Numerical control machine tool reliability evaluation method and device, equipment and storage medium

Abstract

The invention relates to a numerical control machine tool reliability evaluation method and device, equipment and a storage medium. The method comprises the following steps: determining a Bayesian network model corresponding to the numerical control machine tool according to a fault logic relationship of each component in the numerical control machine tool, wherein the Bayesian network model comprises a leaf node, a plurality of intermediate nodes, a plurality of root nodes, state transition probability distribution of each root node and conditional probability distribution among the nodes; if the root node is influenced by the external factors, determining a corresponding proportional risk model according to the root node and the external factors so as to update state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model; predicting probability distribution of leaf nodes at the target moment according to the updated Bayesian network model, wherein the probability distribution comprises a fault probability and a normal probability of each node; evaluating the reliability of the numerical control machine tool according to the probability distribution of the leaf nodes. The method can improve the accuracy of the evaluation result.

Description

Technical Field

[0001] The present application relates to the technical field of reliability assessment, and in particular to a reliability assessment method, device, equipment and storage medium for CNC machine tools. Background Art

[0002] CNC machine tools are known as the "mother machines of industry" and are the foundation of the manufacturing industry. Their reliability level determines whether they can operate stably and reliably for a long time, which in turn affects the stability of the machining process and maintenance costs. As a complex system integrating mechanical, electrical and hydraulic systems, the reliability of CNC machine tools is determined by the reliability of their various components.

[0003] In the prior art, the reliability of CNC machine tools can be evaluated through methods such as fault tree, reliability block diagram, Markov model, etc. Most of these methods assume that the failures between the components of the CNC machine tools are independent of each other, resulting in low accuracy of reliability evaluation results. Summary of the invention

[0004] Based on this, it is necessary to provide a CNC machine tool reliability assessment method, device, equipment and storage medium that can improve the accuracy of CNC machine tool reliability assessment results in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for evaluating the reliability of a CNC machine tool, comprising:

[0006] According to the fault logic relationship of each component in the CNC machine tool, a Bayesian network model corresponding to the CNC machine tool is determined. The Bayesian network model includes a leaf node, multiple intermediate nodes, multiple root nodes, state transition probability distribution of each root node, and conditional probability distribution between each node;

[0007] If the root node is affected by external factors, the corresponding proportional risk model is determined according to the root node and the external factors, so as to update the state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model;

[0008] The probability distribution of leaf nodes at the target time is predicted based on the updated Bayesian network model. The probability distribution includes the failure probability and normal probability of each node.

[0009] The reliability of CNC machine tools is evaluated based on the probability distribution of leaf nodes.

[0010] In one embodiment, determining a Bayesian network model corresponding to a numerically controlled machine tool according to a fault logic relationship of each component in the numerically controlled machine tool includes:

[0011] A fault analysis is performed on the CNC machine tool, and a fault tree of the CNC machine tool is established. The top event of the fault tree is the fault of the CNC machine tool, the bottom event is the fault of the basic component in the CNC machine tool, the intermediate event is the fault between the CNC machine tool and the basic component, and the basic component is a part-level component. The leaf nodes, intermediate nodes and root nodes in the Bayesian network model are determined according to the top event, intermediate event and bottom event in the fault tree.

[0012] In one embodiment, determining a Bayesian network model corresponding to a numerically controlled machine tool according to a fault logic relationship of each component in the numerically controlled machine tool includes:

[0013] The state transition probability distribution of each root node in the Bayesian network model and the conditional probability distribution between nodes are determined based on historical fault data.

[0014] In one embodiment, if the root node is affected by external factors, a corresponding proportional risk model is determined according to the root node and the external factors, so as to update the state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model, including:

[0015] Obtain the basic failure rate function of the root node and the quantitative function of the impact of external factors on the failure rate of the root node, wherein the basic failure rate function is related to time, and the quantitative function is related to the state of the external factors; determine the proportional risk model according to the product of the basic failure rate function and the quantitative function; determine the target state transition probability distribution of the root node according to the proportional risk model, and update the state transition probability distribution of the root node in the Bayesian network model according to the target state transition probability distribution.

[0016] In one embodiment, determining the target state transition probability distribution of the root node according to the proportional risk model includes:

[0017] Obtain the state transition probability distribution of external factors; determine the impact value of external factors on the failure probability of the root node under different states based on the proportional risk model and the probability distribution of external factors; determine the target state transition probability distribution of the root node based on the state transition probability distribution of external factors and each impact value.

[0018] In one embodiment, the probability distribution of leaf nodes at a target time is predicted according to an updated Bayesian network model, including: iteratively executing the probability distribution prediction process of leaf nodes multiple times until the number of iterations reaches a target number, and the target number is determined according to the target time; wherein the probability distribution prediction process of the i-th leaf node includes: obtaining the first probability distribution of each root node and the state transition probability distribution of each root node; if the root node is affected by external factors, determining the state transition probability distribution of the root node according to the first probability distribution of the external factor and the updated Bayesian network model, wherein when i is equal to 1, the first probability distribution is the probability distribution at the initial moment, and when i is greater than 1, the first probability distribution is the target probability distribution solved in the probability distribution prediction process of the i-1-th leaf node; determining the target probability distribution of each root node according to the first probability distribution of each root node and the state transition probability distribution of each root node; if the root node is affected by external factors, determining the target probability distribution of the external factor according to the first probability distribution of the external factor and the state transition probability distribution of the external factor; determining the probability distribution of the leaf node according to the target probability distribution of each root node and the conditional probability distribution between each node.

