Mine truck dynamic scheduling method, system, device, equipment and medium

Abstract

The embodiment of the invention relates to a mine truck dynamic scheduling method, system, device and equipment and a medium, and the method comprises the steps: obtaining the working state data of equipment at each loading point, determining the optimal path time for a target truck to reach each loading point based on the real-time road condition information, and obtaining the optimal path time; determining the shoveling efficiency of each piece of loading point equipment based on the working state data of each piece of loading point equipment, determining the loading time of each piece of loading point equipment for the target truck based on the shoveling efficiency of each piece of loading point equipment, predicting the corresponding queuing condition when the target truck reaches each loading point, determining the queuing time of each loading point, and determining the loading time of each piece of loading point equipment. And based on the optimal path time, the loading time and the queuing time, determining the predicted earliest loading time when the target truck arrives at each loading point, and generating a truck scheduling instruction of the target truck based on the predicted earliest loading time, so that the target truck is scheduled according to the truck scheduling instruction. And the dynamic response capability and the overall operation efficiency of mine truck dispatching are improved.

Description

Technical Field

[0001] This disclosure relates to the field of mining, and in particular to a method, system, device, equipment and medium for dynamic scheduling of mining trucks. Background Technology

[0002] With the expansion of mining scale and the increase in operational complexity, the traditional scheduling methods are no longer able to meet the needs of efficient production as the number of vehicles increases and transportation tasks become more diversified.

[0003] Traditional mining truck scheduling relies on fixed rules or manual experience, which cannot respond in real time to dynamic conditions such as fluctuations in the efficiency of loading point equipment and route congestion. This results in long truck waiting times, averaging 30%-40%, and low equipment utilization, at only 60%-70%. In addition, existing algorithms, such as static scheduling based on queuing theory, do not consider the real-time operating capabilities of loading point equipment, with prediction errors exceeding 15% and system response delays often exceeding 10 seconds. Consequently, the efficiency of dynamic scheduling of mining trucks is low. Therefore, how to improve the efficiency of dynamic scheduling of mining trucks has become an urgent technical problem to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a method, system, apparatus, equipment, and medium for dynamic scheduling of mining trucks.

[0005] This disclosure provides a method for dynamic scheduling of mining trucks, the method comprising:

[0006] Acquire the operating status data of the equipment at each loading point;

[0007] Based on real-time traffic information, determine the optimal path time for the target truck to reach each loading point;

[0008] The loading efficiency of each loading point device is determined based on the working status data of each loading point device, and the loading time of each loading point device for the target truck is determined based on the loading efficiency of each loading point device.

[0009] Predict the queuing situation when the target truck arrives at each loading point, and determine the queuing time at each loading point;

[0010] Based on the optimal path time, the loading time, and the queuing time, the earliest estimated loading completion time for the target truck to arrive at each loading point is determined.

[0011] Based on the estimated earliest loading completion time, a truck dispatching instruction is generated for the target truck so that the target truck is dispatched according to the truck dispatching instruction.

[0012] This disclosure also provides a dynamic scheduling system for mining trucks, used to execute the dynamic scheduling method for mining trucks as provided in this disclosure.

[0013] This disclosure also provides a dynamic dispatching device for mining trucks, the device comprising:

[0014] The acquisition module is used to acquire the working status data of the equipment at each loading point;

[0015] The first determining module is used to determine the optimal path time for the target truck to reach each loading point based on real-time traffic information;

[0016] The second determining module is used to determine the loading efficiency of each loading point device based on the working status data of each loading point device, and to determine the loading time of each loading point device for the target truck based on the loading efficiency of each loading point device.

[0017] The third determining module is used to predict the queuing situation when the target truck arrives at each loading point and to determine the queuing time at each loading point.

[0018] The fourth determining module is used to determine the earliest estimated loading completion time of the target truck at each loading point based on the optimal path time, the loading time, and the queuing time.

[0019] The generation module is used to generate a truck dispatching instruction for the target truck based on the earliest expected loading completion time, so that the target truck is dispatched according to the truck dispatching instruction.

[0020] This disclosure also provides an electronic device, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the executable instructions to implement the dynamic scheduling method for mining trucks as provided in this disclosure.

[0021] This disclosure also provides a computer-readable storage medium storing a computer program for executing the mining truck dynamic scheduling method provided in this disclosure.

