Method and apparatus for predicting operating state of manned mining truck, and terminal device

Abstract

A method and apparatus for predicting the operating state of a manned mining truck, and a terminal device. The method comprises: by means of an unmanned mining truck, acquiring positioning data of a manned mining truck to be subjected to prediction; acquiring a directed weighted graph corresponding to a transport operation area and a plurality of routes corresponding to the directed weighted graph, wherein each route represents a travelling path between two parking points in the transport operation area; on the basis of the positioning data and the directed weighted graph, determining a target candidate route of said manned mining truck; and on the basis of a candidate point in the target candidate route that corresponds to said manned mining truck, and a route attribute of the target candidate route, determining the operating state of said manned mining truck. The method enables unmanned mining trucks and manned mining trucks in a transport operation area to better perform coordinated operations, thereby avoiding congestion in the transport operation area, and improving the production and transport efficiency and the transport safety of the entire transport operation area.

Description

A method, device and terminal equipment for predicting the operating state of a manned mine truck

[0001] The present application claims priority from the Chinese patent application No. 202410817794.4 filed on June 24, 2024 in the China Patent Office and entitled "A method, device and terminal equipment for predicting the operating state of a manned mine truck", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the technical field of intelligent scheduling, in particular to a method, device and terminal equipment for predicting the operating state of a manned mine truck. BACKGROUND

[0003] With the continuous development of technology, automatic driving technology has been widely applied in various fields, including the field of mine transportation. In the transportation operation area of the mine, manned mine trucks and unmanned mine trucks are usually mixed for operation. The unmanned mine trucks can realize more intelligent and efficient transportation by using automatic driving technology, while the manned mine trucks cannot directly feed back their own positioning data and operating state. Moreover, since the unmanned mine trucks and the manned mine trucks have overlapping transportation routes, there may be intersections and conflicts of driving routes, thereby affecting the transportation scheduling of the transportation operation area, causing congestion in the transportation operation area, and thus affecting the production and transportation efficiency and transportation safety of the whole transportation operation area.

[0004] Therefore, how to accurately predict the operating state of the manned mine truck and improve the production and transportation efficiency and transportation safety of the transportation operation area is a technical problem to be solved by those skilled in the art at present. TECHNICAL PROBLEM

[0005] The purpose of the present application is to provide a method, device, terminal equipment and computer readable storage medium for predicting the operating state of a manned mine truck, which can make the unmanned mine trucks and the manned mine trucks in the transportation operation area better coordinate operation and avoid congestion in the transportation operation area, and improve the production and transportation efficiency and transportation safety of the whole transportation operation area. TECHNICAL SOLUTION

[0006] In a first aspect, the present application provides a method for predicting the operating state of a manned mine truck. The method comprises:

[0007] obtaining, by an unmanned mine truck, positioning data of a to-be-predicted manned mine truck;

[0008] obtaining a directed and weighted graph corresponding to the transportation operation area and a plurality of routes corresponding to the directed and weighted graph; the route represents a driving path between two stops in the transportation operation area;

[0009] determining a target candidate route of the manned mining truck to be predicted according to the positioning data and the directed weighted graph;

[0010] determining a working state of the manned mining truck to be predicted according to a candidate point corresponding to the manned mining truck in the target candidate route and a route attribute of the target candidate route.

[0011] In one of the embodiments, the determining of the target candidate route of the manned mining truck to be predicted according to the positioning data and the directed weighted graph comprises:

[0012] if the positioning data comprises a predicted trajectory, determining a candidate route according to the predicted trajectory and the directed weighted graph;

[0013] determining an observation probability and a transition probability corresponding to a hidden Markov model according to the predicted trajectory and the candidate route;

[0014] determining a state sequence according to the observation probability and the transition probability to obtain the target candidate route.

[0015] In one of the embodiments, the method further comprises:

[0016] if the positioning data comprises a trajectory point, determining a candidate route according to the trajectory point and the directed weighted graph;

[0017] determining a value of each of the candidate routes;

[0018] determining a target candidate route from the candidate routes according to the values.

[0019] In one of the embodiments, the determining of the value of each of the candidate routes comprises:

[0020] for each of the candidate routes, determining a candidate point corresponding to the trajectory point on the candidate route, and determining a projection distance between the candidate point and the trajectory point;

[0021] determining a heading angle difference between a heading angle of the candidate point and a heading angle of the trajectory point;

[0022] determining the value of the candidate route according to the projection distance and the heading angle difference.

[0023] In one of the embodiments, the determining of the target candidate route from the candidate routes according to the values comprises:

[0024] arranging the values of the candidate routes in a preset order;

[0025] According to the order from high to low of the generation values, the cost difference values of two adjacent generation values are calculated in sequence, and when the cost difference value is less than or equal to a preset cost difference value threshold, the candidate route is determined as a similar candidate route.

[0026] According to the similar candidate route, a target candidate route is determined.

[0027] In one embodiment, the method further comprises:

[0028] The number of candidate routes is determined.

[0029] If the number of candidate routes is 1, the similar candidate route is determined as the target candidate route.

[0030] In one embodiment, the method further comprises:

[0031] If the number of candidate routes is greater than 1, it is determined whether each similar candidate route is an offloading route.

[0032] If each similar candidate route is the offloading route, for each similar candidate route, the number of vehicles in the similar candidate route on the future driving path of the to-be-predicted manned mine truck is determined.

[0033] The similar candidate route with the least number of vehicles is determined as the target candidate route.

[0034] In one embodiment, the method further comprises:

[0035] If each similar candidate route is not the offloading route, according to the vehicle offset of the to-be-predicted manned mine truck and the heading angle of the trajectory point, the target candidate route is determined from a plurality of similar candidate routes.

[0036] In a second aspect, the present application also provides a working state prediction device of a manned mine truck. The device comprises:

[0037] A positioning data acquisition module is configured to acquire positioning data of a to-be-predicted manned mine truck through an unmanned mine truck.