[0019] In a second aspect, the present application also provides a CNC machine tool reliability assessment device, comprising:

[0020] An initial module is used to determine the Bayesian network model corresponding to the CNC machine tool according to the fault logic relationship of each component in the CNC machine tool. The Bayesian network model includes a leaf node, multiple intermediate nodes, multiple root nodes, the state transition probability distribution of each root node, and the conditional probability distribution between each node;

[0021] An updating module, for determining a corresponding proportional risk model according to the root node and the external factors when the root node is affected by external factors, so as to update the state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model;

[0022] A prediction module is used to predict the probability distribution of leaf nodes at the target time according to the updated Bayesian network model. The probability distribution includes the failure probability and normal probability of each node.

[0023] The evaluation module is used to evaluate the reliability of CNC machine tools according to the probability distribution of leaf nodes.

[0024] In one of the embodiments, the initial module is specifically used to perform fault analysis on a CNC machine tool and establish a fault tree for the CNC machine tool, wherein the top event of the fault tree is the fault of the CNC machine tool, the bottom event is the fault of a basic component in the CNC machine tool, the intermediate event is the fault between the CNC machine tool and the basic component, and the basic component is a part-level component; and the leaf nodes, intermediate nodes, and root nodes in the Bayesian network model are respectively determined according to the top event, intermediate event, and bottom event in the fault tree.

[0025] In one of the embodiments, the initial module is specifically used to determine the state transition probability distribution of each root node in the Bayesian network model and the conditional probability distribution between each node according to the historical fault data.

[0026] In one of the embodiments, the update module is specifically used to obtain a basic failure rate function of the root node and a quantification function of the impact of external factors on the failure rate of the root node, the basic failure rate function is related to time, and the quantification function is related to the state of the external factor; determine a proportional risk model based on the product of the basic failure rate function and the quantification function; determine a target state transition probability distribution of the root node based on the proportional risk model, and update the state transition probability distribution of the root node in the Bayesian network model based on the target state transition probability distribution.

[0027] In one of the embodiments, the update module is specifically used to obtain the state transition probability distribution of external factors; determine the impact value of the external factors on the failure probability of the root node under different states based on the proportional risk model and the probability distribution of the external factors; and determine the target state transition probability distribution of the root node based on the state transition probability distribution of the external factors and each impact value.

[0028] In one embodiment, the prediction module is specifically used to iteratively execute the probability distribution prediction process of the leaf nodes multiple times until the number of iterations reaches the target number, and the target number is determined according to the target time; wherein, the probability distribution prediction process of the i-th leaf node includes: obtaining the first probability distribution of each root node and the state transition probability distribution of each root node; if the root node is affected by external factors, the state transition probability distribution of the root node is determined according to the first probability distribution of the external factor and the updated Bayesian network model, wherein when i is equal to 1, the first probability distribution is the probability distribution at the initial moment, and when i is greater than 1, the first probability distribution is the target probability distribution solved in the probability distribution prediction process of the i-1-th leaf node; the target probability distribution of each root node is determined according to the first probability distribution of each root node and the state transition probability distribution of each root node; if the root node is affected by external factors, the target probability distribution of the external factor is determined according to the first probability distribution of the external factor and the state transition probability distribution of the external factor; the probability distribution of the leaf node is determined according to the target probability distribution of each root node and the conditional probability distribution between each node.

[0029] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any of the methods described in the first aspect is implemented.

[0030] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any of the methods described in the first aspect above.

[0031] In a fifth aspect, the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements any of the methods described in the first aspect above.

[0032] The above-mentioned CNC machine tool reliability evaluation method, device, equipment and storage medium determine the Bayesian network model corresponding to the CNC machine tool according to the fault logical relationship of each component in the CNC machine tool. The Bayesian network model includes a leaf node, multiple intermediate nodes, multiple root nodes, the state transition probability distribution of each root node and the conditional probability distribution between each node. Then, if the root node is affected by external factors, the corresponding proportional risk model is determined according to the root node and the external factors to update the state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model. Then, the probability distribution of the leaf node at the target time is predicted according to the updated Bayesian network model. The probability distribution includes the failure probability and normal probability of each node. Finally, the reliability of the CNC machine tool is evaluated according to the probability distribution of the leaf node. The fault correlation between different components is considered through the Bayesian network, and the influence of external factors on some components is considered based on the proportional risk model. In this way, when performing reliability evaluation, the fault correlation between machine tool components and the influence of external factors are considered at the same time. The obtained reliability evaluation result is more in line with the actual situation of the CNC machine tool and can effectively improve the accuracy of the reliability evaluation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 A schematic diagram of a process flow of a method for evaluating the reliability of a CNC machine tool in one embodiment;

[0035] Figure 2 A schematic flow chart of the steps of determining a Bayesian network model corresponding to a numerically controlled machine tool in one embodiment;

[0036] Figure 3 A schematic diagram of a Bayesian network structure in one embodiment;

[0037] Figure 4 A schematic diagram of a flow chart of a step of updating a state transition probability distribution in one embodiment;

[0038] Figure 5 A schematic diagram of the state transition of a root node when affected by external factors in one embodiment;

[0039] Figure 6 A schematic diagram of a flow chart of a step of determining a target state transition probability distribution of a root node in one embodiment;

[0040] Figure 7 A schematic diagram of a process for predicting the probability distribution of leaf nodes at a target time in one embodiment;

[0041] Figure 8 A schematic diagram of a dynamic Bayesian network structure in one embodiment;

[0042] Fig. 9 1 is a failure probability curve of a CNC machine tool at different times in an embodiment.