[0022] Compared with the prior art, the technical solution provided in this disclosure has the following advantages: It acquires the working status data of each loading point device, determines the optimal path time for the target truck to arrive at each loading point based on real-time road condition information, determines the loading efficiency of each loading point device based on the working status data of each loading point device, determines the loading time for the target truck based on the loading efficiency of each loading point device, predicts the queuing situation corresponding to the target truck when it arrives at each loading point, determines the queuing time at each loading point, determines the earliest expected loading completion time for the target truck to arrive at each loading point based on the optimal path time, loading time, and queuing time, and generates a truck dispatching instruction for the target truck based on the earliest expected loading completion time, so that the target truck can be dispatched according to the truck dispatching instruction. By adopting the above technical solution, based on the optimal path time of the target truck to each loading point, the loading time of the equipment at each loading point for the target truck, and the queuing time at each loading point, the estimated earliest loading completion time of the target truck to each loading point is determined. Based on the estimated earliest loading completion time, a truck scheduling instruction is generated so that the target truck is scheduled according to the truck scheduling instruction. This can effectively reduce the waiting time of trucks at loading points, improve the utilization rate of loading point equipment, and thus significantly improve the dynamic response capability and overall operating efficiency of mine truck scheduling. Attached Figure Description

[0023] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0024] Figure 1 A flowchart illustrating a dynamic scheduling method for mining trucks provided in this embodiment of the present disclosure;

[0025] Figure 2 A flowchart illustrating another dynamic scheduling method for mining trucks provided in this embodiment of the present disclosure;

[0026] Figure 3 This is a schematic diagram of the structure of a dynamic dispatching device for mining trucks provided in an embodiment of the present disclosure;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0028] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0029] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0030] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0031] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0032] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0033] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0034] Traditional mining truck scheduling relies on fixed rules or manual experience, which cannot respond in real time to dynamic conditions such as fluctuations in the efficiency of loading point equipment and route congestion. This results in long truck waiting times, averaging 30%-40%, and low equipment utilization, at only 60%-70%. In addition, existing algorithms, such as static scheduling based on queuing theory, do not consider the real-time operating capabilities of loading point equipment, with prediction errors exceeding 15% and system response delays often exceeding 10 seconds. Consequently, the efficiency of dynamic scheduling of mining trucks is low. Therefore, how to improve the efficiency of dynamic scheduling of mining trucks has become an urgent technical problem to be solved.

[0035] To address the aforementioned issues, this disclosure provides a dynamic scheduling method for mining trucks, which will be described below with reference to specific embodiments.

[0036] Figure 1 This is a flowchart illustrating a dynamic scheduling method for mining trucks according to an embodiment of this disclosure. This method can be executed by a dynamic scheduling device for mining trucks, which can be implemented using software and / or hardware, and is generally integrated into an electronic device. Figure 1 As shown, the method includes:

[0037] Step 101: Obtain the working status data of the equipment at each loading point.

[0038] In this context, a loading point refers to a location in a mine used to load ore onto transport trucks. Loading point equipment can be any equipment used to load trucks in the mine, such as electric shovels or excavators, distributed across different loading points. Working status data includes real-time and statistical information on the loading point equipment during operation, including the actual loading time and volume for a single truck, as well as the total volume and time for all loading operations within a shift.

[0039] Step 102: Based on real-time traffic information, determine the optimal path time for the target truck to reach each loading point.

[0040] Real-time traffic information refers to a data set reflecting newly added roads, road repairs, and other road improvements in the road network. A target truck refers to a truck scheduled to proceed to the loading point based on current needs. Optimal path time refers to the time corresponding to the shortest path from the target truck's current location to the loading point, or the shortest travel time from the target truck's current location to the loading point.

[0041] In this embodiment of the disclosure, taking the target loading point as an example, the shortest path for the target truck to reach the target loading point is determined based on real-time traffic information, and the travel time corresponding to the shortest path for the target truck to reach the target loading point is taken as the optimal path time for the target truck to reach the target loading point.

[0042] In another embodiment of this disclosure, taking the target loading point as an example, the travel path of the target truck to the target loading point is determined based on real-time traffic information, and the shortest travel time in the travel path of the target truck to the target loading point is taken as the optimal path time of the target truck to the target loading point.

[0043] In one optional implementation, the optimal path time for the target truck to reach each loading point is determined based on real-time traffic information, including: constructing a dynamic road network topology map based on real-time traffic information, the current location information of the target truck, and the location information of each loading point; and using a shortest path algorithm to search in the dynamic road network topology map to determine the optimal path time for the target truck to reach each loading point.

[0044] The dynamic road network topology graph is a directed graph model that abstracts the real-time changing road network into a directed graph model with key location points in the mining road network as nodes, road segments as edges, and travel time or distance as weights. The shortest path algorithm is a mathematical optimization method that calculates the total weight (such as travel time or distance) of all possible paths from the starting node to the ending node in the road network topology graph and selects the path with the minimum total weight.