[0038] A route acquisition module is configured to acquire a directed weighted graph corresponding to a transportation working area and a plurality of routes corresponding to the directed weighted graph. The route represents a driving path between two stops in the transportation working area.

[0039] A target candidate route determination module is configured to determine a target candidate route of the to-be-predicted manned mine truck according to the positioning data and the directed weighted graph.

[0040] The operation state prediction module is configured to determine the operation state of the to-be-predicted manned mine truck according to a candidate point in the target candidate route corresponding to the to-be-predicted manned mine truck and a routing attribute of the target candidate route.

[0041] In a third aspect, the present application provides a terminal device. The terminal device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method described above when executing the computer program.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and the computer program is executable by a processor to implement the steps of the method described above. Advantages

[0043] The present application provides a method for predicting the operation state of a manned mine truck. The method predicts a target candidate route of a to-be-predicted manned mine truck according to the positioning data of the to-be-predicted manned mine truck obtained by an unmanned mine truck and a directed weighted graph corresponding to a transportation operation area, and determines the operation state of the to-be-predicted manned mine truck according to a candidate point in the target candidate route corresponding to the to-be-predicted manned mine truck and a routing attribute of the target candidate route. By predicting the operation state of the manned mine truck, the unmanned mine truck can be more reasonably dispatched, and the transportation task and the driving route can be arranged. The unmanned mine trucks and the manned mine trucks in the transportation operation area can be better coordinated to avoid congestion in the transportation operation area, and the production and transportation efficiency and the transportation safety of the entire transportation operation area can be improved.

[0044] It can be understood that the manned mine truck operation state prediction device, the terminal device, and the computer-readable storage medium provided by the embodiments of the present application have the same advantages as the manned mine truck operation state prediction method described above, and will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the following description of the embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0046] FIG. 1 is a flowchart of a method for predicting the operation state of a manned mine truck according to an embodiment of the present application;

[0047] FIG. 2 is a schematic diagram of a directed weighted graph according to an embodiment of the present application;

[0048] FIG. 3 is a schematic diagram of a process for determining a target candidate route according to an embodiment of the present application;

[0049] FIGS. 4 and 5 are schematic diagrams of matching results based on a Viterbi dynamic programming algorithm according to an embodiment of the present application;

[0050] FIGS. 6 to 8 are schematic diagrams of matching results between similar candidate routes and track points according to an embodiment of the present application;

[0051] FIG. 9 is a flowchart of another method for predicting an operating state of a manned mine truck according to an embodiment of the present application;

[0052] FIG. 10 is a schematic diagram of a structure of a device for predicting an operating state of a manned mine truck according to an embodiment of the present application;

[0053] FIG. 11 is a schematic diagram of a structure of a terminal device according to an embodiment of the present application. Embodiments of the present application

[0054] In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0055] It is to be understood that the terminology “includes”, “has”, “holds”, “contains” and / or “comprising”, when used in this specification and in the following claims, indicates the presence of the described features, integers, steps, operations, elements, and / or components but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0056] It is also to be understood that the terminology “and / or” when used in this specification and in the following claims, refers to at least one of the items, or any combination of the items, and includes all possible combinations when used in the following claims.

[0057] As used in this specification and in the claims, the term “if’ can be construed to mean “when” or “once” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a described condition or event] is detected” can be construed to mean “once it is determined” or “in response to determining” or “once [the described condition or event] is detected” or “in response to detecting [a described condition or event],” depending on the context.

[0058] In addition, in the description of the present application and the appended claims, the terms "first", "second", "third", etc. are used only to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0059] Reference to "one embodiment" or "some embodiments" or "one implementation" or "some implementations" described in the present application means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Therefore, the statements "in one embodiment", "in some embodiments", "in other some embodiments", "in some other embodiments" and the like appearing in various places in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "include", "contain", "have" and their variants mean "include but not limited to", unless otherwise specifically emphasized. "Multiple" means "two or more".

[0060] The operation state prediction method of the manned mine truck provided by the embodiments of the present application can be executed by the processor of the terminal device when running the corresponding computer program. The terminal device can be an intelligent scheduling device corresponding to the transportation operation area.

[0061] FIG. 1 is a flowchart of an operation state prediction method of a manned mine truck provided by an embodiment of the present application. For ease of illustration, only the part related to the present embodiment is shown, and the method provided by the present embodiment includes the following steps:

[0062] S100: Obtain positioning data of a manned mine truck to be predicted by an unmanned mine truck.

[0063] The positioning data refers to data representing the position information of the manned mine truck to be predicted, including trajectory points and predicted trajectories. The unmanned mine truck determines the positioning data of the manned mine truck to be predicted through pre-installed sensors such as vehicle body cameras, laser radars and millimeter wave radars.

[0064] In one specific example, the surrounding obstacle image is collected by the vehicle body camera, the obstacle image is identified, when the obstacle is determined to be a manned mine truck, the position information of the self is obtained by the positioning device; the distance and direction between the manned mine truck and the self are obtained by the laser radar or the millimeter wave radar; the trajectory point of the manned mine truck is determined according to the position information of the self and the distance and direction between the manned mine truck and the self. Specifically, after the unmanned mine truck determines the positioning data of the to-be-predicted manned mine truck, the positioning data of the to-be-predicted manned mine truck is sent to the intelligent scheduling device through MQTT (Message Queuing Telemetry Transport) to make the intelligent scheduling device obtain the positioning data of the to-be-predicted manned mine truck.

[0065] S200: Obtain a directed weighted graph corresponding to the transportation operation area and a plurality of routes corresponding to the directed weighted graph; the route represents a driving path between two stops in the transportation operation area.

