Real-time construction method and system for dynamic road network in open pit mines

Abstract

The present invention provides a method and system for real-time construction of a dynamic road network in an open-pit mine, which relates to the technical field of open-pit mines. The method includes obtaining a set of loading points and a set of unloading points of operating equipment and trajectory data of transportation equipment between the loading points and the unloading points; constructing the actual trajectory of the transportation equipment between the loading points and the unloading points as an associated path between the loading points and the unloading points based on the spatial distribution of the trajectory data; cutting the associated path using the intersection nodes to obtain a plurality of non-overlapping atomic paths; constructing the atomic paths and the intersection nodes into a directed graph, and assigning values ​​to the directed graph based on the path attributes and the node attributes to obtain a dynamic road network. The present invention realizes the automatic construction of a dynamic road network in an open-pit mine through intelligent analysis of vehicle trajectories and loading and unloading signals, thereby improving the efficiency of road network generation and reducing manual planning costs.

Description

Technical Field

[0001] The present invention relates to open-pit mine technology, and in particular to a method and system for real-time construction of a dynamic road network in an open-pit mine. Background Art

[0002] In open-pit mines, roads change frequently, mining faces are constantly being excavated and expanded, spoil dumps are constantly being filled, and engineering departments are continuously planning and constructing internal roads.

[0003] The rapid changes in routes make it impossible for mine workers to accurately reach a certain location in the open-pit mine. In the field of truck scheduling, it is impossible to accurately judge the distance from the loading point to the unloading point, and it is also impossible to estimate the running time of the truck from the loading point to the unloading point, resulting in waste of resources and reduced efficiency. Summary of the Invention

[0004] The embodiments of the present invention provide a method and system for real-time construction of a dynamic road network in an open-pit mine, which can solve the problems in the prior art.

[0005] A first aspect of an embodiment of the present invention provides a method for constructing a dynamic road network in an open-pit mine, comprising:

[0006] Acquire a set of loading points and a set of unloading points of the operating equipment and trajectory data of the transport equipment between the loading points and the unloading points;

[0007] Based on the spatiotemporal distribution correlation of the loading point, the unloading point, and the trajectory data, an actual trajectory of the transportation equipment between the loading point and the unloading point is constructed as an associated path between the loading point and the unloading point, wherein an intersection node is configured in the associated path, and the intersection node has a node attribute;

[0008] Cutting the associated path using the intersection node to obtain a plurality of non-overlapping atomic paths, wherein the atomic paths have path attributes;

[0009] The atomic paths and the intersection nodes are constructed into a directed graph, and the directed graph is assigned values ​​based on the path attributes and the node attributes to obtain a dynamic road network.

[0010] In an optional embodiment, constructing the actual path of the operating equipment between the loading point and the unloading point based on the spatial distribution of the trajectory data as the associated path between the loading point and the unloading point includes:

[0011] Generating a plurality of trajectory time series according to the time sequence of the trajectory data corresponding to each of the transportation devices;

[0012] Traversing all the loading point and unloading point combination pairs, and selecting the combination pairs in which the same transport equipment has passed through as associated combination pairs;

[0013] Connecting the trajectory time series of the transport equipment in the associated combination pair in chronological order to obtain the actual trajectory;

[0014] The associated path is determined based on the actual trajectory.

[0015] In an optional embodiment, after the trajectory time series of the transport equipment in the associated combination pair are connected in chronological order to obtain the associated path, the method further includes:

[0016] Segmenting the space where the associated path is located to obtain multiple space segments;

[0017] If there are at least two valid path segments in each space segment of the association combination pair, it is confirmed that the association path corresponding to the association combination is valid.

[0018] In an optional embodiment, constructing the actual trajectory of the transportation equipment between the loading point and the unloading point as the associated path between the loading point and the unloading point based on the spatial distribution of the trajectory data includes:

[0019] performing standardization and / or thickening processing on the actual trajectory to obtain preprocessed trajectory data;

[0020] Fitting a first path between the loading point and the unloading point and a second path between the unloading point and the loading point based on a global trend and a local trend of the preprocessed trajectory;

[0021] The associated path is constructed based on the first path and the second path.