[0045] To facilitate understanding of the optimal path time for the target truck to reach each loading point, the specific process is described in detail.

[0046] First, a road network topology is constructed based on mine road data. By collecting the centerlines, nodes, and related attribute information of mine roads, a directed graph G = (V, E) is built for calculating the optimal path between vehicles and loading points. The original road edge set is E = {e1, e2, ... e}. n}, where each roadside e i Represented as: e i =LineString(p i1 ,p i2 ,...p ik ), where p i1 p represents the starting point of the road. i1 V represents the endpoint of a road. The set of all starting and ending points of all road edges is V. raw ={p 11 ,p 1k ,...,p n1 ,p nk}

[0047] To address minor deviations during data stitching, a tolerance value ε ≥ 0 is defined, indicating that two vertices are considered to have the same maximum distance threshold. For any two road vertices p a ,p b ∈V raw Its distance d(p) a ,p b ) is defined as:

[0048] Where, p a =(x a ,y a ),pb =(x b ,y b ).

[0049] If the distance between two vertices is less than or equal to the fault tolerance value, they are merged into the same node. This is defined as: merging. Forming the merged road network node set V nodes ={v1,v2,...,v m}, where each road network node v j For one or more original vertices, the road network node IDj is assigned in ascending order according to the processing sequence. Then, for each road edge e... i =LineString(p i1 ,p i2 ,...p ik Map its start and end points to the node set V respectively. nodes In this context, the process involves finding the road network node closest to the starting or ending point of a road, defined as:

[0050] source(e i =arg min d(p i1 ,v j ),v j ∈V nodes ,target(e i =arg min d(p ik ,v j ),v j ∈V nodes .

[0051] The mapped start and end points must satisfy the condition that their distances from the original start and end points are less than the tolerance value, i.e.: d(p i1 ,source(e i ))≤ε and d(p) ik ,target(e i ))≤ε.

[0052] Ultimately, the road network topology is represented as a directed graph G = (V, E), where V is the set of road network nodes. nodes E is the set of road network edges E = {e1, e2, ..., e} n}, each edge e iEach is a quintuple (e.id, e.geom, e.source, e.target, e.cost), with each attribute defined as follows: e.id is the unique identifier of the edge, e.geom is the geometric representation of the edge, e.source is the identifier of the starting node of the edge, belonging to V, e.target is the identifier of the ending node of the edge, belonging to V; e.cost is the travel cost (distance or travel time) of the edge.

[0053] Based on this, given two nodes A and B in the road network, the optimal path from A to B is calculated using the road network topology G=(V,E) mentioned above. The Dijkstra optimal path calculation process is as follows.

[0054] Define a distance array dist[v], representing the current optimal path estimate from A to v:

[0055]

[0056] Define a priority queue Q, initially sorting all vertices by dist[v]; define a set Represents the vertices for which the optimal path has been determined, initially. Define a predecessor vertex prev[v] = null, which is used to record the vertex data of the traversal.

[0057] Algorithm iterative execution: Extract the vertex from Q that has the smallest distance from dist[u]. It will be added to S:

[0058] u←arg min dist[v],v∈V\S.

[0059] If u = target_id, then the calculation process ends, and the optimal path for dist[B] has been found.

[0060] Then, a relaxation operation is performed on all adjacent vertices v∈adj[u] of u, i.e. (u,v)∈E, and dist[v] is updated:

[0061] dist[v]←min(dist[v],dist[u]+e.cost), where e.cost is the passage cost of (u,v). If dist[v] is updated, the priority of v in Q is adjusted.

[0062] Repeat the vertex selection and relaxation operations above until target_id∈S or Q is empty. If target_id∈S, then dist[target_id] is the optimal path from A to B. The edge sequence of the path is obtained by backtracking the predecessor vertex prev[v]. The travel cost of the path is: Where π represents all paths from A to B; if If Q is empty, then B is unreachable, and an empty path is returned.

[0063] Based on the location of the target vehicle and multiple loading points, multiple paths are calculated. Using the above method, by inputting the location of the target truck and the locations of each loading point, the optimal path and travel cost from the target vehicle to each loading point can be calculated.

[0064] Taking the target loading point as an example, the optimal path can be the shortest path from the target truck to the target loading point or the path with the shortest travel time from the target truck to the target loading point. The time corresponding to the optimal path is the optimal path time.

[0065] Step 103: Determine the loading efficiency of each loading point based on the working status data of each loading point equipment, and determine the loading time of each loading point equipment for the target truck based on the loading efficiency of each loading point equipment.

[0066] Loading efficiency refers to the ability of equipment to complete loading within a unit of time. Loading time refers to the time required for the target truck to be fully loaded after it stops at the loading point. In this embodiment of the disclosure, after determining the loading efficiency of the equipment at each loading point, the loading time of the target truck at each loading point is determined according to the loading requirements of the target truck.

