Reliability distribution method and device for mining dump truck, terminal and storage medium

Abstract

The invention discloses a reliability distribution method and device for a mining dump truck, a terminal and a storage medium in the technical field of engineering machinery, and aims at solving the problems that in the prior art, the subjective evaluation dimension is single, and the importance weight of the evaluation factor of each subsystem is solidified. The method comprises the steps of obtaining structure data of a vehicle, and performing product hierarchy division on the vehicle according to the structure data of the vehicle so as to obtain a plurality of subsystems; determining influence factors influencing reliability distribution, and setting a reliability index target; according to the method, the weights of evaluation factors are calculated by introducing a CRITIC weight method, and the subjective allocation weight of each subsystem is obtained in combination with a score mean value; meanwhile, according to a preset time window, a Hort two-parameter exponential smoothing method is adopted to calculate the historical fault proportion of each subsystem, and the objective distribution weight of each subsystem is further obtained; and the flexibility and accuracy of the reliability distribution result are improved.

Description

Technical Field

[0001] The invention relates to a reliability distribution method for a mining dump truck, and belongs to the technical field of engineering machinery. Background Art

[0002] Reliability allocation refers to the process of assigning a system's reliability indicators to its various components according to specific criteria. Mining dump trucks operate in harsh working environments, and their reliability directly impacts mine production efficiency and safety. Existing reliability allocation methods, such as the equal allocation method, the similar product method, and the AGREE method, while valuable in certain areas, have limitations in complex systems like mining dump trucks. The equal allocation method assumes that each component has the same reliability level, which does not hold true in complex systems with multiple types of components. The similar product method relies on the availability of historical data, but the complex structure and operating conditions of mining dump trucks make historical data acquisition and processing difficult. The AGREE method requires pre-determined operating time information for each component, which is often limited by the accuracy of this information for complex systems. Therefore, a method that comprehensively considers subjective and objective factors is necessary when allocating reliability indicators for mining dump truck reliability design.

[0003] Existing reliability allocation methods, such as the invention with patent number CN114925468A, propose a correction coefficient reliability allocation method based on historical data, including allocating reliability indicators in proportion to the number of failures of similar products; forming an adjustment coefficient based on the characteristics of new products and the evaluation of the demand side and the developer, and revising the aforementioned allocation results; determining the minimum acceptable value and design input value to more effectively convey reliability requirements in various environments such as design, manufacturing, testing and management; existing methods have a single subjective evaluation dimension, the importance weight of the evaluation factors of each subsystem is fixed, and changes in product reliability are not taken into account, which affects the flexibility and accuracy of the reliability allocation results. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a reliability allocation method, device, terminal and storage medium for mining dump trucks. The CRITIC weight method is introduced to calculate the weights of evaluation factors, and the subjective allocation weights of each subsystem are obtained by combining the score averages. At the same time, the Holt two-parameter exponential smoothing method is used to calculate the historical failure proportion of each subsystem according to a preset time window, and the objective allocation weights of each subsystem are further obtained. The comprehensive allocation weights are integrated based on the fusion of subjective allocation weights and objective allocation weights to solve the problem that the current reliability allocation method is not suitable for mining dump trucks, thereby improving the flexibility and accuracy of the reliability allocation results.

[0005] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0006] The present invention provides a reliability allocation method for a mining dump truck, comprising:

[0007] Step a: Obtain vehicle structural data, and divide the vehicle into product hierarchies based on the vehicle structural data to obtain multiple subsystems;

[0008] Step b: Determine the factors that affect reliability allocation and set reliability index targets;

[0009] Step c: Obtain scores from multiple experts on the influencing factors of each subsystem, and form a score mean matrix based on the scores from multiple experts on the influencing factors of each subsystem; use the CRITIC method to calculate the weight of each influencing factor of each subsystem and form a first weight matrix; perform weighted aggregation on the first weight matrix and the score mean matrix to calculate the subsystem reliability subjective allocation weight matrix;

[0010] Step d: Obtain the vehicle type, and based on the vehicle type, obtain historical failure data for each subsystem of similar products. Based on the historical failure data for each subsystem and a preset time window, use the Holt two-parameter exponential smoothing method to calculate the failure count matrix for each subsystem. Based on the failure count matrix for each subsystem, obtain the final smoothed failure count, and calculate the objective weight matrix for subsystem reliability based on the final smoothed failure count.

[0011] Step e: Obtain the trust parameter of the subjective weight, and calculate the subsystem reliability comprehensive distribution weight matrix based on the trust parameter of the subjective weight, the subsystem reliability subjective distribution weight matrix, and the subsystem reliability objective distribution weight matrix;

[0012] Step f: Obtain the reliability model category of each subsystem, comprehensively allocate the weight matrix according to the subsystem reliability, obtain the reliability of each subsystem, calculate the index value according to the reliability model category of each subsystem and the reliability of each subsystem, and compare the index value with the reliability index target. If the index value is greater than the reliability index target, the work is completed; if the index value is not greater than the reliability index target, the feedback adjustment mechanism is triggered and steps c, d, e and f are repeated.

[0013] Furthermore, the multiple subsystems include a power system, a transmission system, a hydraulic system, an electrical system, a driving system, a steering system, a body and a cargo box, a braking system and an auxiliary system.

[0014] Furthermore, the factors affecting reliability distribution include the severity of the mine environment, the severity of the fault hazard, the complexity of subsystem coupling, the maturity of design and manufacturing, maintenance accessibility and the difficulty of improving reliability.

[0015] Furthermore, the step c specifically includes:

[0016] Obtain scores from multiple experts on the influencing factors of each subsystem. Based on the scores from multiple experts on the influencing factors of each subsystem, calculate the mean of each influencing factor of each subsystem and form a score mean matrix. Use the CRITIC method to calculate the weight of each influencing factor of each subsystem and form a first weight matrix. Perform weighted aggregation on the first weight matrix and the score mean matrix to obtain a comprehensive score for each subsystem. Based on the comprehensive score of each subsystem, calculate the subsystem reliability subjective allocation weight matrix.

