Mine vehicle dispatching system and method

Abstract

This invention discloses a mining vehicle dispatching system and method, belonging to the field of mining transportation dispatching technology. It includes a perception layer, a decision-making layer, an execution layer, and an interaction layer. The perception layer accurately acquires multi-source key information about vehicles and excavators and uploads this information to the decision-making layer. The decision-making layer, connected to the perception layer, constructs a vehicle-excavator digital twin mapping through data fusion, executes the tiered cyclic charging rules for electric mining trucks, and makes loading point decisions based on multi-dimensional cost modeling, generating dispatching instructions. The execution layer, connected to the decision-making layer, transmits dispatching instructions in real-time via hierarchical voice and feeds back the execution status to the decision-making layer. The interaction layer, connected to the decision-making layer, centrally displays vehicle status and dispatching progress, and supports switching from automatic dispatching mode to manual dispatching mode under triggered conditions, with manual dispatching instructions having higher priority than automatic dispatching instructions. This invention achieves efficient and accurate mining transportation dispatching.

Description

Technical Field

[0001] This invention relates to the field of mine transportation scheduling technology, specifically to a mine vehicle scheduling system and method. Background Technology

[0002] As mining operations move towards intelligence, greening, and efficiency, the mixed operation of electric and traditional fuel-powered mining trucks has become the mainstream mode of transportation in open-pit mines. With the expansion of mining area transportation scale and the increase in operating equipment, the traditional vehicle dispatching method, which relies on manual experience, has gradually revealed obvious shortcomings.

[0003] The existing mine vehicle dispatching methods have the following drawbacks: First, the system did not design adaptation logic to address the differences in power characteristics between electric and fuel-powered mining trucks. Electric mining trucks are constrained by their battery capacity, while fuel-powered mining trucks have a range advantage. The differences in power characteristics between the two types of vehicles were not fully utilized, resulting in insufficient universality of the dispatching scheme.

[0004] Second, the selection of loading points is based solely on a single queuing status, without incorporating multi-dimensional indicators such as excavator operating efficiency, road access costs, and vehicle compatibility. The existing system is prone to making suboptimal decisions, such as "short-distance but high-waiting-time" or "long-distance but inefficient loading."

[0005] Third, the status verification node is singular, only planning the loading point once after unloading, without considering the dynamic changes in mining operations. Unexpected queuing, excavator malfunctions, and other dynamic factors cause a large amount of ineffective vehicle travel, wasting transportation capacity.

[0006] Fourth, the dual-mode scheduling (automatic / manual) lacks clear switching trigger conditions and operational procedures. Manual intervention lacks data support, leading to highly subjective scheduling decisions.

[0007] Therefore, there is an urgent need to build a vehicle dispatching method that features multi-dimensional collaboration, dual-node verification, and dual-mode adaptation, taking into account both versatility and accuracy. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a mining vehicle scheduling system and method to solve the problems of low efficiency and poor accuracy in mining transportation scheduling.

[0009] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows.

[0010] The mining vehicle dispatching system and method include a perception layer, a decision-making layer, an execution layer, and an interaction layer. These layers are interconnected through a communication network to form a logical framework of "state perception - multi-dimensional modeling - dual-node verification - dual-mode execution". The perception layer is used to accurately acquire multi-source key information about vehicles and excavators, and upload the information to the decision layer; The decision layer, connected to the perception layer, is used to construct a vehicle-excavator digital twin mapping through data fusion, execute the tiered cyclic charging rules of electric mining trucks, make loading point decisions based on multi-dimensional cost modeling, and generate scheduling instructions. The execution layer is connected to the decision layer and is used to transmit scheduling instructions in real time in hierarchical voice form and to feed back the execution status to the decision layer. The interaction layer, connected to the decision layer, is used to centrally display vehicle status and scheduling progress, and supports switching from automatic scheduling mode to manual scheduling mode under trigger conditions, with manual scheduling instructions having higher priority than automatic scheduling instructions.

[0011] Preferably, the perception layer includes a vehicle status perception unit and an excavator perception unit; The vehicle status perception unit is configured differently for electric mining trucks and fuel-powered mining trucks. Electric mining trucks are equipped with a power sensor to collect the remaining power SOC value and a battery health monitoring module to monitor battery health. Fuel-powered mining trucks are equipped with a fuel quantity sensor to collect the remaining fuel quantity and a fuel consumption rate monitoring module to collect the fuel consumption rate. Both types of vehicles are uniformly equipped with a GPS positioning module and a load sensor. The excavator sensing unit is used to collect the excavator's operating status, loading speed, current number of vehicles in the queue, and loading interface type. The perception layer transmits the collected data to the decision layer in real time via a wireless network, providing data support for differentiated scheduling.

