A battery replacement decision method for an electric unmanned mine car

Abstract

The present application relates to a kind of electric unmanned mine car's power swap decision method, belong to power swap decision technical field, this method includes: constructing electric unmanned mine car simulation system, set the relevant static parameters of electric unmanned mine car;Establish multi-objective function, determine the actual situation of current production site, set the relevant dynamic parameters of electric unmanned mine car in mine car, excavator and power swap station;The parameter space related to electric unmanned mine car static scene is divided into grid, and initial parameter combination is input into electric unmanned mine car simulation system;Start electric unmanned mine car simulation system to start grid search;Output simulation result, record the effective operation time of vehicle fleet and power swap station team leader time sequence standard deviation;Determine whether grid search ends, if the determination result is grid search ends, determine the optimal parameter combination based on entropy weight method, determine power swap decision function according to the optimal parameter combination, according to power swap decision function guide mine car power swap scheduling.

Description

Technical Field

[0001] This invention belongs to the field of battery swapping decision-making technology, and particularly relates to a battery swapping decision-making method for an electric unmanned mining truck. Background Technology

[0002] With the development of autonomous driving technology, the mining transportation sector is entering a new stage of large-scale application. Compared with traditional manual driving, autonomous driving technology effectively solves the problem of driver fatigue caused by long-term operation in harsh environments and significantly reduces the risk of safety accidents. This technological revolution increases effective operating time, greatly improves the overall transportation efficiency of mines, and can create more considerable economic benefits for enterprises. However, as an emerging technology, current unmanned mining truck systems still face challenges in terms of technological maturity in some details, and urgently need to be improved through continuous technological innovation and practical verification.

[0003] Furthermore, the high fuel costs of traditional fuel-powered trucks severely restrict the progress of mines in improving production efficiency. Currently, a type of battery-swapping mining truck is gradually being introduced into mining operations. Thousands of electric mining trucks with battery swapping capabilities are already in operation worldwide. This type of mining truck is of great significance for extending range and reducing downtime. [1] However, due to the short operational time of battery-swapping mining trucks, the corresponding technology is not yet fully mature, and exploration in related fields is still ongoing.

[0004] In the research on battery swapping methods for electric unmanned mining trucks, existing studies mainly focus on the establishment of multi-objective scheduling models: Amini et al. [2] A bi-objective linear mathematical model was developed to reduce the total delay time of outbound mining cars and maximize the reliability of mining car operations; T. Chargui et al. [3] A mixed-integer multi-objective model is proposed to minimize the delay of mining cars entering and leaving the station and reduce the consumption during the transfer process.

[0005] Most existing technologies are based on fixed thresholds (such as switching batteries when the battery level drops below a certain point) or simple rules (such as shortest path priority), focusing on a combination of modeling and heuristic algorithms. They mainly rely on historical data or single objectives, lacking adaptive optimization for dynamic operating conditions such as slope and load. Static thresholds are difficult to adapt to complex mining environments. Switching batteries too early wastes battery cycles, while switching them too late may cause operational interruptions. Failure to fully consider the real-time load of battery swapping stations can easily lead to congestion or resource constraints, greatly limiting the efficiency of global scheduling. Existing simulation optimization is rarely applied to dynamic decision-making during the operation of unmanned mining trucks, resulting in poor real-time responsiveness and difficulty in dynamically adjusting battery swapping parameters in real time based on actual operating conditions, greatly limiting the ability to meet the high-frequency decision-making needs of mining trucks.

[0006] [1]Xiao, Y.; Zhou, W.; Luan, B.; Yang, K.; Yang, Y. TruckTransportation Scheduling for a New Transport Mode of Battery-Swapping Trucks in Open-Pit Mines. Appl. Sci. 2024, 14, 10185.

[0007] [2]Amini, A.; Tavakkoli-Moghaddam, R. A bi-objective truck scheduling problem in a cross-docking center with probability of breakdown for trucks. Comput. Ind. Eng. 2016, 96, 180–191

[0008] [3]Chargui, T.; Bekrar, A.; Reghioui, M.; Trentesaux, D. Multi-objective Truck Scheduling in a Physical Internet Road-Road Crossdocking Hub. In Proceedings of the 17th IFAC Symposium on Information Control Problems inManufacturing (INCOM), Budapest, Hungary, 7–9 June 2021; pp. 647–652. Summary of the Invention

[0009] In view of the shortcomings of the existing technology, the purpose of the invention is to provide a battery swapping decision-making method for electric unmanned mining trucks, which adjusts the battery swapping decision in real time according to the actual situation on the production site, greatly reducing the risks of resource waste and task interruption. Through the design and application of the simulation system, it breaks through the traditional offline optimization mode, enhances the mobility of transportation and battery swapping, and improves production continuity.

[0010] In a first aspect, the present invention proposes a battery swapping decision-making method for electric unmanned mining trucks, comprising six steps S1 to S6:

[0011] S1: Obtain static scene information of the electric unmanned mining truck, construct an electric unmanned mining truck simulation system, and set the relevant static parameters of the electric unmanned mining truck;

[0012] S2: To maximize the total time for all mining trucks to perform transport tasks and minimize the fluctuation of the queue length at the battery swapping station, a multi-objective function is established. The actual situation at the current production site is determined, and the relevant dynamic parameters of the mining trucks, excavators, and battery swapping stations in the electric unmanned mining trucks are set according to the actual situation at the current production site.