[0017] According to the scores of multiple experts on the influencing factors of each subsystem, an expert scoring matrix is ​​formed ,in, Indicates the Experts on The first subsystem Rating of influencing factors;

[0018] According to the expert scoring matrix, the mean value of each influencing factor of each subsystem is calculated. The specific expression is as follows:

[0019] ;

[0020] Where: For the The first subsystem The mean of the influencing factors, The expert's label, is the total number of experts; is the subsystem label, ; is the total number of subsystems; is the label of the influencing factor, , is the total number of influencing factors;

[0021] Forming the mean score matrix ;

[0022] The CRITIC method is used to calculate the weights of each influencing factor of each subsystem and form the first weight matrix, which specifically includes:

[0023] Summarize the experts’ scores on the influencing factors of each subsystem and obtain the independent scoring matrix of each subsystem , an independent scoring matrix for each subsystem Perform standardization to generate a standardized matrix , the specific expression is as follows:

[0024] Obtain positive and negative indicators based on influencing factors. For positive indicators:

[0025] ;

[0026] For negative indicators:

[0027] ;

[0028] Generate a standardized matrix based on positive and negative indicators:

[0029] ;

[0030] Among them, negative indicators include the severity of fault hazards and the maturity of design and manufacturing; positive indicators include the severity of the mining environment, the complexity of subsystem coupling, maintenance accessibility and the difficulty of improving reliability; Indicates the Experts on The first subsystem The standardized value of the influencing factor score, Indicates that all experts The first subsystem The minimum score of the influencing factors, Indicates that all experts The first subsystem The maximum score of the influencing factors;

[0031] Calculate the standard deviation of each influencing factor of each subsystem , the specific expression is as follows:

[0032] ;

[0033] ;

[0034] Where: express Experts on The first subsystem The average score standardized value of the influencing factors;

[0035] Calculate the The first subsystem factors and other influencing factors Correlation coefficient , and obtain the Pearson correlation coefficient matrix between each influencing factor in each subsystem , the specific expression is as follows:

[0036] ;

[0037] Where: Indicates the Experts on The first subsystem The standardized score value of each influencing factor, express Experts on The first subsystem The average score standardization value of the influencing factors, For the The system's The standard deviation of the standardized values ​​of the scores of the influencing factors;

[0038] According to the standard deviation of each influencing factor of each subsystem And the Pearson correlation coefficient matrix between each influencing factor in each subsystem , calculate the information amount of each influencing factor in each subsystem , the specific expression is as follows:

[0039] ;

[0040] in, Indicates the independent contribution between indicators;

[0041] The amount of information about each influencing factor in each subsystem Perform normalization to form the first weight matrix , the specific expression is as follows:

[0042] ;

[0043] Where: For the The first subsystem The amount of information after normalization of the influencing factors;

[0044] Perform weighted aggregation on the first weight matrix and the score mean matrix to obtain the weighted matrix , the specific expression is as follows:

[0045] ;

[0046] Further obtain the comprehensive score of each subsystem , the specific expression is as follows:

[0047] ;

[0048] According to the comprehensive score of each subsystem , calculate the subjective weight matrix of subsystem reliability , the specific expression is as follows:

[0049] ;

[0050] Where: For the The subjective weight of each subsystem.

[0051] Furthermore, the step d specifically includes:

[0052] Obtain the vehicle type, and based on the vehicle type, obtain the historical fault data of each subsystem of similar products, and summarize the historical fault count sequence of each subsystem within a preset time window;

[0053] Holt's two-parameter exponential smoothing method is used to iteratively calculate the historical fault number sequence to obtain the fault number matrix of each subsystem , the specific expression is as follows:

[0054] ;

[0055] Where: is the time window, which is a positive integer greater than zero; For the subsystems in the time window The number of smoothing failures, For the subsystems in the time window The number of historical failures, For the subsystems in the time window The smoothing failure times, smoothing level coefficient The value range is ; For the subsystems in the time window The smoothed failure trend number, For the subsystems in the time window The smoothed fault trend number is calculated as follows:

[0056]

[0057] Where, is the smoothing trend coefficient, and its value range is ;

[0058] According to the failure number matrix of each subsystem , get the final smoothed fault count matrix ;in, The last period within the preset time window The value of For the The final smoothed failure count of each subsystem in the time window T;

[0059] Calculate the objective distribution weight matrix of subsystem reliability based on the final smoothed failure number , the specific expression is as follows:

[0060] ;

[0061] Where: For the The objective weight of each subsystem.

[0062] Furthermore, the step e specifically includes:

[0063] A dynamic weighted objective function is established to solve each subsystem. The specific expression is as follows:

[0064] ;

[0065] Where: To be solved The comprehensive weight of each subsystem, For the The subjective weight of each subsystem, For the Trust parameters of the subjective weights of each subsystem;

[0066] The trust parameter of obtaining subjective weight , specifically including:

[0067] Calculate the predicted failure count matrix of each subsystem in the next X years , the specific expression is as follows:

[0068] ;

[0069] Where: For the The predicted number of failures of each subsystem in year X;

[0070] According to the predicted failure number matrix , obtain the trust parameter of subjective weight , the specific expression is as follows:

[0071] ;

[0072] According to the subjective weight trust parameter, the subsystem reliability subjective distribution weight matrix and the subsystem reliability objective distribution weight matrix, the dynamic weighted objective function is solved by constructing the Lagrangian function to obtain the subsystem reliability comprehensive distribution weight matrix , the specific expression is as follows:

[0073] ;

[0074] Where: For the The comprehensive weight of each subsystem.

[0075] Furthermore, the step f specifically includes:

[0076] Obtain the reliability model category of each subsystem;

[0077] According to the comprehensive allocation weight matrix of subsystem reliability, the reliability of each subsystem is obtained, including:

[0078] Convert reliability target into failure rate requirement , the specific expression is as follows:

[0079] ;

[0080] Where: It is the reliability index target;

[0081] According to the failure rate requirements , calculate the failure rate metric for the distribution , the specific expression is as follows:

[0082] ;

[0083] Where: Allocate redundancy for reliability;

[0084] Get the failure rate indicator matrix , , the specific expression is as follows:

[0085] ;

[0086] Where: For the Failure rate indicators of each subsystem;

[0087] According to the failure rate indicator matrix , obtain the reliability of each subsystem , the specific expression is as follows:

[0088] ;

[0089] Where: For the Reliability of each subsystem; is a natural constant; is the target time;

[0090] Calculate the index value based on the reliability model category and reliability of each subsystem, and compare the index value with the reliability index target. If the index value is greater than the reliability index target, the work is completed. Specifically, it includes:

[0091] If the reliability model category of each subsystem is a series model, the index value is ;

[0092] If the reliability model category of each subsystem is a parallel model, the index value is ;

[0093] If the reliability model category of each subsystem is other than the series model and the parallel model, the model is converted to the series model or the parallel model, and the index value is recalculated;

[0094] If the indicator value is not greater than the reliability indicator target, the feedback adjustment mechanism is triggered and steps c, d, e, and f are repeated. The feedback adjustment mechanism specifically includes:

[0095] Improve reliability by allocating redundancy, adding new scoring experts, and re-evaluating historical failure data for each subsystem.