[0067] In one optional implementation, the loading efficiency of each loading point is determined based on the working status data of each loading point device, and the loading time for each loading point device to the target truck is determined based on the loading efficiency of each loading point device. This includes: obtaining the historical loading time and historical loading volume of each loading point device from the working status data of each loading point device, and determining the loading efficiency of each loading point device based on the historical loading time and historical loading volume; wherein, the loading efficiency of each loading point device is dynamically updated based on the loading time and loading volume of trucks that have completed loading operations in the current shift, and the loading time for each loading point device to the target truck is determined based on the loading volume of the target truck and the loading efficiency of each loading point device.

[0068] In this embodiment of the disclosure, taking the target loading point as an example, when the target truck is the first truck in the current shift, the loading efficiency of the loading point equipment at the target loading point can be determined based on a certain historical shift of the loading point equipment at the target loading point. For example, the average loading efficiency of the loading point equipment at the target loading point in a certain historical shift (such as the shift before the current shift) can be used as the loading efficiency of the loading point equipment at the target loading point. When the target truck is not the first truck, the loading efficiency of the loading point equipment at the target loading point can be the loading efficiency obtained by the loading time and loading volume of the loading point equipment at the target loading point for the trucks that have completed the operation in the current shift. That is, the loading efficiency of the loading point equipment at the target loading point is dynamically updated based on the loading time and loading volume of the trucks that have completed the operation.

[0069] For example, if the target truck is the second truck of the current shift, the loading efficiency of the loading point equipment at the target loading point is determined by the loading time and loading volume of the first truck of the current shift; if the target truck is the third truck of the current shift, the loading efficiency of the loading point equipment at the target loading point is determined by the total loading time and total loading volume of the first and second trucks of the current shift, and so on, so as to achieve dynamic updating of the loading efficiency of the loading point equipment at the target loading point.

[0070] Single-operation capacity calculation: Taking the target loading point as an example, the actual loading time and loading volume of a single truck by the loading point equipment at the target loading point are obtained in real time, and the instantaneous loading efficiency is calculated.

[0071] (Unit: m) 3 / h), where, The loading point equipment at the target loading point is designed for the instantaneous loading efficiency of truck j. Let the loading volume of truck j be , The loading time for truck j is the loading point equipment at the target loading point.

[0072] For example, if loading point equipment A fills truck B in 5 minutes, such as a standard load of 30m³... 3 Then the instantaneous loading efficiency of loading point equipment A for truck B is 6m. 3 / min.

[0073] Shift Data Cumulative Calculation: Continuing with the target loading point as an example, calculate the average loading efficiency of the loading equipment at the target loading point within one shift. Within one shift (e.g., 8 hours), record the total loading volume of all loading operations. and total loading time

[0074] The average loading efficiency C of the loading point equipment at the target loading point within one shift shiftThe calculation formula is:

[0075]

[0076] For example, if the total loading capacity of the loading equipment at the target loading point in a certain shift is 2400 m³... 3 If the total loading time is 400 minutes, then the average loading efficiency of the loading equipment at the target loading point during this shift is 6m. 3 / min.

[0077] Dynamic loading capacity prediction:

[0078] Initial Prediction: When the system starts (i.e., when the target truck is the first truck in the current shift), the default or historical shift data (i.e., the average loading efficiency within historical shifts) is used as the initial prediction value (C). predicted This refers to the loading efficiency of the equipment at each loading point mentioned above. During the current shift, the efficiency is updated after each loading operation is completed. and And recalculate C shift This allows the loading efficiency of equipment at each loading point to be dynamically updated based on the loading time and volume of trucks that have completed loading operations in the current shift.

[0079] Step 104: Predict the queuing situation when the target truck arrives at each loading point, and determine the queuing time at each loading point.

[0080] The queuing status can be the number of trucks waiting to be loaded at each loading point at a given moment.

[0081] In one optional implementation, the loading point includes a target loading point. Predicting the queuing situation when the target truck arrives at each loading point and determining the queuing time at each loading point includes: obtaining the total loading time of all queuing trucks at the target loading point; determining whether the optimal path time of the target truck to the target loading point is greater than the total loading time: if the optimal path time of the target loading point is greater than the total loading time, then the queuing time of the target truck is 0; if the optimal path time of the target loading point is not greater than the total loading time, then the queuing time of the target truck is the difference between the total loading time and the optimal path time of the target loading point.

[0082] The total loading time is the sum of the loading times required for all queued trucks at the target loading point.