[0013] S3: Divide the parameter space related to the static scene of the electric unmanned mining truck into a grid, and input the initial parameter combination into the electric unmanned mining truck simulation system;

[0014] S4: Start the electric unmanned mining truck simulation system to begin grid search;

[0015] S5: Output simulation results, recording the effective operating time of the fleet and the standard deviation of the time series of the battery swapping station team leader;

[0016] S6: Determine whether the grid search has ended. If the result is that the grid search has ended, determine the optimal parameter combination based on the entropy weight method, determine the battery swapping decision function based on the optimal parameter combination, and guide the battery swapping scheduling of mining trucks based on the battery swapping decision function.

[0017] The initial parameter combination consists of the initial values ​​of the power consumption impact weight, the maximum allowable queue length, and the battery swapping decision threshold. The optimal parameter combination consists of the optimal values ​​of the power consumption impact weight, the maximum allowable queue length, and the battery swapping decision threshold, expressed as follows: .

[0018] Furthermore, the aforementioned method for battery swapping decisions in an electric unmanned mining truck also includes:

[0019] If the result indicates that the grid search has not ended, update the parameter combination and repeat steps S4 to S6.

[0020] Furthermore, in the above-mentioned battery swapping decision method for electric unmanned mining trucks, static scene information of the electric unmanned mining trucks is obtained, an electric unmanned mining truck simulation system is constructed, and relevant static parameters of the electric unmanned mining trucks are set, including:

[0021] Obtain detailed data and basic parameters of the mining farm;

[0022] The detailed data and basic parameters of the mine are mapped to the simulation system to build an electric unmanned mining truck simulation system;

[0023] Set the necessary data for the electric unmanned mining truck simulation system;

[0024] The detailed data of the mine includes at least: the transportable routes of the mine, the location of the battery swapping station, and the location of the spoil heap; the basic parameters include at least: the number of mine cars, the battery capacity of the mine cars, the number of spare batteries at the battery swapping station, the number of excavators, the excavator loading point, the excavator loading speed, the excavator loading time, the excavator unloading point, the excavator unloading speed, the excavator unloading time, the machine changeover cycle, the machine changeover downtime, and the number of spoil heaps; the necessary data includes at least: the status of mine cars and battery swapping stations, the status of excavators and spoil heaps, the distance traveled during the transportation process, and the speed and power consumption of mine cars under specific circumstances.

[0025] Furthermore, in the above-mentioned battery swapping decision-making method for electric unmanned mining trucks, the multi-objective function is formulated with the objectives of maximizing the total time for all mining trucks to perform transportation tasks and minimizing the fluctuation of queue length at battery swapping stations as follows:

[0026]

[0027] Determine the current actual conditions at the production site, and based on these conditions, set the relevant dynamic parameters for the mining trucks, excavators, and battery swapping stations in the electric unmanned mining truck system, including:

[0028] Determine the relevant parameters of the mining truck, and set the simulation status, location, and initial power of the online mining truck;

[0029] Determine the relevant parameters of the excavator and set the status and location information of the excavator;

[0030] Determine the relevant parameters of the battery swapping station, and set the backup battery capacity and quantity for the battery swapping station;

[0031] in, They represent parameter combinations respectively. The weighting of electricity consumption, the maximum allowable queue length, and the battery swapping decision threshold are all important factors in this context. They represent parameter combinations respectively. The index score is the standard deviation of the time series of effective operation time of the fleet and the queue length of the battery swapping station. These represent the weights of the scores for the effective operation time of the fleet and the standard deviation of the queue length of the battery swapping station in the multi-objective evaluation function.

[0032] Furthermore, in the aforementioned battery swapping decision-making method for an electric unmanned mining truck, the initial parameter combination is input into the electric unmanned mining truck simulation system, including:

[0033] Input the initial values ​​of the power consumption impact weight, the maximum allowable queue length, and the battery swapping decision threshold into the electric unmanned mining truck simulation system;

[0034] Start the electric unmanned mining truck simulation system to begin grid search, including:

[0035] Step 1: Set the simulation duration and search step size for the electric unmanned mining truck, and initialize the mining truck information, excavator information, and battery swapping station information;

[0036] Step 2: Update the simulation time pointer;

[0037] Step 3: Select the online mining trucks in sequence and set them as the current mining trucks;

[0038] Step 4: Determine the operating logic based on the simulation status of the mining truck;

[0039] Step 5: Determine whether all mining trucks have been traversed. If the result is that all mining trucks have been traversed, update the excavator status, excavator location, and backup battery power at the battery swapping station. If the result is that not all mining trucks have been traversed, return to Step 3.

[0040] Step 6: Determine whether the simulation time exceeds the set simulation duration. If the result is that the simulation time exceeds the set simulation duration, output the simulation result; if the result is that the simulation time does not exceed the set simulation duration, return to Step 3.

[0041] Furthermore, in the above-mentioned battery swapping decision-making method for electric unmanned mining trucks, the optimal parameter combination is determined based on the entropy weight method, including:

[0042] Range standardization parameter combination The effective operating time of the fleet and the standard deviation of the queue length time at the battery swapping station;

[0043] Based on the parameter combination after range standardization The effective operating time of the fleet and the standard deviation of the queue length of the battery swapping station are determined by the parameter combination. The index score is based on the effective working time and the standard deviation of the queue length time series at the battery swapping station.

[0044] Based on parameter combinations The index score of the effective working time and the standard deviation of the queue length time series of the battery swapping station is determined in the parameter combination. The entropy of the standard deviation of the effective working time of the vehicle convoy and the queue length of the battery swapping station;

[0045] Based on parameter combinations The entropy of the time series standard deviation of the effective operating time of the vehicle fleet and the queue length of the battery swapping station determines the coefficient of variation of the time series standard deviation of the effective operating time of the vehicle fleet and the queue length of the battery swapping station.