[0096] In a second aspect, the present invention provides a reliability allocation device for a mining dump truck, comprising:

[0097] Product hierarchy division module: used to obtain vehicle structural data, divide the vehicle into product hierarchies based on the vehicle structural data to obtain multiple subsystems; determine the factors affecting reliability allocation and set reliability indicator targets;

[0098] Subjective allocation weight determination module: used to obtain scores of multiple experts on the influencing factors of each subsystem, calculate the mean of each influencing factor of each subsystem based on the scores of multiple experts on the influencing factors of each subsystem, and form a score mean matrix; use the CRITIC method to calculate the weight of each influencing factor of each subsystem and form a first weight matrix; perform weighted aggregation on the first weight matrix and the score mean matrix to obtain the comprehensive score of each subsystem; calculate the subsystem reliability subjective allocation weight matrix based on the comprehensive score of each subsystem;

[0099] Objective allocation weight determination module: used to obtain vehicle type, obtain historical failure data of each subsystem of similar products based on the vehicle type, calculate the failure count matrix of each subsystem based on the Holt two-parameter exponential smoothing method based on the historical failure data of each subsystem and a preset time window, obtain the final smoothed failure count based on the failure count matrix of each subsystem, and calculate the objective allocation weight matrix of subsystem reliability based on the final smoothed failure count;

[0100] Comprehensive allocation weight calculation module: used to obtain the trust parameters of subjective weights, and calculate the comprehensive allocation weight matrix of subsystem reliability based on the trust parameters of subjective weights, the subjective allocation weight matrix of subsystem reliability, and the objective allocation weight matrix of subsystem reliability;

[0101] Reliability index allocation module: used to obtain the reliability model category of each subsystem, comprehensively allocate weight matrix according to the subsystem reliability, obtain the reliability of each subsystem, calculate the index value according to the reliability model category of each subsystem and the reliability of each subsystem, compare the index value with the reliability index target, if the index value is greater than the reliability index target, the work is completed; if the index value is not greater than the reliability index target, the feedback adjustment mechanism is triggered, and the steps of subjective allocation weight determination module, objective allocation weight determination module, comprehensive allocation weight calculation module and reliability index allocation module are repeated.

[0102] In a third aspect, the present invention provides a terminal including a processor and a storage medium;

[0103] The storage medium is used to store instructions;

[0104] The processor is configured to operate according to the instructions to execute the steps of the method according to the first aspect.

[0105] In a fourth aspect, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0106] Compared with the prior art, the present invention has the following beneficial effects:

[0107] 1. This invention uses the CRITIC weighting method to calculate the weights of evaluation factors, and combines the average score to obtain the subjective allocation weight of each subsystem. At the same time, the Holt two-parameter exponential smoothing method is used to calculate the historical failure proportion of each subsystem according to a preset time window, and further obtain the objective allocation weight of each subsystem. Based on the fusion of subjective and objective allocation weights, a comprehensive allocation weight is generated to address the problem that current reliability allocation methods are not suitable for mining dump trucks, thereby improving the flexibility and accuracy of reliability allocation results.

[0108] 2. The present invention takes into account the importance weights of the evaluation factors of each subsystem: a comprehensive evaluation is conducted on the importance of the evaluation factors of each subsystem based on the characteristics of each subsystem, thereby solving the problem of inconsistent importance of various influencing factors of each subsystem; considering the factors that the reliability level of the product changes over time, and considering the continuous improvement of the reliability level of most subsystems, by calculating the number of historical failure levels and trend numbers according to a preset time window, the calculation proportion results of historical failures are more in line with engineering reality, ensuring the flexibility and accuracy of the reliability distribution results. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 The figure is a flow chart of a reliability allocation method for a mining dump truck provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0110] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0111] The term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " generally indicates an "or" relationship between the related objects.

[0112] Example 1:

[0113] like Figure 1 As shown, the present invention provides a reliability allocation method for mining dump trucks, comprising:

[0114] Step a: Obtain vehicle structural data, and divide the vehicle into product hierarchies based on the vehicle structural data to obtain multiple subsystems;

[0115] Step b: Determine the factors that affect reliability allocation and set reliability index targets;

[0116] Step c: Obtain scores from multiple experts on the influencing factors of each subsystem, and form a score mean matrix based on the scores from multiple experts on the influencing factors of each subsystem; use the CRITIC method to calculate the weight of each influencing factor of each subsystem and form a first weight matrix; perform weighted aggregation on the first weight matrix and the score mean matrix to calculate the subsystem reliability subjective allocation weight matrix;

[0117] Step d: Obtain the vehicle type, and based on the vehicle type, obtain historical failure data for each subsystem of similar products. Based on the historical failure data for each subsystem and a preset time window, use the Holt two-parameter exponential smoothing method to calculate the failure count matrix for each subsystem. Based on the failure count matrix for each subsystem, obtain the final smoothed failure count, and calculate the objective weight matrix for subsystem reliability based on the final smoothed failure count.

[0118] Step e: Obtain the trust parameter of the subjective weight, and calculate the subsystem reliability comprehensive distribution weight matrix based on the trust parameter of the subjective weight, the subsystem reliability subjective distribution weight matrix, and the subsystem reliability objective distribution weight matrix;

[0119] Step f: Obtain the reliability model category of each subsystem, comprehensively allocate the weight matrix according to the subsystem reliability, obtain the reliability of each subsystem, calculate the index value according to the reliability model category of each subsystem and the reliability of each subsystem, and compare the index value with the reliability index target. If the index value is greater than the reliability index target, the work is completed; if the index value is not greater than the reliability index target, the feedback adjustment mechanism is triggered and steps c, d, e and f are repeated.

[0120] In one embodiment, the multiple subsystems include a power system, a transmission system, a hydraulic system, an electrical system, a driving system, a steering system, a body and a cargo box, a braking system and an auxiliary system; optionally, each subsystem is a series model.

[0121] Specifically, the power system is equipped with a high-power diesel engine, a turbocharger, a high-pressure common rail fuel system, a multi-stage air filter, an SCR / DPF after-treatment system, a radiator group and a starter, etc.; the core of the transmission system is a generator set, an IGBT rectifier / inverter power cabinet, a two-wheel traction motor, a wheel-side reducer, a main reducer, a differential and a half-axle, etc.; the hydraulic system integrates a hydraulic pump, a lifting cylinder, a steering priority valve, a multi-way control valve, an accumulator, a fuel tank and a hydraulic oil radiator, etc.; the electrical system relies on the vehicle controller, the engine power generation controller, the traction controller, the The vehicle body and cargo box are equipped with a cab, cargo box, floor anti-collision beam, etc. The auxiliary system covers a centralized lubrication pump, an automatic fire extinguishing device in the engine compartment, a tire pressure monitoring sensor, a high-power air-conditioning compressor, and a weighing system.

[0122] In one embodiment, the factors affecting reliability distribution include mine environment severity, fault hazard severity, subsystem coupling complexity, design and manufacturing maturity, maintenance accessibility, and reliability improvement difficulty.