[0012] Preferably, the decision-making layer includes a data fusion module, a job monitoring and scheduling module, a charging optimization scheduling module, and a dual-mode scheduling control module; The data fusion module is used to integrate static road network data and dynamic operation data to build a unified data foundation; The operation monitoring and scheduling module is used to verify whether the SOC value of the electric mining truck is greater than or equal to the low power threshold for transportation, or whether the remaining fuel of the fuel mining truck is greater than or equal to the minimum fuel consumption for a single transportation after the vehicle unloads. The charging optimization scheduling module is used to maintain the charging queue A and the waiting queue B, and execute the tiered cyclic charging logic: when there are vehicles waiting to be charged in queue B, the module filters out vehicles in queue A with a remaining power of ≥20% SOC, and selects the vehicle with the highest remaining power to terminate charging and drive away, so as to release the charging pile. The dual-mode scheduling and control module is used to manage the switching between automatic decision-making and manual intervention, and to record the person making the adjustment, the time of adjustment, and the reason for adjustment.

[0013] Preferably, the decision-making layer further includes a cost assessment module; The cost assessment module is used to construct a cost assessment model to calculate the total time cost T. Bi The formula is T Bi =α×travel time+β×loading waiting time, where α is the weighting coefficient of travel time and β is the weighting coefficient of loading waiting time; The cost assessment module is configured to adjust the weighting coefficients according to the vehicle type: for fuel-powered mining trucks, the weighting value of travel time α is higher than that of electric mining trucks; for electric mining trucks, the weighting value of loading waiting time β is higher than that of fuel-powered mining trucks. The cost assessment module is also configured to handle situations where two or more excavators have a T... Bi If the values ​​are equal, the excavator with the shorter travel time is selected as the optimal solution.

[0014] Preferably, the execution layer includes a voice broadcast terminal and an in-vehicle confirmation terminal; The voice broadcast terminal is deployed in the vehicle's driver's cab to receive dispatch instructions and convey them in real time in a hierarchical voice format, providing key reminders for scenarios such as changes in loading points and adjustments to charging priorities. The vehicle-mounted confirmation terminal is used to send an arrival confirmation signal or an execution feedback signal to the decision-making level in response to the driver's operation. The interaction layer includes a dispatch center console and a mobile operation terminal; The dispatch center console displays vehicle status, excavator occupancy, and dispatch progress through a centralized interface. The mobile operating terminal provides a remote intervention channel, allowing dispatchers to manually select loading points and issue manual dispatch instructions when they receive emergency production orders or discover abnormal system decisions.

[0015] A method for dispatching mining vehicles, applied to a mining vehicle dispatching system, includes the following steps: S1. Vehicle operating condition initialization verification: After the vehicle completes unloading, the remaining SOC value of the electric mining car or the remaining fuel of the fuel mining car is collected to verify whether the operating conditions are met. If not, the electric mining car will perform tiered cyclic charging and the fuel car will be refueled. S2. Designated Excavator Status Assessment and Initial Decision: If the vehicle meets the operating conditions, query the excavator A specified in the schedule. If its operation is normal and the matching degree is M... A ≥Preset threshold M0 and estimated total loading time T A If the value is less than or equal to the preset threshold T0, then an instruction to proceed to excavator A is generated; otherwise, proceed to step S3. S3. Selection and Cost Modeling of Candidate Excavators: Select candidate excavators and calculate the total time cost T for each candidate excavator based on differentiated weights. Bi Select T Bi The smallest one is the optimal candidate excavator B; S4. First-stage dispatch instruction generation and execution: Generate dispatch instructions to excavator B, the vehicle travels according to the instructions, and the system tracks the vehicle's location in real time; S5. Secondary verification and dynamic correction of mining area fence boundary: When the vehicle travels to a preset distance outside the mining area fence boundary, the status of excavator B is verified. If it is queuing or malfunctioning, the optimal loading point Z is re-selected and the instructions are updated. S6. Loading completion and scheduling closed loop: When the vehicle arrives at the loading point, the system records the entire process data and stores it in the scheduling log.

[0016] Preferably, in step S1, the tiered cyclic charging specifically includes: The preset minimum allowable operating power is 20% SOC, and the target charge for a cycle is 80% SOC. Establish a queue B of vehicles waiting to be charged and sort them in ascending order by remaining battery power; establish a queue A of vehicles currently being charged. When a charging station is available, dispatch the first vehicle in queue B to enter the charging station; When there are vehicles waiting to be charged in queue B, the vehicles in queue A are sorted in descending order of remaining battery power. The vehicle with the highest remaining battery power among those with ≥20% SOC is selected, and an "emergency power-yielding" voice command is issued to it to leave. Then, the vehicle at the head of the new queue in queue B is scheduled to be charged.

