Mine vehicle control method and device, electronic equipment and readable storage medium

Abstract

Embodiments of the present application provide a kind of mine vehicle control method, device, electronic equipment and readable storage medium, involve unmanned driving technical field.The method comprises: in response to vehicle reaches the target operating face of mining area, the network state between determination and cloud;Target operating face represents the set of multiple operating areas in the same mining height and the same regional range;If it is determined that the network state does not meet the preset condition, first environment information is acquired, and according to the first environment information and the current stored map information, it is determined and travels to target operating area;Target operating area is any operating area in target operating face;Map information includes the position information and path information of each operating face and each operating area in mining area;In response to vehicle reaches target operating area, the point information of target point in target operating area is acquired, and target path traveled to target point is determined.The present application realizes the continuous operation of mine car in weak network environment, improves the work efficiency of vehicle in mining area.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and more specifically, to a control method, device, electronic equipment, and readable storage medium for mining vehicles. Background Technology

[0002] With the large-scale application of unmanned driving technology in the mining industry, the level of automation and intelligence in mining operations has been significantly improved, effectively reducing the intensity of manual labor, increasing operational efficiency, and avoiding safety risks for personnel.

[0003] However, existing unmanned mining architectures rely heavily on real-time cloud-based scheduling and data transmission, making the vehicle highly dependent on the cloud. When there is a weak or no network connection, cloud-based scheduling instructions cannot be issued in a timely manner, and the vehicle's status cannot be reported in real time, leading to interruptions in vehicle operations, increased ineffective energy consumption, and serious impact on mining efficiency. Summary of the Invention

[0004] This application provides a control method, device, electronic equipment, and readable storage medium for mining vehicles, which solves the technical problem that mining vehicles cannot operate continuously in a weak network environment, resulting in low operating efficiency.

[0005] According to a first aspect of the embodiments of this application, a method for controlling mining vehicles is provided, the method comprising: determining the network status with the cloud in response to the vehicle arriving at a target working face in the mining area; the target working face being used to characterize a set of multiple working areas at the same excavation height and within the same area. If the network status is determined not to meet the preset conditions, the first environmental information is obtained, and the target work area is determined and driven to based on the first environmental information and the currently stored map information; the target work area is any work area within the target work face; the map information includes the location information and path information of each work face and each work area within the mining area; In response to the vehicle arriving at the target work area, the location information of the target point in the target work area is obtained, and the target route to the target point is determined.

[0006] In one possible implementation, a first request is sent to the cloud; the first request is used to request the location information and target path of the target point in the target work area; If the location information and the first path of the first point are received from the cloud, and it is determined that the first point is located at the end of the first path based on the location information and the first path, then the first point is taken as the target point and the first path is taken as the target path. If it is determined from the location information of the first point and the first path that the first point is not located at the end of the first path, then the second environmental information and the currently stored map information are obtained. The map information also includes the location information of the target point in the target work area. The target path is determined based on the second environmental information and the currently stored map information.

[0007] In another possible implementation, if the location information and the first path of the first location sent from the cloud are not received, the second environmental information and the currently stored map information are obtained. The target path is determined based on the second environmental information and the currently stored map information.

[0008] In another possible implementation, the map information also includes the location information of target points and drivable routes in each work area; Send retrieval requests to the cloud at a preset first frequency; Update the locally stored map information based on the latest acquired map information; If the current network latency is greater than the first threshold, the location information of the target points in each work area is obtained, and the latest map information is the location information of the target points in each work area. If the current network latency is not greater than the first threshold, the request is used to request the location information and drivable paths of the target points in each work area. The latest map information is the location information and drivable paths of the target points in each work area.

[0009] In yet another possible implementation, the map information is updated by the cloud in the following way: Receive at least one sensor information sent by each vehicle; the sensor information is collected by at least one sensor installed by the corresponding vehicle when it determines that any one of the first conditions is met; The map information stored in the cloud is updated based on information from at least one sensor of each vehicle. The first condition includes at least one of the following: The corresponding vehicles complete their tasks upon arrival at the target location; The vehicle has traveled a distance exceeding the preset distance; The vehicle has not sent at least one sensor message to the cloud for more than a preset period of time since then.

[0010] In another possible implementation, after determining the target path to the target point, the system responds by entering the vicinity of the target point. Acquire information about the third environment, the vehicle's current pose, and the target location; generate an adjustment path based on the information about the third environment, the vehicle's current pose, and the target location. Move the vehicle according to the adjusted route to reach the target location.

[0011] In another possible implementation, if it is determined that a first instruction sent from the cloud has been received, the target work surface is obtained from the first instruction; the first instruction is used to instruct the vehicle to drive to the target work surface to carry out the work. If it is determined that the first instruction sent from the cloud has not been received, and the vehicle is currently in a dispatchable state, then the target work area for this operation is determined based on the work area where the most recently completed task was located.

[0012] In another possible implementation, the first instruction is also used to instruct the vehicle to travel to the first working area of ​​the target working face to carry out the work; based on the real-time acquired operating information of each vehicle in the mining area and the global work plan, the target working face and the first working area corresponding to the vehicle are determined; the first working area is the working area within the target working face that meets at least one of the second conditions, the target working face is the working face within the mining area that meets at least one of the second conditions, the operating information is information related to the position and work progress of the corresponding vehicle, and the global work plan is used to characterize the set of work tasks to be performed; Determine the network status with the vehicle; the determination of the network status is performed according to a preset second frequency; If the network status between the device and the vehicle is determined to meet the preset conditions, a first instruction is generated and sent to the vehicle.

[0013] In another possible implementation, after determining the network status with the cloud, if the network status with the cloud meets the preset conditions, the first job area indicated in the received first instruction is taken as the target job area.

[0014] In yet another possible implementation, the second condition includes at least one of the following: The load level is below the corresponding threshold. The distance to the vehicle is the shortest.

[0015] In yet another possible implementation, the environmental information includes at least one of the following: The driving intentions of other vehicles within the vehicle's preset range; The load level of the work area within the vehicle's preset range.

[0016] According to a second aspect of the embodiments of this application, a control device for mining vehicles is provided, the device comprising: The determination module is used to determine the network status with the cloud in response to the arrival of the vehicle at the target working face in the mining area; the target working face is used to represent a set of multiple working areas at the same excavation height and within the same area. The driving module is used to obtain first environmental information if it is determined that the network status does not meet the preset conditions, and determine and drive to the target work area based on the first environmental information and the currently stored map information; the target work area is any work area within the target work face; the map information includes the location information and path information of each work face and each work area in the mining area; The acquisition module is used to acquire the location information of the target point in the target work area in response to the vehicle arriving at the target work area, and to determine the target route to the target point.

[0017] According to a third aspect of the embodiments of this application, an electronic device is provided, the electronic device including a memory, a processor and a computer program stored in the memory, wherein the processor executes the program to implement the steps of the method provided in the first aspect.

[0018] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps of the method provided in the first aspect.

[0019] According to a fifth aspect of the present application, a computer program product is provided, the computer program product including computer instructions stored in a computer-readable storage medium, wherein when a processor of a computer device reads the computer instructions from the computer-readable storage medium, the processor executes the computer instructions, causing the computer device to perform steps implementing the method provided in the first aspect.

[0020] The beneficial effects of the technical solutions provided in this application are: The control method for mining vehicles provided in this application determines the network status with the cloud in response to the vehicle arriving at the target working face of the mining area, thereby realizing real-time determination of the communication status with the cloud.

[0021] If the network status is determined to be inconsistent with the preset conditions, it indicates that the current communication with the cloud is poor. In order to avoid the interruption of the operation due to poor communication with the cloud, the first environmental information is obtained. Based on the first environmental information and the currently stored map information, the vehicle determines and drives to the target work area. This realizes the selection of the optimal work area most suitable for the current vehicle to carry out the operation based on the real-time environmental information and the stored location and path information of each work face and work area in the mining area. The vehicle then drives to the target work area. This enables the vehicle to continue to operate in a weak network environment without relying on real-time communication with the cloud, but relying on the environmental data collected by the vehicle itself and the stored map information, thus ensuring the continuous operation of the vehicle.

