Reliability Analysis Method for Vehicle-Road-Cloud Collaborative Operation and Computer-Readable Storage Medium

Abstract

This invention relates to a reliability analysis method for vehicle-road-cloud collaborative operation and a computer-readable storage medium. The method includes determining the initial state of the system, determining the reliability of the unmanned mining truck group (UMC), first determining the reliability of a single UMC, and then determining the overall reliability of the entire UMC group by analyzing the reliability of the group under different numbers of UMC failures; determining the reliability of the system under different operating modes, and determining the reliability of the system under different operating modes after the next UMC is deployed, based on whether the next UMC is successfully deployed. This invention achieves reliability analysis and prediction under different operating modes by separating the reliability of the UMC fleet, roadside equipment, and cloud control platform. Based on this, the subsequent operation of the system can be predicted, and the operating mode of the system can be rationally selected and maintenance and upkeep planned accordingly.

Description

Technical Field

[0001] This invention relates to the field of reliability analysis technology for unmanned transportation systems, specifically to a reliability analysis method for vehicle-road-cloud collaborative operation and a computer-readable storage medium. Background Technology

[0002] Besides policy support, demand is also accelerating the implementation of unmanned mining operations. For a long time, mines have faced pain points such as difficulty in recruiting workers, high labor costs, and safety issues, which have also spurred huge demand in the unmanned driving market. The intelligent and unmanned construction of mines has become the only way to solve these pain points.

[0003] Unmanned mining transportation systems are an organic whole composed of unmanned mining trucks, roadside equipment, and a cloud control platform. They involve a wide range of disciplines, advanced technologies, and high economic benefits. Therefore, from research and development to engineering commissioning and operation and maintenance, all stages require the use of reliability management and other methods to conduct a comprehensive analysis, design, and optimization of the reliability of vehicle-road-cloud collaborative operation, in order to ensure the smooth progress of unmanned transportation system projects.

[0004] Traditional quality management and reliability engineering tools have varying degrees of shortcomings and deficiencies when performing reliability modeling and analysis of unmanned transportation systems in open-pit mines:

[0005] ① The established mathematical model for system reliability is too complex, making it impossible to calculate and solve it;

[0006] ② Too many theoretical assumptions make the reliability model difficult to understand, thus losing its practical application significance;

[0007] ③ It is difficult to make timely adjustments based on changes in the structure of the unmanned transportation system, resulting in poor model adaptability;

[0008] ④ The model is too sensitive to changes in system parameters, resulting in poor robustness.

[0009] ⑤ Reliability assessment relies on extensive debugging and testing to acquire data; however, the weights of various events change dynamically, making it difficult to comprehensively respond to real-time reliability changes during the operation of the unmanned transportation system.

[0010] Unmanned transportation systems typically include a cloud control platform, roadside equipment, and a fleet of unmanned vehicles. This fleet usually comprises multiple mining trucks operating at a mining site, which can be coordinated and controlled via the cloud platform to form work teams. For such physically complex unmanned systems, current technology lacks specific reliability methods. Failure mode analysis during system design and maintenance plan design during system operation both rely heavily on system reliability analysis. Therefore, a suitable reliability analysis method for such complex systems with fleets of unmanned vehicles is needed from both a design and usage perspective. Summary of the Invention

[0011] Given that there is currently no solution in the technology for reliability analysis of complex unmanned transportation systems such as unmanned mining operation systems, which include unmanned vehicle fleets, roadside equipment, and cloud control platforms, this invention provides a reliability analysis method for vehicle-road-cloud collaborative operation and a computer-readable storage medium.

[0012] The technical solution of the present invention is as follows:

[0013] A reliability analysis method for vehicle-road-cloud cooperative operation includes the following steps:

[0014] S1. Determine the initial state of the system by collecting data from the cloud control platform, roadside equipment, and unmanned mining trucks to determine the current operating status of each part of the system;

[0015] S2. Determine the reliability of the unmanned mining truck unit. The unmanned mining truck unit refers to the subsystem composed of all unmanned mining trucks that have been put into operation. First, determine the reliability of a single unmanned mining truck, and then determine the overall reliability of the entire unmanned truck unit by the reliability of the unit under different numbers of unmanned vehicle failures.