[0017] Preferably, in step S3, cost modeling specifically includes: Calculate travel time T 行驶 =(L Bi / V 车 )×60, where L Bi For the vehicle's current location to the th i The actual driving distance of the candidate excavator Bi, in km; V 车 The rated speed of the vehicle, expressed in km / h; Calculate the loading waiting time T 装料等待 =(N Bi / V Bi )×60+(60 / V Bi ), where N Bi For the first i The current number of vehicles in the queue for the alternative excavator Bi, in units of vehicles; V Bi For the first i The loading speed of the alternative excavator Bi, in units of vehicles per hour; The total time cost T is calculated using weighting coefficients α=0.4 and β=0.6. Bi Calculation; If there are 2 or more excavators in the T BiIf the values ​​are equal, then the travel time is further compared, and the excavator with the shorter travel time is selected as the optimal solution.

[0018] Preferably, in step S5, the secondary verification specifically includes: If excavator B is operating normally and the current number of vehicles in the queue is N Bi =0, the system maintains the original scheduling instructions and does not repeat the voice broadcast; If excavator B has a malfunction or the current number of vehicles in the queue is N Bi If the value is ≥1, the rescheduling process will be initiated, repeating the candidate excavator selection and cost modeling logic, and generating a new scheduling instruction for excavator Z to overwrite the original instruction.

[0019] Preferably, it includes a dual-mode scheduling switching step: The automatic scheduling mode is enabled by default, and the entire process from steps S1 to S6 is executed automatically by the system. When the dispatch center detects abnormal system decisions, receives emergency production orders, or reports temporary road closures from drivers, it triggers the manual dispatch mode. The dispatcher manually selects the loading point and issues instructions through the dispatch center console or mobile operating terminal, and the system records the adjustment information. After manual scheduling is completed, the system will automatically revert to automatic scheduling mode or the scheduler can manually switch to automatic scheduling mode.

[0020] Due to the adoption of the above technical solutions, the technical progress achieved by this invention is as follows.

[0021] This invention achieves differentiated adaptation and collaborative scheduling optimization: Differentiated perception and operational condition verification logic is designed to address the differences in power characteristics between electric and fuel-powered mining trucks. In the time cost model, weight coefficients are adaptively adjusted based on vehicle type, with higher weights for fuel-powered truck travel time and higher weights for electric truck loading waiting time, achieving precise adaptation between the two types of vehicles within the same scheduling framework. Simultaneously, through accurate power prediction and a tiered charging strategy, the risk of breakdowns due to low battery levels is avoided, significantly improving the utilization rate of charging stations.

[0022] This invention improves transportation efficiency: based on a multi-dimensional weighted cost model, it dynamically avoids excavator queuing congestion, accurately plans the optimal loading point and driving route, reduces ineffective vehicle driving and queuing time, improves the overall transportation efficiency of the mine, and significantly shortens the transportation cycle.

[0023] The invention's dual-node verification improves scheduling reliability: a second status verification mechanism is introduced when a vehicle enters the boundary of the mining area fence, and the scheduling instructions are dynamically corrected according to the real-time status of the excavator, effectively avoiding invalid driving caused by sudden queuing or failure, and overcoming the defect that one-time decision-making cannot cope with dynamic changes in operations.

[0024] This invention achieves a balance between scheduling reliability and flexibility: a dual-mode scheduling system of "automatic decision-making + manual intervention" not only enables intelligent operation of the entire process in normal scenarios, reducing the cost of manual scheduling, but also allows for rapid response to emergencies such as equipment failures and task changes. It ensures the continuity and stability of transportation operations through remote manual intervention, while recording intervention information for easy traceability.

[0025] This invention improves ease of operation: the hierarchical voice broadcast mechanism provides enhanced reminders for key scheduling changes, and combined with clear instruction delivery logic, it reduces the difficulty of driver operation, reduces errors in the execution of scheduling instructions, and improves the safety factor and the quality of the scheduling loop. Attached Figure Description

[0026] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0027] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0028] A mining vehicle dispatching system and method, combined with Figure 1 As shown, it includes a perception layer, a decision-making layer, an execution layer, and an interaction layer. These layers are interconnected through a communication network to form a logical framework of "state perception - multi-dimensional modeling - dual-node verification - dual-mode execution".

[0029] The perception layer is used to accurately acquire multi-source key information about vehicles and excavators, and then upload the information to the decision layer.

[0030] Specifically, the perception layer includes a vehicle status perception unit and an excavator perception unit.

[0031] The vehicle status perception unit is configured differently for electric mining trucks and fuel-powered mining trucks. Electric mining trucks are equipped with a power sensor to collect the remaining power SOC value and a battery health monitoring module to monitor battery health. Fuel-powered mining trucks are equipped with a fuel quantity sensor to collect the remaining fuel quantity and a fuel consumption rate monitoring module to collect the fuel consumption rate. Both types of vehicles are uniformly equipped with a GPS positioning module and a load sensor.

[0032] The excavator sensing unit is used to collect the excavator's operating status (normal operation / malfunction shutdown), loading speed (vehicles / hour), current number of vehicles in the queue, and loading interface type.

[0033] The perception layer transmits the collected data to the decision-making layer in real time via a wireless network, providing data support for differentiated scheduling.