[0022] By responding to the arrival of the vehicle in the target work area, obtaining the location information of the target point in the target work area, and determining the target route to the target point, the vehicle can carry out the current work task after arriving at the target point. This enables the vehicle to operate continuously in a weak network environment and improves the operating efficiency of vehicles in the mining area. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0024] Figure 1 A flowchart illustrating a method for controlling vehicles in a mining area, provided as an embodiment of this application; Figure 2 A flowchart illustrating the method for obtaining target locations and target paths in a control method for mining vehicles provided in this application embodiment; Figure 3 A schematic diagram of the system architecture of a control method for mining vehicles provided in this application embodiment; Figure 4 A flowchart illustrating a method for controlling vehicles in a mining area, provided as an embodiment of this application; Figure 5 A schematic flowchart illustrating the cloud-based scheduling method in a mining vehicle control method provided in this application embodiment; Figure 6 A schematic diagram illustrating a method for generating environmental information in a control method for mining vehicles provided in this application embodiment; Figure 7 A schematic diagram of the structure of a control device for a mining vehicle provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0026] Those skilled in the art will understand that, unless otherwise stated, the singular forms “a,” “an,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.”

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0028] The relevant technologies are explained below: Currently, the control of mining vehicles largely relies on real-time cloud-based scheduling and data transmission, making the vehicles highly dependent on the cloud. When a weak or outage occurs, cloud-based scheduling instructions cannot be issued in a timely manner, and vehicle status cannot be reported in real time, leading to vehicle operation interruptions and severely impacting mining efficiency. Furthermore, existing scheduling methods mostly employ a single-level scheduling approach, lacking a layered redundancy mechanism designed for weak network scenarios, thus failing to achieve collaborative fault tolerance between the cloud and the vehicle. In addition, existing perception modules largely rely solely on onboard sensor data, without integrating vehicle-to-vehicle (V2V) information, resulting in insufficient perception accuracy and reliability, making it difficult to support autonomous operational decisions during weak or outage scenarios. Regarding path planning, cloud-based maps often employ a high-frequency update mode, which not only increases network transmission pressure but also fails to synchronize in a timely manner during weak network scenarios, further exacerbating operational interruptions.

[0029] In related technologies, some solutions attempt to address weak network issues by optimizing vehicle-to-cloud communication links. However, these solutions suffer from drawbacks such as link establishment relying on preset conditions and poor dynamic adaptability, failing to meet the demands of dynamic mining vehicle operations. Other solutions only implement simple autonomous obstacle avoidance on the vehicle side, without incorporating hierarchical scheduling logic for the work surface and work area, thus failing to guarantee the continuity and standardization of operations under weak network conditions. Therefore, these technologies suffer from problems such as operation interruptions, inefficient scheduling, and insufficient perception due to weak or broken networks.

[0030] In response to at least one of the aforementioned technical problems or areas requiring improvement in related technologies, this application proposes a control method for mining vehicles. This method determines the network status with the cloud in response to the vehicle's arrival at the target working face in the mining area, thereby achieving real-time determination of the communication status with the cloud.

[0031] If the network status is determined to be inconsistent with the preset conditions, it indicates that the current communication with the cloud is poor. In order to avoid the interruption of the operation due to poor communication with the cloud, the first environmental information is obtained. Based on the first environmental information and the currently stored map information, the vehicle determines and drives to the target work area. This realizes the selection of the optimal work area most suitable for the current vehicle to carry out the operation based on the real-time environmental information and the stored location and path information of each work face and work area in the mining area. The vehicle then drives to the target work area. This enables the vehicle to continue to operate in a weak network environment without relying on real-time communication with the cloud, but relying on the environmental data collected by the vehicle itself and the stored map information, thus ensuring the continuous operation of the vehicle.

[0032] By responding to the arrival of the vehicle in the target work area, obtaining the location information of the target point in the target work area, and determining the target route to the target point, the vehicle can carry out the current work task after arriving at the target point. This enables the vehicle to operate continuously in a weak network environment and improves the operating efficiency of vehicles in the mining area.

[0033] The technical solutions of this application and their effects are described below through several exemplary embodiments. It should be noted that the following embodiments can be referenced, borrowed from, or combined with each other. Identical terms, similar features, and similar implementation steps in different embodiments will not be repeated.

[0034] This application provides a method for controlling vehicles in a mining area, such as... Figure 1 As shown, the method includes: S101 determines the network status with the cloud in response to the vehicle arriving at the target working face in the mining area.

[0035] In this embodiment of the application, the vehicle refers to an unmanned vehicle that operates in the mining area. After determining the target working face to be operated, the vehicle drives to the target working face. In response to the vehicle arriving at the target working face in the mining area, the network status with the cloud is first determined. By determining the network relationship with the cloud, the vehicle can determine whether the current communication situation is suitable for the next operation based on the data transmitted from the cloud.

[0036] In this embodiment of the application, it can be determined whether the cloud is within effective communication range by detecting the communication heartbeat status with the cloud.

[0037] In this embodiment of the application, there are multiple working faces in the mining area. The target working face is the working face that is most suitable for the current vehicle to carry out the work (e.g., the one closest to the vehicle). The target working face is used to represent a set of multiple working areas that are at the same excavation height and within the same area. In other words, there are multiple working areas within the working face.

[0038] S102, if it is determined that the network status does not meet the preset conditions, the first environmental information is obtained, the target work area is determined based on the first environmental information and the currently stored map information, and the vehicle travels to the target work area based on the first environmental information and the currently stored map information.

[0039] In the implementation of this application, if it is determined that the network status does not meet the preset conditions, it means that the current communication status with the cloud is poor and there is no condition to carry out further operations based on the information sent by the cloud. When the network status is confirmed by detecting the communication heartbeat status, the preset condition can be receiving the response information from the cloud within a preset time period.

[0040] In this embodiment of the application, when it is determined that the conditions for further operation based on the information sent from the cloud are not met, the vehicle enters an autonomous decision-making mode, that is, the terminal autonomously decides to proceed with the next operation based on the real-time collected environmental information and the stored map information.

[0041] In this embodiment of the application, the target work area is any work area within the target work surface. Since the vehicle needs to determine which work area to go to for further work after arriving at the target work surface, it is currently necessary to select the target work area from multiple work areas in the target work surface. When selecting the target work area, priority is given to the work area with no work conflict (i.e., no other vehicles are working in the current work area), the closest to the vehicle, and the best working conditions as the target work area.

[0042] In this application embodiment, the environmental information includes at least one of the following: The driving intentions of other vehicles within the vehicle's preset range; Information on obstacles within the vehicle's preset range; In this embodiment of the application, the driving intention can be the current location of the vehicle. Based on the driving intentions of other vehicles within a preset range, it can be determined that there are no vehicles operating in the current target work area. The obstacle information refers to whether there are obstacles around the vehicle that affect the vehicle's driving. Based on the obstacle information, it can be determined which work area the vehicle can drive to most quickly.

[0043] In this embodiment of the application, in order to select a target work area from multiple work areas, environmental information and currently stored map information are obtained. The map information includes the location information and path information of each work face and each work area in the mining area. Therefore, the target work area is determined by combining the environmental information and the map information. After the target work area is determined, the vehicle travels to the target work area according to the location of the target work area in the map information.

[0044] In one example, based on the driving intentions of other vehicles within a preset range, unoccupied work areas within the target work area can be identified as candidate work areas. Obstacle information can be used to determine whether the paths to each candidate work area are blocked, and the candidate work areas can be further eliminated to obtain available work areas. For each available work area, the path length to the available work area can be determined based on map information, as can the ground flatness and other information of the work area. Combining the path length and ground flatness, the work area with the shortest path length and the highest flatness can be selected as the target work area.

[0045] In this embodiment, the environmental information is obtained by fusing vehicle-to-vehicle information based on sensor information collected by the current vehicle and other vehicles. The sensors that collect sensor information include: lidar, millimeter-wave radar, camera, GPS / BeiDou positioning module, etc. By collecting data such as the vehicle's own pose, surrounding environment (such as obstacles, work surface terrain), and work site status in real time, basic perception information is provided for vehicle driving and operation.