[0043] Fig.10 A schematic diagram of a flow chart of a method for evaluating the reliability of a CNC machine tool in another embodiment;

[0044] Fig.11 A structural block diagram of a reliability evaluation device for a numerically controlled machine tool in one embodiment;

[0045] Fig.12 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] CNC machine tools are called "industrial mother machines" and are the foundation of the manufacturing industry. Their reliability level determines whether CNC machine tools can operate stably and reliably for a long time, which in turn affects the stability of the processing process and maintenance costs. As a complex system integrating mechanical, electrical and hydraulic systems, the reliability of CNC machine tools is determined by the reliability of its various components, and there is a certain correlation between the failures of different components. In addition, the reliability of each component will be affected by external factors such as its working environment and working load. The above situation makes the reliability modeling and evaluation of CNC machine tools more complicated. Therefore, how to accurately and effectively evaluate the reliability of CNC machine tools is a problem that technical personnel in this field need to solve.

[0048] In the prior art, the reliability of CNC machine tools can be evaluated through methods such as fault tree, reliability block diagram, Markov model, etc. Most of these methods assume that the failures between the components of the CNC machine tools are independent of each other, resulting in low accuracy of reliability evaluation results.

[0049] In view of this, the present application provides a CNC machine tool reliability assessment method that can improve the accuracy of CNC machine tool reliability assessment results. The CNC machine tool reliability assessment method provided in the embodiment of the present application, its execution subject can be a CNC machine tool reliability assessment device, and the CNC machine tool reliability assessment device can be implemented by software, hardware, or a combination of software and hardware, and can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software. In the following method embodiments, the execution subject is taken as an example of a server.

[0050] In an exemplary embodiment, Figure 1 As shown, a method for evaluating the reliability of a numerically controlled machine tool is provided, comprising the following steps 101 to 104. Among them:

[0051] S101, determining a Bayesian network model corresponding to the CNC machine tool according to the fault logic relationship of each component in the CNC machine tool.

[0052] The Bayesian network model includes a leaf node, multiple intermediate nodes, multiple root nodes, the state transition probability distribution of each root node, and the conditional probability distribution between each node.

[0053] Optionally, in the embodiment of the present application, the CNC machine tool may be a complete CNC machine tool, a subsystem or a component of a CNC machine tool.

[0054] Optionally, the CNC machine tool can be divided into multiple components. When dividing the CNC machine tool, the components can be divided according to the functional independence or structural independence of the CNC machine tool. The order of the division levels can be from high to low as whole machine level → subsystem level → component level → part level, etc. The division level can be flexibly selected according to the actual needs of the CNC machine tool reliability assessment. The embodiment of the present application does not limit the division method.

[0055] Optionally, the root node may be a basic component, such as a part, etc., and the leaf node may be a CNC machine tool.

[0056] Optionally, the logical relationship between the failures of each component can be determined based on historical failure data, or by acquiring domain knowledge and expert experience. For example, the failure of one component may directly cause the failure of another component, and there is a logical relationship between the two components.

[0057] Optionally, the Bayesian network can be a directed acyclic graph, in which nodes represent variables, edges represent conditional dependencies between variables, and each node has a conditional probability distribution, which describes the probability distribution of the node under different values ​​of its parent node.

[0058] Optionally, according to the fault logic relationship of each component, each component is regarded as a node and connected with directed edges, that is, from the cause variable to the result variable or from the condition variable to the dependent variable. It can be understood that after determining the Bayesian network model corresponding to the CNC machine tool according to the fault logic relationship, it is necessary to check whether the Bayesian network model is a directed acyclic graph. The cyclic structure will lead to logical contradictions and computational problems, because it means that there is a mutually circular causal relationship between components, which does not meet the definition of the Bayesian network and requires re-examination and adjustment of the fault logic relationship between components.

[0059] Optionally, the state transition probability distribution of each root node and the conditional probability distribution between nodes may be determined by obtaining prior knowledge, historical fault data or expert estimation.

[0060] S102: If the root node is affected by external factors, a corresponding proportional risk model is determined according to the root node and the external factors, so as to update the state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model.

[0061] Optionally, the state transition probability distribution may describe the change of the node state over time, including the distribution of the probability law of the node transitioning from any state at the current time to any state at the next time.

[0062] For example, the state transition matrix can be used to represent the state transition probability distribution of a node. If a node has n discrete states S1, S2, ..., S n , then the state transfer matrix P is an n×n matrix, where the element P ij Indicates that from state S i Transfer to state S j The probability of ij =P(X t+1 =S j |X t =S i ) means that the node state at time t is S i , at time t+1 the node state changes to S j probability.