[0034] The decision-making layer, connected to the perception layer, is used to construct a digital twin mapping between vehicles and excavators through data fusion, execute the tiered cyclic charging rules of electric mining trucks, make loading point decisions based on multi-dimensional cost modeling, and generate scheduling instructions.

[0035] Specifically, the decision-making layer includes a data fusion module, an operation monitoring and scheduling module, a charging optimization scheduling module, a dual-mode scheduling and control module, and a cost assessment module.

[0036] The data fusion module is used to integrate static road network data and dynamic operation data to build a unified data foundation.

[0037] The operation monitoring and scheduling module is used to verify, after unloading, whether the SOC value of electric mining trucks is greater than or equal to the low battery threshold for transportation (preset based on the average transportation distance in the mining area, default ≥10%), or whether the remaining fuel of fuel-powered mining trucks is greater than or equal to the minimum fuel consumption for a single transportation trip (calculated based on the average transportation distance in the mining area × fuel consumption rate). The operation monitoring and scheduling module is also used to trigger charging or dispatch decisions for electric mining trucks based on the comparison of the battery threshold after unloading.

[0038] The charging optimization and scheduling module maintains the charging queue A and the waiting queue B, and executes the tiered cyclic charging logic: It monitors the charging queue A and the waiting queue B in real time. When vehicles enter queue B, they are sorted in ascending order of remaining battery power, with the vehicle at the bottom of the queue receiving priority charging access. When there are vehicles waiting to be charged in queue B, vehicles in queue A with remaining battery power ≥ the preset minimum allowable operating battery power (20% SOC) are selected, and the vehicle with the highest remaining battery power is terminated and leaves, releasing the charging station. Charging automatically stops when a vehicle reaches the preset cyclic charging target of 80% SOC. After the removed vehicle leaves, the charging station is released, queue B automatically reorders vehicles in ascending order of remaining battery power, and immediately schedules a new vehicle to enter the charging queue, with the charging target remaining at 80% SOC.

[0039] The dual-mode scheduling control module manages the switching between automatic decision-making and manual intervention, and records the person making the adjustment, the time of adjustment, and the reason for the adjustment. Manual scheduling instructions have higher priority than automatic scheduling instructions.

[0040] The cost assessment module is used to build a cost assessment model and calculate the total time cost T. Bi The formula is T Bi =α×travel time + β×loading waiting time, where α is the weighting coefficient of travel time and β is the weighting coefficient of loading waiting time.

[0041] The cost assessment module is configured to adjust weighting coefficients based on vehicle type: for fuel-powered mining trucks, the travel time weight α is higher than that for electric mining trucks; for electric mining trucks, the loading waiting time weight β is higher than that for fuel-powered mining trucks. These weighting coefficients can be parameterized through the dispatch center to adapt to the different mines' "distance priority" or "waiting priority" operational preferences.

[0042] The cost assessment module is also configured to handle T-values ​​from two or more excavators. Bi If the values ​​are equal, the excavator with the shorter travel time is selected as the optimal solution.

[0043] The execution layer, connected to the decision-making layer, is used to transmit scheduling instructions in real time in hierarchical voice format and to feed back the execution status to the decision-making layer.

[0044] Specifically, the execution layer includes a voice broadcast terminal and an in-vehicle confirmation terminal.

[0045] The voice broadcast terminal, deployed in the vehicle's driver's cab, receives dispatch instructions and delivers them in real-time using a tiered voice format, providing focused reminders for scenarios such as changes in loading points and adjustments to charging priorities. Specifically, the tiered voice format uses a normal volume for initial dispatch instructions and a higher volume for dispatch change instructions.

[0046] The vehicle-mounted confirmation terminal is used to send an arrival confirmation signal or execution feedback signal to the decision-making level in response to the driver's operation.

[0047] The interaction layer, connected to the decision-making layer, is used to centrally display vehicle status and scheduling progress, and supports switching from automatic scheduling mode to manual scheduling mode under triggered conditions.

[0048] Specifically, the interaction layer includes the dispatch center console and mobile operation terminals.

[0049] The dispatch center console displays vehicle status, excavator occupancy, and dispatch progress through a centralized interface.

[0050] The mobile operating terminal provides a remote intervention channel, allowing dispatchers to manually select loading points and issue manual dispatch instructions when receiving urgent production orders or detecting system decision anomalies. If no loading point is available, the system pushes an alarm to the dispatch center, and the vehicle remains in place awaiting manual intervention.

[0051] A method for dispatching mining vehicles, applied to a mining vehicle dispatching system, such as... Figure 2 As shown, it includes the following steps: S1. Vehicle Operation Condition Initialization Verification: After the vehicle completes unloading, the on-board sensing unit collects core data and uploads it to the dispatch system. This includes collecting the remaining SOC value, battery health, current location coordinates, vehicle load (no-load confirmation), and vehicle model (matching the excavator loading interface) of the electric mining truck, or the remaining fuel quantity, fuel consumption rate, current location coordinates, vehicle load (no-load confirmation), and vehicle model of the fuel mining truck. The system verifies whether the operation conditions are met. If not, the electric mining truck performs a tiered cyclic charging, and the fuel truck is refueled or repaired.