[0046] In this embodiment of the application, since short-range wireless communication is not affected by the weak network conditions in the mine, vehicles can exchange sensor information with other vehicles through short-range wireless communication, ensuring that even in weak network conditions, they can determine and drive to the target work area by using environmental information and stored map information.

[0047] In this embodiment, after receiving sensor information from other vehicles, the vehicle fuses its own sensor information with that of other vehicles, eliminates redundant information, corrects information errors, and outputs environmental information that includes the vehicle's own operating status, the driving intentions of other vehicles within a preset range, and obstacle information. This provides reliable data support for determining the target work area and driving to the target work area, solving the problems of limited sensing range and susceptibility to environmental interference of a single sensor.

[0048] In this embodiment, the map information is a global operation map pre-generated in the cloud based on information such as mine terrain data, distribution of work faces, and location of work areas. It includes path information for each work face and distribution information of work areas. The map information can be updated based on sensor information uploaded by each vehicle. In other words, the vehicle has the core data of the mine map cached locally. When it is impossible to carry out operations based on information transmitted from the cloud in a weak network environment, it can carry out operations based on the currently stored map information and real-time environmental information, thereby reducing the dependence on the real-time performance of the cloud map.

[0049] In this embodiment of the application, when determining the path to the target work area based on map information, it is necessary to consider the path intersection of each work area within the target work area. By using a path optimization algorithm, path interference between different vehicles can be avoided, and work conflicts between vehicles can be reduced.

[0050] S103, in response to the vehicle arriving at the target work area, obtains the location information of the target point in the target work area and determines the target route to the target point.

[0051] In this embodiment of the application, each work area contains a target point, such as an excavation point or a loading point. The vehicle needs to move to the target point to carry out the work. Therefore, in response to the vehicle arriving at the target work area, the point information of the target point in the target work area is first obtained. The point information is information related to the location of the target point.

[0052] In this embodiment of the application, when the communication quality with the cloud is poor, the location information of the target point in the target work area can be obtained through the currently stored map information, and the target path to the target point can be determined based on the map information and the real-time acquired environmental information.

[0053] In this embodiment of the application, even when the communication quality with the cloud is poor, the target path can still be determined by the currently stored map information and the real-time acquired environmental information. This avoids the vehicle stopping directly due to poor communication quality, reduces ineffective energy consumption, and achieves efficient vehicle operation. While ensuring production safety and efficiency, it also achieves a significant green and low-carbon goal.

[0054] In this embodiment of the application, when the communication quality with the cloud is good, the location information of the target point and the target path to the target point can be obtained through the cloud.

[0055] In this embodiment of the application, after determining the target path, the vehicle travels to the target location according to the target path to carry out the operation.

[0056] The control method for mining vehicles provided in this application determines the network status with the cloud in response to the vehicle arriving at the target working face of the mining area, thereby realizing real-time determination of the communication status with the cloud.

[0057] If the network status is determined to be inconsistent with the preset conditions, it indicates that the current communication with the cloud is poor. In order to avoid the interruption of the operation due to poor communication with the cloud, the first environmental information is obtained. Based on the first environmental information and the currently stored map information, the vehicle determines and drives to the target work area. This realizes the selection of the optimal work area most suitable for the current vehicle to carry out the operation based on the real-time environmental information and the stored location and path information of each work face and work area in the mining area. The vehicle then drives to the target work area. This enables the vehicle to continue to operate in a weak network environment without relying on real-time communication with the cloud, but relying on the environmental data collected by the vehicle itself and the stored map information, thus ensuring the continuous operation of the vehicle.

[0058] By responding to the arrival of the vehicle in the target work area, obtaining the location information of the target point in the target work area, and determining the target route to the target point, the vehicle can carry out the current work task after arriving at the target point. This enables the vehicle to operate continuously in a weak network environment and improves the operating efficiency of vehicles in the mining area.

[0059] Based on the above embodiments, as an optional embodiment, a method for obtaining target locations and target paths is provided, such as... Figure 2 As shown, the specific content is as follows: S201, Send the first request to the cloud; the first request is used to request the location information and target path of the target point in the target work area; S202, upon receiving the location information and first path of the first location sent by the cloud, if it is determined that the first location is located at the end of the first path based on the location information and first path, then the first location is taken as the target location and the first path is taken as the target path. S203, if it is determined from the location information of the first point and the first path that the first point is not located at the end of the first path, then obtain the second environment information and the currently stored map information. S204, Determine the target path based on the second environmental information and the currently stored map information.

[0060] In S201 of this application embodiment, after the vehicle arrives at the target work area, it sends a first request to the cloud to request the location information and target path of the target point in the target work area. Since the map information is updated by the cloud, the latest location information and target path of the target point can be obtained by sending the first request to the cloud, thereby assisting the vehicle to reach the target point in the target work area more accurately and quickly.

[0061] In S202 of this application embodiment, when the location information of the first location and the first path sent by the cloud are received, since the location information and the path sent by the cloud may not match, when the location information and the first path are received, it is first determined whether the first location is located at the end of the first path. If the first location is located at the end of the first path, it means that the sent location information and the first path match, and the information sent by the cloud is usable. Therefore, the first location is taken as the target location and the first path is taken as the target path, so as to travel to the target location based on the target path.

[0062] In S203 of this embodiment, if it is determined that the first point is not located at the end of the first path based on the point information of the first point and the first path, it indicates that the point information of the first point and the first path sent by the cloud are unavailable. Since the map information also includes the point information of the target point in the target work area, the vehicle starts the autonomous decision-making mode to obtain the second environmental information and the currently stored map information.

[0063] In S204 of this application embodiment, the location information of the target point is obtained from the currently stored map information. Based on the location information, the current location of the vehicle, and the obstacle information within a preset range included in the environmental information, the target path from the current location to the target point is determined.

[0064] In the above scheme, upon reaching the target work area, a first request is sent to the cloud to request the location information and target path of the target point in the target work area. Upon receiving the location information and target path from the cloud, the system first determines whether the first location and target path match. If they do not match, it indicates that the cloud may not provide available locations and paths due to network latency or other issues. Therefore, the vehicle activates autonomous decision-making mode, acquires second environmental information in real time, and determines the target location and target path to the target location based on the second environmental information and the currently stored map information. This ensures that even when the next step of the operation cannot be based on the data sent from the cloud, the vehicle can autonomously make decisions about the target location and target path, avoiding interruptions in the operation and ensuring that the vehicle can successfully reach the work point. This scheme effectively adapts to weak or even extremely unstable network scenarios in mining areas, improving the efficiency of vehicle operations within the mining area.

[0065] Based on the above embodiments, as an optional embodiment, after sending a first request to the cloud, if the location information of the first point and the first path are not received from the cloud, the second environmental information and the currently stored map information are obtained; and the target path is determined based on the second environmental information and the currently stored map information.

[0066] In this embodiment of the application, if the location information and the first path of the first point are not received from the cloud within a preset time period after sending the first request to the cloud, it indicates that the current communication quality between the cloud and the vehicle is extremely poor and a communication interruption has occurred between the cloud and the vehicle. In this case, it is impossible to drive to the target point based on the information sent by the cloud. In order to ensure the continuity of vehicle operation and avoid interruption of operation, the location information of the target point and the path to the target point are determined by the currently stored map information. The path is adjusted in combination with the real-time environmental information to finally obtain the target path to the target point.

[0067] In the above scheme, if the location information and path sent by the cloud are not received within the preset time period, it means that the communication between the cloud and the vehicle has been interrupted. Therefore, the target location and target path are determined autonomously based on the real-time environmental information and the currently stored map information, thereby ensuring that the vehicle can continue to work in a weak network environment and improving the efficiency of vehicle operation in mining areas under weak network scenarios.

[0068] Based on the above embodiments, as an optional embodiment, the map information also includes the location information of target points in each work area and the drivable path; sending acquisition requests to the cloud at a preset first frequency; and updating the locally stored map information based on the latest acquired map information.

[0069] In this embodiment of the application, the vehicle sends a request to the cloud to obtain the latest map information at a first frequency. After obtaining the latest map information, it updates the locally stored map information so that the locally stored map information is consistent with the actual operation in the mining area.