[0016] S3. Determine the reliability of the system under different operating modes. Based on whether the next unmanned mining truck is successfully deployed, determine the reliability of the system under different operating modes after the next unmanned mining truck is deployed. Different operating modes include cloud control platform operation control mode, roadside equipment collaborative control mode, and single vehicle autonomous control mode.

[0017] Preferably, in step S2, which determines the reliability of the unmanned mining truck group, the overall reliability P of the entire unmanned truck group is... s,k for:

[0018]

[0019] In the formula, M represents the total number of unmanned mining vehicles in the vehicle group, and R n,k Let R represent the failure rates of the k unmanned mining trucks in the nth case, 1-R n,jThis represents the individual reliability of each of the Mk normally operating unmanned mining trucks in the nth case, i.e., the individual unmanned mining truck reliability P corresponding to each unmanned mining truck. s .

[0020] Preferably, the reliability P of each unmanned mining truck is... s for:

[0021]

[0022] Preferably, in step S3, which determines the reliability of the system under different operating modes,

[0023] The system reliability under the cloud control platform's operating control mode is:

[0024] P G，i =P G1，i +P G2，i

[0025] The system reliability under the roadside equipment cooperative control mode is:

[0026] P M，i =P M1，i +P M2，i

[0027] The system reliability under the three autonomous vehicle control modes is as follows:

[0028] P S，i =P S1，i +P S2，i

[0029] Where, p g,i Indicates the reliability of the cloud control platform; p m,i Indicates the reliability of roadside equipment; p s,i Indicates the operational reliability of the unmanned vehicle fleet; p G,i Indicates the reliability of the cloud control platform's operating control mode; p M,i Indicates the reliability of the roadside equipment's supported modes; p S,i This indicates the reliability of the autonomous operation mode of the unmanned vehicle fleet. C1, C2, and C3 are predetermined coefficients. Subscript 1 indicates the case where the next unmanned mining truck is successfully deployed, and subscript 2 indicates the case where the next unmanned mining truck fails to be deployed.

[0030] Preferably,

[0031] If the i-th unmanned mining truck successfully departs...

[0032] The system reliability under the cloud control platform's operating control mode is:

[0033] P G1，i =C1P r，i +C2Ps，i +C3P g，i

[0034] The system reliability under the roadside equipment cooperative control mode is:

[0035] P M1，i =(1-P G1，i )P m，i

[0036] The system reliability under the three autonomous vehicle control modes is as follows:

[0037] P s1，i =[1-(1-P G1，i )P m，i ]P s，i +(1-P G1，i (1-P) m，i )P s，i

[0038] If the i-th unmanned mining truck fails to depart successfully

[0039] The system reliability under the cloud control platform's operating control mode is:

[0040] P G2，i =C1(1-P r，i +C2P s，i-1 +C3P g，i

[0041] The system reliability under the roadside equipment cooperative control mode is:

[0042] P M1，i =(1-P G2，i )P m，i

[0043] Three modes of autonomous vehicle control

[0044] P s2，i =[1-(1-P G2，i) P m，i ]P s，i-1 +(1-P G2，i (1-P) m，i )P s，i-1 .

[0045] Preferably, the reliability p of the cloud control platform g,i Roadside equipment reliability p m,i Reliability of unmanned vehicle fleet operation p s,i The weights between them are

[0046]

[0047] in:

[0048]

[0049] r n，1 Let i be the weighting factor, i be the dimension of the weighting factor, and n take values ​​of 1, 2, and 3 to represent the reliability p of the cloud control platform, respectively. g,i Roadside equipment reliability p m,i Reliability of unmanned vehicle fleet operation p s,i The weight.

[0050] Preferably, the step of S1, which determines the initial state of the system, includes determining whether a given unmanned mining vehicle is put into operation.