[0052] The specific components of tiered cyclic charging include: The preset minimum allowable operating power is 20% SOC, and the target charge for a cycle is 80% SOC. The charging optimization scheduling module monitors the charging queue A and the waiting-to-charge queue B in real time. Establish a queue B of vehicles waiting to be charged and sort them in ascending order of remaining battery power. The vehicle with the lowest battery power is placed at the front of the queue and has priority to obtain charging rights. Establish a queue A of vehicles that are currently being charged. When a charging station is idle, the vehicle at the head of queue B is dispatched to enter the charging station, and queue B is automatically reordered in ascending order of remaining battery power to continue dispatching new vehicles at the head of the queue to charge. When there are vehicles waiting to be charged in queue B, the vehicles in queue A are sorted in descending order of remaining battery power. The vehicle with the highest remaining battery power among those with ≥20% SOC is selected and given a voice command "Emergency power supply, minimum working power has been met, please leave the charging station immediately" is issued to the vehicle to be dispatched. After the vehicle leaves the charging area, step S3 is entered to make a decision, and then the vehicle at the head of the new queue in queue B is dispatched to charge.

[0053] S2. Designated Excavator Status Assessment and Initial Decision: If the vehicle meets the operating conditions, query the scheduled excavator A. The dispatching system queries the real-time status data (collected by the excavator sensing unit) of the target excavator (denoted as excavator A) specified in the vehicle's scheduling plan, including the operating status (normal operation / fault shutdown) and loading speed (vehicles / hour, denoted as V). A The current number of vehicles in the queue (denoted as N) A The compatibility between the excavator's loading interface type and the current vehicle model (denoted as M). A (Match = 1.0, Partial match = 0.8, No match = 0).

[0054] Calculate the estimated total loading time T for excavator A. A :T A =(N A / V A )×60+(60 / V A(Unit: minutes), i.e., the estimated total loading time = queuing time + loading time per vehicle. Loading time per vehicle = 60 minutes / loading speed V A Queue waiting time = number of vehicles in queue N A × Loading time per vehicle.

[0055] If its operation is normal and the matching degree is M A ≥0.8 and the estimated total loading time T A If the value is less than or equal to the preset threshold T0 (default T0 = 15 minutes), then an instruction to proceed to excavator A is generated; otherwise, proceed to step S3.

[0056] S3. Selection of Candidate Excavators and Cost Modeling: The scheduling system selects all candidate excavators (denoted as set {B1, B2, ..., Bn}) from the mining area excavator database that meet the following conditions: normal operating status and a loading interface matching degree ≥ 0.8 with the current vehicle model. The selection of candidate excavators involves collecting real-time status data for each candidate excavator Bi, including excavator position coordinates and loading speed V. Bi (vehicles / hour), current number of vehicles in the queue N Bi And the vehicle's current position coordinates and the vehicle's rated driving speed V 车 The total time cost T for each candidate excavator is calculated based on differentiated weights. Bi Select T Bi The smallest one is the optimal candidate excavator B.

[0057] Cost modeling specifically includes: Calculate travel time T 行驶 =(L Bi / V 车 )×60, where L Bi For the vehicle's current location to the th i The actual travel distance of the candidate excavator Bi, in km, is the shortest path length calculated by the GPS positioning module, directly using the actual road network distance; V 车 The rated speed of the vehicle, expressed in km / h; Calculate the loading waiting time T 装料等待 =(N Bi / V Bi )×60+(60 / V Bi ), where N Bi For the first i The current number of vehicles in the queue for the alternative excavator Bi, in units of vehicles; V Bi For the first i The loading speed of the alternative excavator Bi, in units of vehicles per hour; Total time cost T Bi=α × travel time + β × loading waiting time, with weighting coefficients α=0.4 and β=0.6 for the total time cost T. Bi The calculation (β weight is higher than α, giving priority to ensuring the core need of "less waiting", which is in line with the core goal of mine transportation to "reduce idling waiting and improve operational efficiency"). The total time cost T of the scheduling system for all candidate excavators Bi Sort in ascending order, select T Bi The smallest excavator is selected as the optimal candidate. If there are two or more excavators with a T... Bi If the values ​​are equal, the travel time is further compared, and the excavator with the shorter travel time is selected as the optimal solution to ensure route economy.

[0058] S4. First-stage dispatch instruction generation and execution: Generate a dispatch instruction to excavator B. The dispatch instruction is broadcast in a low volume via a voice broadcast terminal as "Go to excavator B for loading". The vehicle moves according to the instruction. The system tracks the vehicle's position in real time and monitors the status changes of excavator B (queue number, loading speed).