[0070] In this embodiment of the application, if the current network latency is greater than a first threshold, a request is made to obtain the location information of the target points in each work area, and the latest map information is the location information of the target points in each work area.

[0071] In this embodiment of the application, when sending the acquisition request, it is first determined whether the current network latency is greater than a first threshold. If it is determined that the current network latency is greater than the first value, it indicates that the current network is extremely unstable, such as network latency ≥1000ms or frequent disconnections. In order to ensure the priority acquisition of core data, the location information of the target points in each work area is requested first, so as to ensure that even in the case of network disconnection, the vehicle can be guided to carry out further work based on the cached map information.

[0072] In this embodiment of the application, if the current network latency is not greater than the first threshold, the request is used to obtain the location information and drivable path of the target point in each work area, and the latest map information is the location information and drivable path of the target point in each work area.

[0073] In this embodiment of the application, when the network latency is not greater than the first threshold, it indicates that the current network status is relatively stable. Therefore, the request is used to request the location information and drivable path of the target point in each work area.

[0074] In one example, the location information of target points in each work area can be retrieved from the cloud as a separate module, and the drivable path can be retrieved separately as a separate module. This avoids the entire map becoming unusable due to the failure of data transmission of a single module. Furthermore, the module to be retrieved can be adaptively selected based on network status. For example, when network instability is detected, the acquisition of target point information can be prioritized, and the drivable path can be retrieved after the target work point data is acquired. Alternatively, when the network status is good, both the target point information and the drivable path can be retrieved. In other words, the acquisition priority of target point information in each work area is set higher than that of drivable paths within the mining area.

[0075] In the above scheme, in order to adapt to the weak network environment in the mining area, the vehicle locally caches the core data in the map information (the location information of the target point and the driving path), sends the acquisition request to the cloud at a preset frequency, and classifies the acquisition request. In the weak network environment, only the most core point information is acquired, and the driving path can be acquired when the network is better. This ensures that even in the case of network outage, basic operations such as work area guidance and path driving can be completed based on the cached points and paths.

[0076] Based on the above embodiments, as an optional embodiment, at least one sensor information sent by each vehicle is received; the sensor information is collected by at least one sensor installed by the corresponding vehicle when it determines that any one of the first conditions is met; the map information stored in the cloud is updated according to the at least one sensor information of each vehicle.

[0077] In this embodiment of the application, the updating of the map information stored in the cloud depends on the sensor information uploaded to the cloud by each vehicle in the mining area. After obtaining the sensor information, the map information stored in the cloud is updated according to at least one sensor information of each vehicle.

[0078] In this embodiment, the sensor information includes environmental information, road surface information, and obstacle information around the vehicle. The cloud processes the sensor information of different vehicles within the same radius together to determine the updated part of the map information and update the map information based on the updated part.

[0079] In one example, the cloud platform pre-generates a global operation map based on mine terrain data, workface distribution, and work area locations. This map includes path information for each workface and work area distribution. When generating paths, the platform prioritizes the intersection and overlap of paths between different work areas within the same workface. Path optimization algorithms are used to avoid path interference between different vehicles and reduce operational conflicts. Specifically, when generating a dedicated path for each work area, the platform actively avoids work points and vehicle waiting points in other work areas, preventing paths from overlapping or intersecting with core work points and waiting areas in other work areas. Furthermore, the path design near work points is optimized to be as close as possible to straight lines parallel to the work point direction. This ensures vehicles can quickly and smoothly reach work points, reducing path detours and positional adjustments, while further avoiding interference with paths and points in other work areas, thus ensuring path independence and safety during multi-vehicle parallel operations.

[0080] In this application embodiment, the first condition includes at least one of the following: The corresponding vehicles complete their tasks upon arrival at the target location; The corresponding vehicle has traveled a distance exceeding the preset distance; The corresponding vehicle has been away from sending at least one sensor message to the cloud for more than a preset period of time.

[0081] In this embodiment of the application, since the update of map information depends on the sensor information uploaded by the vehicle, in order to ensure that the amount of data received by the cloud is not too large while also being able to capture the dynamic changes of the working environment in a timely manner, the vehicle is configured to send at least one sensor information to the cloud when the first condition is met.

[0082] In this embodiment of the application, after the vehicle completes the task at the target location, the environment and working conditions near the target location may change. Therefore, after the vehicle completes the task, it sends at least one sensor information (including surrounding terrain, obstacles, actual status of the work location, etc.) acquired in real time to the cloud.

[0083] In this embodiment of the application, after the vehicle travels a distance exceeding a preset distance, the sensor information is automatically triggered to upload, ensuring that the dynamic changes in the working environment can be fed back to the cloud in a timely manner, thereby updating the map information.

[0084] In this embodiment, sensor information can also be uploaded at preset time intervals. When the time elapsed since the last transmission of sensor information exceeds a preset time, it indicates that sensor information needs to be uploaded again.

[0085] In the above scheme, by setting multiple trigger conditions, the frequency of vehicle transmitting sensor information to the cloud is reduced, while ensuring that the updated map information can capture changes in the mining area environment in a timely manner.

[0086] Based on the above embodiments, as an optional embodiment, after determining the target path to the target location, in response to entering the vicinity of the target location, third environmental information, the vehicle's current pose, and the target location's location information are obtained; an adjusted path is generated based on the third environmental information, the vehicle's current pose, and the target location's location information; and the vehicle is moved according to the adjusted path to reach the target location.

[0087] In this embodiment, after the vehicle travels to the vicinity of the target point, it no longer travels based on the target path. Instead, it acquires third-party environmental information, the vehicle's current pose, and the target point's location information in real time to generate an adjustment path. The vehicle is then moved according to the adjustment path, i.e., the vehicle's pose is adjusted, so that the vehicle reaches the target point, ensuring the accuracy of subsequent operations, such as the alignment accuracy of excavation and loading.

[0088] In the above scheme, the target path determined based on map information only guides the vehicle to the vicinity of the target location. The subsequent precise selection of locations and fine-tuning of position are confirmed by the vehicle based on real-time acquired third-party environmental information, which improves the accuracy of vehicle fine-tuning.

[0089] Based on the above embodiments, as an optional embodiment, if it is determined that a first instruction sent from the cloud has been received, the target work surface is obtained from the first instruction; the first instruction is used to instruct the vehicle to drive to the target work surface to carry out the work.

[0090] In this embodiment of the application, the vehicle can determine the target work surface for the current cloud-instructed operation based on the received first instruction. If the vehicle is in a dispatchable state and has good communication with the cloud, it will receive the first instruction sent by the cloud. The first instruction instructs the cloud to drive to the target work surface for operation. Therefore, the cloud can obtain the information of the target work surface from the first instruction and drive to the target work surface.

[0091] In this embodiment of the application, if it is determined that the first instruction sent by the cloud has not been received and the vehicle is currently in a dispatchable state, the target work surface for this operation is determined based on the work surface where the most recently completed task is located.

[0092] In this embodiment, the vehicle can also determine the target work surface for the current operation based on the work surface where the most recently completed task was located. When the cloud is in a dispatchable state but has not received the first instruction sent by the cloud, it indicates that the communication quality between the cloud and the vehicle is poor, and the cloud cannot know that the vehicle is currently in a dispatchable state. Alternatively, the cloud may choose not to send the first instruction to the vehicle based on the current network conditions between the cloud and the vehicle. To ensure continuous operation of the vehicle, if the vehicle does not receive the first instruction sent by the cloud within a preset time period while in a dispatchable state, it will enter an autonomous decision-making mode and autonomously select the target work surface. To ensure that the vehicle minimizes its travel distance, the target work surface for the current operation is determined based on the work surface where the most recently completed task was located. The preset time period can be set according to the dispatch frequency of the cloud. For example, if the cloud sends the first instruction to the currently dispatchable vehicle every three minutes, then if the vehicle does not receive the first instruction sent by the cloud within three minutes of entering the dispatchable state, it will autonomously proceed with the decision-making process for the target work surface.