[0022] In an optional embodiment, fitting the first path between the loading point and the unloading point and the second path between the unloading point and the loading point based on the global trend and the local trend of the preprocessed trajectory respectively includes:

[0023] Segmenting the actual trajectory to obtain multiple trajectory segments;

[0024] Standardizing the coordinate scale and density of the trajectory segment to obtain a standardized trajectory segment;

[0025] The standardized trajectory segment is thickened by an interpolation method according to the fitting sizes of the first path and the second path.

[0026] In an optional embodiment, constructing the actual trajectory of the transportation equipment between the loading point and the unloading point as the associated path between the loading point and the unloading point based on the spatial distribution of the trajectory data includes:

[0027] detecting intersection areas between the associated paths;

[0028] The intersection node and the node attributes of the intersection node are configured based on the geometric features of the associated paths and / or the intersection points of the associated paths in the intersection area.

[0029] In an optional embodiment, the step of constructing the atomic paths and the intersection nodes into a directed graph and assigning values ​​to the directed graph based on the path attributes and the node attributes to obtain a dynamic road network further includes:

[0030] The shortest path weight between each pair of intersection nodes in the directed graph is dynamically planned based on the path attributes and the node attributes to generate a complete path matrix, wherein each element in the path matrix represents the shortest path cost between any two intersection nodes.

[0031] In an optional embodiment, obtaining the loading point set and the unloading point set of the operating equipment includes:

[0032] Obtain loading and unloading data of operating equipment;

[0033] The loading data and the unloading data are clustered respectively to obtain a set of loading points and a set of unloading points.

[0034] According to a second aspect of the embodiments of the present invention,

[0035] Provided is a system for real-time construction of a dynamic road network in an open-pit mine, comprising: a data acquisition device for collecting loading and unloading data of operating equipment and trajectory data of transport equipment; a server connected to the data acquisition device, comprising a processor and a memory for storing instructions executable by the processor;

[0036] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0037] According to a third aspect of the embodiments of the present invention,

[0038] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0039] The beneficial effects of this application are as follows:

[0040] The method obtains a set of loading points and unloading points of the operating equipment and trajectory data of the transport equipment between the loading points and the unloading points; constructs the actual trajectory of the transport equipment between the loading points and the unloading points based on the spatial distribution of the trajectory data as an associated path between the loading points and the unloading points, wherein the associated path is configured with intersection nodes; uses the intersection nodes to cut the associated path to obtain a number of non-overlapping atomic paths, wherein the atomic paths have path attributes; constructs the atomic paths and the intersection nodes into a directed graph, and encapsulates the directed graph to obtain a dynamic road network. Based on the actual driving trajectory of all transport equipment, the actual driving path of the transport equipment between the loading points and the unloading points of the operating equipment is fitted. The geometric position of the path is highly consistent with the road centerline and has high standardization and consistency. After the path fitting is completed, the intersection nodes are configured to ensure the topological integrity of the road network and the accuracy of the navigation function. Through path cutting, atomic path extraction and directed graph encapsulation, the efficient generation of dynamic road networks and optimal path planning capabilities can be achieved, providing accurate technical support for real-time navigation and updating of open-pit mine road networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Schematic diagram of the process of a method for real-time construction of a dynamic road network in an open-pit mine according to an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the DBSCAN algorithm according to an embodiment of the present invention;

[0043] Figure 3 This is the schematic diagram of Graham's convex edge scanning algorithm;

[0044] Figure 4 This is a schematic diagram of an overview of the cutting path according to an embodiment of the present invention;

[0045] Figure 5 A schematic diagram of cutting path details according to an embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram showing a complete fitted road network according to an embodiment of the present invention;

[0047] Figure 7 This is an overall schematic diagram of the bidirectional fitting results of an embodiment of the present invention;

[0048] Figure 8 Detailed schematic diagram of the bidirectional fitting results of an embodiment of the present invention. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0050] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0051] The present invention provides a method for constructing a dynamic road network in an open pit mine. Figure 1 As shown, the following steps may be included:

[0052] S10. Obtain a set of loading points and unloading points for the operating equipment, as well as trajectory data for the transport equipment between the loading and unloading points. In this embodiment, the operating equipment may be operating equipment in an open-pit mine, for example, loading and unloading equipment such as an excavator or an electric shovel. Sensors for collecting loading data may be installed on the loading equipment. In this embodiment, taking the operating equipment as an excavator, positioning sensors and loading sensors are installed on the electric shovel. During the excavation process, the sensors collect loading data from the excavator, which may include a loading signal and a loading positioning signal. The transport equipment may be a truck equipped with an onboard terminal. The onboard terminal may include a positioning module and an unloading sensor. The positioning module and unloading sensor collect unloading data, including the unloading signal and the unloading positioning signal. The positioning module may be located in the cab, and the unloading sensor may be a lift sensor installed on the vehicle body. The lift sensor collects the unloading signal at the dump, coal pile, or crushing station nodes using the lift sensor collected by the truck's onboard terminal. The positioning module may use trajectory data such as positioning data, speed, and heading angle during transportation. Of course, during unloading, the operating signal generated by the electric shovel may also be used as the unloading signal.

[0053] In open-pit mines, there are often multiple operating equipment for loading, and there are also multiple transportation equipment for transportation and unloading. Therefore, there are multiple loading points on the loading operation surface of the mine, there is trajectory data during transportation, and there are multiple unloading points on the unloading surface of the mine. The loading points and unloading points are often scattered. Therefore, in this embodiment, the loading data and unloading data can be screened and clustered by clustering methods to obtain a set of loading points and a set of unloading points.

[0054] In this embodiment, the following can be used Figure 2The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is used to filter and cluster loading and unloading data. Specifically, the collected loading and unloading data coordinates can be clustered using DBSCAN. Based on the density and neighborhood radius of the loading and unloading points, a number of loading and unloading fences are determined. The loading fences can represent the loading surface within the mine, and the unloading fences can represent the unloading surface within the mine.

[0055] In this embodiment, a neighborhood with a radius of r can be pre-set. A point can be selected from a number of loading or unloading points as designated point P. The neighborhood of designated point P is the set of all points within a circle with a radius of r, centered around designated point P. This set contains the coordinates of all loading and unloading points. If the r-neighborhood of designated point P contains at least minPts points, designated point P is considered a core point. If designated point P does not meet the core point criteria, but is within the r-neighborhood of a core point, it is considered a boundary point. Points that are neither core points nor boundary points are marked as noise points, which are invalid interference data.

[0056] Specifically, all points are marked as unvisited. Traverse all unvisited points and take them as designated points P in turn. Calculate the set of points in their r neighborhood. If the number of neighborhood points is greater than or equal to minPts, mark the designated point P as a core point and start expanding the cluster. If the number of neighborhood points is less than minPts, mark it as a noise point. When expanding the cluster, if the neighborhood point is unvisited, mark it as visited and continue calculating its r neighborhood. If the neighborhood point is also a core point, merge the points in its neighborhood into the current cluster. If the neighborhood point is a boundary point, directly add it to the current cluster.

[0057] After traversing all unvisited points in the above manner, several clusters are obtained, each of which is a loading working surface or an unloading point working surface.

[0058] Specifically, after obtaining the coordinates of each loading working surface or unloading point working surface, the convex edge boundary of each cluster is calculated. Figure 3 As shown in Figure 2, the Graham scan algorithm based on polar angle sorting and stack operation obtains accurate loading and unloading points. Figure 3As shown, in each cluster, find the point with the smallest y coordinate. If there are multiple points, select the point with the smallest x coordinate. Using this point as the base, sort the other points by polar angle. Use a stack to store polygon vertices, and check each point to see if they form a convex hull boundary. If the angle formed by a point with the two top points in the stack is not a left turn, pop the closest point from the stack. Repeat this process until all points have been processed. Finally, the landfill area of ​​the dump can be calculated based on the unloading point coordinates, effectively calculating a representative unloading point set. Furthermore, the excavation or loading area can be calculated based on the loading point coordinates, effectively calculating a representative loading point set.

[0059] The loading and unloading data collected by the working equipment's sensor data are used to determine the loading and unloading areas using a density-based clustering algorithm. The coordinates of the loading and unloading points are extracted in the loading and unloading areas to ensure high-precision definition of the loading and unloading point data, providing a reliable basis for subsequent path planning.

[0060] S20. Based on the temporal and spatial distribution correlation of the loading point, the unloading point and the trajectory data, the actual trajectory of the transportation equipment between the loading point and the unloading point is constructed as an associated path between the loading point and the unloading point, wherein an intersection node is configured in the associated path, and the intersection node has node attributes.