[0066] The transportation operation area refers to a mine unloading area in a mine production line, and the manned mine truck and the unmanned mine truck operate in the operation area. In the transportation operation area, the driving path of the unmanned mine truck is generally fixed, and the driving path of the manned mine truck has randomness, but due to the limitation of geographical conditions, it will not deviate greatly from the operation path of the unmanned mine truck.

[0067] Specifically, the intelligent scheduling device obtains the road network data of the transportation operation area, and constructs a corresponding directed weighted graph according to the road network data, and determines a plurality of routes according to the directed weighted graph. The road network data includes map information of stops and driving paths in the transportation operation area; in actual application, the road network data corresponding to the transportation operation area can be obtained through GRPC (Google Remote Procedure Call).

[0068] FIG. 2 is a schematic diagram of a directed weighted graph provided by an embodiment of the present application; as shown in FIG. 2, the nodes in the directed weighted graph are stops in the transportation operation area, and the stops include loading and unloading points and starting points; the edges in the directed weighted graph are driving paths in the transportation operation area; the route represents a driving path between two stops in the transportation operation area. The corresponding route is determined according to the shortest path between any two nodes in the directed weighted graph, and one route represents the driving path corresponding to two nodes.

[0069] Specifically, for each node in the directed weighted graph, the shortest path corresponding to the node and each of the other nodes is determined, that is, the corresponding route is determined. In actual application, the Dijkstra algorithm can be used to search for the shortest path of the node to the remaining nodes, so that the shortest path between any two nodes in the directed weighted graph can be obtained, that is, the corresponding route is determined and pre-stored in the cache of the intelligent scheduling device.

[0070] In this step, the directed weighted graph is constructed in advance according to the road network data, and the shortest path between any two nodes is determined through path searching to obtain the corresponding route. When subsequent route matching is performed according to the positioning data of the to-be-predicted manned mine truck, the route-based matching can be directly performed, so that the calculation time consumption can be reduced and the real-time performance of map matching can be improved.

[0071] S300: determining the target candidate route of the to-be-predicted manned mine truck according to the positioning data and the directed weighted graph.

[0072] The target candidate route refers to the route that is determined from the routes and that is most matched with the to-be-predicted manned mine truck.

[0073] In this embodiment, the target candidate route of the to-be-predicted manned mine truck is determined according to the positioning data and the directed weighted graph. Specifically, the target candidate route of the to-be-predicted manned mine truck is determined according to the mapping relationship between the positioning data and the directed weighted graph and the routes corresponding to the directed weighted graph that are determined in advance.

[0074] S400: determining the operation state of the to-be-predicted manned mine truck according to the candidate point corresponding to the to-be-predicted manned mine truck in the target candidate route and the route attribute of the target candidate route.

[0075] The route attribute includes the task type, the starting point, each loading and unloading point, and the destination corresponding to the route, and the specific content of the route attribute is not limited in this embodiment.

[0076] Specifically, the candidate point corresponding to the to-be-predicted manned mine truck in the target candidate route is determined according to the mapping relationship between the trajectory point of the to-be-predicted manned mine truck and the target candidate route. The route attribute of the target candidate route is obtained, and the operation state of the to-be-predicted manned mine truck is determined according to the candidate point and the route attribute. The operation state includes the task type, the destination, the current route, and the actual position.

[0077] The embodiment of the present application provides a manned mine truck operation state prediction method, the method predicts the target candidate route of the manned mine truck to be predicted according to the positioning data of the unmanned mine truck to be predicted and the directed weighted graph corresponding to the transportation operation area, and determines the operation state of the manned mine truck to be predicted according to the candidate point corresponding to the manned mine truck to be predicted in the target candidate route and the route attribute of the target candidate route. Through the prediction of the operation state of the manned mine truck, the unmanned mine truck can be more reasonably dispatched, and the transportation task and the driving route can be arranged. The unmanned mine trucks and the manned mine trucks in the transportation operation area can better coordinate operation, and the transportation operation area congestion can be avoided. The production and transportation efficiency and the transportation safety of the whole transportation operation area are improved.

[0078] On the basis of the above-mentioned embodiment, the technical scheme is further described and optimized in the embodiment, specifically, in the embodiment, the target candidate route of the manned mine truck to be predicted is determined according to the positioning data and the directed weighted graph, comprising:

[0079] If the positioning data includes the predicted trajectory, the candidate route is determined according to the predicted trajectory and the directed weighted graph;

[0080] The observation probability and the transition probability corresponding to the hidden Markov model are determined according to the predicted trajectory and the candidate route;

[0081] The state sequence is determined according to the observation probability and the transition probability, and the target candidate route is obtained.

[0082] The predicted trajectory refers to the predicted trajectory point sequence of the manned mine truck to be predicted determined by the unmanned mine truck. Fig. 3 is a process schematic diagram for determining the target candidate route provided by the embodiment of the present application; in the embodiment, if the positioning data includes the predicted trajectory, the candidate route is determined according to the predicted trajectory and the directed weighted graph; specifically, after the scene graph is constructed according to the road network data of the transportation operation area and the corresponding directed weighted graph is determined, the corresponding route is determined based on the directed weighted graph;

[0083] Each predicted trajectory point in the predicted trajectory is mapped to the directed weighted graph to obtain the corresponding candidate route. In actual application, the trajectory data can be preprocessed, and the candidate route is determined according to the predicted trajectory after the preprocessing operation and the directed weighted graph.

[0084] In one specific example, for each route, the sub-segments corresponding to the route are determined, the projection distances of the predicted trajectory point to the respective sub-segments are determined, the sub-segment with the shortest projection distance is determined as the target sub-segment by comparing the sizes of the projection distances, the predicted trajectory point is orthogonally mapped to the target sub-segment to obtain a corresponding candidate point, the projection distance of the predicted trajectory point to the candidate point is obtained, and it is determined whether the projection distance is less than or equal to the candidate radius. If the projection distance is less than or equal to the candidate radius, the route corresponding to the target sub-segment is determined as the candidate route, that is, the route where the candidate point is located is determined as the candidate route.