[0063] Optionally, the reliability of each basic component is easily affected by external factors such as the working environment, working load or other basic components. Therefore, when determining that the root node is affected by external factors, the state transition probability distribution of the root node needs to be determined based on the external factors.

[0064] Optionally, a proportional hazard model (PHM) is a statistical model used to analyze the impact of one or more external factors on the failure of an infrastructure component.

[0065] Optionally, the external factors may be represented by nodes in the Bayesian network model, which are called covariate nodes X, and the covariate nodes X are connected to the basic component nodes affected by them by directed edges.

[0066] Optionally, the covariate node X is represented by a Markov process, and its state transition probability can be expressed as:

[0067]

[0068] in, It represents the probability that the state of the covariate X changes to j at time t+Δt when the state of the covariate X is i at time t.

[0069] Alternatively, the general form of the proportional hazards model can be as follows:

[0070]

[0071] Among them, X t Represents the state of the covariate node X at time t, function (X t ) quantifies the effect of the covariate on the failure rate of the basic component, h0(t) represents the basic failure rate function of the basic component, and h(t, X t ) represents the basic component failure rate function considering the influence of external factors. It can be understood that as time changes, the probability distribution of the covariate node will also change, and the state transition probability of the root node at different times will also be different.

[0072] Optionally, historical observation data of the root node and the external factors may be obtained, and the parameters in the proportional hazard model may be determined by maximum likelihood estimation to determine the proportional hazard model corresponding to the root node and the external factors.

[0073] In one possible implementation, the proportional hazard model, the state transition probability of the covariate node, and the probability distribution of the covariate node can be input into a pre-trained neural network model, the state transition probability distribution of the root node can be determined based on the output of the neural network model, and the corresponding state transition probability distribution in the Bayesian network model can be updated.

[0074] S103, predicting the probability distribution of leaf nodes at the target time according to the updated Bayesian network model.

[0075] The probability distribution includes the failure probability and normal probability of each node.

[0076] In one possible implementation method, the probability distribution of leaf nodes at the target moment can be determined in the following manner: after determining the probability distribution of each root node at the initial moment, the probability distribution of the intermediate nodes can be determined based on the conditional probability of each node and the total probability formula, and then the probability distribution of the leaf nodes at the initial moment can be determined based on the probability distribution of the intermediate nodes and the total probability formula, and then the probability distribution of the root nodes at the next moment can be determined based on the probability distribution of the root nodes and the state transition probability distribution, and the above process of determining the probability distribution of the leaf nodes is repeated to obtain the probability distribution of the leaf nodes at the next moment, and so on, until the probability distribution of the leaf nodes at the target moment is calculated, wherein, when the root node is affected by external factors, it is necessary to determine the state transition probability distribution of the root node based on the updated Bayesian network model, so as to determine the probability distribution of the root node at the next moment based on the updated state transition probability distribution.

[0077] In another possible implementation method, the probability distribution of leaf nodes at the target moment can also be determined in the following way: the Bayesian network model also includes the state transition probability distribution of leaf nodes. After determining the probability distribution of each root node at the initial moment, the probability distribution of the intermediate nodes can be determined based on the conditional probability of each node and the total probability formula, and then the probability distribution of leaf nodes at the initial moment can be determined based on the probability distribution of the intermediate nodes and the total probability formula. Then, the probability distribution of leaf nodes at the next moment can be determined based on the probability distribution of leaf nodes at the initial moment and the state transition probability distribution of leaf nodes, and so on, until the probability distribution of leaf nodes at the target moment is calculated.

[0078] S104, evaluating the reliability of the CNC machine tool according to the probability distribution of the leaf nodes.

[0079] Optionally, the probability distribution of a leaf node may include the probability of a leaf node failure and the probability of a leaf node being normal. If the probability of a leaf node failure is greater than a preset threshold, it indicates that the reliability of the CNC machine tool is poor.

[0080] Optionally, in the process of predicting the probability distribution of leaf nodes at the target moment, the probability distribution of leaf nodes at each moment from the initial moment to the target moment can be obtained, and the failure probability curve of the leaf nodes can be determined based on the probability distribution of leaf nodes at each moment to describe the operation status of the CNC machine tool.

[0081] The above-mentioned CNC machine tool reliability assessment method determines the Bayesian network model corresponding to the CNC machine tool according to the fault logical relationship of each component in the CNC machine tool. The Bayesian network model includes a leaf node, multiple intermediate nodes, multiple root nodes, the state transition probability distribution of each root node, and the conditional probability distribution between each node. Then, if the root node is affected by external factors, the corresponding proportional risk model is determined according to the root node and the external factors to update the state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model. Then, the probability distribution of the leaf node at the target time is predicted according to the updated Bayesian network model. The probability distribution includes the failure probability and normal probability of each node. Finally, the reliability of the CNC machine tool is evaluated according to the probability distribution of the leaf node. The fault correlation between different components is considered through the Bayesian network, and the influence of external factors on some components is taken into account based on the proportional risk model. In this way, when conducting reliability assessment, the fault correlation between machine tool components and the influence of external factors are considered at the same time. The obtained reliability assessment results are more in line with the actual situation of CNC machine tools and can effectively improve the accuracy of reliability assessment results. At the same time, when determining the state transition probability distribution of the root node affected by external factors, the proportional risk model is converted into a Bayesian network model, realizing the effective fusion of the two models.