[0083] In this embodiment of the disclosure, if the optimal path time to the target loading point is greater than the total loading time, it indicates that the target truck does not need to wait when it arrives at the target loading point, that is, the queuing time of the target truck is 0; if the optimal path time to the target loading point is not greater than the total loading time, it indicates that the target truck needs to wait when it arrives at the target loading point, and in order to avoid repeated calculations, the queuing time of the target truck is the difference between the total loading time and the optimal path time to the target loading point.

[0084] In this embodiment of the disclosure, the queuing time is used to quantify the time the target truck needs to wait after arriving at the loading point. Specifically, the calculation formula is as follows:

[0085]

[0086] Where t queue Queuing time at the target loading point C is the total loading time of all queued trucks at the target loading point. predicted The predicted loading efficiency of the loading point equipment at the target loading point is used as the actual loading efficiency (m²) of the equipment at the target loading point. 3 / h), The loading capacity of truck j queuing at the target loading point, i.e., the rated loading capacity of a single truck (e.g., 30m³). 3 ), where n is the number of vehicles queuing at the target loading point, i.e., the current number of trucks queuing at the target loading point, and t is the number of vehicles queuing at the target loading point. travel The optimal path time for the target truck to reach the target loading point is minus t. travel The queuing time consumed during the target truck's journey is taken to avoid duplicate calculations. The maximum value is used to indicate that if the result is negative, it means that the target truck does not need to wait when it arrives at the target loading point, i.e., t. queue =0.

[0087] The loading time of the loading point equipment can be collected from the vehicle terminal status. For example, if the loading takes 5 minutes, the speed of the target truck can be determined based on the real-time location.

[0088] Step 105: Based on the optimal path time, loading time, and queuing time, determine the earliest estimated loading completion time for the target truck to arrive at each loading point.

[0089] In one alternative implementation, the earliest completion time is estimated to be the sum of the optimal path time, loading time, and queuing time.

[0090] Taking the target loading point as an example, the earliest estimated loading time for the target truck to arrive at the target loading point is the sum of the optimal path time for the target truck to arrive at the target loading point, the time required for the target truck to load at the target loading point (i.e., the loading time of the loading point equipment for the target truck), and the queuing time at the target loading point.

[0091] Step 106: Generate truck dispatch instructions for the target truck based on the earliest expected loading completion time, so that the target truck can be dispatched according to the truck dispatch instructions.

[0092] In this embodiment of the disclosure, the loading point corresponding to the minimum value is determined from all the earliest loading completion times of the target truck arriving at each loading point, and the target truck is assigned to the loading point corresponding to the minimum value to generate a truck dispatch instruction for the target truck, instructing the target truck to go to the loading point corresponding to the minimum value to perform loading operations, so as to maximize the overall operation efficiency.

[0093] In this embodiment of the disclosure, the goal of the scheduling decision is to select the loading point that will complete loading earliest for the target truck. Specifically, the estimated earliest loading completion time for each loading point is calculated, the optimal loading point is selected, a target truck scheduling instruction is generated, and the instruction is sent to the target truck terminal.

[0094] Continuing with the example of the target loading point, the formula for calculating the earliest estimated loading completion time of the target truck upon arrival at the target loading point is:

[0095] Among them, t complete t is the earliest estimated time when the target truck will arrive at the target loading point and be fully loaded. travel Let t be the optimal path time for the target truck to reach the target loading point. queue Queuing time at the target loading point; For the target truck's loading capacity, The time required to load the target truck.

[0096] Specifically, iterate through all possible load points: for each load point P i ,calculate Based on the optimal selection rule, select Generate truck dispatch instructions for the target truck and send them to the target truck terminal.

[0097] in, For the target truck to reach loading point P i The earliest estimated completion time.

[0098] In one optional implementation, after predicting the queuing situation when the target truck arrives at each loading point and determining the queuing time at each loading point, the method further includes: if a loading point allocation instruction for the target truck is received, determining the loading point corresponding to the target truck from the loading point allocation instruction, and generating a truck scheduling instruction for the target truck so that the target truck is scheduled according to the truck scheduling instruction.

[0099] Loading point allocation instructions are dispatch commands that designate a target truck to a specific loading point to perform loading operations. These instructions are typically triggered in case of emergencies or changes in priority, enabling dynamic response. For example, loading point allocation instructions may be triggered by the need for emergency task priority loading or the need for priority transport of high-grade ore. Emergency task priority loading refers to a situation outside the normal truck dispatching process where, due to an urgent task, a target truck needs to immediately proceed to a designated loading point to perform loading operations, taking precedence over the original truck dispatch instructions. Priority transport of high-grade ore refers to situations where ore from certain areas has a higher grade and greater economic value, and target trucks are prioritized to go to the corresponding loading point to transport the high-grade ore out as quickly as possible.