[0085] After the candidate route is determined, the observation probability and the transition probability corresponding to the Hidden Markov Model (HMM) are determined according to the predicted trajectory and the candidate route.

[0086] It should be noted that the establishment of the Hidden Markov Model is based on the following two assumptions:

[0087] Assumption 1: The projection distance between the trajectory point and the candidate point obeys a normal distribution, and the smaller the projection distance between the two, the greater the probability that the trajectory point is generated because the to-be-predicted person-driven mining truck is at the candidate point. That is, for the trajectory point p t (corresponding to the trajectory point at time t), the observation probability P(r k ) corresponding to all candidate routes r k respectively indicates the probability that the to-be-predicted person-driven mining truck actually leads to the observation of the trajectory point p k on the candidate route r t , and the calculation method obeys the above assumption 1; therefore, the observation probability is calculated according to the normal distribution probability density function of the projection distance between the trajectory point and the candidate point.

[0088] Assumption 2: The closer the first path distance between two candidate points and the second path distance between two trajectory points at adjacent two time points, the greater the probability that the to-be-predicted person-driven mining truck moves from the previous candidate point to the next candidate point. That is, for the trajectory point p t and its candidate route r k+1 , the trajectory point p t+1 and its candidate route r k+2 , there is a transition probability, which indicates the probability that the vehicle moves from the candidate route r k+1 to the candidate route r k+2 , the calculation method obeys the assumption 2, r k+1 is the candidate route corresponding to the trajectory point p t at time t, and r k+2 is the candidate route corresponding to the trajectory point p t+1The transition probability is calculated according to an exponential distribution probability density function of a difference between the first path distance between the two candidate points and the second path distance between the two trajectory points. The first path distance and the second path distance can be calculated by the Euclidean distance, or can be calculated by other manners, which is not limited in the embodiment.

[0089] In the embodiment, after the observation probability and the transition probability are determined, the state sequence is determined according to the observation probability and the transition probability, and the target candidate route is obtained. Specifically, the optimal state sequence is solved by a Viterbi dynamic programming algorithm, that is, the candidate route with the maximum observation probability and transition probability at each time is determined, that is, the target candidate route is determined.

[0090] FIGS. 4 and 5 are schematic diagrams of a matching result based on the Viterbi dynamic programming algorithm according to the embodiment of the application. As shown in FIGS. 4 and 5, the lines in the area represent a plurality of routes of the directed weighted graph corresponding to the transportation operation area; the round dots in FIG. 4 represent the predicted trajectory point sequence of the vehicle; and the round dots in FIG. 5 are the candidate point sequence obtained after the map matching of the predicted trajectory point sequence, and the route where the candidate point sequence is located is the target candidate route, so that the target candidate route is determined.

[0091] It can be seen that according to the method of the embodiment, the target candidate route can be efficiently and conveniently determined in the case that the positioning data includes the predicted trajectory.

[0092] On the basis of the above-mentioned embodiment, the technical solution is further described and optimized in the embodiment, and specifically, the method further includes:

[0093] If the positioning data includes the trajectory point, the candidate route is determined according to the trajectory point and the directed weighted graph;

[0094] The generation value corresponding to each candidate route is determined.

[0095] The target candidate route is determined from the candidate routes according to the generation values.

[0096] If the positioning data includes the trajectory point, the candidate route is determined according to the trajectory point and the directed weighted graph; specifically, the trajectory point is mapped to the directed weighted graph to obtain the corresponding candidate route.

[0097] In one specific example, for each route, the sub-segments corresponding to the route are determined, the projection distances of the trajectory point to the respective sub-segments are determined, the sub-segment with the shortest projection distance is determined as the target sub-segment by comparing the size relationship of the projection distances, the trajectory point is orthogonally mapped to the target sub-segment to obtain the corresponding candidate point, the projection distance of the trajectory point to the candidate point is obtained, and it is determined whether the projection distance is less than or equal to the candidate radius. If the projection distance is less than or equal to the candidate radius, the route corresponding to the target sub-segment is determined as the candidate route, that is, the route where the candidate point is located is determined as the candidate route.

[0098] After the candidate route is determined, the generation value respectively corresponding to each candidate route is calculated according to the geometric relationship between the trajectory point and each candidate route, and then the target candidate route is determined from each candidate route according to the generation values.

[0099] It can be seen that according to the method of the embodiment, the target candidate route can be efficiently and conveniently determined in the case that the positioning data includes the trajectory point, and the prediction efficiency of the operation state of the manned mine truck can be improved.

[0100] On the basis of the above-mentioned embodiment, the technical solution is further described and optimized in the embodiment, and specifically, in the embodiment, the generation value respectively corresponding to each candidate route includes:

[0101] For each candidate route, the candidate point corresponding to the trajectory point on the candidate route is determined, and the projection distance between the candidate point and the trajectory point is determined.

[0102] The heading angle difference value of the heading angle of the candidate point and the heading angle of the trajectory point is determined.

[0103] The generation value of the candidate route is determined according to the projection distance and the heading angle difference value.

[0104] It should be noted that according to the geometric relationship between the trajectory point and each candidate route, the smaller the projection distance between the candidate point and the trajectory point, the greater the generation value of the corresponding candidate route; the smaller the heading angle difference value of the heading angle of the candidate point and the heading angle of the trajectory point, the greater the generation value of the corresponding candidate route.

[0105] For each candidate route, the candidate point corresponding to the trajectory point on the candidate route is determined, and the projection distance between the candidate point and the trajectory point is determined, the heading angle difference value of the heading angle of the candidate point and the heading angle of the trajectory point is determined, and the generation value of the candidate route is determined according to the projection distance and the heading angle difference value. The projection distance can be the Euclidean distance, or the projection distance can be calculated in other ways, which is not limited in the embodiment.