[0046] The weights of the standard deviations of the time series of effective fleet operation time and queue length at battery swapping stations are determined based on the coefficient of variation of these two values.

[0047] The multi-objective function value is determined based on the index scores of the effective operation time of the fleet and the standard deviation of the queue length of the battery swapping station, as well as the weights of the effective operation time of the fleet and the standard deviation of the queue length of the battery swapping station.

[0048] The optimal parameter combination is determined by identifying the parameter combination that corresponds to the largest multi-objective function value.

[0049] Furthermore, in the above-mentioned battery swapping decision method for electric unmanned mining trucks, the battery swapping decision function is determined based on the optimal parameter combination using the following formula:

[0050]

[0051] in, , These represent the weights of the impact of electricity consumption and the impact of the queue, respectively. This indicates the maximum remaining battery power threshold. Indicates the minimum remaining battery power threshold. This indicates the maximum allowed queue length. This indicates the current remaining power of the mining truck. This indicates the current queue length at the battery swapping station. This represents the value of the battery swapping decision function, where the sum of the weights affecting battery capacity and queue size is 1.

[0052] Furthermore, in the above-mentioned method for battery swapping decision-making of electric unmanned mining trucks, the battery swapping scheduling of mining trucks is guided by the battery swapping decision function, including:

[0053] Determine the value of the battery swapping decision function and the battery swapping decision threshold to guide the scheduling of battery swapping for mining trucks;

[0054] If the judgment result is that the battery swapping decision function value is greater than or equal to the battery swapping decision threshold, the mining truck will go to the battery swapping station to swap batteries; if the judgment result is that the battery swapping decision function value is less than the battery swapping decision threshold, the mining truck will not swap batteries.

[0055] A second aspect of the present invention also provides an electronic device comprising: a processor and a memory;

[0056] The processor executes a battery swapping decision method for an electric unmanned mining truck, such as one of the above methods, by calling programs or instructions stored in memory.

[0057] In a third aspect, the present invention also provides a computer-readable storage medium storing a program or instructions that cause a computer to execute a battery swapping decision method for an electric unmanned mining vehicle as described in any of the preceding claims.

[0058] The beneficial effects of this invention are as follows:

[0059] 1) A battery swapping decision function based on multi-parameter collaborative optimization was designed to determine whether a mining truck should enter a battery swapping station. The specific battery swapping decision function depends on the current queue length of the battery swapping station and the current remaining power of the mining truck. The application of the battery swapping decision function can dynamically adjust the battery swapping trigger threshold, avoiding the one-size-fits-all defects of traditional strategies. It comprehensively considers time and resource factors for adjustment, overcoming the problem of low resource utilization efficiency caused by using static thresholds.

[0060] 2) Simulation can generate massive amounts of data at a low cost to verify the applicability of the battery swapping decision-making method in various scenarios. By dynamically inputting real-time running data into the simulation system, the method verification can be completed in a short time, overcoming the drawback of low actual deployment performance under traditional methods.

[0061] 3) Embed the new battery swapping decision logic into a simulation that conforms to the actual on-site work. By real-time detection of the queuing status of the battery swapping station and the battery power of the mining truck, the simulation parameters are dynamically adjusted to achieve accurate modeling, correct theoretical prediction deviations in a timely manner, quantify and evaluate multi-dimensional indicators in the simulation, find the optimal solution under the trade-off, improve overall efficiency, establish a congestion prevention mechanism, overcome the single decision-making method of traditional battery swapping methods, improve battery utilization and reduce task interruption rate. Attached Figure Description

[0062] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. It is obvious that the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings.

[0063] Figure 1 A diagram illustrating a battery swapping decision-making method for an electric unmanned mining truck provided in an embodiment of the present invention;

[0064] Figure 2 A diagram illustrating a method for constructing and setting up a simulation system, provided by an embodiment of the present invention;

[0065] Figure 3 A diagram illustrating a grid search method provided in an embodiment of the present invention;

[0066] Figure 4 A diagram illustrating a method for guiding the battery swapping scheduling of mining trucks, provided by an embodiment of the present invention;

[0067] Figure 5 This is a schematic block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0068] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0069] Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts disclosed in this invention.

[0070] In the description of this invention, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0071] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of methods and systems consistent with some aspects of the invention as detailed in the appended claims.

[0072] This invention proposes a battery swapping decision-making method, electronic equipment, and storage medium for electric unmanned mining trucks. The battery swapping decision is adjusted in real time according to the actual situation on the production site, which greatly reduces the risks of resource waste and task interruption. Through the design and application of the simulation system, it breaks through the traditional offline optimization mode, enhances the mobility of transportation and battery swapping, and improves production continuity.

[0073] Method Implementation Examples

[0074] Figure 1 This diagram illustrates a battery swapping decision method for an electric unmanned mining truck, as provided in an embodiment of the present invention.

[0075] In a first aspect, this invention proposes a battery swapping decision-making method for electric unmanned mining trucks, combining... Figure 1 It includes six steps, S1 to S6:

[0076] S1: Obtain static scene information of the electric unmanned mining truck, construct an electric unmanned mining truck simulation system, and set the relevant static parameters of the electric unmanned mining truck.

[0077] Specifically, in this embodiment of the invention, the method for obtaining static scene information of the electric unmanned mining truck, constructing an electric unmanned mining truck simulation system, and setting relevant static parameters of the electric unmanned mining truck is described in detail below.

