Unmanned truck active fault-tolerant scheduling method for strip mine equipment faults
Through the active fault-tolerant scheduling method of unmanned trucks, the scheduling solution is optimized using fault warning systems and genetic algorithms, the scheduling problems caused by high frequency and short-term failure of open-pit mine equipment are solved, and the robustness of the scheduling solution and the production efficiency are improved.
Patent Information
- Application Number
- CN202411756416.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-03
- Publication Date
- 2025-03-14
AI Technical Summary
In the face of high frequency and short-term failure of open-pit mine equipment, the scheduling scheme is poor, and it is impossible to effectively optimize for multiple targets, and the calculation complexity is high, making it difficult to find the optimal solution within a limited time.
The unmanned truck's active fault-tolerant scheduling method is adopted. By establishing a fault warning system, receiving fault warning information, determining the benchmark task set, using genetic algorithms to optimize the optimized task set, obtaining the optimal scheduling task set, and performing loading and unloading task scheduling.
Warning and adjustments are carried out about 20 minutes before the failure occurs to ensure the robustness and production continuity of the scheduling scheme. The optimal scheduling scheme that meets the efficiency requirements is quickly solved through genetic algorithms, which significantly improves the flexibility and overall efficiency of scheduling.
Smart Images
Figure CN119620712A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of unmanned driving technology, and in particular to an active fault-tolerant dispatching method for unmanned driving trucks targeting open-pit mine equipment failures. Background Art
[0002] In the production process of open-pit mines, the failure of various equipment will inevitably affect production efficiency and operational safety. Especially when loading and unloading equipment is performing tasks, frequent failures not only increase the maintenance cost of the equipment, but may also cause interruptions and delays in the entire production process. Common types of failures include large rocks stuck in the crushing station, electric shovels stuck in the mud and unable to move forward, electric shovel motors overheating or insufficient voltage, etc. These failures are usually characterized by high frequency and rapid recovery. Although each failure lasts about 20 minutes, due to its high frequency of occurrence, the cumulative impact on production is quite significant.
[0003] To deal with these sudden equipment failures, existing technologies usually adopt regular maintenance and passive response strategies. However, these strategies show certain limitations when facing high-frequency, short-term failures. On the one hand, regular maintenance cannot accurately predict and prevent all failures, resulting in temporary handling after the failure occurs; on the other hand, passive response strategies can only be adjusted after the failure occurs, which often causes certain production losses.
[0004] In this context, active fault-tolerant scheduling methods have emerged, aiming to reduce the impact of failures on production through early warning and reasonable scheduling. Existing active fault-tolerant scheduling methods usually rely on fault prediction models and redundant designs to reduce the risk of production interruption by taking preventive measures before failures occur. However, these methods still have some shortcomings in practical applications.
[0005] First, although the existing fault prediction model can issue an early warning about 20 minutes in advance, the probability of a fault is difficult to accurately determine, resulting in poor robustness of the scheduling scheme. In actual operation, the accuracy and timeliness of the early warning information directly affect the execution effect of the scheduling scheme. Secondly, the existing methods mostly use single-objective optimization, that is, only consider a single factor such as production efficiency or cost, ignoring the importance of multi-objective optimization in practical applications. Finally, traditional optimization methods usually have high computational complexity and it is difficult to find the optimal solution within a limited time, resulting in unsatisfactory implementation of the scheduling scheme. Summary of the invention
[0006] In view of the above problems, the present invention provides an active fault-tolerant scheduling method for unmanned trucks for open-pit mine equipment failures, which solves the technical problems of insufficient utilization of fault warning information in fault-tolerant scheduling and inability to comprehensively optimize scheduling for multiple objectives in the prior art.
[0007] The present invention provides an active fault-tolerant dispatching method for unmanned trucks for open-pit mine equipment failures, comprising the following steps:
[0008] Step S1, establishing an unmanned truck fault warning system, wherein the fault warning system sends fault warning information before a fault occurs;
[0009] Step S2, receiving the fault warning information, and determining a reference task set based on the time when the fault warning information is received, wherein the reference task set includes a plurality of loading and unloading scheduling tasks that are planned to be performed by unmanned trucks;
[0010] Step S3, determining a task set to be optimized from the benchmark task set, and determining constraint conditions and objective functions for optimizing the task set to be optimized;
[0011] Step S4: using a genetic algorithm to optimize the task set to be optimized based on the constraint conditions and the objective function to obtain an optimal scheduling task set;
[0012] Step S5: Based on the optimal scheduling task set, the unmanned truck is scheduled for loading and unloading tasks.