[0061] In this embodiment, the loading point set contains m loading points and the unloading point set contains n unloading points. Theoretically, there are m×n paths between the loading point set and the unloading point set, that is, between the loading and unloading work areas. However, not every path is actually traversed by the transport equipment. Therefore, it is necessary to accurately determine the path association between the loading and unloading points, that is, to determine whether a path exists between each loading point and each unloading point.

[0062] In reality, a transport device must pass through loading points, transport routes, and unloading points during transportation. Therefore, if a path exists between the loading and unloading points, there is a temporal correlation between the loading points, transport routes, and unloading points. Furthermore, there is a spatial correlation between the transport route and the loading points, and between the transport route and the unloading points. That is, if there are loading and unloading points along a path, then records of the same transport device passing near both the loading and unloading points exist, and the trajectory data for the transport path between the loading and unloading points should be temporally continuous. Based on this, in this embodiment, the actual trajectory between the loading and unloading points is filtered by the temporal and spatial distribution correlations between the loading and unloading points, the unloading points, and the trajectory data.

[0063] Exemplarily, multiple trajectory time series are generated based on the time sequence of the trajectory data corresponding to each transport device. The actual trajectory data of the transport device is used to generate multiple trajectory time series with time sequence marks according to the timestamps of the recorded trajectory points. In this embodiment, the trajectory data is grouped by transport device using the unique identifier of the transport device, and the coordinates of the trajectory points of each transport device are sorted according to the timestamp of the trajectory point to generate multiple trajectory time series corresponding to each transport device.

[0064] All loading point and unloading point pairs are traversed, and pairs that have been visited by the same transport equipment are selected as associated pairs. All loading points and unloading points in the calculated loading point set and unloading point set are randomly combined to obtain loading point and unloading point pairs. For example, for each loading point and n unloading points, there are m×n pairs. All loading point and unloading point pairs are traversed, and using the unique identifier of the transport equipment, pairs of loading points and unloading points that have been visited by the same transport equipment are selected. For example, the spatial distribution characteristics of the transport equipment trajectory data are used to select each pair of loading points and unloading points. The presence of a correlation between the loading and unloading points in the same pair is determined by calculating whether there are trajectory points recorded for the same transport equipment within the vicinity of the loading point and unloading area (e.g., within a 30-meter radius of the loading and unloading points). If no transport equipment overlaps between a pair, the path corresponding to the pair is eliminated. If there is overlap, then an actual path exists between the loading and unloading points in the pair. The corresponding combination pairs are regarded as associated combination pairs.

[0065] The actual trajectory is obtained by concatenating the trajectory time series of the transport equipment in the associated combination pair in chronological order. In this embodiment, the trajectory time series corresponding to the transport equipment in the associated combination pair with both loading and unloading points are spliced ​​in chronological order to obtain the actual trajectory of the same transport equipment within a specific time period. The associated path can be determined based on the actual trajectory.

[0066] In one embodiment, the coordinates of the trajectory points may be subject to drift, interference, and other factors, resulting in inaccurate positioning. In addition, some transport equipment may stray into forks in the road, take detours, and so on, which may produce some interfering paths. Therefore, in order to more accurately obtain the associated path between the loading point and the unloading point, in this embodiment, the above-mentioned actual path can also be verified. In this embodiment, the space where the actual trajectory is located is divided to obtain multiple space segments; if there are at least two valid path segments in each space segment of the associated combination pair, then the associated path exists in the space where the actual trajectory is located. If there is an associated path (i.e., a real path) between the loading point and the unloading point, then there is at least one case where the same transport equipment passes through at least twice on the associated path. Therefore, in this embodiment, after the actual trajectory is obtained, the space where the actual trajectory is located is cut and divided into multiple space segments containing the actual trajectory. For details, please refer to Figure 4 and Figure 5 The path after cutting is shown in the figure. After obtaining the spatial segment, if there are more than 2 valid short paths in the spatial segment, it can be considered that the same transport equipment has passed through the area where the current path is located at least twice, and therefore it can be determined that there is a reliable associated path between the loading point and the unloading point.