[0093] In the above scheme, when the first instruction sent by the cloud is received, it indicates that the current communication quality with the cloud is good. The target work area is directly obtained from the first instruction. When the system is in the scheduling state and the first instruction has not been received, it indicates that the communication quality with the cloud is poor. In this case, the system will directly enter the autonomous decision-making mode and determine the target work area for this operation based on the work area where the most recently completed task is located. This ensures that even if the vehicle is in a scheduling state in a weak network environment, it can still enter the target work area to carry out the operation. It also ensures the rationality of the work area selection in a weak network environment.

[0094] Based on the above embodiments, as an optional embodiment, the target working face and the first working area corresponding to the vehicle are determined based on the real-time acquired operating information of each vehicle in the mining area and the global operation plan; the first working area is the working area within the target working face that meets at least one of the second conditions, the target working face is the working face within the mining area that meets at least one of the second conditions, the operating information is information related to the position and operation progress of the corresponding vehicle, and the global operation plan is used to characterize the set of operation tasks to be performed.

[0095] In this embodiment, the cloud dispatches available vehicles within the mining area at a preset frequency. The dispatch frequency is set to low-frequency dispatch, ranging from 1 to 5 minutes per dispatch, to avoid network transmission pressure caused by high-frequency dispatch and to adapt to weak network environments. During the dispatching process, the operating information and global work plan of each vehicle within the mining area are first acquired in real time. The vehicle operating information includes the vehicle's current location, battery level, load, and work progress. The global work plan is a set of tasks to be performed, such as the loading volume required for each work surface. Then, based on the operating status of each vehicle and the global work plan, a target work surface and a first work area are assigned to vehicles in a dispatchable state. The target work surface is the most suitable work surface for the vehicle to perform work compared to other work surfaces, and the first work area is the most suitable work area for the vehicle to perform work compared to other work areas within the target work surface.

[0096] In this embodiment of the application, when determining the target work area and the first work area, the cloud uses a global work optimization algorithm to allocate the target work area and the first work area to each schedulable vehicle, taking into account the work load, vehicle distance, path congestion, and other factors of each work area.

[0097] In one example, the cloud platform calculates the number of vehicles allocated, the remaining workload, and the number of idle work points in each work area based on the operating status of each vehicle and the global work plan. It then establishes a work area load weighting model, with the following specific scheduling rules: For work areas with idle target points and non-empty remaining workload, the cloud platform prioritizes scheduling vehicles to the work area with the shortest path distance, balancing work efficiency and path cost. For work areas without idle target points and non-empty remaining workload, the cloud platform calculates the average number of vehicles allocated per target point based on the number of allocated vehicles and the total number of target points in each work area. It then prioritizes scheduling vehicles to the work area with the lowest average number of vehicles allocated per target point, achieving global work load balancing.

[0098] After determining the target work area, the cloud further selects the optimal work area within that target work area, with the specific allocation rules as follows: 1. When there are available target locations, prioritize assigning work areas that are closest to the vehicle's current travel path to reduce vehicle idle time; 2. When all target locations have loaded vehicles, the cloud platform combines the historical average loading time of each work area with the start time of loading in each work area to estimate the release time of each work area location, and prioritizes the work area with the closest release time to avoid long waiting times for vehicles.

[0099] 3. Combined with dynamic route congestion avoidance, the cloud uses real-time location data reported by vehicles to identify the congestion status of key nodes such as main roads and intersections between work areas in real time. If a path associated with a work area becomes congested, the cloud will temporarily reduce the scheduling priority of that work area and guide vehicles to the work area corresponding to the non-congested path to avoid work interruption due to path blockage and ensure work continuity.

[0100] After the cloud completes the allocation of work surfaces and work areas in the above manner, it issues the dispatch to vehicles at a low frequency (1 minute / time), without relying on high-frequency real-time communication, and is adapted to the weak network environment of the mine.

[0101] In this embodiment of the application, the network status between the network and the vehicle is determined; the determination of the network status is performed according to a preset second frequency; if the network status between the network and the vehicle meets the preset conditions, a first instruction is generated and sent to the vehicle. In this embodiment of the application, the network status between the cloud and the vehicle is determined. The cloud detects the network status between the cloud and each vehicle at a preset second frequency. For example, the cloud detects the communication heartbeat status between the cloud and each vehicle in real time to determine whether the vehicle is within the effective communication range, that is, to determine the network status between the cloud and the vehicle. Only when the communication is detected to be normal will the first instruction be generated and sent to the vehicle.

[0102] In this embodiment of the application, if it is determined that the network status between the vehicle and the vehicle does not meet the preset conditions, then the communication is determined to be interrupted, and the first instruction for scheduling the vehicle to perform the operation is not sent to the vehicle. Instead, the vehicle autonomously executes the decision of the subsequent target work area to ensure that the operation is not interrupted.

[0103] In the above scheme, when scheduling vehicles for operations, the cloud adopts a low-frequency global scheduling method. Based on the real-time location information, operating status, and global operation plan reported by the vehicles, it determines the target work surface and first work area most suitable for the current vehicle to perform the operation. This achieves overall scheduling of all vehicles, does not rely on high-frequency real-time communication, reduces communication pressure in weak network environments, and determines whether to send the first instruction based on the real-time network status before generating the first instruction. This ensures that the first instruction is sent to the vehicle when the communication quality between the vehicle and the cloud is good, and that the vehicle makes its own decision when the communication quality is poor. In other words, the posture factor adjustment strategy ensures continuous operation of vehicles in weak network environments.

[0104] In this application embodiment, the second condition includes at least one of the following: The load level is below the corresponding threshold. The distance between you and the vehicle is less than the corresponding distance threshold.

[0105] In this embodiment of the application, for the target work surface to meet the load level below the corresponding threshold level, that is, to select the work surface with fewer vehicles currently working as the target work surface, so as to avoid the situation where too many vehicles in a certain work surface cause road congestion. For the first work area to meet the load level below the corresponding threshold level, that is, to select the work area with no vehicles currently working, so as to avoid work conflicts.

[0106] In this embodiment of the application, for the target working face, the distance between the current vehicle location and the entrance of the working face is usually taken as the distance between the working face and the vehicle. The working face with a distance less than the distance threshold is selected as the target working face, which avoids the vehicle spending more time traveling to and from the target working face and improves the efficiency of mining truck operation.

[0107] In this embodiment, for the first work area, the distance between the excavator's location and the current vehicle's location within the work area is typically used as the distance between the work area and the vehicle. Work areas with a distance less than a corresponding distance threshold are selected as the first work area, ensuring that the vehicle can quickly enter the work area to perform operations after reaching the target work surface, thus improving work efficiency. Based on the above embodiments, as an optional embodiment, if the network status with the cloud meets preset conditions, the first work area indicated in the received first instruction is used as the target work area.

[0108] In this embodiment of the application, after moving to the target work area according to the received first instruction, if it is determined that the network status meets the preset conditions, it means that the current communication quality with the cloud is good. The first work area indicated in the received first instruction can be used as the target work area for operation. This ensures that the operation is carried out based on the information sent by the cloud while ensuring that the cloud communication is reliable. It avoids making autonomous decisions when the communication with the cloud is good, thereby reducing the operating efficiency of the vehicle.

[0109] The control method for mining vehicles provided in this application reduces communication pressure through low-frequency global scheduling in the cloud. When a vehicle detects poor network conditions, it initiates an autonomous decision-making mode and continues operation based on environmental information, ensuring operational continuity and improving mining efficiency. When determining the target working face and the first working area, the cloud allocates them based on workload and path conditions. Simultaneously, map information generation avoids path interference and reduces vehicle operation conflicts. Environmental information is obtained by fusing sensor information between vehicles, which facilitates the initiation of autonomous decision-making mode, improves decision-making accuracy, and reduces the risks of collisions and operational errors. The cloud uses low-frequency scheduling and low-frequency map information synchronization, reducing the amount of communication data in weak network environments and avoiding environmental congestion. The vehicle caches map information locally, reducing real-time dependence on the cloud and improving the system's anti-interference capability. The control method for mining vehicles proposed in this application fully considers the characteristics of complex mine terrain and unstable network, balancing operational continuity, accuracy, and safety. It can be widely applied to various unmanned driving scenarios in open-pit mines, underground mines, and other mines, and has strong practicality.