[0013] Preferably, in step S1, the fault warning system monitors the status of the open-pit mine unmanned truck in real time and collects monitoring information, and issues fault warning information before the first time length of the fault occurs by analyzing the monitoring information; the monitoring information includes the working status, temperature, voltage and current of the equipment in the unmanned truck.
[0014] Preferably, step S2 specifically includes:
[0015] Step S2-1, taking a second time length after the moment of receiving the fault warning information as a time window; the second time length is greater than the first time length;
[0016] Step S2-2: extracting a set of loading and unloading scheduling tasks within the duration of the time window from the loading and unloading scheduling tasks originally planned to be performed by the driverless truck as a benchmark task set.
[0017] Preferably, step S3 specifically includes:
[0018] Step S3-1: for the loading and unloading scheduling tasks in the benchmark task set, exclude the critical tasks being executed, and select a set of all the loading and unloading scheduling tasks that meet the requirement that the remaining time to reach the work area is less than the first time length and require a new scheduling target as the task set to be optimized;
[0019] Step S3-2, determining an objective function for optimizing the task set to be optimized based on maximizing production efficiency;
[0020] Step S3-3: Taking the efficiency drop of the optimized scheduling solution as being within an acceptable range as a criterion, determining the constraint conditions for optimizing the task set to be optimized.
[0021] Preferably, in step S3-3, the constraint condition is expressed as:
[0022]
[0023] Among them, T(X) represents the production time of the original scheduling plan, X * represents the optimized scheduling scheme, T(X * ) represents the production time of the optimized scheduling scheme, and k represents the allowable efficiency drop threshold.
[0024] Preferably, step S4 specifically includes:
[0025] Step S4-1, decomposing the optimization process into a loading task optimization process and an unloading task optimization process which are independent of each other;
[0026] Step S4-2, using genetic algorithms for the loading task optimization process and the unloading task optimization process respectively, and iteratively solving them with the constraints and objective functions as the optimization goals. During the iterative process, the information of the solutions of the loading task optimization process and the unloading task optimization process are exchanged; finally, the optimal scheduling task set is obtained.
[0027] Preferably, in step S4-2, the process of performing the iterative solution using the genetic algorithm specifically includes:
[0028] The loading and unloading scheduling tasks are encoded as chromosomes to generate an initial population, which includes multiple different chromosomes. The fitness of each chromosome is evaluated according to the objective function and constraints, and chromosomes with high fitness are selected for crossover and mutation operations to generate a new population.
[0029] Preferably, the initial population is generated by using a linear programming method to determine the real-time optimal solution of the loading task optimization process and the unloading task optimization process under fault and non-fault conditions, obtaining the corner point information corresponding to the optimistic solution and the pessimistic solution, and using the corner point information as the initial population of the genetic algorithm.
[0030] Preferably, step S5 specifically includes:
[0031] The loading and unloading scheduling tasks in the optimal scheduling task set are transmitted to the unmanned truck control system to control the driving and operation of the truck.
[0032] Compared with the prior art, the present invention has at least the following beneficial effects:
[0033] (1) The active fault-tolerant scheduling method for unmanned trucks provided by the present invention can provide early warning and adjustment about 20 minutes before a fault occurs, thereby ensuring the robustness and production continuity of the scheduling plan in the event of a sudden fault. By using a genetic algorithm for scheduling optimization, the optimal scheduling plan that meets the efficiency requirements can be quickly solved within a limited time, greatly improving the flexibility and overall efficiency of scheduling.
[0034] (2) The present invention provides a special scheduling adjustment strategy for common high-frequency short-term faults in open-pit mine production, such as large rocks stuck in the crushing station, electric shovel stuck in mud, electric shovel motor overheating, etc. By comprehensively considering the warning time, fault time and recovery time, the present invention can adjust the scheduling task within about 1 hour, realize rapid production recovery, and significantly reduce the frequency of production interruptions.
[0035] (3) The present invention realizes active fault-tolerant scheduling through unmanned trucks, reducing the reliance on manual intervention, thereby reducing labor costs and safety risks. During the adjustment process, the scheduling tasks in the original plan are used as a benchmark to ensure that the updated scheduling tasks meet the production requirements, thereby optimizing the production process and ensuring the overall efficiency of production. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] The drawings are only for the purpose of illustrating particular embodiments and are not to be construed as limiting the invention.
[0037] Figure 1 A flow chart of the active fault-tolerant scheduling method for unmanned trucks for open-pit mine equipment failures provided by the present invention.
[0038] Figure 2 A schematic diagram of the Y-type encoding method for fault-tolerant scheduling provided by the present invention.