[0123] Specifically, the severity of the mining environment is assessed based on the mining environment in which the subsystem is located. The subsystem will be subjected to adverse and harsh environmental conditions during operation and will be rated 10 points, while the subsystem with the best environment will be rated 1 point; the severity of the fault hazard is assessed based on the severity of the subsystem failure. The subsystem will cause a personal safety accident when the failure occurs and will be rated 10 points for the subsystem that does not affect normal operation; the complexity of subsystem coupling is assessed based on the number of components that make up the subsystem and the difficulty of their assembly. The subsystem with the most complex coupling will be rated 10 points, while the simplest subsystem will be rated 1 point; the maintenance accessibility is assessed based on the convenience of subsystem repair and maintenance. The subsystem that requires special tools and has a small operating space will be rated 10 points, while the subsystem that does not require special tools and has a spacious operating space will be rated 1 point. Design and manufacturing maturity is assessed based on the subsystem's design stability and the maturity of its manufacturing process. A subsystem with a highly mature design and standardized and automated manufacturing process will be awarded 1 point, while a subsystem with an unverified design, complex process, and reliance on non-standard customization will be awarded 10 points. The difficulty of improving reliability is assessed based on the technical complexity and resource input cost of improving the subsystem's reliability. A subsystem whose reliability improvement requires disruptive technological breakthroughs or extremely high costs (such as material replacement or full system redesign) will be awarded 10 points, while a subsystem that can be significantly improved through simple optimization will be awarded 1 point.

[0124] In one embodiment, step c specifically includes:

[0125] Obtain scores from multiple experts on the influencing factors of each subsystem. Based on the scores from multiple experts on the influencing factors of each subsystem, calculate the mean of each influencing factor of each subsystem and form a score mean matrix. Use the CRITIC method to calculate the weight of each influencing factor of each subsystem and form a first weight matrix. Perform weighted aggregation on the first weight matrix and the score mean matrix to obtain a comprehensive score for each subsystem. Based on the comprehensive score of each subsystem, calculate the subsystem reliability subjective allocation weight matrix.

[0126] According to the scores of multiple experts on the influencing factors of each subsystem, an expert scoring matrix is ​​formed ,in, Indicates the Experts on The first subsystem Rating of influencing factors;

[0127] According to the expert scoring matrix, the mean value of each influencing factor of each subsystem is calculated. The specific expression is as follows:

[0128] ;

[0129] Where: For the The first subsystem The mean of the influencing factors, The expert's label, is the total number of experts; is the subsystem label, ; is the total number of subsystems; is the label of the influencing factor, , is the total number of influencing factors;

[0130] Forming the mean score matrix ;

[0131] The CRITIC method is used to calculate the weights of each influencing factor of each subsystem and form the first weight matrix, which specifically includes:

[0132] Summarize the experts’ scores on the influencing factors of each subsystem and obtain the independent scoring matrix of each subsystem , an independent scoring matrix for each subsystem Perform standardization to eliminate dimensional differences and generate a standardized matrix , the specific expression is as follows:

[0133] Obtain positive and negative indicators based on influencing factors. For positive indicators:

[0134] ;

[0135] For negative indicators:

[0136] ;

[0137] Generate a standardized matrix based on positive and negative indicators:

[0138] ;

[0139] Among them, negative indicators include the severity of fault hazards and the maturity of design and manufacturing; positive indicators include the severity of the mining environment, the complexity of subsystem coupling, maintenance accessibility and the difficulty of improving reliability; Indicates the Experts on The first subsystem The standardized value of the influencing factor score, Indicates that all experts The first subsystem The minimum score of the influencing factors, Indicates that all experts The first subsystem The maximum score of the influencing factors;

[0140] Calculate the standard deviation of each influencing factor of each subsystem , the specific expression is as follows:

[0141] ;

[0142] ;

[0143] Where: express Experts on The first subsystem The average score standardized value of the influencing factors;

[0144] Calculate the The first subsystem factors and other influencing factors Correlation coefficient , and obtain the Pearson correlation coefficient matrix between each influencing factor in each subsystem , the specific expression is as follows:

[0145] ;

[0146] Where: Indicates the Experts on The first subsystem The standardized score value of each influencing factor, express Experts on The first subsystem The average score standardization value of the influencing factors, For the The system's The standard deviation of the standardized values ​​of the scores of the influencing factors;

[0147] According to the standard deviation of each influencing factor of each subsystem And the Pearson correlation coefficient matrix between each influencing factor in each subsystem , calculate the information amount of each influencing factor in each subsystem , the specific expression is as follows:

[0148] ;

[0149] in, Indicates the independent contribution between indicators;

[0150] The amount of information about each influencing factor in each subsystem Perform normalization to form the first weight matrix , the specific expression is as follows:

[0151] ;

[0152] Where: For the The first subsystem The amount of information after normalization of the influencing factors;

[0153] Perform weighted aggregation on the first weight matrix and the score mean matrix to obtain the weighted matrix , the specific expression is as follows:

[0154] ;

[0155] Further obtain the comprehensive score of each subsystem , the specific expression is as follows:

[0156] ;

[0157] According to the comprehensive score of each subsystem , calculate the subjective weight matrix of subsystem reliability , the specific expression is as follows:

[0158] ;

[0159] Where: For the The subjective weight of each subsystem.

[0160] Optional, the system has subsystems, each with factors, organization Experts scored, the scoring range is ; The method for calculating the weight of each influencing factor of each subsystem can be a variety of weight calculation methods such as hierarchical analysis method and priority diagram method. All of them can be used without affecting the accuracy of the calculation and can achieve the same purpose.

[0161] In one embodiment, step d specifically includes:

[0162] Obtain the vehicle type, and based on the vehicle type, obtain the historical fault data of each subsystem of similar products, and summarize the historical fault count sequence of each subsystem within a preset time window;

[0163] Specifically, obtain the number of failure records for each subsystem of the vehicle model or similar products within a preset time window, and summarize them to obtain a historical failure number sequence for each subsystem within the preset time window. Optionally, obtain historical failure data for similar products based on similar products. Similar products refer to products with similar working environments, the same functional implementation principles, and similar working loads (for complete machines, the load tonnage is within ±20%).

[0164] Holt's two-parameter exponential smoothing method is used to iteratively calculate the historical fault number sequence to obtain the fault number matrix of each subsystem , the specific expression is as follows:

[0165] ;

[0166] Where: is the time window, which is a positive integer greater than zero; For the subsystems in the time window The number of smoothing failures, For the subsystems in the time window The number of historical failures, For the subsystems in the time window The smoothing failure times, smoothing level coefficient The value range is ; For the subsystems in the time window The smoothed failure trend number, For the subsystems in the time window The smoothed fault trend number is calculated as follows:

[0167]

[0168] Where, is the smoothing trend coefficient, and its value range is ;

[0169] According to the failure number matrix of each subsystem , get the final smoothed fault count matrix ;in, The last period within the preset time window The value of For the The final smoothed failure count of each subsystem in the time window T;

[0170] Calculate the objective distribution weight matrix of subsystem reliability based on the final smoothed failure number , the specific expression is as follows:

[0171] ;

[0172] Where: For the The objective weight of each subsystem.