[0078] S2: To maximize the total time for all mining trucks to perform transport tasks and minimize the fluctuation of the queue length at the battery swapping station, a multi-objective function is established. The actual situation at the current production site is determined, and the relevant dynamic parameters of the mining trucks, excavators, and battery swapping stations in the electric unmanned mining trucks are set according to the actual situation at the current production site.

[0079] Specifically, in this embodiment of the invention, a multi-objective function is established with the goal of maximizing the total time for all mining trucks to perform transportation tasks and minimizing the fluctuation of the queue length at the battery swapping station. The method for determining the current actual situation at the production site and setting the relevant dynamic parameters of the mining trucks, excavators, and battery swapping stations in the electric unmanned mining trucks based on the current actual situation at the production site is described in detail below.

[0080] S3: Divide the parameter space related to the static scene of the electric unmanned mining truck into a grid, and input the initial parameter combination into the electric unmanned mining truck simulation system.

[0081] Specifically, in this embodiment of the invention, the parameter space related to the static scene of the electric unmanned mining truck is divided into grids using a high-density grid division strategy to comprehensively cover every point in the relevant parameter space as much as possible, ensuring that the area where the global optimum is located is not missed, and that the key parameter combinations are fully covered. The method of inputting the initial parameter combinations into the electric unmanned mining truck simulation system is described in detail below.

[0082] S4: Start the electric unmanned mining truck simulation system to begin grid search.

[0083] Specifically, in this embodiment of the invention, the method for starting the grid search of the electric unmanned mining truck simulation system is described in detail below.

[0084] S5: Output simulation results, recording the effective operating time of the fleet and the standard deviation of the time series of the battery swapping station leader.

[0085] Specifically, in this embodiment of the invention, after starting the grid search process, the simulation results are output, the simulation is run, and the effective operating time of the fleet under this parameter combination is recorded. Standard deviation of time series of queue length at battery swapping stations Due to factors such as fluctuations in loading time and different distances to unloading points, the simulation results will exhibit a certain degree of randomness. To reduce the impact of randomness on the results, each parameter combination will be simulated multiple times, and the average value of relevant statistical indicators will be used as the final result.

[0086] S6: Determine whether the grid search has ended. If the result is that the grid search has ended, determine the optimal parameter combination based on the entropy weight method, determine the battery swapping decision function based on the optimal parameter combination, and guide the battery swapping scheduling of mining trucks based on the battery swapping decision function.

[0087] Specifically, in this embodiment of the invention, the optimal parameter combination is determined based on the entropy weight method, the battery swapping decision function is determined based on the optimal parameter combination, and the method of guiding the battery swapping scheduling of mining trucks based on the battery swapping decision function is described in detail below.

[0088] The initial parameter combination consists of the initial values ​​of the power consumption impact weight, the maximum allowable queue length, and the battery swapping decision threshold. The optimal parameter combination is the optimal value of the power consumption impact weight, the maximum allowable queue length, and the battery swapping decision threshold, expressed as follows: .

[0089] Furthermore, the aforementioned method for battery swapping decisions in an electric unmanned mining truck also includes:

[0090] If the result indicates that the grid search has not ended, update the parameter combination and repeat steps S4 to S6.

[0091] Figure 2 This diagram illustrates a method for constructing and setting up a simulation system, as provided in an embodiment of the present invention.

[0092] Furthermore, in the aforementioned method for battery swapping decisions of an electric unmanned mining truck, static scene information of the electric unmanned mining truck is obtained, an electric unmanned mining truck simulation system is constructed, relevant static parameters of the electric unmanned mining truck are set, and combined with... Figure 2 It includes three steps, S21 to S23:

[0093] S21: Obtain detailed data and basic parameters of the mining site;

[0094] S22: Map the detailed data and basic parameters of the mine to the simulation system to build a simulation system for electric unmanned mining trucks;

[0095] S23: Set the necessary data for the electric unmanned mining truck simulation system;

[0096] The detailed data of the mine should include at least the following: the transportable routes of the mine, the location of the battery swapping station, and the location of the spoil heap; the basic parameters should include at least the following: the number of mine cars, the battery capacity of the mine cars, the number of spare batteries at the battery swapping station, the number of excavators, the excavator loading point, the excavator loading speed, the excavator loading time, the excavator unloading point, the excavator unloading speed, the excavator unloading time, the changeover cycle, the changeover downtime, and the number of spoil heaps; the necessary data should include at least the following: the status of mine cars and battery swapping stations, the status of excavators and spoil heaps, the distance traveled during the transportation process, and the speed and power consumption of mine cars under specific circumstances.

[0097] Specifically, in this embodiment of the invention, the simulation system adopts a discrete-time simulation framework based on Python. The electric unmanned mining truck simulation system mainly includes the following core components: 1) Battery: manages battery power and controls charging and discharging logic; 2) Mining truck: simulates vehicle operation and state transition; 3) Battery swapping station: manages the battery swapping queue, backup battery charging, and battery swapping operations; 4) Excavator: manages the loading queue and loading operations, and simulates normal excavator movement. The electric unmanned mining truck simulation system advances the simulation through a time pointer, mapping detailed mine data and basic parameters to the simulation system for modeling and construction. In the process of building the electric unmanned mining truck simulation system, due to the existence of uphill and downhill road conditions in the actual mining production environment, and the difference between loaded and unloaded states of the mining trucks, the speed and power consumption of the mining trucks under different conditions are set accordingly to maximize the simulation system's resemblance to reality.