[0059] S5. Secondary verification and dynamic correction of mining area fence boundary: When the vehicle travels to a preset distance outside the mining area fence boundary (the trigger distance is 50 meters outside the fence boundary to ensure time redundancy for verification and instruction issuance), the status of excavator B is verified. If it is queuing or malfunctioning, the optimal loading point Z is reselected and the instruction is updated.

[0060] The second verification specifically includes: The system initiates a second status check. If excavator B is operating normally and the current queue number N vehicles is... Bi =0 (no queue), the system maintains the original scheduling instructions, does not repeat the voice broadcast, and the vehicle continues to proceed to excavator B for loading along the original route; If excavator B has a malfunction or the current number of vehicles in the queue is N Bi If ≥1 (queueing occurs), the rescheduling process is initiated, repeating the candidate excavator selection and cost modeling logic, generating a new scheduling instruction for excavator Z to overwrite the original instruction, and broadcasting a new voice prompt "Loading point has been updated to excavator Z".

[0061] S6. Loading completion and scheduling closed loop: When the vehicle arrives at the loading point, the on-board terminal sends a "arrival at loading point" confirmation signal to the scheduling system. The system records the entire scheduling process data (loading point selection process, status change record, execution time) and stores it in the scheduling log for subsequent algorithm optimization and traceability.

[0062] This method also includes a dual-mode scheduling switching step: The automatic scheduling mode is enabled by default, and the entire process from S1 to S6 is executed automatically by the system without manual intervention. Triggering conditions include: when the dispatch center detects abnormal system decision-making (such as deviation in comprehensive cost calculation), receives an emergency production order (such as a certain excavator requiring priority allocation), or when the driver reports a special situation (such as temporary road closure), the manual dispatch mode is triggered. The dispatcher manually selects the loading point and issues instructions through the dispatch center console or mobile operating terminal, and the system records the adjustment information. Access control: Manual scheduling instructions have higher priority than automatic scheduling instructions, and the system records adjustment information after execution for easy traceability; After manual scheduling is completed, the system will automatically revert to automatic scheduling mode or the scheduler can manually switch to automatic scheduling mode.

[0063] The working principle of this invention is as follows: This intelligent scheduling method is based on the logical framework of "state perception - multi-dimensional modeling - dual-node verification - dual-mode execution". By collecting real-time data of vehicles (electric / fuel) and excavators, a cost evaluation model is constructed. The scheduling plan is corrected by verifying the state twice after unloading and at the boundary of the mining area fence, so as to ensure the effectiveness of the decision.

[0064] The perception layer collects multi-source status data of vehicles and excavators in real time through differentiated sensors (electric power sensor and battery health monitoring module for electric mining trucks, fuel quantity sensor and fuel consumption rate monitoring module for fuel mining trucks, and uniformly equipped GPS positioning module and load sensor), and uploads it to the decision-making layer via wireless network to build the data foundation for scheduling decisions.

[0065] The decision-making layer's data fusion module integrates the received real-time data with static road network data to construct a vehicle-excavator digital twin mapping, forming a unified data foundation. The operation monitoring and scheduling module performs initialization checks after vehicle unloading: electric mining trucks check if their SOC value is ≥ the low battery threshold for transportation (default ≥ 10%), and fuel-powered mining trucks check if their remaining fuel level is ≥ the minimum fuel consumption for a single transport. Electric mining trucks that do not meet the conditions enter the charging optimization scheduling module for tiered cyclic charging, while fuel-powered trucks are refueled or undergo maintenance.

[0066] The loading point decision-making process is divided into two stages: In the first stage, the operation monitoring and scheduling module queries the scheduled excavator A and calculates the estimated total loading time T. A And determine whether the direct scheduling conditions are met. If the specified excavator is operating normally, the matching degree meets the standard, and T... A If the time is ≤15 minutes, direct scheduling is performed; otherwise, the cost assessment module is triggered to select candidate excavators from the mining excavator database that are operating normally and have a suitability of ≥0.8, based on the total time cost model T. BiThe comprehensive time cost of each candidate excavator is calculated as α × travel time + β × loading waiting time, and the excavator with the lowest cost is selected as the target excavator. The weighting coefficients α and β are configured differently according to the vehicle type. The travel time of fuel-powered mining trucks has a higher weight, while the loading waiting time of electric mining trucks has a higher weight. The weights can be parameterized by the dispatch center to adapt to the different mines' "distance priority" or "waiting priority" operation preferences.

[0067] In the second phase, the vehicle drives towards the target excavator according to the dispatch instructions. When it reaches 50 meters outside the mining area fence boundary, the system initiates a second status check to re-acquire the real-time queuing and operation status of the target excavator: if the operation is normal and there is no queuing, the original instructions are maintained; if queuing or a malfunction occurs, the screening logic of the cost evaluation module is re-executed to generate an updated optimal loading point and overwrite the original instructions. This dual-node verification mechanism effectively addresses the dynamic changes in the operation status during vehicle movement.