[0173] Optionally, the historical failure proportion can also be divided according to the same type of products and similar products, and assigned different weights, so that the calculation results are more in line with the actual engineering purpose, and the same purpose can be achieved.

[0174] In one embodiment, the step e specifically includes:

[0175] A dynamic weighted objective function is established to solve each subsystem. The specific expression is as follows:

[0176] ;

[0177] Where: To be solved The comprehensive weight of each subsystem, For the The subjective weight of each subsystem, For the Trust parameters of the subjective weights of each subsystem;

[0178] The trust parameter of obtaining subjective weight , specifically including:

[0179] Calculate the predicted failure count matrix of each subsystem in the next X years , the specific expression is as follows:

[0180] ;

[0181] Where: For the The predicted number of failures of each subsystem in year X;

[0182] According to the predicted failure number matrix , obtain the trust parameter of subjective weight , the specific expression is as follows:

[0183] ;

[0184] According to the subjective weight trust parameter, the subsystem reliability subjective distribution weight matrix and the subsystem reliability objective distribution weight matrix, the dynamic weighted objective function is solved by constructing the Lagrangian function to obtain the subsystem reliability comprehensive distribution weight matrix , the specific expression is as follows:

[0185] ;

[0186] Where: For the The comprehensive weight of each subsystem.

[0187] Optionally, the trust parameters of the subjective weights can also be determined using methods such as the hierarchical analysis method and the expert scoring method to achieve the same purpose; the fusion model of the subjective allocation weights and the objective allocation weights can also be fused using models such as game theory and the OWA operator weight method to achieve the same purpose.

[0188] In one embodiment, step f specifically includes:

[0189] Obtain the reliability model category of each subsystem;

[0190] According to the comprehensive distribution weight matrix of subsystem reliability, the reliability of each subsystem is distributed and calculated, the reliability distribution of the mining dump truck is completed, and the reliability of each subsystem is obtained, including:

[0191] Convert reliability target into failure rate requirement , the specific expression is as follows:

[0192] ;

[0193] Where: It is the reliability index target;

[0194] According to the failure rate requirements , calculate the failure rate metric for the distribution , the specific expression is as follows:

[0195] ;

[0196] Where: Allocate redundancy for reliability; based on engineering experience, the reliability redundancy can be 10% to 20%;

[0197] Get the failure rate indicator matrix , , the specific expression is as follows:

[0198] ;

[0199] Where: For the Failure rate indicators of each subsystem;

[0200] According to the failure rate indicator matrix , obtain the reliability of each subsystem , the specific expression is as follows:

[0201] ;

[0202] Where: For the Reliability of each subsystem; is a natural constant; is the target time;

[0203] Calculate the index value based on the reliability model category and reliability of each subsystem, and compare the index value with the reliability index target. If the index value is greater than the reliability index target, the work is completed. Specifically, it includes:

[0204] If the reliability model category of each subsystem is a series model, the index value is ; The specific comparison expression is ;

[0205] If the reliability model category of each subsystem is a parallel model, the index value is ; The specific comparison expression is ;

[0206] If the reliability model category of each subsystem is other than the series model and the parallel model, the model is converted to the series model or the parallel model, and the index value is recalculated;

[0207] If the indicator value is not greater than the reliability indicator target, the feedback adjustment mechanism is triggered and steps c, d, e, and f are repeated. The feedback adjustment mechanism specifically includes:

[0208] Improve reliability by allocating redundancy, adding new scoring experts, and re-evaluating historical failure data for each subsystem.

[0209] Specifically, the feedback adjustment mechanism includes but is not limited to increasing the reliability allocation redundancy from 10% to 20%; inviting new scoring experts to obtain scoring values ​​and combine them with the original scoring values ​​to calculate the weights; and re-evaluating the historical fault data of each subsystem to determine whether there are misjudgments or outdated fault data.

[0210] In one embodiment, a mining dump truck has 9 subsystems, each subsystem has 6 influencing factors. In this embodiment, 5 experts score the influencing factors of each subsystem according to the above scoring criteria and collect data; the mining dump truck expert scoring matrix is ​​obtained. .in (Subsystem numbers represent power system, transmission system, hydraulic system, electrical system, driving system, steering system, braking system, body and cargo box, and auxiliary system respectively); (The influencing factor numbers represent the severity of the mining environment, the severity of the fault hazard, the complexity of subsystem coupling, maintenance accessibility, design and manufacturing maturity, and the difficulty of improving reliability); (The scoring expert numbers represent Expert 1, Expert 2, Expert 3, Expert 4, and Expert 5 respectively). The expert scoring matrix is ​​as follows;

[0211] ;

[0212] ;

[0213] ;

[0214] ;

[0215] ;

[0216] According to the expert scoring matrix, calculate the mean of each influencing factor of each subsystem to form a scoring mean matrix , as follows:

[0217] ;

[0218] The CRITIC method is used to calculate the weight of each influencing factor of each subsystem and form the first weight matrix. The scores of each expert on the influencing factors of each subsystem are summarized to obtain the independent score matrix of each subsystem. , an independent scoring matrix for each subsystem Perform standardization to generate a standardized matrix , taking the power system as an example, the power system is the first subsystem, the details are as follows:

[0219] ;

[0220] ;

[0221] Calculate the standard deviation of each influencing factor of each subsystem , as shown in Table 1:

[0222]

[0223] Calculate the The first subsystem factors and other influencing factors Correlation coefficient , and obtain the Pearson correlation coefficient matrix between each influencing factor in each subsystem , as shown in Table 2:

[0224] According to the standard deviation of each influencing factor of each subsystem And the Pearson correlation coefficient matrix between each influencing factor in each subsystem , calculate the information amount of each influencing factor in each subsystem , as follows:

[0225] ;

[0226] The amount of information about each influencing factor in each subsystem Perform normalization processing to form the first weight matrix, which is as follows:

[0227] ;

[0228] Repeat the above steps to calculate the weight values ​​of the influencing factors of the transmission system, hydraulic system, electrical system, driving system, steering system, braking system, body and cargo box, and auxiliary system respectively, and obtain the weight matrix of each influencing factor of each system, that is, the first weight matrix , as follows:

[0229] ;

[0230] The first weight matrix and the score mean matrix are weighted and aggregated as follows:

[0231] ;

[0232] Further obtain the comprehensive score of each subsystem , as follows:

[0233] ;

[0234] in, represents transpose;

[0235] According to the comprehensive score of each subsystem , calculate the subjective weight matrix of subsystem reliability, as follows:

[0236] ;

[0237] Obtain the fault data records of each subsystem of the model product or similar products within the preset time window (the last 3 years), and the time series are , the year before last , the most recent year , the historical fault number sequence of each subsystem within the preset time window is summarized, see Table 3:

[0238]

[0239] Holt's two-parameter exponential smoothing method is used to iteratively calculate the historical fault number sequence to obtain the fault number matrix of each subsystem , taking the power system as an example, the details are as follows:

[0240] Smoothing horizontal coefficient , smoothing level coefficient ;

[0241] Calculate the failure count matrix of the power system :

[0242] Initial value : ;

[0243] : ;

[0244] : ;

[0245] That is, the power system smoothed fault number matrix ;

[0246] Calculate the smoothed fault count matrix of the power system = :

[0247] Initial value : ;

[0248] : ;

[0249] : ;

[0250] We can further obtain the predicted number of failures of the power system in the next year ;

[0251] ;

[0252] Predicted number of failures in the next year =166.4.