[0098] The specific model makes the following assumptions: Assumption 1: All mining trucks are battery-swapping trucks with the same battery capacity; Assumption 2: The battery-swapping station has queuing constraints, and at any given time, it can only swap batteries for one mining truck; Assumption 3: The mining truck is fully charged after battery swapping; Assumption 4: The loading time, unloading time, and running speed of the mining truck all follow the same distribution; Assumption 5: The mining truck can only swap batteries after a transportation task is completed, and the task will not be interrupted; Assumption 6: Empty mining trucks will go to the excavator with the shortest queue length for loading. If there are excavators with the same queue length, they will go to the excavator with the longest distance so that subsequent mining trucks can choose the nearest excavator for loading. The excavator team leader includes the number of mine cars en route, the number of mine cars waiting, and the number of mine cars in operation; Assumption 7: When a mine car goes to the spoil heap to unload, the unloading location will be randomly selected; Assumption 8: The average speed and average power consumption of the mine car in flat and sloping conditions, and in no-load and loaded conditions, are set respectively; Assumption 9: The position of the excavator will move during normal operation, so the normal moving speed of the excavator during the loading process needs to be set; In addition, considering the excavator changing station requirements, the changing station cycle is input accordingly, and the excavator stops accepting new mine cars during the changing station time.

[0099] Furthermore, in the above-mentioned battery swapping decision-making method for electric unmanned mining trucks, the multi-objective function is formulated with the objectives of maximizing the total time for all mining trucks to perform transportation tasks and minimizing the fluctuation of queue length at battery swapping stations as follows:

[0100]

[0101] Determine the current actual conditions at the production site, and based on these conditions, set the relevant dynamic parameters for the mining trucks, excavators, and battery swapping stations in the electric unmanned mining truck system, including:

[0102] Determine the relevant parameters of the mining truck, and set the simulation status, location, and initial power of the online mining truck;

[0103] Determine the relevant parameters of the excavator and set the status and location information of the excavator;

[0104] Determine the relevant parameters of the battery swapping station, and set the backup battery capacity and quantity for the battery swapping station;

[0105] in, They represent parameter combinations respectively. The weighting of electricity consumption, the maximum allowable queue length, and the battery swapping decision threshold are all important factors in this context. They represent parameter combinations respectively. The index score is the standard deviation of the time series of effective operation time of the fleet and the queue length of the battery swapping station. represents the weights of the scores for the effective operation time of the fleet and the standard deviation of the queue length of the battery swapping station in the multi-objective evaluation function, respectively, and n is the maximum value of the parameter combination i.

[0106] Furthermore, in the aforementioned battery swapping decision-making method for an electric unmanned mining truck, the initial parameter combination is input into the electric unmanned mining truck simulation system, including:

[0107] Input the initial values ​​of the power influence weight, the maximum allowable queue length, and the battery swapping decision threshold into the electric unmanned mining truck simulation system.

[0108] Specifically, in this embodiment of the invention, the initial parameter combination is the minimum value within the search range of the power influence weight, the maximum allowable queue length, and the battery swapping decision threshold.

[0109] Figure 3 A diagram illustrating a grid search method provided in an embodiment of the present invention.

[0110] The electric unmanned mining truck simulation system was activated to begin a grid search, combined with... Figure 3 ,include:

[0111] S31: Set the simulation duration and search step size for the electric unmanned mining truck, and initialize the mining truck information, excavator information, and battery swapping station information.

[0112] Specifically, in this embodiment of the invention, the simulation duration and search step size are flexibly set according to the actual situation. The initial excavator information includes the excavator's latitude and longitude coordinates, queue length, and time until the next station change. The initial mining truck information includes the mining truck's latitude and longitude coordinates and remaining power. The initial battery swapping station information includes obtaining the battery swapping station queue length, number of backup batteries, and remaining power.

[0113] S32: Update the simulation time pointer.

[0114] Specifically, in this embodiment of the invention, the simulation time pointer is updated to the starting point.

[0115] S33: Select the online mining trucks in sequence and set them as the current mining truck.

[0116] Specifically, in this embodiment of the invention, steps S33 to S36 are executed in each time step loop. Here, the excavator status is also checked to see if the excavator is working normally or shut down for another machine, and relevant information is recorded.

[0117] S34: Determine the operating logic based on the simulation status of the mine car.

[0118] Specifically, in this embodiment of the invention, the operating logic is determined based on the simulation state of the mining truck as follows:

[0119] Running (Starting transport task): The minecart status is updated to "in trip" and the transport task begins;

[0120] In trip (performing a transport task): Determine if the minecart transport task is finished. If not, do not change the minecart status. If yes, determine whether a power swap is needed based on the power swap decision function. If yes, update the minecart status to "traveling tostation" and proceed to the power swap station for a power swap. If not, update the minecart status to "running" and prepare to execute a new round of transport tasks.

[0121] Traveling to station: Determine if the minecart has arrived at the swap station. If yes, the minecart's status is updated to "waiting" and it enters the swap station's queue. If no, the minecart's status remains unchanged.

[0122] Waiting (waiting at the battery swapping station): Determine if the mining truck is at the head of the battery swapping station queue and the station is available. If so, update the mining truck status to "swapping" and begin battery swapping; otherwise, do not change the mining truck status.

[0123] Swapping: Determines whether the minecart has completed the battery swap. If yes, the minecart's status is updated to "running," ready to execute a new round of transport tasks; otherwise, the minecart's status remains unchanged.