[0067] In one embodiment, there are intersections between loading and unloading points. Therefore, to ensure the topological integrity of the road network and the accuracy of navigation, intersection nodes are required on the associated paths. This ensures the accuracy and rationality of each intersection node in the road network, thereby optimizing the overall performance and reliability of the road network and laying a technical foundation for the efficient construction and real-time updating of dynamic road networks. To this end, in this embodiment, the intersection areas between the associated paths are detected; the intersection nodes and their node attributes are configured based on the geometric characteristics of the associated paths and / or the intersection points within the intersection areas. In this embodiment, an algorithm is used to automatically detect the intersection areas of the associated paths based on the associated paths. By analyzing the geometric characteristics of the paths (such as intersection points, angles, and overlap areas), potential intersection nodes are automatically identified and set. The accuracy of the intersection node locations is determined by the resolution and density of the associated paths, ensuring that all path intersections have reasonable node annotations.

[0068] In areas with complex terrain or significant dynamic changes, automatic configuration may not be fully covered. In these cases, interactive tools can be used to manually label and adjust intersection nodes. Combined with the path fitting results, users can intuitively add, delete, or move nodes in the intersection area to ensure the integrity of the road network structure.

[0069] The number of actual paths calculated above is analyzed for the corresponding associated path coverage. The connectivity of the associated paths and the integrity of the intersection points are tested to verify the rationality of the intersection nodes. Missing or abnormal intersection nodes are identified. For intersection areas with unlabeled intersection nodes, the corresponding intersection nodes can be supplemented using the associated path coverage, connectivity, and intersection integrity associated with the intersection area. The supplementation conditions can be that the associated path coverage is greater than the preset coverage, the connectivity is greater than the preset connectivity type, and the intersection integrity is greater than the preset integrity. Redundant or offset nodes are adjusted using an optimization algorithm.

[0070] After the intersection nodes are configured, node attributes are assigned to each node (such as the intersection type, the priority of each path and / or transportation equipment at the intersection, or traffic rules), and a topological data table of the road network is generated. The node attributes are combined with the path information to provide accurate basic data support for subsequent navigation algorithms and path planning.

[0071] S30. Using the intersection node to cut the associated path, a plurality of non-overlapping atomic paths are obtained, wherein the atomic paths have path attributes.

[0072] After completing path fitting and intersection node configuration, the round-trip paths between the loading point and the unloading point need to be finely cut to build a complete road network topology and achieve efficient path planning functions.

[0073] The associated paths are segmented based on the configured intersection nodes and their attributes. Critical paths are precisely segmented using node coordinates to generate a number of non-overlapping minimal path units, known as atomic paths. Each atomic path terminates at adjacent intersection nodes and carries path attribute information, such as path length, direction weight, and travel time. This ensures a logically cohesive and low-coupling road network, providing standardized input for subsequent path calculations.

[0074] S40. Construct the atomic paths and the intersection nodes into a directed graph, and assign values ​​to the directed graph based on the path attributes and the node attributes to obtain a dynamic road network.

[0075] Atomic paths and intersection nodes are organized into a directed graph structure, and the relationships between paths and intersection nodes are efficiently encapsulated using an adjacency matrix or adjacency list. Edges between intersection nodes represent paths, and edge weights are dynamically assigned based on path attributes (such as distance or time cost). This allows for a precise representation of the topological relationships of the road network, facilitating path search and updates. Based on this constructed directed graph, the Floyd-Warshall algorithm is used to calculate the global shortest path matrix. This algorithm uses dynamic programming to iteratively optimize the shortest path weights between each pair of nodes to generate a complete path matrix. Each element in the matrix represents the shortest path cost between any two nodes, enabling fast path queries and providing fundamental support for navigation and scheduling algorithms.

[0076] In an exemplary embodiment, the generated shortest path matrix can be further processed according to actual application requirements. For example, a dynamic path update module can be added to adjust the path weight in real time to cope with changes in traffic flow; or a topology optimization algorithm can be used to improve the efficiency of path planning. The final road network data can be directly applied to the vehicle terminal and dispatching system to achieve full process support from path search to navigation guidance. Through path cutting, atomic path extraction and directed graph encapsulation, combined with the shortest path matrix generated by the Floyd-Warshall algorithm, the system realizes the efficient generation of dynamic road networks and optimal path planning capabilities, providing accurate technical support for real-time navigation and updating of open-pit mine road networks. See Figure 6 The road network diagram shown.