[0110] like Figure 3 As shown, a system architecture diagram of a method for controlling mining vehicles is provided. The system architecture of the method for controlling mining vehicles includes a cloud scheduling layer 301, a vehicle execution layer 302, a perception fusion layer 303, and a map service layer 304. Each layer works collaboratively to complete the method for controlling mining vehicles. The specific details are as follows: The cloud scheduling layer 301 includes a global scheduling module 3011 and a communication heartbeat detection module 3012. The global scheduling module 3011 is used to allocate target work surfaces and target work areas to schedulable vehicles based on the operating status reported by the vehicles and the global work plan in a low-frequency global scheduling mode. The communication heartbeat detection module 3012 is used to detect the communication status with the vehicles.

[0111] The vehicle-side execution layer 302 includes an autonomous decision-making module 3021, a pose fine-tuning module 3022, and a path planning module 3023. The autonomous decision-making module 3021 is used to adaptively determine whether to execute the autonomous decision-making mode based on the communication status with the cloud, ensuring that the vehicle can operate continuously in a weak network environment. The pose fine-tuning module 3022 is used to perform pose fine-tuning based on environmental information. The path planning module 3023 is used to perform path planning based on environmental information and map information.

[0112] The perception fusion layer 303 includes a V2V information interaction module 3031, which is used to communicate with other vehicles and obtain sensor information from other vehicles; an on-board sensor module 3032, which is used to collect sensor information from the vehicle itself; and an information fusion module 3033, which is used to fuse the sensor information of the vehicle itself with the sensor information from other vehicles to obtain environmental information, thereby providing data support for autonomous decision-making and operation control at the vehicle end.

[0113] The map service layer 304 includes a cloud map generation module 3041 and a low-frequency map synchronization module 3042. The cloud map generation module 3041 generates map information based on the sensor information uploaded by each vehicle, thereby providing map data support for cloud scheduling and vehicle driving. The cloud map generation module 3041 synchronizes the map to each vehicle by updating the map at a low frequency, reducing network transmission pressure and bandwidth dependence, and adapting to weak network environments.

[0114] In one example, a model architecture for mining vehicles is provided, applicable to unmanned loading and transportation operations in open-pit mines. Specific details include: The cloud-based scheduling layer is equipped with a global scheduling module and a communication detection module, which accesses real-time data (location, status) of all autonomous vehicles. It pre-defines three working faces in the open-pit mine (each working face contains 4-6 working areas, with consistent excavation height within the same working face). The global scheduling module uses a genetic algorithm to assign a target working face to each vehicle based on the principle of workload balancing, with a scheduling frequency set to 3 minutes per time. The communication heartbeat monitoring module checks the communication status with the vehicle every 10 seconds. If no heartbeat feedback is detected for three consecutive times, it is determined that the communication is interrupted.

[0115] Each unmanned mining truck is equipped with an autonomous decision-making module and a pose fine-tuning module. After receiving the first instruction from the cloud, it autonomously drives to the vicinity of the target working face. Upon arrival, it checks the communication heartbeat: if communication is normal, it drives according to the working area recommended by the cloud and performs loading operations; if communication is interrupted, the autonomous decision-making module selects the nearest working area with no other vehicles based on perception fusion data and starts loading operations. After entering the target working area, it drives to the vicinity of the loading point based on the low-frequency path sent by the cloud. Through the pose fine-tuning module, combined with real-time perception data, it adjusts the vehicle's pose to ensure loading alignment accuracy (error ≤ 5cm). The vehicle will adaptively judge the matching of the point and the path. When matching, it drives to the vicinity of the point according to the path and relies on real-time perception to fine-tune the end path. When not matching, the vehicle autonomously plans a global path to the point. The accurate selection of the point depends on real-time perception data and does not rely on the precise endpoint information of the cloud map.

[0116] The perception fusion layer is equipped with vehicle-mounted sensor modules, which include lidar (detection range ≥100m), millimeter-wave radar, high-definition cameras, and GPS / BeiDou positioning modules (positioning accuracy ±1cm). These modules collect data such as vehicle pose, surrounding obstacles, and work area status in real time. Vehicles exchange location and work status information every 2 seconds. The information fusion module uses a Kalman filter algorithm to fuse sensor data and V2V data, remove interference data, and output accurate information about the surrounding environment and vehicle status.

[0117] The map service layer deploys a cloud-based map generation module. Based on open-pit mine terrain data, this module generates a global operational map, including path information for each work face, work area distribution, and drivable area information. When generating paths, the A* algorithm is used to avoid path intersections between different work areas within the same work face, ensuring uninterrupted vehicle movement. The low-frequency map synchronization module is set to update every 5 minutes. The vehicle-side uses a mode of acquiring target points and drivable paths separately. Upon startup, target point information is prioritized, while drivable paths are set as a low priority. Subsequently, data from each module is synchronized and updated every 5 minutes, and core map data is cached locally. When extreme network instability is detected, priority is continuously given to acquiring point data, while the acquisition of low-priority drivable path data is temporarily suspended.

[0118] In this embodiment, when the mine experiences a weak network (network latency ≥ 500ms), low-frequency dispatch commands from the cloud can still be issued normally, and the vehicle does not require high-frequency interaction, ensuring continuous operation. When a network outage occurs, the vehicle makes autonomous decisions based on perception fusion data to complete the selection of the work area and loading operations. Within 30 minutes of network outage, there is no significant decrease in work efficiency. After the network is restored, the vehicle automatically synchronizes the first command from the cloud and switches to cloud control mode to achieve seamless connection.

[0119] The control method for mining vehicles provided in this application is applicable to complex terrain and unstable network signal scenarios in mines, enabling continuous, efficient, and safe operation of unmanned vehicles. It focuses on solving the problem of operational continuity under weak network and network outage conditions, and improves the anti-interference capability and operational stability of mining vehicles.

[0120] like Figure 4 The diagram shows a flowchart of a method for controlling vehicles in a mining area. The specific details are as follows: S401, receiving a first instruction for instructing a vehicle to proceed to the first work area of ​​the target work surface to perform operations; S402 travels to the target work area based on locally stored map information; S403, check if the communication with the cloud is normal. If yes, proceed to S404; otherwise, proceed to S405. S404, take the first job area in the first instruction as the target job area; S405 acquires environmental information in real time and selects the target work area based on the environmental information and the currently stored map information; S406, enters the target work area; S407, Obtain the location information and first path of the target point; S408, determine whether the target point is located at the end of the first path. If yes, execute S409; otherwise, execute S410. S409, proceed along the first route to the target location; S410 determines the target route to the target location based on the currently stored map information and the real-time acquired environmental information, and then travels to the target location according to the target route. S411 responds to the vehicle's arrival in the vicinity of the target location by adjusting the vehicle's position based on real-time environmental information until the vehicle reaches the target location and performs excavation or loading operations.

[0121] like Figure 5 The diagram shows a flowchart of a scheduling method for cloud-based execution. S501 receives operational information reported by various vehicles within the mining area; S502, Obtain the overall mine operation plan; S503 uses a global optimal scheduling algorithm to process the operation information of each vehicle and the global work plan to determine the target work surface and the first work area of ​​the vehicle. S504: If it is determined that the communication status with the current vehicle is normal and the vehicle is dispatchable, then send the first instruction to the vehicle. S505, waiting for the next scheduling cycle.

[0122] like Figure 6 The diagram illustrates a method for generating environmental information, the details of which are as follows: The vehicle-mounted sensor module 3032 is equipped with a lidar, millimeter-wave radar, high-definition camera and GPS positioning system.

[0123] The V2V information interaction module 3031 is used to obtain vehicle location information, operation status information, and driving intention information.

[0124] The information fusion module 3033 performs data preprocessing, Kalman filtering algorithm, redundant information removal and perception error correction on the information acquired by the vehicle sensor module 3032 and the surrounding vehicle V2V information interaction module 3031 to obtain environmental information. The environmental information includes surrounding environmental information and the intentions of other vehicles in the surrounding environment. The surrounding environmental information is used to support the position and pose adjustment of the work point and path planning, and the intentions of other vehicles in the surrounding environment are used to support the vehicle end to select the target work area.