[0253] According to the above method, the smooth fault times of the transmission system, hydraulic system, electrical system, driving system, steering system, braking system, body and cargo box, and auxiliary system are calculated respectively to obtain the smooth fault times matrix of each system. and the predicted failure count matrix for the next year , specifically as follows;

[0254] ;

[0255] Predicted failure count matrix for the next year , as follows:

[0256] ;

[0257] According to the failure number matrix of each subsystem , get the final smoothed fault count matrix ;in, The last period within the preset time window The value of , as follows:

[0258] ;

[0259] The objective distribution weight matrix of subsystem reliability is calculated based on the final smoothed failure count, as follows:

[0260] ;

[0261] The cross-entropy-based adaptive weight fusion model is used to aggregate the subjective allocation weights and the objective allocation weights to generate the comprehensive reliability allocation weights of each subsystem of the product. The specific steps are as follows:

[0262] According to the predicted failure number matrix , obtain the trust parameter of subjective weight , as follows:

[0263] ;

[0264] By constructing the Lagrangian function to solve the dynamic weighted objective function, the reliability comprehensive distribution weight matrix is ​​obtained. , as follows:

[0265] .

[0266] Based on the comprehensive reliability allocation weight and the reliability index of the whole machine, the reliability allocation of the mining dump truck is completed, including:

[0267] Convert reliability target into failure rate requirement , optional, reliability indicator target is Not less than 0.8, the specific expression is as follows:

[0268] ;

[0269] Here, the failure rate is reduced by 10% according to the reliability distribution principle, that is, ;

[0270] Get the failure rate indicator matrix , as follows:

[0271] ;

[0272] According to the failure rate indicator matrix , obtain the reliability of each subsystem , according to the reliability of each subsystem , get the reliability matrix ;

[0273] Verify whether the reliability index of the subsystem distribution meets the reliability index requirements of the whole machine. Since the reliability model between the subsystems of the mining dump truck is a series model, the reliability index of the subsystem is Method verification, the verification results are as follows:

[0274] ;

[0275] Through verification, the reliability index allocation results of each subsystem of the mining dump truck meet the reliability index requirements of the whole machine, and the reliability allocation work is completed.

[0276] If the reliability requirements of the entire machine are not met, the feedback adjustment mechanism is triggered, that is, returning to step S2 for reallocation, and taking one or more of the following measures, but not limited to the following three, until the reliability index requirements of the entire machine are met: increase the reliability allocation redundancy from 10% to 20%; invite new scoring experts, obtain the scoring values, and combine them with the original scoring values ​​to calculate the weight; re-evaluate the fault data of each subsystem to determine whether there are misjudgments or outdated fault data.

[0277] The present invention calculates the weights of evaluation factors by introducing the CRITIC weight method, and obtains the subjective allocation weight of each subsystem by combining the score average; at the same time, the Holt two-parameter exponential smoothing method is used to calculate the historical failure proportion of each subsystem according to a preset time window, and further obtains the objective allocation weight of each subsystem; based on the fusion of subjective allocation weights and objective allocation weights, comprehensive allocation weights are obtained to solve the problem that the current reliability allocation method is not suitable for mining dump trucks, and improve the flexibility and accuracy of reliability allocation results; the present invention considers the importance weights of the evaluation factors of each subsystem: comprehensively evaluates the importance of the evaluation factors of each subsystem according to the characteristics of each subsystem, and then solves the problem of inconsistent importance of various influencing factors of each subsystem; considering the factors that the reliability level of the product changes over time, considering the continuous improvement of the reliability level of most subsystems, by calculating the number of historical failure levels and the number of trend numbers according to a preset time window, the historical failure calculation proportion results are more in line with engineering practice, ensuring the flexibility and accuracy of the reliability allocation results.

[0278] The evaluation dimensions of the present invention are rich. The present invention considers the comprehensive impact of mining working conditions on mining dump trucks from six dimensions, such as the severity of the mining environment and the severity of fault hazards. The evaluation dimensions are comprehensive and highly consistent with the actual working conditions. The trust parameter of the subjective weight is introduced to dynamically adjust the ratio of the subjective allocation weight and the objective allocation weight. The adaptive weight fusion model based on cross entropy is used to fuse the subjective and objective allocation weights, thereby improving the flexibility and accuracy of the reliability allocation results.

[0279] Example 2:

[0280] The present invention provides a reliability distribution device for a mining dump truck, comprising:

[0281] Product hierarchy division module: used to obtain vehicle structural data, divide the vehicle into product hierarchies based on the vehicle structural data to obtain multiple subsystems; determine the factors affecting reliability allocation and set reliability indicator targets;

[0282] Subjective allocation weight determination module: used to obtain scores of multiple experts on the influencing factors of each subsystem, calculate the mean of each influencing factor of each subsystem based on the scores of multiple experts on the influencing factors of each subsystem, and form a score mean matrix; use the CRITIC method to calculate the weight of each influencing factor of each subsystem and form a first weight matrix; perform weighted aggregation on the first weight matrix and the score mean matrix to obtain the comprehensive score of each subsystem; calculate the subsystem reliability subjective allocation weight matrix based on the comprehensive score of each subsystem;

[0283] Objective allocation weight determination module: used to obtain vehicle type, obtain historical failure data of each subsystem of similar products based on the vehicle type, calculate the failure count matrix of each subsystem based on the Holt two-parameter exponential smoothing method based on the historical failure data of each subsystem and a preset time window, obtain the final smoothed failure count based on the failure count matrix of each subsystem, and calculate the objective allocation weight matrix of subsystem reliability based on the final smoothed failure count;

[0284] Comprehensive allocation weight calculation module: used to obtain the trust parameters of subjective weights, and calculate the comprehensive allocation weight matrix of subsystem reliability based on the trust parameters of subjective weights, the subjective allocation weight matrix of subsystem reliability, and the objective allocation weight matrix of subsystem reliability;

[0285] Reliability index allocation module: used to obtain the reliability model category of each subsystem, comprehensively allocate weight matrix according to the subsystem reliability, obtain the reliability of each subsystem, calculate the index value according to the reliability model category of each subsystem and the reliability of each subsystem, compare the index value with the reliability index target, if the index value is greater than the reliability index target, the work is completed; if the index value is not greater than the reliability index target, the feedback adjustment mechanism is triggered, and the steps of subjective allocation weight determination module, objective allocation weight determination module, comprehensive allocation weight calculation module and reliability index allocation module are repeated.