[0068] The dual-mode scheduling control module manages the switching between automatic scheduling and manual intervention modes: During normal operation, the system runs in automatic mode; when there is an abnormal system decision, an emergency production order, or special circumstances reported by the driver, the dispatcher can manually issue a manual scheduling order through the interaction layer. The manual order has higher priority than the automatic order, and the entire operation is recorded and traceable (recording the person making the adjustment, the time of adjustment, and the reason for the adjustment). After completion, the system automatically returns to automatic mode.

[0069] The execution layer uses a voice broadcast terminal deployed in the driver's cab to convey dispatch instructions in real time in a hierarchical voice format: the initial dispatch instructions are broadcast at a normal volume, and the dispatch change instructions are broadcast at an enhanced volume; when there are no available loading points, an alarm is pushed to the dispatch center, and the driver provides feedback on the execution result through the on-board confirmation terminal, forming a complete dispatch closed loop.

[0070] After the vehicle arrives at the excavator, the onboard terminal sends a "arrival at loading point" confirmation signal to the dispatch system. The system records the entire dispatch process data (loading point selection process, status change records, execution time) and stores it in the dispatch log for subsequent algorithm optimization and traceability.

Claims

1. A mining vehicle dispatching system, characterized in that: It includes a perception layer, a decision-making layer, an execution layer, and an interaction layer. These layers are interconnected through a communication network, forming a logical framework of "state perception - multi-dimensional modeling - dual-node verification - dual-mode execution". The perception layer is used to accurately acquire multi-source key information about vehicles and excavators, and upload the information to the decision layer; The decision layer, connected to the perception layer, is used to construct a vehicle-excavator digital twin mapping through data fusion, execute the tiered cyclic charging rules of electric mining trucks, make loading point decisions based on multi-dimensional cost modeling, and generate scheduling instructions. The execution layer is connected to the decision layer and is used to transmit scheduling instructions in real time in hierarchical voice form and to feed back the execution status to the decision layer. The interaction layer, connected to the decision layer, is used to centrally display vehicle status and scheduling progress, and supports switching from automatic scheduling mode to manual scheduling mode under trigger conditions, with manual scheduling instructions having higher priority than automatic scheduling instructions.

2. The mining vehicle dispatching system according to claim 1, characterized in that: The perception layer includes a vehicle status perception unit and an excavator perception unit; The vehicle status perception unit is configured differently for electric mining trucks and fuel-powered mining trucks. Electric mining trucks are equipped with a power sensor to collect the remaining power SOC value and a battery health monitoring module to monitor battery health. Fuel-powered mining trucks are equipped with a fuel quantity sensor to collect the remaining fuel quantity and a fuel consumption rate monitoring module to collect the fuel consumption rate. Both types of vehicles are uniformly equipped with a GPS positioning module and a load sensor. The excavator sensing unit is used to collect the excavator's operating status, loading speed, current number of vehicles in the queue, and loading interface type. The perception layer transmits the collected data to the decision layer in real time via a wireless network, providing data support for differentiated scheduling.

3. The mining vehicle dispatching system according to claim 1, characterized in that: The decision-making layer includes a data fusion module, an operation monitoring and scheduling module, a charging optimization scheduling module, and a dual-mode scheduling and control module. The data fusion module is used to integrate static road network data and dynamic operation data to build a unified data foundation; The operation monitoring and scheduling module is used to verify whether the SOC value of the electric mining truck is greater than or equal to the low power threshold for transportation, or whether the remaining fuel of the fuel mining truck is greater than or equal to the minimum fuel consumption for a single transportation after the vehicle unloads. The charging optimization scheduling module is used to maintain the charging queue A and the waiting queue B, and execute the tiered cyclic charging logic: when there are vehicles waiting to be charged in queue B, the module filters out vehicles in queue A with a remaining power of ≥20% SOC, and selects the vehicle with the highest remaining power to terminate charging and drive away, so as to release the charging pile. The dual-mode scheduling and control module is used to manage the switching between automatic decision-making and manual intervention, and to record the person making the adjustment, the time of adjustment, and the reason for adjustment.

4. The mining vehicle dispatching system according to claim 3, characterized in that: The decision-making layer also includes a cost assessment module; The cost assessment module is used to construct a cost assessment model to calculate the total time cost T. Bi The formula is T Bi =α×travel time+β×loading waiting time, where α is the weighting coefficient of travel time and β is the weighting coefficient of loading waiting time; The cost assessment module is configured to adjust the weighting coefficients according to the vehicle type: for fuel-powered mining trucks, the weighting value of travel time α is higher than that of electric mining trucks; for electric mining trucks, the weighting value of loading waiting time β is higher than that of fuel-powered mining trucks. The cost assessment module is also configured to handle situations where two or more excavators have a T... Bi If the values ​​are equal, the excavator with the shorter travel time is selected as the optimal solution.