[0039] Figure 3 A flowchart based on a collaborative genetic algorithm provided by the present invention. DETAILED DESCRIPTION
[0040] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. In addition, the present invention can also be implemented in other ways different from those described herein, and therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.
[0041] The present invention proposes an active fault-tolerant scheduling method for unmanned trucks for open-pit mine equipment failures. The method aims to solve the scheduling problems caused by sudden equipment failures in the open-pit mine production process. Through a pre-planned fault-tolerant scheduling plan, it ensures that high production efficiency can be maintained in the event of equipment failure.
[0042] In order to illustrate the effectiveness of the method proposed by the present invention, the above technical solution of the present invention is described in detail below through a specific embodiment. Figure 1 As shown, an active fault-tolerant dispatching method for unmanned trucks for open-pit mine equipment failures is disclosed, and the specific implementation steps are as follows:
[0043] Step S1: Establish an unmanned truck fault warning system, wherein the fault warning system sends fault warning information when predicting that the unmanned truck is about to fail.
[0044] Equipment failure is inevitable in the production process of open-pit mines. However, many failures, such as large rocks stuck in the crushing station, electric shovel stuck in mud and unable to move, electric shovel motor overheating or insufficient voltage, will have early warning information before they occur. These early warning information provides a valuable time window for scheduling adjustments before the failure actually occurs.
[0045] In order to implement the present invention, it is first necessary to establish an unmanned truck fault warning system, which monitors the status of the open-pit mine unmanned truck in real time and collects various monitoring information. These monitoring information may include key parameters such as the working status, temperature, voltage and current of the equipment in the unmanned truck. Through data analysis, it is possible to identify which parameter changes may indicate that the equipment failure of the unmanned truck is about to occur, and at this time, the fault warning system sends fault warning information.
[0046] In some embodiments, the unmanned truck fault warning system of the present invention sends out fault warning information 20 minutes in advance. The fault warning information can be used as a basis for subsequent unmanned truck task scheduling.
[0047] Step S2: receiving the fault warning information, and determining a benchmark task set based on the time when the fault warning information is received, wherein the benchmark task set includes a plurality of loading and unloading scheduling tasks that are planned to be performed by unmanned trucks.
[0048] After receiving the fault warning information, it is necessary to determine a time window and adjust the loading and unloading scheduling tasks that the unmanned truck plans to perform within the time window. This time window should be long enough to cover the warning time, failure time and recovery time after receiving the fault warning information. In some embodiments, the time window can be set to 1 hour. Next, from the loading and unloading scheduling tasks that the original unmanned truck plans to perform, the loading and unloading scheduling tasks within the duration of the time window are extracted. These tasks constitute the scheduling task set to be optimized, which is determined as the benchmark task set X0. The production efficiency of the benchmark task set X0 will be used as the efficiency evaluation benchmark for the optimized scheduling scheme. The subsequent optimized and updated scheduling tasks need to meet the production requirements within the time window.
[0049] Step S3: determining a task set to be optimized from the benchmark task set, and determining constraints and objective functions for optimizing the task set to be optimized.
[0050] In the open-pit mine production process, the impact of mine car equipment failure in different areas on production is different. Therefore, it is necessary to divide the loading and unloading scheduling tasks into different areas, and formulate different scheduling adjustment strategies for different areas. Specifically, the loading and unloading scheduling tasks in the benchmark task set can be divided into red areas and non-red areas. Red areas refer to those areas where critical tasks are being performed and cannot be easily changed. In these areas, even if early warning information of equipment failure is received, scheduling adjustments are not made to ensure the stability of production. Non-red areas refer to those areas where tasks are relatively flexible and loading and unloading tasks can be interchanged or adjusted to a certain extent.
[0051] In the non-red area, a set of all the loading and unloading scheduling tasks that meet the requirement of having a remaining time to reach the work area of less than 20 minutes and require a new scheduling target is selected as the task set to be optimized and as the object of subsequent scheduling adjustment.
[0052] In order to adjust various loading and unloading scheduling tasks and form an optimized scheduling plan, the present invention establishes an objective function to evaluate the production efficiency of different scheduling plans. At the same time, it also establishes constraint conditions to ensure the feasibility of the scheduling plan.
[0053] The objective function is F(X * ), where X * represents the optimized scheduling scheme, F(X * ) value, the higher the production efficiency of the scheduling scheme.
[0054] The constraint condition expression is:
[0055]
[0056] Among them, T(X) represents the production time of the original scheduling plan, T(X * ) represents the production time of the optimized scheduling scheme, and k represents the allowable efficiency drop threshold. The constraint condition ensures that the efficiency drop of the optimized scheduling scheme is within an acceptable range, thereby ensuring production efficiency.