[0286] Example 3:

[0287] An embodiment of the present invention further provides a terminal, including a processor and a storage medium;

[0288] The storage medium is used to store instructions;

[0289] The processor is configured to operate according to the instructions to execute the steps of the method described in embodiment 1.

[0290] One or more processors, a memory, and one or more programs stored in the memory, wherein the program is configured to be executed by the processor and includes instructions for implementing the method described in any one of the first embodiments; specifically, the processor performs product hierarchy division, influencing factor determination, subjective allocation weight calculation, objective allocation weight calculation, comprehensive weight calculation, and reliability index allocation; if it is detected that the reliability of the entire machine does not meet the standard, a feedback mechanism is triggered to readjust the trust parameters of the subsystem subjective weight until the output meets the preset indicators; the memory supports non-volatile storage media to ensure the persistence of program code and running data.

[0291] Example 4:

[0292] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method described in the first embodiment are implemented.

[0293] Since the storage medium provided in the embodiment of the present invention can execute the method provided in the first embodiment of the present invention, it has the corresponding functional modules and beneficial effects of the execution method.

[0294] The present invention may be implemented entirely in hardware, entirely in software, or in a combination of hardware and software. In software embodiments, the method is embodied as a computer program product, comprising executable code stored on a computer-readable medium (e.g., a disk drive, CD-ROM, or cloud server). When the program is loaded into a general-purpose computer, embedded controller, or other programmable device, the instructions drive the processor to perform the steps described in the claims, including parsing sensor data streams, running optimization algorithms, generating allocation plans, and feedback control instructions. Program modules interact with each other via logical interfaces; for example, the data acquisition module and the core algorithm module share memory to achieve low-latency communication.

[0295] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0296] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0297] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0298] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0299] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A reliability allocation method for mining dump trucks, characterized in that: include: Step a: Obtain vehicle structural data, and divide the vehicle into product hierarchies based on the vehicle structural data to obtain multiple subsystems; Step b: Determine the factors that affect reliability allocation and set reliability index targets; Step c: Obtain scores from multiple experts on the influencing factors of each subsystem, and form a score mean matrix based on the scores from multiple experts on the influencing factors of each subsystem; The CRITIC method is used to calculate the weight of each influencing factor of each subsystem and form the first weight matrix; the first weight matrix is ​​weighted and aggregated with the score mean matrix to calculate the subsystem reliability subjective distribution weight matrix; Step d: Obtain the vehicle type, and based on the vehicle type, obtain historical failure data for each subsystem of similar products. Based on the historical failure data for each subsystem and a preset time window, use the Holt two-parameter exponential smoothing method to calculate the failure count matrix for each subsystem. Based on the failure count matrix for each subsystem, obtain the final smoothed failure count, and calculate the objective weight matrix for subsystem reliability based on the final smoothed failure count. Step e: Obtain the trust parameter of the subjective weight, and calculate the subsystem reliability comprehensive distribution weight matrix based on the trust parameter of the subjective weight, the subsystem reliability subjective distribution weight matrix, and the subsystem reliability objective distribution weight matrix; Step f: Obtain the reliability model category of each subsystem, assign a weight matrix based on the subsystem reliability, obtain the reliability of each subsystem, calculate the index value based on the reliability model category and reliability of each subsystem, and compare the index value with the reliability index target. If the index value is greater than the reliability index target, the work is completed; If the indicator value is not greater than the reliability indicator target, the feedback adjustment mechanism is triggered and steps c, d, e, and f are repeated.

2. The reliability allocation method for mining dump trucks according to claim 1, characterized in that: The multiple subsystems include a power system, a transmission system, a hydraulic system, an electrical system, a driving system, a steering system, a body and a cargo box, a braking system and an auxiliary system.

3. The reliability allocation method for mining dump trucks according to claim 1, characterized in that: The factors affecting reliability distribution include the severity of the mine environment, the severity of fault hazards, the complexity of subsystem coupling, the maturity of design and manufacturing, maintenance accessibility and the difficulty of improving reliability.

4. The reliability allocation method for mining dump trucks according to claim 3, characterized in that: The step c specifically includes: Obtain scores from multiple experts on the influencing factors of each subsystem. Based on the scores from multiple experts on the influencing factors of each subsystem, calculate the mean of each influencing factor of each subsystem and form a score mean matrix. Use the CRITIC method to calculate the weight of each influencing factor of each subsystem and form a first weight matrix. Perform weighted aggregation on the first weight matrix and the score mean matrix to obtain a comprehensive score for each subsystem. Based on the comprehensive score of each subsystem, calculate the subsystem reliability subjective allocation weight matrix. According to the scores of multiple experts on the influencing factors of each subsystem, an expert scoring matrix is ​​formed ,in, Indicates the Experts on The first subsystem Rating of influencing factors; According to the expert scoring matrix, the mean value of each influencing factor of each subsystem is calculated. The specific expression is as follows: ； Where: For the The first subsystem The mean of the influencing factors, The expert's label, is the total number of experts; is the subsystem label, ; is the total number of subsystems; is the label of the influencing factor, , is the total number of influencing factors; Forming a score mean matrix ; The CRITIC method is used to calculate the weights of each influencing factor of each subsystem and form the first weight matrix, which specifically includes: Summarize the experts’ scores on the influencing factors of each subsystem and obtain the independent scoring matrix of each subsystem , an independent scoring matrix for each subsystem Perform standardization to generate a standardized matrix , the specific expression is as follows: Obtain positive and negative indicators based on influencing factors. For positive indicators: ； For negative indicators: ； Generate a standardized matrix based on positive and negative indicators: ； Among them, negative indicators include the severity of fault hazards and the maturity of design and manufacturing; positive indicators include the severity of the mining environment, the complexity of subsystem coupling, maintenance accessibility and the difficulty of improving reliability; Indicates the Experts on The first subsystem The standardized value of the influencing factor score, Indicates that all experts The first subsystem The minimum score of the influencing factors, Indicates that all experts The first subsystem The maximum score of the influencing factors; Calculate the standard deviation of each influencing factor of each subsystem , the specific expression is as follows: ； ； Where: express Experts on The first subsystem The average score standardized value of the influencing factors; Calculate the The first subsystem factors and other influencing factors Correlation coefficient , and obtain the Pearson correlation coefficient matrix between each influencing factor in each subsystem , the specific expression is as follows: ； Where: Indicates the Experts on The first subsystem The standardized score value of each influencing factor, express Experts on The first subsystem The average standardized value of the scores of the influencing factors, For the The system's The standard deviation of the standardized values ​​of the scores of the influencing factors; According to the standard deviation of each influencing factor of each subsystem And the Pearson correlation coefficient matrix between each influencing factor in each subsystem , calculate the information amount of each influencing factor in each subsystem , the specific expression is as follows: ； in, Indicates the independent contribution between indicators; The amount of information about each influencing factor in each subsystem Perform normalization to form the first weight matrix , the specific expression is as follows: ； Where: For the The first subsystem The amount of information after normalization of the influencing factors; Perform weighted aggregation on the first weight matrix and the score mean matrix to obtain the weighted matrix , the specific expression is as follows: ； Further obtain the comprehensive score of each subsystem , the specific expression is as follows: ； According to the comprehensive score of each subsystem , calculate the subjective weight matrix of subsystem reliability , the specific expression is as follows: ； Where: For the The subjective weight of each subsystem.