[0077] In this application, paths within the road network are generated based on loading and unloading data from operating equipment within the open-pit mine, as well as the actual driving trajectories of all transport equipment. The path geometry closely matches the road centerline and exhibits high standardization and consistency, ensuring the accuracy and reliability of the road network data. This shortest path planning supports efficient navigation on in-vehicle terminals and mobile devices, providing users with precise and convenient path guidance services, significantly improving operational efficiency and convenience.

[0078] To refine the paths in a dynamic road network, actual trajectories or associated paths can be fitted and optimized to improve the accuracy of the paths that make up the road network, thereby improving the accuracy of the dynamic road network. Based on this, in this embodiment, actual paths or associated paths obtained by filtering actual paths can be further fitted and optimized to construct standardized associated paths.

[0079] Specifically, the actual trajectory or the associated path determined by the actual trajectory is thickened to obtain pre-processed trajectory data. In this embodiment, the object of the thickening process can be an actual trajectory that has not been cut, or it can be an associated path that has been verified as an effective short path within a spatial segment in which the space where the actual trajectory is located is cut. In this embodiment, the associated path after cutting can be used as an example for illustration. Among them, the reliable associated path between the determined loading point and the unloading point can be presented in the form of a path segment after cutting. The coordinates of the path after cutting often show uneven distribution and irregular characteristics. Direct local fitting may cause fitting failure or large errors in certain sections. Therefore, the cutting path is first subjected to data standardization processing to unify the coordinate scale and density of the path. Combined with the fitting radius r, the path is thickened by the interpolation method so that the distribution density of the path segment matches the fitting requirements, thereby improving the stability and accuracy of the fitting.

[0080] In addition, the actual trajectory can also be thickened. In this embodiment, the actual trajectory is segmented to obtain multiple trajectory segments; the coordinate scale and density of each trajectory segment are standardized to obtain standardized trajectory segments; and the standardized trajectory segments are thickened using an interpolation method based on the fitted dimensions of the first and second paths. For specific methods for fitting and thickening the first and second paths, refer to the methods for fitting and thickening the segmented associated paths in the above embodiments.

[0081] After obtaining the thickened path, a round-trip bidirectional path can be fitted to the space (region) where the actual trajectory resides based on the thickened path to ensure path continuity and symmetry. In this embodiment, a first path between the loading point and the unloading point and a second path between the unloading point and the loading point can be fitted based on the global and local trends of the pre-processed trajectory. The first and second paths can correspond to the forward and reverse paths, respectively, to obtain a bidirectional path between the loading point and the unloading point. The associated path is constructed based on the first and second paths.

[0082] Specifically, the thickened path is segmented and fitted, and the path trend is gradually modeled using the recursive circle center moving method: with the loading point as the initial circle center, the path data within the radius r is intercepted to analyze the path direction of the area. A fitting curve is fitted based on the trend of the local path, and the intersection of the curve and the circle boundary is the next circle center position. Move the circle center to the new position, repeat the above interception and fitting process, and gradually expand the fitting range until the path is fitted to the unloading point position. Use the same method to construct a reverse path fitting model from the unloading point to the loading point. By bidirectional fitting of the forward and reverse paths, the continuity and symmetry of the path are ensured. For details, please refer to Figure 7 and Figure 8 The bidirectional path fitting results are shown.

[0083] During the fitting process, the global direction is captured by analyzing path trends. Local fitting results are combined to generate an overall path model. All path segments are optimized and smoothed to improve the accuracy and practicality of path fitting. This path fitting and optimization approach generates highly accurate, connected paths between loading and unloading points, providing reliable basic data for the construction of dynamic road networks and ensuring bidirectional path connectivity and optimal planning.

[0084] An embodiment of the present application also provides an open-pit mine dynamic road network construction system, including: a data acquisition device and a server, the data acquisition device is used to collect loading data and unloading data of operating equipment, as well as trajectory data of transportation equipment, and transmit the loading data, unloading data and trajectory data to the server, and the server executes the above-mentioned open-pit mine dynamic road network construction method.

[0085] In this embodiment, data acquisition devices are installed on the operating equipment and transport equipment. The data acquisition devices may include a vehicle-mounted terminal and sensors. For example, the vehicle-mounted terminal is securely mounted in a predetermined location within the cab. The vehicle-mounted terminal should avoid interfering with the driver's operation and ensure reliable power connection and signal transmission. The vehicle-mounted terminal can collect positioning data from the operating equipment and transport equipment to determine loading and unloading point coordinates and transport equipment trajectory data.