[0082] In an exemplary embodiment, Figure 2 As shown, optionally, determining the Bayesian network model corresponding to the CNC machine tool according to the fault logic relationship of each component in the CNC machine tool includes the following steps 201 to 202. Among them:

[0083] S201, perform fault analysis on the CNC machine tool and establish a fault tree of the CNC machine tool.

[0084] Among them, the top event of the fault tree is the fault of the CNC machine tool, the bottom event is the fault of the basic component in the CNC machine tool, the middle event is the fault between the CNC machine tool and the basic component, and the basic component is a part-level component.

[0085] Exemplarily, the embodiment of the present application takes the CNC machine tool as the CNC machine tool spindle system as an example, and divides the CNC machine tool into components. The whole machine level is the spindle system, and the component level includes 4 categories: motor component, bearing component, cooling component and tool. The top event may be a spindle system failure, the intermediate event may be a poor cutting function of the spindle system or abnormal operation of the spindle system, and the bottom event may be a tool failure, a cooling component failure, a bearing component failure and a motor component failure.

[0086] S202, determining leaf nodes, intermediate nodes and root nodes in a Bayesian network model respectively according to the top event, intermediate event and bottom event in the fault tree.

[0087] Optionally, the top event in the fault tree can be used as a leaf node in the Bayesian network model, the middle event in the fault tree can be used as an intermediate node in the Bayesian network model, and the bottom event in the fault tree can be used as a root node in the Bayesian network model.

[0088] Optionally, events with logical relationships in the fault tree are connected with directed edges in the Bayesian network to represent the correlation of faults between nodes.

[0089] For example, Figure 3 As shown in FIG. 1 , it is a Bayesian network model determined according to the above fault tree, that is, there is a correlation between the spindle system failure and the poor cutting function of the spindle system and the abnormal operation of the spindle system, there is a correlation between the tool failure and the cooling component failure and the poor cutting function of the spindle system, and there is a correlation between the bearing component failure and the motor component failure and the abnormal operation of the spindle system. The representation of each node in the Bayesian network model is shown in Table 1:

[0090] Table 1

[0091]

[0092] By conducting fault analysis on the CNC machine tool and establishing the fault tree of the CNC machine tool, the leaf nodes, intermediate nodes and root nodes in the Bayesian network model are determined according to the top events, intermediate events and bottom events in the fault tree, and the Bayesian network model corresponding to the CNC machine tool can be accurately determined.

[0093] Optionally, a Bayesian network model corresponding to the CNC machine tool is determined according to the fault logic relationship of each component in the CNC machine tool, including: determining the state transition probability distribution of each root node in the Bayesian network model and the conditional probability distribution between each node according to historical fault data.

[0094] Exemplarily, as shown in Tables 2 to 6, they are the state transition probability distribution of each root node and the conditional probability distribution between each node, where Table 2 is the conditional probability distribution of leaf node S, Table 3 is the conditional probability distribution of intermediate node F1, Table 4 is the conditional probability distribution of intermediate node F2, Table 5 is the state transition probability distribution of root nodes C1 and C3, and Table 6 is the state transition probability of root node C2.

[0095] Table 2

[0096]

[0097] Table 3

[0098]

[0099] Table 4

[0100]

[0101] Table 5

[0102]

[0103] Table 6

[0104]

[0105] In an exemplary embodiment, Figure 4 As shown, optionally, if the root node is affected by external factors, a corresponding proportional risk model is determined according to the root node and the external factors, so as to update the state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model, including the following steps 401 to 403. Among them:

[0106] S401, obtaining a basic failure rate function of a root node and a quantitative function of the impact of external factors on the failure rate of the root node.

[0107] Among them, the basic failure rate function is related to time, and the quantification function is related to the state of external factors.

[0108] Optionally, there may be at least one external influencing factor. At the same time, the external influencing factor of the root node may be a working environment factor, a working load factor, and other root nodes in the Bayesian network model may also serve as external factors of the root node.

[0109] Optionally, the basic failure rate function of the root node may be the failure rate when all covariates are 0, that is, the basic failure rate is a function of time t, and the basic failure rate function may be obtained from a plurality of preset function pools according to the identification information of the root node.

[0110] Optionally, when there are multiple external factors, the quantization function may be the product of the influence of each external factor on the root node.

[0111] Optionally, the parameters of the quantization function may be determined according to a maximum likelihood estimation method.

[0112] S402, determining a proportional hazard model according to the product of the basic failure rate function and the quantization function.

[0113] For example, according to the analysis of historical fault data, the external factor of the motor component failure may be a cooling component failure, that is, the cooling component failure may cause the motor component failure. The determined proportional risk model is as follows:

[0114]

[0115] Among them, the basic failure rate function of motor component failure is , the quantitative function of the effect of cooling component failure on motor component failure rate is , is the state probability of cooling component failure at time t, where, as shown in Table 2, the state of cooling component failure may include {normal, degraded, faulty}. When the target state transition probability distribution of the root node is subsequently determined according to the proportional risk model, the state of cooling component failure may be digitally represented as {1, 2, 3}, and the distribution corresponds to different states.