5. The reliability allocation method for mining dump trucks according to claim 4, characterized in that: The step d specifically includes: Obtain the vehicle type, and based on the vehicle type, obtain the historical fault data of each subsystem of similar products, and summarize the historical fault count sequence of each subsystem within a preset time window; Holt's two-parameter exponential smoothing method is used to iteratively calculate the historical fault number sequence to obtain the fault number matrix of each subsystem , the specific expression is as follows: ； Where: is the time window, which is a positive integer greater than zero; For the subsystems in the time window The number of smoothing failures, For the subsystems in the time window The number of historical failures, For the subsystems in the time window The smoothing failure times, smoothing level coefficient The value range is ; For the subsystems in the time window The smoothed failure trend number, For the subsystems in the time window The smoothed fault trend number is calculated as follows: ； Where, is the smoothing trend coefficient, and its value range is ; According to the failure number matrix of each subsystem , get the final smoothed fault count matrix ;in, The last period within the preset time window The value of For the subsystems in the time window The final smoothing failure count of Calculate the objective distribution weight matrix of subsystem reliability based on the final smoothed failure number , the specific expression is as follows: ； Where: For the The objective weight of each subsystem.

6. The reliability allocation method for mining dump trucks according to claim 5, characterized in that: The step e specifically includes: A dynamic weighted objective function is established to solve each subsystem. The specific expression is as follows: ； Where: To be solved The comprehensive weight of each subsystem, For the The subjective weight of each subsystem, For the Trust parameters of the subjective weights of each subsystem; The trust parameter of obtaining subjective weight , specifically including: Calculate the predicted failure count matrix of each subsystem in the next X years , the specific expression is as follows: ； Where: For the The predicted number of failures of each subsystem in year X; According to the predicted failure number matrix , obtain the trust parameter of subjective weight , the specific expression is as follows: ； According to the subjective weight trust parameter, the subsystem reliability subjective distribution weight matrix and the subsystem reliability objective distribution weight matrix, the dynamic weighted objective function is solved by constructing the Lagrangian function to obtain the subsystem reliability comprehensive distribution weight matrix , the specific expression is as follows: ； Where: For the The comprehensive weight of each subsystem.

7. The reliability allocation method for mining dump trucks according to claim 6, characterized in that: The step f specifically includes: Obtain the reliability model category of each subsystem; According to the comprehensive allocation weight matrix of subsystem reliability, the reliability of each subsystem is obtained, including: Convert reliability target into failure rate requirement , the specific expression is as follows: ； Where: It is the reliability index target; According to the failure rate requirements , calculate the failure rate metric for the distribution , the specific expression is as follows: ； Where: Allocate redundancy for reliability; Get the failure rate indicator matrix , , the specific expression is as follows: ； Where: For the Failure rate indicators of each subsystem; According to the failure rate indicator matrix , obtain the reliability of each subsystem , the specific expression is as follows: ； Where: For the Reliability of each subsystem; is a natural constant; is the target time; Calculate the index value based on the reliability model category and reliability of each subsystem, and compare the index value with the reliability index target. If the index value is greater than the reliability index target, the work is completed. Specifically, it includes: If the reliability model category of each subsystem is a series model, the index value is ; If the reliability model category of each subsystem is a parallel model, the index value is ; If the reliability model category of each subsystem is other than the series model and the parallel model, the model is converted to the series model or the parallel model, and the index value is recalculated; If the indicator value is not greater than the reliability indicator target, the feedback adjustment mechanism is triggered and steps c, d, e, and f are repeated. The feedback adjustment mechanism specifically includes: Improve reliability by allocating redundancy, adding new scoring experts, and re-evaluating historical failure data for each subsystem.

8. A reliability distribution device for a mining dump truck, characterized in that: include: Product hierarchy division module: used to obtain the structural data of the vehicle and divide the vehicle into product hierarchies based on the structural data to obtain multiple subsystems; Determine the factors that affect reliability allocation and set reliability indicator targets; Subjective allocation weight determination module: used to obtain scores of multiple experts on the influencing factors of each subsystem, calculate the mean of each influencing factor of each subsystem based on the scores of multiple experts on the influencing factors of each subsystem, and form a score mean matrix; use the CRITIC method to calculate the weight of each influencing factor of each subsystem and form a first weight matrix; perform weighted aggregation on the first weight matrix and the score mean matrix to obtain the comprehensive score of each subsystem; calculate the subsystem reliability subjective allocation weight matrix based on the comprehensive score of each subsystem; Objective allocation weight determination module: used to obtain vehicle type, obtain historical failure data of each subsystem of similar products based on the vehicle type, calculate the failure count matrix of each subsystem based on the Holt two-parameter exponential smoothing method based on the historical failure data of each subsystem and a preset time window, obtain the final smoothed failure count based on the failure count matrix of each subsystem, and calculate the objective allocation weight matrix of subsystem reliability based on the final smoothed failure count; Comprehensive allocation weight calculation module: used to obtain the trust parameters of subjective weights, and calculate the comprehensive allocation weight matrix of subsystem reliability based on the trust parameters of subjective weights, the subjective allocation weight matrix of subsystem reliability, and the objective allocation weight matrix of subsystem reliability; Reliability index allocation module: used to obtain the reliability model category of each subsystem, comprehensively allocate the weight matrix according to the subsystem reliability, obtain the reliability of each subsystem, calculate the index value according to the reliability model category and reliability of each subsystem, and compare the index value with the reliability index target. If the index value is greater than the reliability index target, the work is completed; If the index value is not greater than the reliability index target, the feedback adjustment mechanism is triggered and the steps of subjective allocation weight determination module, objective allocation weight determination module, comprehensive allocation weight calculation module and reliability index allocation module are repeated.

9. A terminal, characterized in that: including processors and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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