[0106] In one specific example, the calculation formula of the generation value is as follows: k = w1 * (R - d k) / R + w2*cos(h k ) / 2;

[0107] wherein S k represents the generation value of the candidate route; d k represents the projection distance between the candidate point and the track point; h k represents the heading angle difference between the heading angle of the candidate point and the heading angle of the track point; w1 represents the distance weight coefficient, w2 represents the heading angle weight coefficient, and R represents the candidate radius.

[0108] It can be seen that, according to the method of the embodiment, the generation value of the candidate route can be determined efficiently and conveniently, and the efficiency of determining the target candidate route can be improved, and the prediction efficiency of the operation state of the manned mine truck can be improved.

[0109] On the basis of the above embodiment, the technical solution is further described and optimized in the embodiment, and specifically, in the embodiment, the target candidate route is determined from the candidate routes according to the generation values, including:

[0110] arranging the generation values of the candidate routes in a preset order;

[0111] calculating the cost difference value of two adjacent generation values in turn according to the generation values from high to low, and determining the candidate route as a similar candidate route when the cost difference value is less than or equal to a preset cost difference threshold value;

[0112] determining the target candidate route according to the similar candidate route.

[0113] In the embodiment, after the generation values corresponding to the candidate routes are determined respectively, the generation values of the candidate routes are arranged in a preset order; for example, the generation values can be arranged in ascending order or in descending order, which is not limited in the embodiment.

[0114] In a specific example, the candidate routes are first arranged in descending order of the generation values, and the candidate route with the first ranked generation value is determined as a similar candidate route; then the generation value difference values corresponding to two adjacent generation values are calculated in turn. Specifically, the generation value difference value of the current candidate route and the generation value of the last candidate route is calculated from the second ranked generation value, and the cost difference value of the current candidate route is obtained; it is determined whether the cost difference value is less than or equal to a preset cost difference threshold value; if the cost difference value is less than or equal to the preset cost difference threshold value, the current candidate route is determined as a similar candidate route; otherwise, if the cost difference value is greater than the preset cost difference threshold value, it is determined that the current candidate route does not belong to the similar candidate route, and the calculation of the cost difference value is terminated. After the traversal ends and all the similar candidate routes are determined, the target candidate route is determined from the similar candidate routes.

[0115] It can be seen that, according to the method of the embodiment, by determining the similar candidate routes and determining the target candidate route according to the similar candidate routes, the matching degree of the finally determined target candidate route and the trajectory point is the highest, thereby improving the accuracy of the operation state prediction of the manned mine truck.

[0116] On the basis of the above-mentioned embodiment, the technical solution is further described and optimized in the embodiment, specifically, in the embodiment, the target candidate route is determined according to the similar candidate routes, comprising:

[0117] determining the candidate number of the similar candidate routes;

[0118] if the candidate number is 1, the similar candidate route is determined as the target candidate route.

[0119] In the embodiment, after all the similar candidate routes are determined, the candidate number of the similar candidate routes is determined, and whether the candidate number of the similar candidate routes is 1 is determined; if the candidate number of the similar candidate routes is 1, it indicates that only the candidate route with the highest generation value is determined as the similar candidate route, the generation value of the similar candidate route is much higher than that of other candidate routes, that is, the geometric features of the similar candidate route are obviously similar to the geometric features of the trajectory point, and therefore the similar candidate route is determined as the target candidate route.

[0120] In the embodiment, when the candidate number of the similar candidate routes is 1, the similar candidate route is directly determined as the target candidate route, and therefore the target candidate route can be efficiently and conveniently determined.

[0121] On the basis of the above-mentioned embodiment, the technical solution is further described and optimized in the embodiment, specifically, in the embodiment, the method further comprises:

[0122] if the candidate number is greater than 1, it is determined whether each similar candidate route is an unloading route;

[0123] if each similar candidate route is an unloading route, for each similar candidate route, the number of vehicles in the similar candidate route on the future driving path of the manned mine truck to be predicted is determined;

[0124] the similar candidate route with the least number of vehicles is determined as the target candidate route.

[0125] In the embodiment, when the candidate number of the similar candidate routes is greater than 1, the target candidate route is determined from the multiple similar candidate routes. If the candidate number of the similar candidate routes is greater than 1, it indicates that the geometric features of multiple candidate routes with higher generation values are similar, and other algorithms need to be combined with the actual scene to further determine the target candidate route from the multiple similar candidate routes.

[0126] It should be noted that when the candidate quantity of the similar candidate routes is multiple, firstly, it is determined whether each similar candidate route is an unloading route, and the unloading route refers to a route attribute of the route being a loading and unloading task; if each similar candidate route is an unloading route, when the driver performs the loading and unloading task, the driver often selects the idle or the loading and unloading point with the shortest waiting time to perform the loading and unloading task, so as to improve the operation efficiency.

[0127] Therefore, for each similar candidate route, the number of vehicles in the future driving path of the to-be-predicted manned mine truck in the similar candidate route is determined; that is, according to the trajectory point and the driving direction of the to-be-predicted manned mine truck, the future driving path of the to-be-predicted manned mine truck is determined; then the number of vehicles corresponding to the future driving path is determined, and the number of vehicles represents the waiting time required for performing the loading and unloading task; the similar candidate route with the least number of vehicles is selected as the target candidate route.

[0128] In the embodiment, when the candidate quantity of the similar candidate routes is greater than 1 and each similar candidate route is an unloading route, the similar candidate route with the least number of vehicles in the future driving path of the to-be-predicted manned mine truck is determined as the target candidate route, and the loading and unloading waiting time corresponding to the target candidate route is the shortest, so that the loading and unloading efficiency of the to-be-predicted manned mine truck can be improved.