[0100] In this embodiment, when a loading point allocation instruction for a target truck is received, the instruction is parsed to determine the loading points that the target truck needs to be allocated to, and a truck dispatch instruction for the target truck is generated so that the target truck can execute the dispatch task according to the truck dispatch instruction. This mechanism achieves coordinated control of manual intervention and automatic dispatch, ensuring that critical tasks are prioritized and improving overall dispatch flexibility and operational efficiency.

[0101] The dynamic scheduling scheme for mining trucks provided in this disclosure acquires the working status data of equipment at each loading point, determines the optimal path time for target trucks to arrive at each loading point based on real-time road condition information, determines the loading efficiency of equipment at each loading point based on the working status data, determines the loading time for target trucks at each loading point based on the loading efficiency, predicts the queuing situation when target trucks arrive at each loading point, determines the queuing time at each loading point, and determines the earliest expected completion time for target trucks to arrive at each loading point based on the optimal path time, loading time, and queuing time. Based on the expected earliest completion time, a truck scheduling instruction is generated for the target trucks so that they can be scheduled according to the truck scheduling instruction. By adopting the above technical solution, based on the optimal path time for target trucks to arrive at each loading point, the loading time of equipment at each loading point for target trucks, and the queuing time at each loading point, the earliest expected completion time for target trucks to arrive at each loading point is determined, and a truck scheduling instruction is generated based on the expected earliest completion time so that target trucks can be scheduled according to the truck scheduling instruction. This effectively reduces the waiting time of trucks at loading points, improves the utilization rate of loading point equipment, and thus significantly improves the dynamic response capability and overall operating efficiency of mining truck scheduling.

[0102] Figure 2This is a flowchart illustrating another dynamic scheduling method for mining trucks provided in this embodiment. First, real-time vehicle location and status data (i.e., the working status data of the equipment at each loading point) are collected. Based on real-time road conditions, the optimal path time for the target truck to reach each loading point is determined (i.e., optimal path planning). Based on the working status data of the equipment at each loading point, the loading efficiency of each loading point is determined (i.e., loading capacity calculation). The queuing situation corresponding to the target truck when it reaches each loading point is predicted, and the queuing time at each loading point is determined. Taking the target loading point as an example, it is determined whether the optimal path time for the target truck to reach the target loading point is greater than the total loading time (i.e., whether the queuing time is greater than a threshold). If not, the queuing time is 0; if so, the queuing time is equal to the queue time multiplied by the loading time minus the travel time (i.e., the queuing time of the target truck is the difference between the total loading time and the optimal path time of the target loading point). Then, it is determined whether manual intervention is required. If there is no manual intervention, the optimal loading point ID is output. If there is manual intervention, the optimal loading point ID is forcibly output at the specified loading point to generate an instruction (i.e., the truck scheduling instruction mentioned above) and send it to the terminal so that the target truck can be scheduled according to the truck scheduling instruction.

[0103] In this embodiment, a dynamic efficiency perception mechanism is used to calculate the loading capacity in real time based on the loading time and volume of the loading point equipment, and to accurately predict the loading speed affected by working conditions. A composite time calculation model is constructed that integrates the remaining loading capacity, the number of vehicles in the queue, and the truck travel time to scientifically estimate the earliest expected loading completion time of the target trucks to each loading point. The optimal truck scheduling scheme is dynamically calculated with the real-time operating capacity of the loading point equipment as the core indicator. At the same time, a human-machine collaboration interface is designed, and an innovative human-machine interaction decision system is implemented to support the intelligent integration of manual intervention and algorithmic decision-making, so as to achieve flexible and efficient task allocation.

[0104] As can be seen, the dynamic scheduling method for mining trucks provided in this disclosure is applicable to the intelligent management of transportation resources in open-pit mines. It can solve the problems of truck idleness, low equipment utilization, and high response delay caused by dynamic changes in working conditions in traditional scheduling methods. Through comparison of actual test data, it can reduce truck waiting time by 25%-35% and increase the utilization rate of loading point equipment by 12%-18% under the same transportation task volume. The system response delay is controlled within 3 seconds, providing an efficient algorithm-level solution for intelligent mining transportation.

[0105] This disclosure also provides a dynamic scheduling system for mining trucks, used to execute the dynamic scheduling method for mining trucks as provided in this disclosure.

[0106] Specifically, the mining truck dynamic dispatching system includes an equipment layer, a computing layer, and an interaction layer. The equipment layer includes trucks / excavators, vehicle terminals, and base stations for vehicle positioning and command display. The computing layer includes data preprocessing, dynamic models, and a decision engine for dynamic efficiency perception modules, composite time calculation models, and human-machine collaboration interfaces. The interaction layer includes a web page (map) and a web page (operation platform) for real-time display of vehicle location and status and manual intervention.