[0086] High-precision sensors are installed on excavators and other operating equipment to collect loading and unloading data. For example, high-precision sensors installed on the bucket monitor digging motion and location in real time. Lift sensors are deployed near truck lifts to collect real-time data on lift movements and corresponding unloading points. All sensors must undergo calibration testing to ensure the accuracy and completeness of the collected data. After installation, a comprehensive inspection of equipment connections, signal quality, and data transmission is required to confirm that they meet system operational requirements.

[0087] The server-side program is deployed to a dedicated server within the open-pit mine. Before deployment, the server environment must be configured, including operating system optimization, database initialization, and the installation and debugging of related dependencies to ensure that the system operating environment fully matches the program requirements. After deployment, functional testing and performance stress testing are conducted to verify the server's stability and efficiency in data reception, processing, and storage. To ensure system security, network access control policies, encrypted communication channels, and backup mechanisms are also configured to prevent external attacks or data loss from impacting system operations.

[0088] A program capable of executing the dynamic open-pit mine network construction method is deployed on the server. This requires configuration of relevant parameters, including parameter tuning for the path fitting algorithm, setting up timed scheduling tasks, and formatting the output results. Furthermore, scheduled tasks (such as daily or weekly updates) are set within the server based on business needs to ensure that the network data reflects the latest dynamic changes in the mining area in real time. The system also requires integrated logging and monitoring modules to track operational status and anomalies, facilitating subsequent maintenance and optimization. After deployment, full-process testing is conducted, including input data simulation, path generation verification, and result visualization, to ensure the accuracy and timeliness of the generated network.

[0089] The server provided in an embodiment of the present application includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus, the memory is used to store computer programs; the processor is used to execute the method in any of the above embodiments by running the computer program stored in the memory.

[0090] Optionally, in this embodiment, the communication bus may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0091] The communication interface is used for communication between the above-mentioned computer device and other devices.

[0092] The memory may include RAM, or may include non-volatile memory, such as at least one disk memory. Alternatively, the memory may also be at least one storage device located away from the aforementioned processor.

[0093] The above-mentioned processor can be a general-purpose processor, which can include but is not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processing), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0094] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0095] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, which can include: a flash drive, ROM, RAM, a magnetic disk or an optical disk, etc.

[0096] As an exemplary embodiment, the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute any one of the method steps of the present embodiment when run.

[0097] Optionally, in this embodiment, the above-mentioned storage medium can be used to execute the program code of the method steps of the embodiment of the present application.

[0098] Optionally, in this embodiment, the storage medium is configured to store data for executing the method in the above embodiment.

[0099] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, which will not be described in detail in this embodiment.

[0100] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a ROM, a RAM, a mobile hard disk, a magnetic disk, or an optical disk.

[0101] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0102] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above-mentioned computer-readable storage medium. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes a number of instructions for causing one or more computer devices (such as personal computers, servers, or network devices) to execute all or part of the steps of the method in the above embodiments.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed client can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, there may be other division methods, such as combining or integrating multiple units or components into another system, or ignoring or not implementing some features. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interface, indirect coupling or communication connection of units or modules, and may be electrical or other forms.

[0104] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the purpose of the solution provided in this embodiment.

[0105] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0106] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0107] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for constructing a dynamic road network in an open pit mine, characterized in that: include: Acquire a set of loading points and a set of unloading points of the operating equipment and trajectory data of the transport equipment between the loading points and the unloading points; Determine whether there is an associated path between each loading point and each unloading point, including: All loading points and unloading points in the calculated loading point set and unloading point set are randomly combined to obtain loading point and unloading point combination pairs. The correlation between the loading points and unloading points in the corresponding combination pair is determined by calculating whether there are track point records of the same transport equipment within a 30-meter range centered on the loading point and unloading point. If there are overlapping transport equipment, there is an actual path between the loading points and unloading points in the corresponding combination pair. The corresponding combination pairs are used as associated combination pairs; the trajectory time series of the transportation equipment in the associated combination pairs are connected in chronological order to obtain an actual trajectory; the space where the actual trajectory is located is divided into multiple space segments; if there are at least two valid path segments in each space segment of the associated combination pair, then the associated path exists in the space where the actual trajectory is located; Based on the spatiotemporal distribution correlation of the loading point, the unloading point, and the trajectory data, an actual trajectory of the transportation equipment between the loading point and the unloading point is constructed as an associated path between the loading point and the unloading point, wherein an intersection node is configured in the associated path, and the intersection node has a node attribute; Cutting the associated path using the intersection node to obtain a plurality of non-overlapping atomic paths, wherein the atomic paths have path attributes; The atomic paths and the intersection nodes are constructed into a directed graph, and the directed graph is assigned values ​​based on the path attributes and the node attributes to obtain a dynamic road network.