[0051] Preferably, the step of S1 in determining the initial state of the system includes the process of analyzing the reliability of the unmanned mining trucks and determining the order of the unmanned mining trucks to be deployed in the next round.

[0052] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the vehicle-road-cloud cooperative operation reliability analysis method described in any of the above claims.

[0053] This invention achieves reliability analysis and prediction under different system operating modes by separating the reliability of the unmanned mining truck fleet, roadside equipment, and cloud control platform. Based on fundamental reliability data, reliability prediction for different system operating states can be simplified and conveniently completed. On this basis, the system's future operating conditions can be predicted, and the appropriate operating mode and maintenance plan can be selected accordingly.

[0054] This technical solution analyzes the vehicle-road-cloud collaborative operation mode of an unmanned transportation system in open-pit mines. Based on a part-to-whole reliability analysis approach, and combining dynamic and static reliability analysis methods, a comprehensive framework for collaborative reliability risk analysis of the unmanned transportation system is constructed. Based on long-term system operation data, the reliability analysis model of the unmanned transportation system under different collaborative modes in open-pit mines is refined, and weak links in the system are examined and optimized. This will provide strong support for the allocation of system reliability indicators and the improvement of service continuity performance. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0056] Figure 2 This is a schematic diagram illustrating the system reliability analysis under different modes of the present invention. Detailed Implementation

[0057] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. In this specification, the dimensions of the drawings do not represent the actual dimensions. They are only used to illustrate the relative positional and connection relationships between the components. Components with the same name or the same reference numeral represent similar or identical structures and are limited to illustrative purposes.

[0058] One implementation of the unmanned transportation system of this invention is an unmanned operation system applied to open-pit mines. It includes a cloud control platform, roadside equipment, and unmanned mining vehicles within the site. The cloud platform fully utilizes technologies such as the Internet of Things, cloud computing, (mobile) internet, and artificial intelligence to collect traffic information from unmanned mining vehicles in cement mining areas. It incorporates intersection traffic rules and intelligent traffic management algorithms to proactively control complex operations such as mixed formations, parallel groupings, and traffic conflicts. By sending traffic management commands (such as acceleration, deceleration, and temporary stopping) to each unmanned mining vehicle, it fully ensures the traffic safety of the entire open-pit mine fleet, improves overall operational efficiency, and enhances intersection safety. The roadside equipment is a ground system deployed at intersections or areas with high traffic volume in the work area. It collects and identifies information such as traffic flow, emergencies, intersection information, road debris intrusion, and road surface slipperiness, and transmits the sensing results to the cloud control platform in real time. Optionally, it can also achieve local communication between the roadside equipment and the unmanned mining vehicles, independent of the cloud control platform. The unmanned mining truck receives work scheduling instructions, makes decisions based on its own status and environmental perception data, and issues control instructions to the vehicle's drive system, braking system, steering system, lifting system and lighting system to drive the vehicle to perform the scheduling work tasks.

[0059] The system can operate in various modes to maximize resource utilization, depending on different application requirements and actual operating conditions. Specifically, its selectable vehicle-road-cloud collaborative operation modes are mainly divided into three types: cloud control platform operation control, roadside equipment collaborative control, and single-vehicle autonomous control. In cloud control platform operation control, the cloud control platform integrates information uploaded by the unmanned mining truck and roadside equipment to generate a global path, which is then sent to the unmanned mining truck. The unmanned mining truck performs local path planning based on the received global path, and the three work together to achieve the orderly operation of the unmanned mining truck group. In roadside equipment collaborative control mode, the cloud control platform temporarily detaches from its operational state, and direct data interaction between the roadside equipment and the unmanned mining truck enables the information collected by the roadside equipment to be provided to the unmanned mining truck. The unmanned mining truck plans its driving path based on its own sensor information and the data provided by the roadside equipment. Single-vehicle autonomous control relies solely on the unmanned mining truck's own sensor information for path planning.