[0116] S403, determining the target state transition probability distribution of the root node according to the proportional risk model, and updating the state transition probability distribution of the root node in the Bayesian network model according to the target state transition probability distribution.

[0117] Optional, such as Figure 5 As shown, it is a schematic diagram of the state transition of the root node when it is affected by external factors, that is, the state transition of the motor component failure is affected by the cooling component failure.

[0118] Optional, such as Figure 6 As shown, determining the target state transition probability distribution of the root node according to the proportional risk model includes the following steps 601 to 603. Among them:

[0119] S601, obtaining the state transition probability distribution of external factors.

[0120] Optionally, the state transition probability distribution of the external factors is known and determined.

[0121] S602: Determine the impact value of the external factor on the root node failure probability under different states according to the proportional risk model and the probability distribution of the external factor.

[0122] Optionally, the first result can be determined by integrating the basic failure rate function in the proportional hazard model, with the upper limit of the integral being the next moment , the lower limit of the integral is the current time t; multiple second results are determined according to the quantization function and the external factors in different states; the influence value of the external factors on the root node failure probability in different states is determined according to the product of the first result and each second result, which can be expressed by the following formula:

[0123]

[0124] Among them, R(t+Δt, t, X t =i) represents the impact value on the root node failure probability when the external factor X is in state i. The probability of the external factor X being in state i is determined according to the probability distribution of the external factor at time t.

[0125] S603, determining the target state transition probability distribution of the root node according to the state transition probability distribution of the external factors and each influence value.

[0126] Optionally, the target state transition probability distribution of the root node can be as shown in Table 7. When the root node fails at time t, the probability of the root node failure at time t+Δt is 1 and the probability of normal is 0. When the root node is normal at time t, the probability of the root node normal at time t+Δt is Ts and the probability of failure is 1-Ts.

[0127] Table 7

[0128]

[0129] Optionally, Ts is calculated as:

[0130]

[0131] in, is the state transition probability of the external factor. When determining the state of the external factor at the current moment, the target transition probability distribution of the root node can be determined by the above expression. It can be understood that there are multiple target state transition probability distributions of the root node. According to the different states of the external factors at the current moment, the corresponding target state transition probability of the root node is determined.

[0132] Exemplarily, the specific value of the target transition probability distribution of the motor component failure can be determined in the following manner:

[0133]

[0134] in, It can be determined by querying the state transition probability of the root node C2 in Table 6, is the state of C2 at the current moment, For the next moment ( ) The possible states of C2 are determined according to the current state of C2 and Table 6.

[0135] In an exemplary embodiment, optionally, predicting the probability distribution of leaf nodes at a target time according to an updated Bayesian network model includes: iteratively executing the probability distribution prediction process of leaf nodes multiple times until the number of iterations reaches a target number.

[0136] Among them, the target number is determined according to the target time.

[0137] Optionally, get the preset time interval The target number can be determined based on the target time and the preset time interval. The time corresponding to the i-th iteration is t, and the time corresponding to the i+1-th iteration is .

[0138] Optional, such as Figure 7As shown, the probability distribution prediction process of the i-th leaf node includes the following steps 701 to 705. Among them:

[0139] S701, obtaining a first probability distribution of each root node and a state transition probability distribution of each root node.

[0140] S702: If the root node is affected by external factors, determine the state transition probability distribution of the root node according to the first probability distribution of the external factors and the updated Bayesian network model.

[0141] Among them, when i is equal to 1, the first probability distribution is the probability distribution at the initial moment, and when i is greater than 1, the first probability distribution is the target probability distribution solved in the probability distribution prediction process of the i-1th leaf node.

[0142] Optionally, when determining the state transition probability distribution of the root node based on the first probability distribution of external factors and the updated Bayesian network model, the above steps 601 to 603 can be followed, which will not be elaborated in the embodiments of the present application.

[0143] S703 , determining a target probability distribution of each root node according to the first probability distribution of each root node and the state transition probability distribution of each root node.

[0144] Optionally, the target probability distribution of the root node may include the probability of the root node being normal and the probability of the root node being faulty. The target probability distribution of some root nodes may also include the probability of the root node being degraded.

[0145] Optionally, the probability that the root node is normal, the probability that the root node is faulty, and the probability that the root node is returned can be determined respectively according to the total probability formula.

[0146] Exemplarily, the normal state of the root node W is represented by S, and the fault state is represented by R. The target probability distribution of the root node W can be determined by the following formula:

[0147]

[0148]

[0149] in, , , and is the state transition probability distribution of the root node W, and is the first probability distribution of the root node W.

[0150] S704: If the root node is affected by an external factor, determine a target probability distribution of the external factor according to a first probability distribution of the external factor and a state transition probability distribution of the external factor.

[0151] Optionally, the target probability distribution of external factors can also be determined based on the total probability formula, which is the same as the above method and will not be described in detail in the embodiments of the present application.

[0152] S705 , determining the probability distribution of leaf nodes according to the target probability distribution of each root node and the conditional probability distribution between each node.

[0153] Optionally, the probability distribution of the intermediate nodes may be determined based on the target probability distribution of each root node and the conditional probability distribution between each node, and the probability distribution of the leaf nodes may be determined based on the probability distribution of the intermediate nodes and the conditional probability distribution between each node.