[0057] Step S4: Use a genetic algorithm to optimize the task set to be optimized to obtain the optimal scheduling task set.
[0058] In order to solve the optimization problem of adjusting the loading and unloading scheduling tasks, the present invention adopts a genetic algorithm. The genetic algorithm is an optimization algorithm that simulates the biological evolution process. The genetic algorithm continuously iterates the optimization solution through operations such as selection, crossover and mutation.
[0059] In the solution process, the interchangeability of loading and unloading tasks needs to be considered. Since loading tasks are usually fixed, and unloading tasks are often adjusted according to the update of loading tasks, the optimization process is decomposed into two different optimization problems: loading task optimization problem and unloading task optimization problem. These two problems can be solved using genetic algorithms respectively.
[0060] For the process of solving the two genetic algorithms, the loading and unloading scheduling tasks are encoded as chromosomes, and each chromosome represents a possible loading and unloading scheduling process. An initial population is generated, which includes multiple different chromosomes. The fitness of each chromosome is evaluated according to the objective function and constraints provided by the present invention, and the chromosomes with higher fitness are selected for crossover and mutation operations to generate a new population. This process is iterated continuously, and the information of the solution is exchanged in each round of iteration, so that they converge toward a common solution until the chromosome that meets the constraints and has the highest objective function value is found, that is, the optimal scheduling task set.
[0061] like Figure 2 As shown in the figure, the chromosome evolution process of the Y-type encoding method for fault-tolerant scheduling is shown. In the figure, s1, s2, ... represent each loading point, d1, d2, ... represent each unloading point, π k (s1, d2, p) is the kth loading and unloading scheduling task, which means the whole task process of completing loading from loading point s1, driving on path p, and completing unloading at unloading point d2. There are multiple paths between a pair of loading and unloading points, and p is one of the paths. At time t0, after receiving the fault warning information that mining truck k is about to fail, since mining truck k executes the task (s1, d2, p), crossover and mutation operations are performed, and the task π1 (s1, d2, p) of mining truck 1 is adjusted to π1 (d2, s3, p) after a period of time. If no failure occurs at time t1, crossover and mutation operations are performed, mining truck 1 continues to execute π1 (s3, d2, p), and mining truck 2 continues to execute the task π1 (s1, d2, p). If a failure does occur at time t1, crossover and mutation operations are performed according to the requirements of obstacle avoidance to determine the new tasks of mining truck 1 and mining truck 2, and the process is repeated to form an iterative process of the genetic algorithm.
[0062] like Figure 3 As shown, in the process of solving the two genetic algorithms, the present invention determines the optimal solution in real time optimization under fault and non-fault conditions by linear programming method, and obtains the corner point information corresponding to the optimistic solution and the pessimistic solution;
[0063] The process of solving the two genetic algorithms can be seen as the optimization goal from one corner point to another corner point, which is to obtain a better solution by comprehensively considering the fault and non-fault conditions.
[0064] In some embodiments, a linear programming method is used to determine the real-time optimization optimal solution of the loading task optimization process and the unloading task optimization process under fault and non-fault conditions, and the corner point information corresponding to the optimistic solution and the pessimistic solution is obtained, and the corner point information is used as the initial population of the genetic algorithm.
[0065] Step S5: Based on the optimal scheduling task set, the unmanned truck is scheduled for loading and unloading tasks.
[0066] After finding the optimal scheduling solution, it needs to be applied to the actual open-pit mine production process. This requires integration with the open-pit mine production management system, transmitting the optimized scheduling solution with the optimal scheduling task set to the unmanned truck control system, and controlling the driving and operation of the truck. During the implementation process, it is necessary to monitor the operating status and operation progress of the truck in real time, and make necessary adjustments based on the actual situation. For example, if a truck fails and cannot complete the operation on time, other trucks need to be dispatched in time to replace it to ensure the continuity of production.
[0067] After the scheduling plan is implemented, it needs to be evaluated and optimized. The purpose of the evaluation is to verify whether the actual effect of the scheduling plan has achieved the expected improvement in production efficiency. The pros and cons of the scheduling plan can be evaluated by comparing indicators such as production efficiency and equipment utilization before and after optimization. If the evaluation results show that the scheduling plan has deficiencies or needs improvement, it needs to be optimized. Optimization can include adjusting the parameters of the objective function or constraints, improving the solution method, adding new scheduling strategies, etc. Through continuous evaluation and optimization, the performance and adaptability of the scheduling plan can be continuously improved, bringing greater value to open-pit mine production.
[0068] Although the specific embodiments of the present invention depict various actions or steps in a specific order, this should be understood as requiring such actions or steps to be performed in the specific order shown or in a sequential order, or requiring all illustrated actions or steps to be performed to obtain the desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination. The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.