5. The mining vehicle dispatching system according to claim 1, characterized in that: The execution layer includes a voice broadcast terminal and an in-vehicle confirmation terminal; The voice broadcast terminal is deployed in the vehicle's driver's cab to receive dispatch instructions and convey them in real time in a hierarchical voice format, providing key reminders for scenarios such as changes in loading points and adjustments to charging priorities. The vehicle-mounted confirmation terminal is used to send an arrival confirmation signal or an execution feedback signal to the decision-making level in response to the driver's operation. The interaction layer includes a dispatch center console and a mobile operation terminal; The dispatch center console displays vehicle status, excavator occupancy, and dispatch progress through a centralized interface. The mobile operating terminal provides a remote intervention channel, allowing dispatchers to manually select loading points and issue manual dispatch instructions when they receive emergency production orders or discover abnormal system decisions.

6. A mining vehicle dispatching method, applied to the mining vehicle dispatching system according to any one of claims 1 to 5, characterized in that, Includes the following steps: S1. Vehicle operating condition initialization verification: After the vehicle completes unloading, the remaining SOC value of the electric mining car or the remaining fuel of the fuel mining car is collected to verify whether the operating conditions are met. If not, the electric mining car will perform tiered cyclic charging and the fuel car will be refueled. S2. Designated Excavator Status Assessment and Initial Decision: If the vehicle meets the operating conditions, query the excavator A specified in the schedule. If its operation is normal and the matching degree is M... A ≥Preset threshold M0 and estimated total loading time T A If the value is less than or equal to the preset threshold T0, then an instruction to proceed to excavator A is generated; otherwise, proceed to step S3. S3. Selection and Cost Modeling of Candidate Excavators: Select candidate excavators and calculate the total time cost T for each candidate excavator based on differentiated weights. Bi Select T Bi The smallest one is the optimal candidate excavator B; S4. First-stage dispatch instruction generation and execution: Generate dispatch instructions to excavator B, the vehicle travels according to the instructions, and the system tracks the vehicle's location in real time; S5. Secondary verification and dynamic correction of mining area fence boundary: When the vehicle travels to a preset distance outside the mining area fence boundary, the status of excavator B is verified. If it is queuing or malfunctioning, the optimal loading point Z is re-selected and the instructions are updated. S6. Loading completion and scheduling closed loop: When the vehicle arrives at the loading point, the system records the entire process data and stores it in the scheduling log.

7. The mining vehicle dispatching method according to claim 6, characterized in that: In step S1, the tiered cyclic charging specifically includes: The preset minimum allowable operating power is 20% SOC, and the target charge for a cycle is 80% SOC. Establish a queue B of vehicles waiting to be charged and sort them in ascending order by remaining battery power; establish a queue A of vehicles currently being charged. When a charging station is available, dispatch the first vehicle in queue B to enter the charging station; When there are vehicles waiting to be charged in queue B, the vehicles in queue A are sorted in descending order of remaining battery power. The vehicle with the highest remaining battery power among those with ≥20% SOC is selected and given an "emergency power-yielding" voice command to leave. Then, the vehicle at the head of the new queue in queue B is scheduled to be charged.

8. The mining vehicle dispatching method according to claim 6, characterized in that: In step S3, cost modeling specifically includes: Calculate travel time T 行驶 =(L Bi / V 车 )×60, where L Bi For the vehicle's current location to the th i The actual driving distance of the candidate excavator Bi, in km; V 车 The rated speed of the vehicle, expressed in km / h; Calculate the loading waiting time T 装料等待 =(N Bi / V Bi )×60+(60 / V Bi ), where N Bi For the first i The current number of vehicles in the queue for the alternative excavator Bi, in units of vehicles; V Bi For the first i The loading speed of the alternative excavator Bi, in units of vehicles per hour; The total time cost T is calculated using weighting coefficients α=0.4 and β=0.

6. Bi Calculation; If there are 2 or more excavators in the T Bi If the values ​​are equal, then the travel time is further compared, and the excavator with the shorter travel time is selected as the optimal solution.

9. The mining vehicle dispatching method according to claim 6, characterized in that: In step S5, the secondary verification specifically includes: If excavator B is operating normally and the current number of vehicles in the queue is N Bi =0, the system maintains the original scheduling instructions and does not repeat the voice broadcast; If excavator B has a malfunction or the current number of vehicles in the queue is N Bi If the value is ≥1, the rescheduling process will be initiated, repeating the candidate excavator selection and cost modeling logic, and generating a new scheduling instruction for excavator Z to overwrite the original instruction.

10. The mining vehicle scheduling method according to claim 6, characterized in that: It also includes a dual-mode scheduling switching step: The automatic scheduling mode is enabled by default, and the entire process from steps S1 to S6 is executed automatically by the system. When the dispatch center detects abnormal system decisions, receives emergency production orders, or reports temporary road closures from drivers, it triggers the manual dispatch mode. The dispatcher manually selects the loading point and issues instructions through the dispatch center console or mobile operating terminal, and the system records the adjustment information. After manual scheduling is completed, the system will automatically revert to automatic scheduling mode or the scheduler can manually switch to automatic scheduling mode.

Images