[0124] S35: Determine whether all mining trucks have been traversed. If the result is that all mining trucks have been traversed, update the excavator status, excavator location, and battery level of the battery swapping station. If the result is that all mining trucks have not been traversed, return to S33.

[0125] S36: Determine whether the simulation time exceeds the set simulation duration. If the result is that the simulation time exceeds the set simulation duration, output the simulation result; if the result is that the simulation time does not exceed the preset simulation duration, return to S33.

[0126] Furthermore, in the above-mentioned battery swapping decision-making method for electric unmanned mining trucks, the optimal parameter combination is determined based on the entropy weight method, including:

[0127] Range standardization in parameter combinations The formula for the standard deviation of the effective operating time of the fleet and the queue length of the battery swapping station is as follows:

[0128]

[0129]

[0130] in, , Let i represent the effective operating time of the vehicle fleet and the standard deviation of the queue length of the battery swapping station under parameter combination i after range standardization. , ), , They represent the parameter combinations respectively. The effective operating time of the fleet and the standard deviation of the queue length of the battery swapping station are calculated.

[0131] Based on the parameter combination after range standardization The effective operating time of the fleet and the standard deviation of the queue length of the battery swapping station are determined by the parameter combination. The formula for the index score of the effective working time and the standard deviation of the queue length time series of the battery swapping station is expressed as follows:

[0132]

[0133]

[0134] in, They represent the parameter combinations respectively. The index score is the effective working time and the standard deviation of the queue length time series of the battery swapping station.

[0135] Based on parameter combinations The index score of the effective working time and the standard deviation of the queue length time series of the battery swapping station is determined in the parameter combination. The formula for the entropy of the standard deviation of the effective operating time of the vehicle fleet and the queue length of the battery swapping station is as follows:

[0136]

[0137]

[0138] in, These represent the entropy values ​​of the effective working time and the standard deviation of the queue length time series at the battery swapping station, respectively.

[0139] Based on parameter combinations The formula for determining the coefficient of variation of the time series standard deviation of the effective operating time of the vehicle fleet and the queue length of the battery swapping station is as follows:

[0140]

[0141]

[0142] in, These represent the coefficients of variation of the effective working time and the standard deviation of the queue length time series at the battery swapping station, respectively.

[0143] The formula for determining the weights of the standard deviations of the time series of effective fleet operation time and queue length at battery swapping stations based on the coefficient of variation of these time series is as follows:

[0144]

[0145]

[0146] in, , These represent the weights of the effective operating time of the fleet and the standard deviation of the queue length of the battery swapping station, respectively.

[0147] The formula for determining the multi-objective function value based on the index scores of the effective operation time of the fleet and the standard deviation of the queue length at the battery swapping station, along with the weights of the effective operation time of the fleet and the standard deviation of the queue length at the battery swapping station, is expressed as follows:

[0148]

[0149] The optimal parameter combination is determined by identifying the parameter combination that corresponds to the largest multi-objective function value.

[0150] Specifically, in this embodiment of the invention, the higher the evaluation function value under a parameter combination, the more the battery swapping decision function under that parameter combination can provide the mining truck with a battery swapping opportunity that better meets the needs of the enterprise. The sum of the power influence weight and the queue influence weight is 1, so only the power influence weight is used as a parameter. The power influence weight, the maximum allowable queue length, and the battery swapping decision threshold affect the battery swapping decision function, and thus affect various indicators of the simulation system. Therefore, it is necessary to search for combinations of these three parameters.

[0151] Furthermore, in the above-mentioned battery swapping decision method for electric unmanned mining trucks, the battery swapping decision function is determined based on the optimal parameter combination using the following formula:

[0152]

[0153] in, , These represent the weights of the impact of electricity consumption and the impact of the queue, respectively. This indicates the maximum remaining battery power threshold. Indicates the minimum remaining battery power threshold. This indicates the maximum allowed queue length. This indicates the current remaining power of the mining truck. This indicates the current queue length at the battery swapping station. This represents the value of the battery swapping decision function, where the sum of the weights affecting battery capacity and queue size is 1.

[0154] Specifically, in this embodiment of the invention, after determining the optimal parameter combination, the battery swapping decision function is determined based on the power influence weight and the maximum allowable queue length in the optimal parameter combination.

[0155] Figure 4 This diagram illustrates a method for guiding the scheduling of battery swapping for mining trucks, as provided in an embodiment of the present invention.

[0156] Furthermore, in the aforementioned method for battery swapping decision-making for electric unmanned mining trucks, the battery swapping scheduling of the mining trucks is guided by the battery swapping decision function, combined with... Figure 4 It includes two steps, S41 and S42:

[0157] S41: Determine the value of the battery swapping decision function and the battery swapping decision threshold to guide the scheduling of battery swapping for mining trucks;

[0158] S42: If the judgment result is that the value of the battery swapping decision function is greater than or equal to the battery swapping decision threshold, the mining truck will go to the battery swapping station to swap batteries. If the judgment result is that the value of the battery swapping decision function is less than the battery swapping decision threshold, the mining truck will not swap batteries.

[0159] Specifically, in this embodiment of the invention, after determining the optimal parameter combination, the scheduling of mining truck battery swapping is guided by the value of the battery swapping decision function and the magnitude of the battery swapping decision threshold in the optimal parameter combination.

[0160] A second aspect of the present invention also provides an electronic device comprising: a processor and a memory;

[0161] The processor executes a battery swapping decision method for an electric unmanned mining truck, such as one of the above methods, by calling programs or instructions stored in memory.