[0069] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.
Claims
1. An active fault-tolerant dispatching method for unmanned trucks for open-pit mine equipment failures, characterized in that: The following steps are involved: Step S1, establishing an unmanned truck fault warning system, wherein the fault warning system sends fault warning information before a fault occurs; Step S2, receiving the fault warning information, and determining a reference task set based on the time when the fault warning information is received, wherein the reference task set includes a plurality of loading and unloading scheduling tasks that are planned to be performed by unmanned trucks; Step S3, determining a task set to be optimized from the benchmark task set, and determining constraint conditions and objective functions for optimizing the task set to be optimized; Step S4: using a genetic algorithm to optimize the task set to be optimized based on the constraint conditions and the objective function to obtain an optimal scheduling task set; Step S5: Based on the optimal scheduling task set, the unmanned truck is scheduled for loading and unloading tasks.
2. The active fault-tolerant dispatching method for unmanned trucks for open-pit mine equipment failure according to claim 1 is characterized in that: In the step S1, the fault warning system monitors the status of the open-pit mine unmanned truck in real time, collects monitoring information, and issues fault warning information before the first time length of the fault occurs by analyzing the monitoring information; The monitoring information includes the operating status, temperature, voltage and current of the equipment in the unmanned truck.
3. The active fault-tolerant dispatching method for unmanned trucks for open-pit mine equipment failure according to claim 2 is characterized in that: Step S2 specifically includes: Step S2-1, taking a second time length after the moment of receiving the fault warning information as a time window; the second time length is greater than the first time length; Step S2-2: extracting a set of loading and unloading scheduling tasks within the duration of the time window from the loading and unloading scheduling tasks originally planned to be performed by the driverless truck as a benchmark task set.
4. The active fault-tolerant dispatching method for unmanned trucks for open-pit mine equipment failure according to claim 3 is characterized in that: Step S3 specifically includes: Step S3-1: for the loading and unloading scheduling tasks in the benchmark task set, exclude the critical tasks being executed, and select a set of all the loading and unloading scheduling tasks that meet the requirement that the remaining time to reach the work area is less than the first time length and require a new scheduling target as the task set to be optimized; Step S3-2, determining an objective function for optimizing the task set to be optimized based on the criterion of maximizing production efficiency; Step S3-3: Taking the efficiency drop of the optimized scheduling solution as being within an acceptable range as a criterion, determining the constraint conditions for optimizing the task set to be optimized.
5. The active fault-tolerant dispatching method for unmanned trucks for open-pit mine equipment failure according to claim 4 is characterized in that: In step S3-3, the constraint condition is expressed as: Among them, T(X) represents the production time of the original scheduling plan, X * represents the optimized scheduling scheme, T(X * ) represents the production time of the optimized scheduling scheme, and k represents the allowable efficiency drop threshold.
6. The active fault-tolerant dispatching method for unmanned trucks for open-pit mine equipment failure according to claim 5 is characterized in that: Step S4 specifically includes: Step S4-1, decomposing the optimization process into a loading task optimization process and an unloading task optimization process which are independent of each other; Step S4-2, using genetic algorithms for the loading task optimization process and the unloading task optimization process respectively, and iteratively solving them with the constraints and objective functions as the optimization goals. During the iterative process, the information of the solutions of the loading task optimization process and the unloading task optimization process are exchanged; finally, the optimal scheduling task set is obtained.
7. The active fault-tolerant dispatching method for unmanned trucks for open-pit mine equipment failure according to claim 6 is characterized in that: In step S4-2, the process of performing the iterative solution using the genetic algorithm specifically includes: The loading and unloading scheduling tasks are encoded as chromosomes to generate an initial population, which includes multiple different chromosomes. The fitness of each chromosome is evaluated according to the objective function and constraints, and chromosomes with high fitness are selected for crossover and mutation operations to generate a new population.
8. The method for active fault-tolerant dispatching of unmanned trucks for open-pit mine equipment failure according to claim 7 is characterized in that: The initial population is generated by using a linear programming method to determine the real-time optimal solution of the loading task optimization process and the unloading task optimization process under fault and non-fault conditions, obtaining the corner point information corresponding to the optimistic solution and the pessimistic solution, and using the corner point information as the initial population of the genetic algorithm.
9. The active fault-tolerant dispatching method for unmanned trucks for open-pit mine equipment failure according to claim 8, characterized in that: Step S5 specifically includes: The loading and unloading scheduling tasks in the optimal scheduling task set are transmitted to the unmanned truck control system to control the driving and operation of the truck.