[0060] Based on the aforementioned system architecture and selectable operating modes, to reduce the complexity of reliability analysis, this invention provides a reliability analysis method. Its core lies in determining the reliability of the unmanned mining truck unit and, based on this, predicting the reliability level under different operating modes. This allows for determining subsequent operating mode selection and the implementation of backup measures, thereby ensuring the safe and stable operation of the system. Figure 1 The general steps of this vehicle-road-cloud collaborative operation reliability analysis method include:

[0061] S1. Determine the initial system state. The current operating status of each part of the system is determined by collecting data from the cloud control platform, roadside equipment, and unmanned mining vehicles. Preferably, for unmanned mining vehicles, this also includes information on whether the unmanned mining vehicle is in operation. This is because real-time reliability analysis of the system does not require analysis of unmanned mining vehicles not being used for human tasks.

[0062] Of course, for unmanned mining trucks that have not yet been deployed, their reliability can still be analyzed in the initial stage, and the order of unmanned mining trucks to be deployed in the next round can be determined accordingly.

[0063] S2. Determine the reliability of the unmanned mining truck unit. An unmanned mining truck unit refers to a subsystem consisting of all unmanned mining trucks already in operation. An unmanned mining truck unit is composed of multiple unmanned mining trucks. Analyzing the reliability of the unit first requires analyzing the reliability of individual unmanned mining trucks. The reliability of an unmanned mining truck refers to its ability to perform its intended functions within a specified time and under specified operating conditions. This is a relatively complex comprehensive performance characteristic, and its main evaluation indicators are the probability of no failure, the cumulative probability of failure, and the failure rate, etc.

[0064]

[0065] Where: P s Indicates the availability status of the unmanned mining truck, MTBF LT Mean Time Between Failures (MTBF) ST Mean Time Between Failures (MTBF) M This indicates the time during which unmanned mining vehicles cannot be used due to other preconditions not being met. In the initial stage of operation, these three parameters are generally obtained through lifespan experiments or tentatively based on relevant empirical values. After long-term operation, these three parameters can be evaluated based on a large amount of unmanned vehicle operation status data over a period of time.

[0066] After obtaining the reliability of a single vehicle, the reliability of the entire unmanned vehicle group under different numbers of vehicle failures can be calculated, and thus the overall reliability of the entire unmanned vehicle group can be obtained. Let P be the reliability of the unmanned vehicle group when k unmanned mining vehicles fail. s,k Its specific value depends on the reliability of each unmanned mining truck under all combinations of conditions, i.e.

[0067]

[0068] In the formula, M represents the total number of unmanned mining vehicles in the vehicle group, and R n，k Let R represent the failure rates of the k unmanned mining trucks in the nth case, 1-R n，j This represents the individual reliability of each of the Mk normally operating unmanned mining trucks in the nth case, i.e., the reliability of P corresponding to each unmanned mining truck. s .

[0069] S3. Determine the system's reliability under different operating modes. See also Figure 2 The calculation diagram is shown below. The reliability of the system in the next round depends on the successful deployment of the next (i-th) unmanned mining truck. This result is not currently confirmed, so the success probability can be considered as P. r，j Success probabilities can usually be estimated in advance based on historical data. Consider smoothing out inconsistencies between different mining trucks by using global data for estimation. Alternatively, after a sufficiently long usage period, it's preferable to use historical data from a single mining truck for estimation, thus ensuring consistency between different mining trucks and improving the accuracy of probability estimation.

[0070] Regardless of the operating mode, reliability depends on whether the unmanned mining truck can successfully depart.

[0071] If the i-th unmanned mining truck successfully departs...