[0162] In a third aspect, the present invention also provides a computer-readable storage medium storing a program or instructions that cause a computer to execute a battery swapping decision method for an electric unmanned mining vehicle as described in any of the preceding claims.

[0163] Figure 5 This is a schematic block diagram of an electronic device provided in an embodiment of the present invention.

[0164] like Figure 5 As shown, the electronic device includes at least one processor 501, at least one memory 502, and at least one communication interface 503. The various components in the electronic device are coupled together via a bus system 504. The communication interface 503 is used for information transmission with external devices. It is understood that the bus system 504 is used to implement communication between these components. In addition to a data bus, the bus system 504 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 5 The general designated all buses as Bus System 504.

[0165] It is understood that the memory 502 in this embodiment may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0166] In some implementations, memory 502 stores elements such as executable units or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0167] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application functions. The program implementing any method in the battery swapping decision-making method for an electric unmanned mining truck provided in this embodiment of the invention can be included in the application programs.

[0168] In this embodiment of the invention, the processor 501 executes the steps of various embodiments of the battery swapping decision method for an electric unmanned mining truck provided by the present invention by calling the program or instructions stored in the memory 502, specifically, the program or instructions stored in the application program.

[0169] S1: Obtain static scene information of the electric unmanned mining truck, construct an electric unmanned mining truck simulation system, and set the relevant static parameters of the electric unmanned mining truck;

[0170] S2: To maximize the total time for all mining trucks to perform transport tasks and minimize the fluctuation of the queue length at the battery swapping station, a multi-objective function is established. The actual situation at the current production site is determined, and the relevant dynamic parameters of the mining trucks, excavators, and battery swapping stations in the electric unmanned mining trucks are set according to the actual situation at the current production site.

[0171] S3: Divide the parameter space related to the static scene of the electric unmanned mining truck into a grid, and input the initial parameter combination into the electric unmanned mining truck simulation system;

[0172] S4: Start the electric unmanned mining truck simulation system to begin grid search;

[0173] S5: Output simulation results, recording the effective operating time of the fleet and the standard deviation of the time series of the battery swapping station team leader;

[0174] S6: Determine whether the grid search has ended. If the result is that the grid search has ended, determine the optimal parameter combination based on the entropy weight method, determine the battery swapping decision function based on the optimal parameter combination, and guide the battery swapping scheduling of mining trucks based on the battery swapping decision function.

[0175] The initial parameter combination consists of the initial values ​​of the power consumption impact weight, the maximum allowable queue length, and the battery swapping decision threshold. The optimal parameter combination consists of the optimal values ​​of the power consumption impact weight, the maximum allowable queue length, and the battery swapping decision threshold, expressed as follows: .

[0176] Any method in the battery swapping decision-making method for an electric unmanned mining truck provided in this embodiment of the invention can be applied to, or implemented by, the processor 501. The processor 501 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by the integrated logic circuits in the hardware of the processor 501 or by instructions in software form. The processor 501 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor.

[0177] In the battery swapping decision-making method for an electric unmanned mining truck provided in this embodiment of the invention, any step can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software units in the decoding processor. The software units can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 502, and processor 501 reads the information in memory 502 and combines it with hardware to complete the steps of the method.

[0178] Those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0179] Those skilled in the art will understand that the descriptions of the various embodiments have different focuses, and for parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0180] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention. All such modifications and variations fall within the scope defined by the appended claims. The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0181] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A battery swapping decision-making method for an electric unmanned mining truck, characterized in that, It includes six steps, S1 to S6: S1: Obtain detailed mine data and basic parameters of the electric unmanned mining truck, map the detailed mine data and basic parameters to the simulation system for modeling to build an electric unmanned mining truck simulation system, and set the relevant static parameters of the electric unmanned mining truck simulation system. S2: To maximize the total time for all mining trucks to perform transport tasks and minimize the fluctuation of the queue length at the battery swapping station, a multi-objective function is established. The actual situation at the current production site is determined, and the relevant dynamic parameters of the mining trucks, excavators, and battery swapping stations in the electric unmanned mining truck simulation system are set according to the actual situation at the current production site. S3: Divide the parameter space related to the static scene in the electric unmanned mining truck simulation system into a grid, and input the initial parameter combination into the electric unmanned mining truck simulation system. S4: Start the electric unmanned mining truck simulation system to begin grid search; S5: Output simulation results, recording the effective operating time of the vehicle fleet and the standard deviation of the queue length time series of the battery swapping station; S6: Determine whether the grid search has ended. If the result is that the grid search has ended, determine the optimal parameter combination based on the entropy weight method, determine the battery swapping decision function based on the optimal parameter combination, and guide the battery swapping scheduling of mining trucks based on the battery swapping decision function. The initial parameter combination consists of the initial values ​​of the power consumption impact weight, the maximum allowable queue length, and the battery swapping decision threshold. The optimal parameter combination consists of the optimal values ​​of the power consumption impact weight, the maximum allowable queue length, and the battery swapping decision threshold, expressed as follows: ; The detailed data of the mine includes at least: the mine's transportable routes, the location of the battery swapping station, and the location of the spoil heap; the basic parameters include at least: the number of mine trucks, the battery capacity of the mine trucks, the number of spare batteries at the battery swapping station, the number of excavators, the excavator loading point, the excavator loading speed, the excavator loading time, the excavator unloading point, the excavator unloading speed, the excavator unloading time, the machine changeover cycle, the machine changeover downtime, and the number of spoil heaps; The formula for establishing a multi-objective function is expressed as follows: They represent parameter combinations respectively. The weighting of electricity consumption, the maximum allowable queue length, and the battery swapping decision threshold are all important factors to consider. They represent parameter combinations respectively. The scores are based on the effective operating time of the fleet and the standard deviation of the queue length at the battery swapping station. These represent the weights of the scores for the effective operating time of the vehicle fleet and the standard deviation of the queue length time series at the battery swapping station in the multi-objective function, respectively. The battery swapping decision function is determined based on the optimal parameter combination using the following formula: in, , These represent the weights of the impact of electricity consumption and the impact of the queue, respectively. This indicates the maximum remaining battery power threshold. Indicates the minimum remaining battery power threshold. This indicates the maximum allowed queue length. This indicates the current remaining power of the mining truck. This indicates the current queue length at the battery swapping station. This represents the value of the battery swapping decision function, where the sum of the weights affecting battery capacity and queue size is 1.