[0107] In addition, the current scheduling plan can be displayed through a visual interface, such as the truck allocation list and manual intervention options; the queue number at each loading point, equipment utilization rate, and predicted time comparison can be displayed through a real-time data panel.

[0108] Figure 3 This is a schematic diagram of a dynamic scheduling device for mining trucks provided in an embodiment of this disclosure. The device can be implemented by software and / or hardware, and is generally integrated into an electronic device. Figure 3 As shown, it includes:

[0109] The acquisition module 301 is used to acquire the working status data of the equipment at each loading point;

[0110] The first determining module 302 is used to determine the optimal path time for the target truck to reach each loading point based on real-time traffic information;

[0111] The second determining module 303 is used to determine the loading efficiency of each loading point device based on the working status data of each loading point device, and to determine the loading time of each loading point device for the target truck based on the loading efficiency of each loading point device.

[0112] The third determining module 304 is used to predict the queuing situation when the target truck arrives at each loading point and determine the queuing time at each loading point.

[0113] The fourth determining module 305 is used to determine the earliest estimated loading completion time of the target truck at each loading point based on the optimal path time, the loading time, and the queuing time.

[0114] The generation module 306 is used to generate a truck dispatching instruction for the target truck based on the estimated earliest loading time, so that the target truck is dispatched according to the truck dispatching instruction.

[0115] In one optional implementation, the first determining module 302 is configured to:

[0116] A dynamic road network topology map is constructed based on real-time traffic information, the current location information of the target truck, and the location information of each loading point.

[0117] The shortest path algorithm is used to search the dynamic road network topology map to determine the optimal path time for the target truck to reach each loading point.

[0118] In one optional implementation, the second determining module 303 is configured to:

[0119] The historical loading time and historical loading volume of each loading point device are obtained from the working status data of each loading point device;

[0120] The loading efficiency of the equipment at each loading point is determined based on the historical loading time and the historical loading volume; wherein, the loading efficiency of the equipment at each loading point is dynamically updated based on the loading time and loading volume of the trucks that have completed loading operations in the current shift.

[0121] Based on the loading capacity of the target truck and the loading efficiency of the equipment at each loading point, the loading time for the target truck at each loading point is determined.

[0122] In one optional implementation, the loading point includes a target loading point, and the third determining module 304 is configured to:

[0123] Obtain the total loading time of all queued trucks at the target loading point;

[0124] Determine whether the optimal path time for the target truck to reach the target loading point is greater than the total loading time:

[0125] If the optimal path time to the target loading point is greater than the total loading time, then the queuing time for the target truck is 0.

[0126] If the optimal path time to the target loading point is not greater than the total loading time, then the queuing time for the target truck is the difference between the total loading time and the optimal path time to the target loading point.

[0127] In one optional implementation, the estimated earliest completion time is the sum of the optimal path time, the loading time, and the queuing time.

[0128] In one optional embodiment, the apparatus further includes:

[0129] The fifth determining module is used to, if a loading point allocation instruction for the target truck is received, determine the loading point corresponding to the target truck from the loading point allocation instruction, and generate a truck scheduling instruction for the target truck so that the target truck is scheduled according to the truck scheduling instruction.

[0130] The mining truck dynamic scheduling device provided in this disclosure can execute the mining truck dynamic scheduling method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of the method execution.

[0131] This disclosure also provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the steps of the above-described dynamic scheduling method for mining trucks.

[0132] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure.

[0133] The following is a detailed reference. Figure 4 The diagram illustrates a structural schematic suitable for implementing the electronic device 400 in the embodiments of this disclosure. The electronic device 400 in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, personal digital assistants (PDAs), tablet computers (PADs), portable media players (PMPs), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0134] like Figure 4 As shown, the electronic device 400 may include a processing unit 401 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from storage device 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device 400. The processing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0135] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic device 400 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 An electronic device 400 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0136] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 409, or installed from storage device 408, or installed from ROM 402. When the computer program is executed by processing device 401, it performs the functions defined in the dynamic scheduling method for mining trucks according to embodiments of this disclosure.

[0137] It should be noted that the computer-readable medium described above in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an electrically erasable programmable read-only memory (EPROM), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, radio frequency (RF), etc., or any suitable combination thereof.