[0129] On the basis of the above embodiment, the technical scheme is further described and optimized in the embodiment, and specifically, the method further includes:

[0130] If each similar candidate route is not an unloading route, the target candidate route is determined from the multiple similar candidate routes according to the vehicle offset of the to-be-predicted manned mine truck and the heading angle of the trajectory point.

[0131] In the embodiment, the target candidate route is determined from the multiple similar candidate routes when it is determined that each similar candidate route is not an unloading route, that is, the similar candidate route includes an unloading route and other routes.

[0132] Specifically, for each similar candidate route, the following operations are performed to determine whether the similar candidate route is the target candidate route:

[0133] The route center line of the similar candidate route is determined, and a preset width is expanded to both sides along the route center line to obtain a corresponding route area polygon, that is, the vehicle offset range is determined; wherein the specific value of the preset width is not limited in the embodiment, for example, the value can be 3m;

[0134] The area heading angle is obtained, and the difference between the heading angle of the trajectory point and the area heading angle is calculated;

[0135] determining whether the trajectory point is within the area range of the route area polygon, that is, determining whether the vehicle offset is within the vehicle offset range, and determining whether the difference is within the preset error tolerance range; wherein the embodiment does not limit the specific value of the preset error tolerance range, which can be 15° for example;

[0136] If the trajectory point is within the area range of the route area polygon and the difference is within the preset error tolerance range, it is determined that the similar candidate route matches the trajectory point successfully, and the similar candidate route is the target candidate route; otherwise, if the trajectory point is not within the area range of the route area polygon or the difference is not within the preset error tolerance range, it is determined that the similar candidate route is not the target candidate route.

[0137] FIGS. 6-8 are schematic diagrams of matching results of similar candidate routes and trajectory points according to the embodiment of the present application. In FIG. 6, the trajectory point is not within the area range of the route area polygon, so the matching of the similar candidate route and the trajectory point fails. In FIG. 7, the difference between the area heading angle and the heading angle of the trajectory point is greater than the preset error tolerance range, so the matching of the similar candidate route and the trajectory point fails. In FIG. 8, the trajectory point is within the area range of the route area polygon and the difference is within the preset error tolerance range, so it is determined that the similar candidate route matches the trajectory point successfully.

[0138] As can be seen, according to the method of the embodiment, the target candidate route can be determined from a plurality of similar candidate routes.

[0139] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described in detail below in combination with actual application scenarios. In the embodiments of the present application, in combination with the flowchart of another manned mine truck operation state prediction method provided by the embodiments of the present application shown in FIG. 9, the specific steps of the manned mine truck operation state prediction method are as follows:

[0140] obtaining positioning data of the manned mine truck to be predicted by the unmanned mine truck;

[0141] obtaining a directed weighted graph corresponding to the transportation operation area and a plurality of routes corresponding to the directed weighted graph; the route represents a driving path between two stops in the transportation operation area;

[0142] If the positioning data comprises a predicted trajectory, the target candidate route is determined according to a map matching algorithm: the candidate route is determined according to the predicted trajectory and the directed and weighted graph; the observation probability is calculated according to a normal distribution probability density function of the projection distance between the trajectory points and the candidate points; the transition probability is calculated according to an exponential distribution probability density function of the difference between the first path distance between two candidate points and the second path distance between two trajectory points; the state sequence is determined according to the observation probability and the transition probability, and the target candidate route is obtained;

[0143] If the positioning data comprises a trajectory point, the candidate route is determined according to the trajectory point and the directed and weighted graph;

[0144] The generation cost of each candidate route is determined: for each candidate route, the candidate points corresponding to the trajectory points on the candidate route are determined, and the projection distances between the candidate points and the trajectory points are determined; the heading angle difference between the heading angle of the candidate points and the heading angle of the trajectory points is determined; the generation cost of the candidate route is determined according to the projection distances and the heading angle difference;

[0145] The candidate routes are sorted and the similar candidate routes are determined: the generation costs of the candidate routes are arranged in a predetermined order; the cost difference between two adjacent generation costs is calculated in order from high to low according to the generation cost, and when the cost difference is less than or equal to a predetermined cost difference threshold, the candidate route is determined as a similar candidate route;

[0146] The number of candidate routes of the similar candidate routes is determined; if the number of candidate routes is 1, the candidate route with the highest generation cost is determined as the target candidate route;

[0147] If the number of candidate routes is greater than 1, it is determined whether each similar candidate route is an unloaded route;

[0148] If each similar candidate route is an unloaded route, the target candidate route is determined according to a quantity balancing algorithm: for each similar candidate route, the number of vehicles in the similar candidate route on the future driving path of the to-be-predicted manned mine truck is determined; the similar candidate route with the least number of vehicles is determined as the target candidate route;

[0149] If each similar candidate route is not an unloaded route, the target candidate route is determined according to a region matching algorithm: the target candidate route is determined from the multiple similar candidate routes according to the vehicle offset of the to-be-predicted manned mine truck and the heading angle of the trajectory point;

[0150] The operation state of the to-be-predicted manned mine truck is determined according to the candidate point in the target candidate route corresponding to the to-be-predicted manned mine truck and the route attribute of the target candidate route;

[0151] The operation state of the to-be-predicted manned mine truck is output.

[0152] The embodiment of the present application provides a manned mine truck operation state prediction method, the method predicts a target candidate route of a to-be-predicted manned mine truck according to positioning data of the to-be-predicted manned mine truck obtained by an unmanned mine truck and a directed weighted graph corresponding to a transportation operation area, and determines an operation state of the to-be-predicted manned mine truck according to a candidate point corresponding to the to-be-predicted manned mine truck in the target candidate route and a route attribute of the target candidate route. Through prediction of the operation state of the manned mine truck, the unmanned mine truck can be more reasonably dispatched, and a transportation task and a driving route can be arranged. The unmanned mine trucks and the manned mine trucks in the transportation operation area can be better coordinated to avoid congestion in the transportation operation area. The production and transportation efficiency and the transportation safety of the whole transportation operation area are improved.