[0125] This application provides a control device for mining vehicles, such as... Figure 7 As shown, the device 70 may include: a determining module 701, a driving module 702, and an acquiring module 703.

[0126] Specifically, module 701 is used to determine the network status with the cloud in response to the arrival of the vehicle at the target working face in the mining area; the target working face is used to represent a set of multiple working areas at the same excavation height and within the same area. The driving module 702 is used to obtain first environmental information if it is determined that the network status does not meet the preset conditions, and determine and drive to the target work area based on the first environmental information and the currently stored map information; the target work area is any work area within the target work face; the map information includes the location information and path information of each work face and each work area in the mining area; The acquisition module 703 is used to acquire the location information of the target point in the target work area in response to the vehicle arriving at the target work area, and to determine the target route to the target point.

[0127] The control device for mining vehicles provided in this application determines the network status with the cloud in response to the vehicle arriving at the target working face of the mining area, thereby realizing real-time determination of the communication status with the cloud.

[0128] If the network status is determined to be inconsistent with the preset conditions, it indicates that the current communication with the cloud is poor. In order to avoid the interruption of the operation due to poor communication with the cloud, the first environmental information is obtained. Based on the first environmental information and the currently stored map information, the vehicle determines and drives to the target work area. This realizes the selection of the optimal work area most suitable for the current vehicle to carry out the operation based on the real-time environmental information and the stored location and path information of each work face and work area in the mining area. The vehicle then drives to the target work area. This enables the vehicle to continue to operate in a weak network environment without relying on real-time communication with the cloud, but relying on the environmental data collected by the vehicle itself and the stored map information, thus ensuring the continuous operation of the vehicle.

[0129] By responding to the arrival of the vehicle in the target work area, obtaining the location information of the target point in the target work area, and determining the target route to the target point, the vehicle can carry out the current work task after arriving at the target point. This enables the vehicle to operate continuously in a weak network environment and improves the operating efficiency of vehicles in the mining area.

[0130] The apparatus in this application embodiment can execute the method provided in this application embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0131] Furthermore, in one possible implementation, a first request is sent to the cloud; the first request is used to request the location information and target path of the target point in the target work area; If the location information and the first path of the first point are received from the cloud, and it is determined that the first point is located at the end of the first path based on the location information and the first path, then the first point is taken as the target point and the first path is taken as the target path. If it is determined from the location information of the first point and the first path that the first point is not located at the end of the first path, then the second environmental information and the currently stored map information are obtained. The map information also includes the location information of the target point in the target work area. The target path is determined based on the second environmental information and the currently stored map information.

[0132] In another possible implementation, if the location information and the first path of the first location sent from the cloud are not received, the second environmental information and the currently stored map information are obtained. The target path is determined based on the second environmental information and the currently stored map information.

[0133] In another possible implementation, the map information also includes the location information of target points and drivable routes in each work area; Send retrieval requests to the cloud at a preset first frequency; Update the locally stored map information based on the latest acquired map information; If the current network latency is greater than the first threshold, the location information of the target points in each work area is obtained, and the latest map information is the location information of the target points in each work area. If the current network latency is not greater than the first threshold, the request is used to request the location information and drivable paths of the target points in each work area. The latest map information is the location information and drivable paths of the target points in each work area.

[0134] In yet another possible implementation, the map information is updated by the cloud in the following way: Receive at least one sensor information sent by each vehicle; the sensor information is collected by at least one sensor installed by the corresponding vehicle when it determines that any one of the first conditions is met; The map information stored in the cloud is updated based on information from at least one sensor of each vehicle. The first condition includes at least one of the following: The corresponding vehicles complete their tasks upon arrival at the target location; The vehicle has traveled a distance exceeding the preset distance; The vehicle has not sent at least one sensor message to the cloud for more than a preset period of time since then.

[0135] In another possible implementation, after determining the target path to the target point, the system responds by entering the vicinity of the target point. Acquire information about the third environment, the vehicle's current pose, and the target location; generate an adjustment path based on the information about the third environment, the vehicle's current pose, and the target location. Move the vehicle according to the adjusted route to reach the target location.

[0136] In another possible implementation, if it is determined that a first instruction sent from the cloud has been received, the target work surface is obtained from the first instruction; the first instruction is used to instruct the vehicle to drive to the target work surface to carry out the work. If it is determined that the first instruction sent from the cloud has not been received, and the vehicle is currently in a dispatchable state, then the target work area for this operation is determined based on the work area where the most recently completed task was located.

[0137] In another possible implementation, the first instruction is also used to instruct the vehicle to travel to the first working area of ​​the target working face to carry out the work; based on the real-time acquired operating information of each vehicle in the mining area and the global work plan, the target working face and the first working area corresponding to the vehicle are determined; the first working area is the working area within the target working face that meets at least one of the second conditions, the target working face is the working face within the mining area that meets at least one of the second conditions, the operating information is information related to the position and work progress of the corresponding vehicle, and the global work plan is used to characterize the set of work tasks to be performed; Determine the network status with the vehicle; the determination of the network status is performed according to a preset second frequency; If the network status between the device and the vehicle is determined to meet the preset conditions, a first instruction is generated and sent to the vehicle.

[0138] In another possible implementation, after determining the network status with the cloud, if the network status with the cloud meets the preset conditions, the first job area indicated in the received first instruction is taken as the target job area.

[0139] In yet another possible implementation, the second condition includes at least one of the following: The load level is below the corresponding threshold. The distance to the vehicle is the shortest.

[0140] In yet another possible implementation, the environmental information includes at least one of the following: The driving intentions of other vehicles within the vehicle's preset range; The load level of the work area within the vehicle's preset range.

[0141] According to a second aspect of the embodiments of this application, a control device for mining vehicles is provided, the device comprising: The determination module is used to determine the network status with the cloud in response to the arrival of the vehicle at the target working face in the mining area; the target working face is used to represent a set of multiple working areas at the same excavation height and within the same area. The driving module is used to obtain first environmental information if it is determined that the network status does not meet the preset conditions, and determine and drive to the target work area based on the first environmental information and the currently stored map information; the target work area is any work area within the target work face; the map information includes the location information and path information of each work face and each work area in the mining area; The acquisition module is used to acquire the location information of the target point in the target work area in response to the vehicle arriving at the target work area, and to determine the target route to the target point.

[0142] This application provides an electronic device (computer device / equipment / system) including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of a method for controlling mining vehicles. Compared with related technologies, this method can achieve: by responding to the arrival of the vehicle at the target working face in the mining area, the network status with the cloud is determined, thereby realizing real-time determination of the communication status with the cloud.

[0143] If the network status is determined to be inconsistent with the preset conditions, it indicates that the current communication with the cloud is poor. In order to avoid the interruption of the operation due to poor communication with the cloud, the first environmental information is obtained. Based on the first environmental information and the currently stored map information, the vehicle determines and drives to the target work area. This realizes the selection of the optimal work area most suitable for the current vehicle to carry out the operation based on the real-time environmental information and the stored location and path information of each work face and work area in the mining area. The vehicle then drives to the target work area. This enables the vehicle to continue to operate in a weak network environment without relying on real-time communication with the cloud, but relying on the environmental data collected by the vehicle itself and the stored map information, thus ensuring the continuous operation of the vehicle.

[0144] By responding to the arrival of the vehicle in the target work area, obtaining the location information of the target point in the target work area, and determining the target route to the target point, the vehicle can carry out the current work task after arriving at the target point. This enables the vehicle to operate continuously in a weak network environment and improves the operating efficiency of vehicles in the mining area.

[0145] In one alternative embodiment, an electronic device is provided, such as Figure 8 As shown, Figure 8 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of this application.

[0146] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0147] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0148] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium capable of carrying or storing computer programs and capable of being read by a computer, without limitation herein.

[0149] The memory 4003 stores computer programs that execute embodiments of this application, and its execution is controlled by the processor 4001. The processor 4001 executes the computer programs stored in the memory 4003 to implement the steps shown in the foregoing method embodiments.

[0150] The electronic device package may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 8 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0151] This application provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program can implement the steps and corresponding content of the aforementioned method embodiments. Compared with the prior art, it enables: determining the network status with the cloud in response to a vehicle arriving at the target working face in the mining area, thus achieving real-time determination of the communication status with the cloud.