2. The battery swapping decision method for an electric unmanned mining truck according to claim 1, characterized in that, The method further includes: If the result indicates that the grid search has not ended, update the parameter combination and repeat steps S4 to S6.

3. The battery swapping decision method for an electric unmanned mining truck according to claim 1, characterized in that, The relevant static parameters of the electric unmanned mining truck simulation system are set as follows: Set the relevant static parameters in the electric unmanned mining truck simulation system; The relevant static parameters include at least: the condition of the mining truck and the battery swapping station, the condition of the excavator and the spoil heap, the distance traveled during the transportation process, and the speed and power consumption of the mining truck under specific circumstances.

4. The battery swapping decision method for an electric unmanned mining truck according to claim 1, characterized in that, Determine the current actual conditions of the production site, and based on these conditions, set the relevant dynamic parameters for the mining truck, excavator, and battery swapping station in the electric unmanned mining truck simulation system, including: Determine the relevant parameters of the mining truck, and set the simulation status, location, and initial power of the online mining truck; Determine the relevant parameters of the excavator and set the status and location information of the excavator; Determine the relevant parameters of the battery swapping station and set the backup battery capacity and quantity.

5. The battery swapping decision method for an electric unmanned mining truck according to claim 1, characterized in that, The initial parameter combination is input into the electric unmanned mining truck simulation system, including: Input the initial values ​​of the power consumption impact weight, the maximum allowable queue length, and the battery swapping decision threshold into the electric unmanned mining truck simulation system; Start the electric unmanned mining truck simulation system to begin grid search, including: Step 1: Set the simulation duration and search step size of the electric unmanned mining truck simulation system, and initialize the mining truck information, excavator information, and battery swapping station information; Step 2: Update the simulation time pointer; Step 3: Select the online mining trucks in sequence and set them as the current mining trucks; Step 4: Determine the operating logic based on the current simulation status of the mining truck; Step 5: Determine whether all mining trucks have been traversed. If the result is that all mining trucks have been traversed, update the excavator status, excavator location, and backup battery power at the battery swapping station. If the result is that not all mining trucks have been traversed, return to Step 3. Step 6: Determine whether the simulation time exceeds the set simulation duration. If the result is that the simulation time exceeds the set simulation duration, output the simulation result; if the result is that the simulation time does not exceed the set simulation duration, return to Step 2.

6. The battery swapping decision method for an electric unmanned mining truck according to claim 1, characterized in that, Determining the optimal parameter combination based on the entropy weight method includes: Standard deviation of fleet effective operating time and battery swapping station queue length time series under range standardization parameter combination i; The standard deviation of the effective operation time of the fleet and the queue length of the battery swapping station under parameter combination i, after range standardization, is determined based on the time series standard deviation of the time series of the parameter combination i. The index score is the standard deviation of the effective operating time of the fleet and the queue length of the battery swapping station. Based on parameter combinations The entropy value of the standard deviation of the time series of effective operation time of the fleet and queue length of the battery swapping station is determined by the index score of the effective operation time of the fleet and queue length of the battery swapping station. The coefficient of variation of the standard deviation of the time series of effective operation time of the fleet and queue length of the battery swapping station is determined based on the entropy value of the time series standard deviation of the effective operation time of the fleet and the queue length of the battery swapping station. The weights of the standard deviations of the time series of effective fleet operation time and queue length at battery swapping stations are determined based on the coefficient of variation of these two values. The multi-objective function value under parameter combination i is determined based on the index scores of the effective operation time of the fleet and the standard deviation of the queue length of the battery swapping station under parameter combination i, as well as the weights of the effective operation time of the fleet and the standard deviation of the queue length of the battery swapping station under parameter combination i. The optimal parameter combination is determined by identifying the parameter combination that corresponds to the largest multi-objective function value.

7. The battery swapping decision method for an electric unmanned mining truck as described in claim 1, characterized in that, The scheduling of battery swapping for mining trucks is guided by the battery swapping decision function, including: The battery swapping decision function value and the battery swapping decision threshold are used to guide the scheduling of mining truck battery swapping. If the battery swapping decision function value is greater than or equal to the battery swapping decision threshold, the mining truck will go to the battery swapping station to swap batteries; if the battery swapping decision function value is less than the battery swapping decision threshold, the mining truck will not swap batteries.

8. An electronic device, characterized in that, include: Processor and memory; The processor executes a battery swapping decision method for an electric unmanned mining truck as described in any one of claims 1 to 7 by calling the program or instructions stored in the memory.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that cause a computer to execute a battery swapping decision method for an electric unmanned mining truck as described in any one of claims 1 to 7.

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