[0153] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0154] FIG. 10 shows a structure schematic diagram of a manned mine truck operation state prediction device provided by the embodiment of the present application. As shown in FIG. 10, the manned mine truck operation state prediction device of the embodiment includes a positioning data acquisition module 1010, a route acquisition module 1020, a target candidate route determination module 1030 and an operation state prediction module 1040; wherein,

[0155] The positioning data acquisition module 1010 is configured to acquire positioning data of a to-be-predicted manned mine truck through an unmanned mine truck.

[0156] The route acquisition module 1020 is configured to acquire a directed weighted graph corresponding to a transportation operation area and a plurality of routes corresponding to the directed weighted graph. The route is a driving path representing two stop points in the transportation operation area.

[0157] The target candidate route determination module 1030 is configured to determine a target candidate route of the to-be-predicted manned mine truck according to the positioning data and the directed weighted graph.

[0158] The operation state prediction module 1040 is configured to determine an operation state of the to-be-predicted manned mine truck according to a candidate point corresponding to the to-be-predicted manned mine truck in the target candidate route and a route attribute of the target candidate route.

[0159] The manned mine truck operation state prediction device provided by the embodiment of the present application has the same beneficial effects as the manned mine truck operation state prediction method.

[0160] In one embodiment, the target candidate route determination module includes:

[0161] The first candidate route determination submodule is configured to determine a candidate route according to the predicted trajectory and the directed and weighted graph if the positioning data comprises the predicted trajectory.

[0162] The probability calculation submodule is configured to determine observation probability and transition probability corresponding to the hidden Markov model according to the predicted trajectory and the candidate route.

[0163] The first target candidate route determination submodule is configured to determine a state sequence according to the observation probability and the transition probability, and obtain a target candidate route.

[0164] In one of the embodiments, the operation state prediction device of the manned mine truck further comprises:

[0165] The second candidate route determination submodule is configured to determine a candidate route according to the trajectory point and the directed and weighted graph if the positioning data comprises the trajectory point.

[0166] The generation value determination submodule is configured to determine a generation value corresponding to each candidate route.

[0167] The second target candidate route determination submodule is configured to determine a target candidate route from the candidate routes according to the generation values.

[0168] In one of the embodiments, the generation value determination submodule comprises:

[0169] The projection distance calculation unit is configured to determine a candidate point corresponding to the trajectory point on the candidate route, and determine a projection distance between the candidate point and the trajectory point for each candidate route.

[0170] The heading angle difference calculation unit is configured to determine a heading angle difference between a heading angle of the candidate point and a heading angle of the trajectory point.

[0171] The generation value calculation unit is configured to determine a generation value of the candidate route according to the projection distance and the heading angle difference.

[0172] In one of the embodiments, the second target candidate route determination submodule comprises:

[0173] The generation value sorting unit is configured to arrange the generation values of the candidate routes in a preset order.

[0174] The similar candidate route determination unit is configured to calculate a cost difference value between two adjacent generation values in a descending order of the generation values, and determine a candidate route as a similar candidate route when the cost difference value is less than or equal to a preset cost difference value threshold.

[0175] The target candidate route determination unit is configured to determine a target candidate route according to the similar candidate route.

[0176] In one of the embodiments, the target candidate route determination unit comprises:

[0177] a candidate number determination sub-unit configured to determine a candidate number of the similar candidate routes;

[0178] a first target candidate route determination sub-unit configured to determine the similar candidate route as the target candidate route if the candidate number is 1.

[0179] In one of the embodiments, the operation state prediction device of the manned mine truck further comprises:

[0180] an unloading route determination sub-unit configured to determine whether each of the similar candidate routes is an unloading route if the candidate number is greater than 1;

[0181] a vehicle number determination sub-unit configured to determine, for each of the similar candidate routes, a vehicle number of vehicles on a future driving path of the manned mine truck to be predicted in the similar candidate route if each of the similar candidate routes is the unloading route;

[0182] a second target candidate route determination sub-unit configured to determine the similar candidate route with the least vehicle number as the target candidate route.

[0183] In one of the embodiments, the operation state prediction device of the manned mine truck further comprises:

[0184] a third target candidate route determination sub-unit configured to determine the target candidate route from the similar candidate routes according to a vehicle offset of the manned mine truck to be predicted and a heading angle of the trajectory point if not all of the similar candidate routes are the unloading route.

[0185] It should be noted that the information interaction, execution process and the like between the above devices / units are based on the same concept as the method embodiments of the present application, and the specific functions and the technical effects brought by the same can be referred to the method embodiments part, and will not be described here.

[0186] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0187] FIG. 11 is a structural schematic diagram of a terminal device provided by an embodiment of the present application. As shown in FIG. 11, the terminal device 1100 of this embodiment includes a memory 1101, a processor 1102, and a computer program 1103 stored in the memory 1101 and executable on the processor 1102; the processor 1102 implements the steps in each of the above manned mine truck operation state prediction method embodiments when executing the computer program 1103; or the processor 1102 implements the functions of each module / unit in the above device embodiments when executing the computer program 1103.

[0188] For example, the computer program 1103 can be divided into one or more modules / units, which are stored in the memory 1101 and executed by the processor 1102 to implement the method of the embodiments of the present application. One or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 1103 in the terminal device 1100. For example, the computer program 1103 can be divided into a positioning data acquisition module, a route acquisition module, a target candidate route determination module, and an operation state prediction module, and the specific functions of each module are as follows:

[0189] The positioning data acquisition module is configured to acquire positioning data of a manned mine truck to be predicted by an unmanned mine truck.

[0190] The route acquisition module is configured to acquire a directed and weighted graph corresponding to a transportation operation area and a plurality of routes corresponding to the directed and weighted graph; the route represents a driving path between two stops in the transportation operation area.