[0072] The system reliability under the cloud control platform's operating control mode is:

[0073] P G1，i =C1P r，i +C2P s，i +C3P g，i

[0074] The system reliability under the roadside equipment cooperative control mode is:

[0075] P M1，i =(1-P G1，i )P m，i

[0076] The system reliability under the three autonomous vehicle control modes is as follows:

[0077] P s1，i =[1-(1-P G1，i )P m，i ]P s，i +(1-P G1，i (1-P) m，i )P s，i

[0078] If the i-th unmanned mining truck fails to depart successfully

[0079] The system reliability under the cloud control platform's operating control mode is:

[0080] P G2，i =C1(1-P r，i +C2P s，i-1 +C3P g，i

[0081] The system reliability under the roadside equipment cooperative control mode is:

[0082] P M1，i =(1-P G2，i )P m，i

[0083] Three modes of autonomous vehicle control

[0084] P s2，i =[1-(1-P G2，i )P m，i ]P s，i-1 +(1-P G2，i (1-P) m，i )P s，i-1

[0085] Therefore, by combining the two scenarios above, the reliability of the system in the next round under different operating modes is obtained as follows:

[0086] The system reliability under the cloud control platform's operating control mode is:

[0087] P G，i =P G1，i +P G2，i =C1P r，i +C2P s，i +C3P g，i

[0088] The system reliability under the roadside equipment cooperative control mode is:

[0089] P M，i =P M1，i +P M2，i =(2-P) G，i )P m，i =2P m，i -[C1+C2(P s，i +P s，i-1 )+C3P g，i ]P m，i

[0090] The system reliability under the three autonomous vehicle control modes is as follows:

[0091] P S，i =PS1，i +P S2，i

[0092] =[1-(1-P G1，i )P m，i ]P S，i +(1-P G1，i (1-P) m，i )P S，i +[1-(1-P G2，i )P m，i ]P S，i-1 +(1-P G2，i (1-P) m，i )P S，i-1

[0093] In the above formulas, p g，i Indicates the reliability of the cloud control platform; p m，i Indicates the reliability of roadside equipment; p s，i Indicates the operational reliability of the unmanned vehicle fleet; p G，i Indicates the reliability of the cloud control platform's operating control mode; p M，i Indicates the reliability of the roadside equipment's supported modes; p S，i This indicates the reliability of the autonomous operation mode of the unmanned vehicle fleet. C1, C2, and C3 are predetermined coefficients.

[0094] Regarding the reliability P of the cloud control platform g The cloud control platform provides communication links, real-time data on all traffic elements, and a real-time operating environment for collaborative applications. Based on vehicle and traffic operation optimization needs, the cloud control platform provides unified control and management of the cloud control infrastructure and collaborative applications. To better support collaborative applications with varying requirements for real-time performance and service intensity, the cloud control platform features an edge cloud, regional cloud, and central cloud hierarchical architecture, each level further composed of multiple systems / devices. Taking a single server as an example, its reliability calculation can select server parameter information as a feature quantity to establish a Markov model (HMM), enabling the judgment of the reliability status of a single server, estimating its development trend, obtaining fault prediction, and determining the reliability P of the cloud control platform. g Based on the completion of the fault prediction model at the individual device level, a system-level fault prediction model can be further constructed according to the logical relationships between each device in the system. The relationships between individual devices and the system in a cloud control platform often manifest as intricate network relationships, which can be simplified into a description of the logical relationships between devices using a Bayesian network model.

[0095] For roadside equipment reliability P mCalculations. Roadside equipment is mainly electronic. When analyzing the reliability of electronic equipment, common reliability models can be broadly categorized as follows: exponential distribution model, Weibull distribution model, (log-)normal distribution model, gamma distribution model, and extreme value distribution model. In practical applications, an appropriate mathematical model should be scientifically selected based on the actual situation of the research objective. Then, based on relevant data from the usage period, simulation tests of the distribution should be conducted to ultimately confirm the suitability of the selected model.

[0096] Furthermore, the reliability assessment can be refined by summarizing several influencing factors affecting the reliability of the vehicle-road-cloud collaborative operation of the unmanned transportation system in open-pit mines. This allows for a more comprehensive system-level evaluation. Based on the above analysis, the unmanned transportation system in open-pit mines can be categorized into three parts: the cloud control platform, roadside equipment, and the unmanned vehicle fleet. The influencing factors for each part are then analyzed to determine reliability allocation factors. These factors include importance, recoverability, operating time, vehicle-grade maturity, and spare parts inventory levels, allocating weight coefficients (r) between 1 and 10. n，i If n = 1, 2, 3, i = 1, 2, 3, 4, 5, then the total weight of the system is...