2. The open-pit mine dynamic road network construction method according to claim 1, characterized in that: The constructing, based on the spatial distribution of the trajectory data, the actual path of the operating equipment between the loading point and the unloading point as the associated path between the loading point and the unloading point includes: Generating a plurality of trajectory time series according to the time sequence of the trajectory data corresponding to each of the transportation devices; Traversing all the loading point and unloading point combination pairs, and selecting the combination pairs in which the same transport equipment has passed through as associated combination pairs; Connecting the trajectory time series of the transport equipment in the associated combination pair in chronological order to obtain the actual trajectory; The associated path is determined based on the actual trajectory.

3. The method for constructing a dynamic road network in an open pit mine according to claim 2, wherein: Determining the associated path based on the actual trajectory includes: Segmenting the space where the actual trajectory is located to obtain multiple space segments; If there are at least two valid path segments in each space segment of the associated combination pair, then the associated path exists in the space where the actual trajectory is located.

4. The method for constructing a dynamic road network in an open-pit mine according to any one of claims 1 to 3, wherein: The constructing the actual trajectory of the transport equipment between the loading point and the unloading point as the associated path between the loading point and the unloading point based on the spatial distribution of the trajectory data includes: performing standardization and / or thickening processing on the actual trajectory to obtain preprocessed trajectory data; Fitting a first path between the loading point and the unloading point and a second path between the unloading point and the loading point based on a global trend and a local trend of the preprocessed trajectory; The associated path is constructed based on the first path and the second path.

5. The method for constructing a dynamic road network in an open-pit mine according to claim 4, wherein: The fitting of the first path between the loading point and the unloading point and the second path between the unloading point and the loading point based on the global trend and the local trend of the preprocessed trajectory respectively includes: Segmenting the actual trajectory to obtain multiple trajectory segments; Standardizing the coordinate scale and density of the trajectory segment to obtain a standardized trajectory segment; The standardized trajectory segment is thickened by an interpolation method according to the fitting sizes of the first path and the second path.

6. The method for constructing a dynamic road network in an open pit mine according to claim 1, wherein: The constructing the actual trajectory of the transport equipment between the loading point and the unloading point as the associated path between the loading point and the unloading point based on the spatial distribution of the trajectory data includes: detecting intersection areas between the associated paths; The intersection node and the node attributes of the intersection node are configured based on the geometric features of the associated paths and / or the intersection points of the associated paths in the intersection area.

7. The method for constructing a dynamic road network in an open pit mine according to claim 1, wherein: The step of constructing the atomic paths and the intersection nodes into a directed graph and assigning values ​​to the directed graph based on the path attributes and the node attributes to obtain a dynamic road network further includes: The shortest path weight between each pair of intersection nodes in the directed graph is dynamically planned based on the path attributes and the node attributes to generate a complete path matrix, wherein each element in the path matrix represents the shortest path cost between any two intersection nodes.

8. The method for constructing a dynamic road network in an open pit mine according to claim 1, wherein: The obtaining of the loading point set and the unloading point set of the operating equipment includes: Obtain loading and unloading data of operating equipment; The loading data and the unloading data are clustered respectively to obtain a set of loading points and a set of unloading points.

9. A real-time construction system for dynamic road network in open pit mines, characterized in that: include: A data acquisition device for collecting loading and unloading data of operating equipment and trajectory data of transport equipment; The server is connected to the data acquisition device and includes a processor and a memory for storing instructions executable by the processor; The processor is configured to call instructions stored in the memory to execute the open-pit mine dynamic road network construction method described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that Computer program instructions are stored, and when the computer program instructions are executed by a processor, the method for constructing a dynamic road network in an open-pit mine as described in any one of claims 1 to 8 is implemented.

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