[0191] The target candidate route determination module is configured to determine a target candidate route of the manned mine truck to be predicted according to the positioning data and the directed and weighted graph.

[0192] a job state prediction module, configured to determine a job state of the to-be-predicted manned mine truck according to a candidate point in the target candidate route corresponding to the to-be-predicted manned mine truck and a route attribute of the target candidate route.

[0193] In applications, the terminal device 1100 can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The terminal device 1100 can include but is not limited to a memory 1101 and a processor 1102. Those skilled in the art can understand that FIG. 11 is merely an example of the terminal device and does not constitute a limitation on the terminal device, and can include more or fewer components than the diagram, or combine certain components, or different components, for example, the terminal device can also include an input / output device, a network access device, a bus, and the like; the input / output device can include a camera, an audio collection / player device, a display screen, and the like; the network access device can include a communication module for wireless communication with external devices.

[0194] In applications, the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0195] In applications, the memory can be an internal storage unit of the terminal device, for example, a hard disk or a memory of the terminal device; can also be an external storage device of the terminal device, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like equipped on the terminal device; and can also include both the internal storage unit and the external storage device of the terminal device. The memory is used to store an operating system, application programs, a boot loader, data, and other programs, for example, program codes of computer programs, and the like. The memory can also be used to temporarily store data that has been output or will be output.

[0196] The embodiment of the present application further provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps in each method embodiment.

[0197] The embodiment of the present application realizes all or part of the processes in the above method embodiments, and can be completed by a computer program instructing related hardware. The computer program can be stored in a computer readable storage medium, and the computer program realizes the steps in each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program code to a terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk.

[0198] The embodiment of the present application provides a computer readable storage medium, which has the same beneficial effects as the method for predicting the operation state of the manned mine truck.

[0199] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0200] Those skilled in the art can appreciate that the devices and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0201] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, and the coupling or direct coupling or communication connection between the displayed or discussed elements can be through some interface, indirect coupling or communication connection between devices can be electrical, mechanical or other forms.

[0202] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for predicting the operational status of a manned mining truck, characterized in that, The method includes: Location data of manned mining trucks to be predicted are obtained through unmanned mining trucks; Obtain a directed weighted graph corresponding to the transportation operation area and multiple routes corresponding to the directed weighted graph; the routes represent the driving paths between two stops in the transportation operation area. The target candidate route for the manned mining truck to be predicted is determined based on the positioning data and the directed weighted graph. The operational status of the manned mining truck to be predicted is determined based on the candidate points in the target candidate routes corresponding to the manned mining truck to be predicted and the routing attributes of the target candidate routes.

2. The method according to claim 1, characterized in that, The step of determining the target candidate route for the manned mining truck to be predicted based on the positioning data and the directed weighted graph includes: If the location data includes a predicted trajectory, then a candidate route is determined based on the predicted trajectory and the directed weighted graph; The observation probability and transition probability corresponding to the Hidden Markov Model are determined based on the predicted trajectory and the candidate route. The state sequence is determined based on the observation probability and the transition probability to obtain the target candidate route.

3. The method according to claim 1, characterized in that, The method further includes: If the location data includes trajectory points, then candidate routes are determined based on the trajectory points and the directed weighted graph; Determine the cost corresponding to each of the candidate routes; The target candidate route is determined from the candidate routes based on the respective cost values.

4. The method according to claim 3, characterized in that, Determining the cost value corresponding to each of the candidate routes includes: For each candidate route, a candidate point corresponding to the trajectory point is determined on the candidate route, and the projection distance between the candidate point and the trajectory point is determined. Determine the difference between the heading angle of the candidate point and the heading angle of the trajectory point; The cost of the candidate route is determined based on the difference between the projected distance and the heading angle.

5. The method according to claim 4, characterized in that, The step of determining the target candidate route from the candidate routes based on the respective cost values ​​includes: The cost values ​​of each candidate route are arranged in a preset order; The cost difference between two adjacent cost values ​​is calculated sequentially from high to low according to the cost value, and when the cost difference is less than or equal to a preset cost difference threshold, the candidate route is determined as a similar candidate route. The target candidate route is determined based on the similar candidate routes.

6. The method according to claim 5, characterized in that, The step of determining the target candidate route based on the similar candidate routes includes: Determine the number of candidate routes for the similar candidate routes; If the number of candidates is 1, then the similar candidate routes are determined as the target candidate routes.

7. The method according to claim 6, characterized in that, The method further includes: If the number of candidates is greater than 1, then determine whether each of the similar candidate routes is an offload route; If all the similar candidate routes are the offloading routes, then for each similar candidate route, determine the number of vehicles on the future driving path of the manned mining truck to be predicted in the similar candidate route; The nearest candidate route with the fewest vehicles is determined as the target candidate route.

8. The method according to claim 7, characterized in that, The method further includes: If not all of the similar candidate routes are the unloading routes, then the target candidate route is determined from the multiple similar candidate routes based on the vehicle offset and heading angle of the trajectory point of the manned mining truck to be predicted.

9. A device for predicting the operational status of a manned mining truck, characterized in that, The device includes: The positioning data acquisition module is used to acquire the positioning data of the manned mining truck to be predicted through the unmanned mining truck; The route acquisition module is used to acquire a directed weighted graph corresponding to the transportation operation area and multiple routes corresponding to the directed weighted graph; the routes represent the driving paths between two stops in the transportation operation area. The target candidate route determination module is used to determine the target candidate route of the manned mining truck to be predicted based on the positioning data and the directed weighted graph. The operation status prediction module is used to determine the operation status of the manned mining truck to be predicted based on the candidate points in the target candidate routes corresponding to the manned mining truck to be predicted and the routing attributes of the target candidate routes.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 8.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 8.

Images