used for communication between the aforementioned computer equipment and other devices.

[0104] The memory may include RAM, or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0105] The processor mentioned above can be a general-purpose processor, including but not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0106] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated here.

[0107] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, ROM, RAM, disk or optical disk, etc.

[0108] As an exemplary embodiment, this application also provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the method steps of any one of the embodiments in this application at runtime.

[0109] Optionally, in this embodiment, the storage medium described above can be used to execute program code for the method steps of the embodiments of this application.

[0110] Optionally, in this embodiment, the storage medium is configured to store methods for performing the above embodiments.

[0111] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated in this embodiment.

[0112] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0113] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0114] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods in the above embodiments.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed client can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0116] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the solution provided in this embodiment, depending on actual needs.

[0117] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0118] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0119] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for overtaking control in an open-pit mine, characterized in that, include: Acquire multi-source driving data, which includes preset first vehicle data and first other vehicle data, as well as real-time collected second vehicle data, second other vehicle data, and environmental dynamic parameters; Based on the multi-source driving data, the vehicle type is identified to obtain the vehicle model identification result; Based on the second vehicle data, the second other vehicle data, and environmental dynamic parameters, multi-dimensional identification is performed to confirm the overtaking intention of other vehicles; When the overtaking intention exists, the corresponding overtaking control strategy is executed based on the vehicle type identification result. Specifically, when the other vehicle and the vehicle itself are of the same type, an overtaking prohibition control strategy is triggered; when the other vehicle and the vehicle itself are of different types, the multi-source driving data is input into a dynamic finite state machine model to execute a bidirectional interactive overtaking permission management process. The dynamic finite state machine model includes multiple predefined states and basic transition parameters corresponding to the predefined states. Each predefined state corresponds one-to-one with multiple pre-set overtaking permission management sub-processes in the overtaking permission management process. The predefined states include: a state of pending overtaking permission, a state of overtaking permission requesting, a state of granting overtaking permission, a state of overtaking execution, a state of completed overtaking, and a state of overtaking failure. The basic transfer parameters include: The first basic transition parameter for the transition from the pending overtaking permission state to the overtaking permission request state includes whether the longitudinal distance between the other vehicle and the current vehicle is less than a preset longitudinal distance and whether the lateral distance is less than a preset lateral distance. The second basic transition parameter for the state transition from the overtaking permission request to the state of granting overtaking permission includes whether there is a confirmation of overtaking request within a first preset time period. The third basic transition parameter for the transition from the overtaking permission state to the overtaking execution state includes whether there is an overtaking behavior within the second preset time period; The fourth basic transition parameter for the transition from the overtaking execution state to the overtaking completed state includes whether the overtaking has been completed; The fifth basic transfer parameter for transitioning from the completed overtaking state to the overtaking failed state includes whether an overtaking request was confirmed within a first preset time period or whether the overtaking was completed within a third preset time period. During the execution of the overtaking permission management process, after transitioning to the current predefined state, a current dynamic threshold of the basic transfer parameter corresponding to the current predefined state is constructed based on the multi-source driving data. The current transfer judgment parameter value is calculated in real time based on the multi-source driving data. When the current transfer judgment parameter value is greater than the current dynamic threshold, the process transitions to the next predefined state until the overtaking permission management process terminates.

2. The control method as described in claim 1, characterized in that, The multi-dimensional identification based on the second vehicle data, the second other vehicle data, and environmental dynamic parameters to confirm the overtaking intention of the other vehicle includes: Determine whether the environmental dynamic parameters meet the conditions for overtaking; When the overtaking conditions are met, the longitudinal distance, lateral distance, and relative speed between the vehicle and the other vehicle are calculated based on the second vehicle data and the second other vehicle data. When the longitudinal distance is less than a preset longitudinal distance, the lateral distance is less than a preset lateral distance, and the relative speed is greater than a preset relative speed, it is confirmed that the other vehicle has the intention to overtake.

3. The control method as described in claim 2, characterized in that, The current dynamic threshold for constructing the basic transfer parameters corresponding to the current predefined state based on the multi-source driving data includes: Confirm the predefined state of the target currently being executed; Extract target data corresponding to the predefined target state from the multi-source driving data; The current dynamic threshold is calculated based on the target data.

4. The control method as described in claim 1, characterized in that, The environmental dynamic parameters include: road slope data, road slip rate, and dust concentration data; When entering the state of requesting overtaking permission, the road slope data, the road slip rate and the dust concentration data are extracted from the environmental dynamic parameters. Based on the preset longitudinal distance, the preset longitudinal distance is corrected using the road slope data, the road slip ratio, and the dust concentration data as correction coefficients to obtain a longitudinal dynamic threshold. The road slope data and the road slip ratio are positively correlated with the longitudinal dynamic threshold, while the dust concentration data is negatively correlated with the longitudinal dynamic threshold.

5. The control method as described in claim 4, characterized in that, When entering the overtaking permission request state, the communication latency data and communication packet loss rate are determined based on the data of the second vehicle and the data of the other vehicle. The road slope data, radius of curvature and dust concentration data are extracted from the environmental dynamic parameters. A communication quality coefficient is determined based on the communication latency data and the communication packet loss rate; an environmental visibility coefficient is determined based on the dust concentration data; a vehicle-to-vehicle situation coefficient is determined based on the longitudinal distance; and a road condition coefficient is determined based on the road slope data and the radius of curvature. Based on the first preset duration, and using the communication quality coefficient, the environmental visibility coefficient, the vehicle situation coefficient, and the road condition coefficient as correction coefficients, the first preset duration is corrected according to the following formula to obtain the dynamic request duration threshold; T1 = max(T 10 ,min( T 11 , T base ×K comm ×K env ×K pos ×K road )) Where T1 is the dynamic request duration threshold, T 10 The duration is constrained by the lower limit of the dynamic request duration threshold, T 11 To constrain the duration of dynamic request duration threshold, T base K is the first preset duration. comm K is the communication quality coefficient. env K represents the environmental visibility factor. pos K represents the inter-vehicle situation coefficient. road This represents the road condition coefficient.

6. The control method as described in claim 5, characterized in that, When entering the state of granting overtaking permission, the road slope data and the road slip ratio are extracted from the environmental dynamic parameters; based on the first vehicle data and the first other vehicle data, the vehicle type difference coefficient between the other vehicle and the vehicle is determined, and the vehicle type difference coefficient represents the ratio of the volume of the other vehicle to the vehicle or the ratio of the load. Based on the third preset duration, the road slope data, the road slip rate, and the vehicle model difference coefficient are used as correction coefficients to correct the third preset duration, thereby obtaining a dynamic authorization duration threshold. The road slope data, the road slip rate, and the vehicle model difference coefficient are positively correlated with the dynamic authorization duration threshold.

7. An overtaking control system for an open-pit mine, comprising a vehicle-mounted equipment layer, a communication layer, and an edge computing layer, as well as a cloud management layer communicatively connected to the equipment layer, wherein, The device layer is used to store the data of the first vehicle and collect the data of the second vehicle; the communication layer is used to communicate with other vehicles to receive the data of the first other vehicle and the data of the second other vehicle; and the edge computing layer is used to execute the control method according to any one of claims 1-6.

8. The overtaking control system for open-pit mines as described in claim 7, characterized in that, The backup layer includes a positioning module, a wireless communication module, and an on-board control unit deployed in the vehicle. The positioning module is used to locate the vehicle. The on-board control unit is used to calculate the vehicle's position and speed information based on the positioning information. The on-board control unit is communicatively connected to the cloud management layer.

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