[0097]

[0098] Therefore, the allocation values ​​for each part are obtained:

[0099]

[0100] By combining the assigned values ​​of each part with the failure rate, the reliability assessment results of each part can be obtained.

[0101] S4. Fault Prevention and Diagnosis

[0102] Based on the system's reliability results under the different modes described above, the optimal operating mode for the system is determined. Potential failure risks are scored, and for failures with high severity, the system may consider self-checks, and failure risk reports should be issued in advance so that maintenance personnel can be aware of relevant issues and develop contingency plans as early as possible. This improves the timeliness of fault handling.

[0103] This analysis process requires: ① Identifying the logical relationships between components during business function execution based on various task information, and outlining the functional architecture of each part; ② Constructing an unmanned transportation system operation management database, primarily for storing key operational data, fault data, performance data, and operation records; ③ Comprehensively utilizing expert knowledge, prior information, and observation data to evaluate the reliability of each component based on Markov chains; ④ Constructing a system state diagnosis relationship network based on Bayesian networks, and further building reliability models for various operational tasks under different operating modes; ⑤ Evaluating the overall reliability of the mine's unmanned transportation system. Classified by business type, the main business types of the unmanned transportation system include: shoveling-loading-transporting-unloading-stopping-entry / exiting the garage. Therefore, the system's functional architecture needs to be based on time, defining the key components for collaborative operation of the unmanned transportation system according to the hierarchical relationship of system-subsystem-key module. Establishing a system operation database based on the operation management data analysis of the unmanned transportation system is an important tool for improving system operational capabilities and the management capabilities of platform maintenance personnel. Based on system operation and fault data, the system's performance can be effectively reflected and predicted, fault handling plans can be improved, and the recurrence of various faults can be avoided in a timely manner. Therefore, the database can rely on actual system operation data to support research on unmanned mining truck fault prediction technology and roadside equipment fault prediction and rapid interruption recovery technology.

[0104] In the process of system status diagnosis, each business function of the unmanned transportation system can be used as a basic diagnostic object. Through comprehensive calculation of posterior probabilities, an optimally matched functional diagnostic Bayesian network structure is constructed. Taking the reliability analysis of loading and parking operations as an example, the time synchronization operation and fault information of unmanned mining trucks and the cloud control platform during a certain period of system operation are statistically analyzed to determine various fault modes, fault causes, impact levels, collaborative operation equipment, compensation measures, and other information. Based on the "loading-transportation-unloading" operation reliability model constructed based on the Bayesian network, and using Markov chains to calculate the reliability of each node, the conditional probability table of key nodes in the Bayesian network shown in the figure below can be obtained. The analysis results show that the operational reliability of the unmanned transportation system can reach 98%. If the loading operation is in a "fault state," it is necessary to determine the fault state probability of each module in turn. Among them, the system software fault rate is 85%, the network transmission fault rate is 18%, and the fault state rate of other products is less than 2%. Therefore, the weak link of the current system is mainly the system software implementation.

[0105] This technical solution analyzes the vehicle-road-cloud collaborative operation mode of an unmanned transportation system in open-pit mines. Based on a part-to-whole reliability analysis approach, and combining dynamic and static reliability analysis methods, a comprehensive framework for collaborative reliability risk analysis of the unmanned transportation system is constructed. The feasibility of this solution is preliminarily verified through the analysis results of the unmanned vehicle fleet reliability, cloud control platform reliability, and roadside equipment reliability. Due to the uncertainties and constraints of various conditions and resources during the collaborative operation of the unmanned transportation system in open-pit mines, comprehensively utilizing expert knowledge, prior information, and observational data is an important approach for conducting quantitative and qualitative analyses of various business operations in the collaborative operation of the unmanned transportation system in open-pit mines. Therefore, refining the reliability analysis model of the unmanned transportation system in open-pit mines under different collaborative modes based on long-term system operation data, and examining and optimizing weak links in the system, will provide strong support for the allocation of system reliability indicators and the improvement of service continuity performance.