[0154] Optionally, the probability distribution can be determined by a total probability formula, which will not be described in detail in the embodiments of the present application.

[0155] For example, Figure 8 As shown, it is a schematic diagram of the dynamic Bayesian network structure. The leaf node S is a CNC machine tool. In the probability distribution prediction process of a leaf node, the state transition probability distribution of the root node C4 can be determined according to the first probability distribution, the state transition probability distribution and the proportional risk model of the root node C2. The target probability distribution of the corresponding root node can be determined according to the state transition probability distribution of each root node. The probability distribution of the intermediate node F1 can be determined according to the target probability distribution of the root nodes C1 and C2 and the conditional probability distribution in Table 3. The probability distribution of the intermediate node F2 can be determined according to the target probability distribution of the root nodes C3 and C4 and the conditional probability distribution in Table 4. Then, the probability distribution of the leaf node S can be determined according to the probability distribution of F1 and F2 and the conditional probability distribution in Table 2.

[0156] For example, Fig. 9 As shown in Figure 1, this is the failure probability curve of the leaf node S at different times when iterating to the target time. The horizontal axis is time. Taking the target time of 2000 as an example, the vertical axis is the failure probability of node S. As time goes by, the failure probability of node S gradually approaches 1. The reliability of the CNC machine tool can be evaluated through this failure probability curve.

[0157] As an optional implementation, Fig.10 As shown, the CNC machine tool reliability assessment method provided in the embodiment of the present application may include the following specific steps:

[0158] S1001, perform fault analysis on the CNC machine tool and establish a fault tree for the CNC machine tool.

[0159] Among them, the top event of the fault tree is the fault of the CNC machine tool, the bottom event is the fault of the basic component in the CNC machine tool, the middle event is the fault between the CNC machine tool and the basic component, and the basic component is a part-level component.

[0160] S1002, determining leaf nodes, intermediate nodes and root nodes in a Bayesian network model respectively according to the top event, intermediate event and bottom event in the fault tree.

[0161] S1003, determining the state transition probability distribution of each root node and the conditional probability distribution between each node in the Bayesian network model according to the historical fault data.

[0162] The Bayesian network model includes a leaf node, multiple intermediate nodes, multiple root nodes, the state transition probability distribution of each root node, and the conditional probability distribution between each node.

[0163] S1004, obtaining a first probability distribution of each root node and a state transition probability distribution of each root node.

[0164] S1005: If the root node is affected by external factors, a basic failure rate function of the root node and a quantization function of the impact of the external factors on the failure rate of the root node are obtained.

[0165] Among them, the basic failure rate function is related to time, and the quantification function is related to the state of external factors.

[0166] S1006, determining a proportional hazard model according to the product of the basic failure rate function and the quantization function.

[0167] S1007, obtaining the state transition probability distribution of external factors.

[0168] S1008: Determine the impact value of the external factor on the root node failure probability under different states according to the proportional risk model and the first probability distribution of the external factor.

[0169] Among them, when i is equal to 1, the first probability distribution is the probability distribution at the initial moment. When i is greater than 1, the first probability distribution is the target probability distribution solved in the probability distribution prediction process of the i-1th leaf node, and i is the number of iterations.

[0170] S1009, determining the target state transition probability distribution of the root node according to the state transition probability distribution of the external factors and each influence value.

[0171] S1010, updating the state transition probability distribution of the root node in the Bayesian network model according to the target state transition probability distribution.

[0172] S1011, determining a target probability distribution of each root node according to the first probability distribution of each root node and the state transition probability distribution of each root node.

[0173] S1012: If the root node is affected by an external factor, a target probability distribution of the external factor is determined according to the first probability distribution of the external factor and the state transition probability distribution of the external factor.

[0174] S1013, determining the probability distribution of leaf nodes according to the target probability distribution of each root node and the conditional probability distribution between each node.

[0175] S1014, repeat the above steps S1004 to 1013 until the number of iterations reaches the target number.

[0176] S1015, evaluating the reliability of the CNC machine tool according to the probability distribution of the leaf nodes.

[0177] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0178] Based on the same inventive concept, the embodiment of the present application also provides a numerical control machine tool reliability assessment device for implementing the numerical control machine tool reliability assessment method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more numerical control machine tool reliability assessment device embodiments provided below can refer to the limitations of the numerical control machine tool reliability assessment method above, and will not be repeated here.

[0179] In an exemplary embodiment, Fig.11 As shown, a numerical control machine tool reliability evaluation device 1100 is provided, comprising: an initial module 1101, an update module 1102, a prediction module 1103 and an evaluation module 1104, wherein:

[0180] The initial module 1101 is used to determine the Bayesian network model corresponding to the CNC machine tool according to the fault logic relationship of each component in the CNC machine tool, and the Bayesian network model includes a leaf node, multiple intermediate nodes, multiple root nodes, the state transition probability distribution of each root node, and the conditional probability distribution between each node;

[0181] An updating module 1102 is used to determine a corresponding proportional risk model according to the root node and the external factor if the root node is affected by an external factor, so as to update the state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model;

[0182] Prediction module 1103, used to predict the probability distribution of leaf nodes at the target time according to the updated Bayesian network model, the probability distribution including the failure probability and normal probability of each node;

[0183] The evaluation module 1104 is used to evaluate the reliability of the CNC machine tool according to the probability distribution of the leaf nodes.