[0152] If the network status is determined to be inconsistent with the preset conditions, it indicates that the current communication with the cloud is poor. In order to avoid the interruption of the operation due to poor communication with the cloud, the first environmental information is obtained. Based on the first environmental information and the currently stored map information, the vehicle determines and drives to the target work area. This realizes the selection of the optimal work area most suitable for the current vehicle to carry out the operation based on the real-time environmental information and the stored location and path information of each work face and work area in the mining area. The vehicle then drives to the target work area. This enables the vehicle to continue to operate in a weak network environment without relying on real-time communication with the cloud, but relying on the environmental data collected by the vehicle itself and the stored map information, thus ensuring the continuous operation of the vehicle.

[0153] By responding to the arrival of the vehicle in the target work area, obtaining the location information of the target point in the target work area, and determining the target route to the target point, the vehicle can carry out the current work task after arriving at the target point. This enables the vehicle to operate continuously in a weak network environment and improves the operating efficiency of vehicles in the mining area.

[0154] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium, a computer-readable medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0155] This application also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments. Compared with the prior art, it can achieve: By responding to the arrival of vehicles at the target working face in the mining area, the network status with the cloud is determined, enabling real-time determination of the communication status with the cloud.

[0156] If the network status is determined to be inconsistent with the preset conditions, it indicates that the current communication with the cloud is poor. In order to avoid the interruption of the operation due to poor communication with the cloud, the first environmental information is obtained. Based on the first environmental information and the currently stored map information, the vehicle determines and drives to the target work area. This realizes the selection of the optimal work area most suitable for the current vehicle to carry out the operation based on the real-time environmental information and the stored location and path information of each work face and work area in the mining area. The vehicle then drives to the target work area. This enables the vehicle to continue to operate in a weak network environment without relying on real-time communication with the cloud, but relying on the environmental data collected by the vehicle itself and the stored map information, thus ensuring the continuous operation of the vehicle.

[0157] By responding to the arrival of the vehicle in the target work area, obtaining the location information of the target point in the target work area, and determining the target route to the target point, the vehicle can carry out the current work task after arriving at the target point. This enables the vehicle to operate continuously in a weak network environment and improves the operating efficiency of vehicles in the mining area.

[0158] The terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text descriptions.

[0159] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0160] The above are only optional implementation methods for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application, without departing from the technical concept of this application, also fall within the protection scope of the embodiments of this application.

Claims

1. A method for controlling vehicles in a mining area, characterized in that, The method includes: In response to a vehicle arriving at the target working face in the mining area, the network status with the cloud is determined; the target working face is used to represent a set of multiple working areas that are at the same excavation height and within the same area. If the network status is determined not to meet the preset conditions, first environmental information is obtained, and the target work area is determined based on the first environmental information and the currently stored map information. The vehicle then travels to the target work area based on the first environmental information and the currently stored map information. The target work area is any work area within the target work surface. The map information includes the location information and path information of each work surface and each work area within the mining area. The first environmental information is obtained by fusing vehicle-to-vehicle information based on sensor information collected by the current vehicle and other vehicles. In response to the vehicle arriving at the target work area, the location information of the target point in the target work area is obtained, and the target route to the target point is determined; The target work surface is obtained through the following methods: If it is determined that no first instruction sent by the cloud is received within the preset time period, and the vehicle is currently in a dispatchable state, then the target work surface for this operation is determined based on the work surface where the most recently completed task is located; the first instruction is used to instruct the vehicle to drive to the target work surface to perform the operation.

2. The method according to claim 1, characterized in that, The step of obtaining the location information of the target point in the target work area and determining the target route to the target point includes: Send a first request to the cloud; the first request is used to request the location information and target path of the target point in the target work area; Upon receiving the location information and first path of the first location sent by the cloud, if it is determined that the first location is located at the end of the first path based on the location information and the first path, then the first location is taken as the target location and the first path is taken as the target path. If it is determined that the first point is not located at the end of the first path based on the point information of the first point and the first path, then the second environmental information and the currently stored map information are obtained. The map information also includes the point information of the target point in the target work area. The target path is determined based on the second environmental information and the currently stored map information.

3. The method according to claim 2, characterized in that, The process of sending the first request to the cloud is followed by: If the location information and the first path of the first location sent by the cloud are not received, the second environmental information and the currently stored map information are obtained. The target path is determined based on the second environmental information and the currently stored map information.

4. The method according to claim 1, characterized in that, The map information also includes the location information of target points and drivable routes in each work area; The method further includes: Send an acquisition request to the cloud at a preset first frequency; Update the locally stored map information based on the latest acquired map information; If the current network latency is greater than the first threshold, the acquisition request is used to request the location information of the target points in each work area, and the latest map information is the location information of the target points in each work area. If the current network latency is not greater than the first threshold, the acquisition request is used to request the location information and drivable path of the target point in each work area, and the latest map information is the location information and drivable path of the target point in each work area.

5. The method according to claim 1, characterized in that, The map information is updated by the cloud in the following ways: Receive at least one sensor information sent by each vehicle; the sensor information is collected by at least one sensor installed by the corresponding vehicle when it determines that any one of the first conditions is met; The map information stored in the cloud is updated based on at least one sensor information from each vehicle; The first condition includes at least one of the following: The corresponding vehicles complete their tasks upon reaching the target location; The corresponding vehicle has traveled a distance exceeding the preset distance; The corresponding vehicle has been away from sending at least one sensor message to the cloud for more than a preset period of time.

6. The method according to claim 1, characterized in that, After determining the target route to the target location, the process further includes: In response to entering the vicinity of the target location; Obtain third environmental information, the current pose of the vehicle, and the location information of the target point; generate an adjustment path based on the third environmental information, the current pose of the vehicle, and the location information of the target point. The vehicle is moved according to the adjusted path so that it reaches the target location.

7. The method according to claim 1, characterized in that, The target work surface is also obtained through the following methods: If it is determined that the first instruction sent by the cloud has been received, the target work surface is obtained from the first instruction; the first instruction is used to instruct the vehicle to drive to the target work surface to carry out the work.

8. The method according to claim 7, characterized in that, The first instruction is also used to instruct the vehicle to travel to the first work area of ​​the target work surface to carry out the work; The first instruction is generated by the cloud in the following way: Based on the real-time acquired operation information of each vehicle in the mining area and the global operation plan, the target working face and the first working area corresponding to the vehicle are determined; the first working area is the working area within the target working face that meets at least one of the second conditions, the target working face is the working face within the mining area that meets at least one of the second conditions, the operation information is information related to the position and operation progress of the corresponding vehicle, and the global operation plan is used to characterize the set of operation tasks to be executed. Determine the network status with the vehicle; The determination of the network state is performed according to a preset second frequency; If it is determined that the network status with the vehicle meets the preset conditions, a first instruction is generated and sent to the vehicle.

9. The method according to claim 8, characterized in that, The process of determining the network status with the cloud also includes: If it is determined that the network status with the cloud meets the preset conditions, then the first work area indicated in the received first instruction is taken as the target work area.

10. The method according to claim 8, characterized in that, The second condition includes at least one of the following: The load level is below the corresponding threshold. The distance between the vehicle and the vehicle is less than the corresponding distance threshold.

11. The method according to claim 1, characterized in that, The environmental information includes at least one of the following: The driving intentions of other vehicles within the preset range of the vehicle; The load level of the work area within the preset range of the vehicle.

12. A control device for mining vehicles, characterized in that, include: The determination module is used to determine the network status with the cloud in response to the arrival of the vehicle at the target working face in the mining area. The target working face is used to represent a set of multiple working areas that are at the same excavation height and within the same area. The driving module is used to obtain first environmental information if it is determined that the network status does not meet the preset conditions, and determine and drive to the target work area based on the first environmental information and the currently stored map information. The target work area is any work area within the target work face; the map information includes the location and path information of each work face and each work area within the mining area. The acquisition module is used to acquire the location information of the target point in the target work area in response to the vehicle arriving at the target work area, and to determine the target route to the target point.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method according to any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-11.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-11.

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