[0138] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol, such as Hypertext Transfer Protocol (HTTP), and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include Local Area Networks (LANs), Wide Area Networks (WANs), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0139] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0140] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire working status data of each loading point device; determine the optimal path time for the target truck to arrive at each loading point based on real-time traffic information; determine the loading efficiency of each loading point device based on the working status data of each loading point device; determine the loading time for the target truck at each loading point device based on the loading efficiency of each loading point device; predict the queuing situation corresponding to the target truck when it arrives at each loading point; determine the queuing time at each loading point; determine the earliest expected loading completion time of the target truck at each loading point based on the optimal path time, loading time, and queuing time; and generate a truck dispatching instruction for the target truck based on the earliest expected loading completion time, so that the target truck is dispatched according to the truck dispatching instruction.

[0141] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including local area networks (LANs) or wide area networks (WANs), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0142] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0143] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0144] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field-Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Parts (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), and so on.

[0145] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0146] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0147] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0148] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0149] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A dynamic scheduling method for mining trucks, characterized in that, The method includes: Acquire the operating status data of the equipment at each loading point; Based on real-time traffic information, determine the optimal path time for the target truck to reach each loading point; The loading efficiency of each loading point device is determined based on the working status data of each loading point device, and the loading time of each loading point device for the target truck is determined based on the loading efficiency of each loading point device. Predict the queuing situation when the target truck arrives at each loading point, and determine the queuing time at each loading point; Based on the optimal path time, the loading time, and the queuing time, the earliest estimated loading completion time for the target truck to arrive at each loading point is determined. Based on the estimated earliest loading completion time, a truck dispatching instruction is generated for the target truck so that the target truck is dispatched according to the truck dispatching instruction.

2. The method according to claim 1, characterized in that, The process of determining the optimal path time for target trucks to reach each loading point based on real-time traffic information includes: A dynamic road network topology map is constructed based on real-time traffic information, the current location information of the target truck, and the location information of each loading point; The shortest path algorithm is used to search the dynamic road network topology map to determine the optimal path time for the target truck to reach each loading point.

3. The method according to claim 1, characterized in that, The process of determining the loading efficiency of each loading point device based on its operating status data, and determining the loading time for each loading point device for the target truck based on its loading efficiency, includes: The historical loading time and historical loading volume of each loading point device are obtained from the working status data of each loading point device; The loading efficiency of the equipment at each loading point is determined based on the historical loading time and the historical loading volume; wherein, the loading efficiency of the equipment at each loading point is dynamically updated based on the loading time and loading volume of the trucks that have completed loading operations in the current shift. Based on the loading capacity of the target truck and the loading efficiency of the equipment at each loading point, the loading time for the target truck at each loading point is determined.

4. The method according to claim 1, characterized in that, Loading points include target loading points. Predicting the queuing situation when the target truck arrives at each loading point and determining the queuing time at each loading point includes: Obtain the total loading time of all queued trucks at the target loading point; Determine whether the optimal path time for the target truck to reach the target loading point is greater than the total loading time: If the optimal path time to the target loading point is greater than the total loading time, then the queuing time for the target truck is 0. If the optimal path time to the target loading point is not greater than the total loading time, then the queuing time for the target truck is the difference between the total loading time and the optimal path time to the target loading point.

5. The method according to claim 1, characterized in that, The estimated earliest completion time is the sum of the optimal path time, the loading time, and the queuing time.

6. The method according to claim 1, characterized in that, After predicting the queuing situation when the target truck arrives at each loading point and determining the queuing time at each loading point, the method further includes: If a loading point allocation instruction for the target truck is received, the loading point corresponding to the target truck is determined from the loading point allocation instruction, and a truck scheduling instruction for the target truck is generated so that the target truck is scheduled according to the truck scheduling instruction.

7. A dynamic dispatching system for mining trucks, characterized in that, Used to perform the dynamic scheduling method for mining trucks as described in any one of claims 1-6.

8. A dynamic dispatching device for mining trucks, characterized in that, The method includes: The acquisition module is used to acquire the working status data of the equipment at each loading point; The first determining module is used to determine the optimal path time for the target truck to reach each loading point based on real-time traffic information; The second determining module is used to determine the loading efficiency of each loading point device based on the working status data of each loading point device, and to determine the loading time of each loading point device for the target truck based on the loading efficiency of each loading point device. The third determining module is used to predict the queuing situation when the target truck arrives at each loading point and to determine the queuing time at each loading point. The fourth determining module is used to determine the earliest estimated loading completion time of the target truck at each loading point based on the optimal path time, the loading time, and the queuing time. The generation module is used to generate a truck dispatching instruction for the target truck based on the earliest expected loading completion time, so that the target truck is dispatched according to the truck dispatching instruction.

9. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the dynamic scheduling method for mining trucks as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for executing the dynamic scheduling method for mining trucks as described in any one of claims 1-6.

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