[0106] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0108] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0110] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A reliability analysis method for vehicle-road-cloud cooperative operation, characterized in that, Includes the following steps: S1. Determine the initial state of the system by collecting data from the cloud control platform, roadside equipment, and unmanned mining trucks to determine the current operating status of each part of the system; S2. Determine the reliability of the unmanned mining truck unit. The unmanned mining truck unit refers to the subsystem consisting of all unmanned mining trucks already in operation. First, determine the reliability of a single unmanned mining truck. Then, determine the overall reliability of the entire unmanned truck unit by assessing the reliability of the unit under different numbers of unmanned truck failures. The overall reliability P of the entire unmanned truck unit... s，k for: M represents the total number of unmanned mining trucks in the vehicle group, R n，i Let R represent the failure rate of each of the i unmanned mining vehicles in the nth case, 1-R n，j This represents the individual reliability of each of the j normally operating unmanned mining trucks in the nth case, i.e., the individual unmanned mining truck reliability P corresponding to each unmanned mining truck. s The reliability P of each unmanned mining truck. s for: MTBF LT Mean Time Between Failures (MTBF) ST Mean Time Between Failures (MTBF) M This indicates the time during which the unmanned mining truck cannot be used due to other preconditions not being met; S3. Determine the system's reliability under different operating modes. Based on whether the next unmanned mining truck is successfully deployed, determine the system's reliability under different operating modes after the next unmanned mining truck is deployed. These different operating modes include cloud control platform operation mode, roadside equipment collaborative control mode, and single-vehicle autonomous control mode. The system reliability under the cloud control platform operation mode is: P G，i =P G1，i +P G2，i The reliability of the system under the roadside equipment cooperative control mode is: P M，i =P M1，i +P M2，i The system reliability under the three autonomous vehicle control modes is: P S，i =P S1，i +P S2，i ; P G，i Indicates the reliability of the cloud control platform's operating control mode; P M，i Indicates the reliability of the roadside equipment support mode; P S，i Let C1, C2, and C3 represent the system reliability under the three autonomous control modes of a single vehicle, where C1, C2, and C3 are predetermined coefficients. If the i-th unmanned mining vehicle successfully departs, the system reliability under the cloud control platform's operation mode is: P G1，i =C1P r，i +C2P s，i +C3P g，i The reliability of the system under the roadside equipment cooperative control mode is: P M1，i =(1-P G1，i )P m，i The system reliability under the three autonomous vehicle control modes is: P S1，i =[1-(1-P G1，i )P m，i ]P s，i +(1-P G1，i (1-P) m，i )P s，i If the i-th unmanned mining vehicle fails to depart, the system reliability under the cloud control platform's operating control mode is: P G2，i =C1(1-P r，i +C2P s，i-1 +C3P g，i The reliability of the system under the roadside equipment cooperative control mode is: P M2，i =(1-P G2，i )P m，i The system reliability under the three autonomous vehicle control modes is: P S2，i =[1-(1-P G2，i )P m，i ]P s，i-1 +(1-P G2，i (1-P) m，i )P s，i-1 ;P g，i Indicates the reliability of the cloud control platform; P m，i Indicates the reliability of roadside equipment; P s，i This indicates the reliability of the unmanned vehicle fleet.

2. The vehicle-road-cloud cooperative operation reliability analysis method as described in claim 1, characterized in that, The step S1, which determines the initial state of the system, includes determining whether a given unmanned mining vehicle should be put into operation.

3. The reliability analysis method for vehicle-road-cloud cooperative operation as described in claim 1, characterized in that, The step S1 in determining the initial state of the system includes analyzing the reliability of the unmanned mining trucks and determining the order in which the unmanned mining trucks are expected to be deployed in the next round.

4. A computer-readable storage medium, characterized in that, The system contains a computer program that, when executed by a processor, causes the processor to perform the steps of the vehicle-road-cloud cooperative operation reliability analysis method as described in any one of claims 1-3.

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