[0184] In an exemplary embodiment, the initial module 1101 is specifically used to perform fault analysis on a CNC machine tool and establish a fault tree for the CNC machine tool, wherein the top event of the fault tree is the fault of the CNC machine tool, the bottom event is the fault of a basic component in the CNC machine tool, the intermediate event is the fault between the CNC machine tool and the basic component, and the basic component is a part-level component; the leaf nodes, intermediate nodes and root nodes in the Bayesian network model are determined respectively according to the top event, intermediate event and bottom event in the fault tree.

[0185] In an exemplary embodiment, the initial module 1101 is specifically used to determine the state transition probability distribution of each root node in the Bayesian network model and the conditional probability distribution between each node according to the historical fault data.

[0186] In an exemplary embodiment, the update module 1102 is specifically used to obtain a basic failure rate function of the root node and a quantification function of the impact of external factors on the failure rate of the root node, wherein the basic failure rate function is related to time, and the quantification function is related to the state of the external factor; a proportional risk model is determined based on the product of the basic failure rate function and the quantification function; a target state transition probability distribution of the root node is determined based on the proportional risk model, and the state transition probability distribution of the root node in the Bayesian network model is updated based on the target state transition probability distribution.

[0187] In an exemplary embodiment, the update module 1102 is specifically used to obtain the state transition probability distribution of external factors; determine the impact value of the external factors on the failure probability of the root node under different states based on the proportional risk model and the probability distribution of the external factors; and determine the target state transition probability distribution of the root node based on the state transition probability distribution of the external factors and each impact value.

[0188] In an exemplary embodiment, the prediction module 1103 is specifically used to iteratively execute the probability distribution prediction process of multiple leaf nodes until the number of iterations reaches the target number, and the target number is determined according to the target time; wherein, the probability distribution prediction process of the i-th leaf node includes: obtaining the first probability distribution of each root node and the state transition probability distribution of each root node; if the root node is affected by external factors, the state transition probability distribution of the root node is determined according to the first probability distribution of the external factor and the updated Bayesian network model, wherein when i is equal to 1, the first probability distribution is the probability distribution at the initial moment, and when i is greater than 1, the first probability distribution is the target probability distribution solved in the probability distribution prediction process of the i-1-th leaf node; according to the first probability distribution of each root node and the state transition probability distribution of each root node, the target probability distribution of each root node is determined; if the root node is affected by external factors, the target probability distribution of the external factor is determined according to the first probability distribution of the external factor and the state transition probability distribution of the external factor; the probability distribution of the leaf node is determined according to the target probability distribution of each root node and the conditional probability distribution between each node.

[0189] Each module in the above-mentioned CNC machine tool reliability assessment device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0190] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.12 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a reliability assessment method for a CNC machine tool is implemented.

[0191] Those skilled in the art will understand that Fig.12The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0192] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps described in any of the above method embodiments when executing the computer program.

[0193] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps described in any of the above method embodiments are implemented.

[0194] In one embodiment, a computer program product is provided, including a computer program, which implements the steps described in any of the above method embodiments when executed by a processor.

[0195] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0196] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0197] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A reliability assessment method for a CNC machine tool, characterized in that: The method comprises: Determine a Bayesian network model corresponding to the CNC machine tool according to the fault logic relationship of each component in the CNC machine tool, wherein the Bayesian network model includes a leaf node, multiple intermediate nodes, multiple root nodes, state transition probability distribution of each of the root nodes, and conditional probability distribution between each node; If the root node is affected by external factors, determining a corresponding proportional risk model according to the root node and the external factors, so as to update the state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model; Predicting the probability distribution of the leaf nodes at the target time according to the updated Bayesian network model, wherein the probability distribution includes the failure probability and normal probability of each of the nodes; The reliability of the CNC machine tool is evaluated according to the probability distribution of the leaf nodes.

2. The method according to claim 1, characterized in that Determining the Bayesian network model corresponding to the CNC machine tool according to the fault logical relationship of each component in the CNC machine tool includes: Performing fault analysis on the CNC machine tool and establishing a fault tree of the CNC machine tool, wherein the top event of the fault tree is the fault of the CNC machine tool, the bottom event is the fault of a basic component in the CNC machine tool, and the intermediate event is the fault between the CNC machine tool and the basic component, wherein the basic component is a part-level component; The leaf nodes, intermediate nodes and root nodes in the Bayesian network model are determined respectively according to the top event, the intermediate event and the bottom event in the fault tree.

3. The method according to claim 1, characterized in that Determining the Bayesian network model corresponding to the CNC machine tool according to the fault logical relationship of each component in the CNC machine tool includes: The state transition probability distribution of each root node in the Bayesian network model and the conditional probability distribution between each node are determined according to the historical fault data.

4. The method according to claim 1, characterized in that: If the root node is affected by external factors, determining a corresponding proportional risk model according to the root node and the external factors, so as to update the state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model, comprises: Obtaining a basic failure rate function of the root node and a quantified function of the influence of the external factor on the failure rate of the root node, wherein the basic failure rate function is related to time, and the quantified function is related to the state of the external factor; determining a proportional hazard model based on the product of the basic failure rate function and the quantization function; The target state transition probability distribution of the root node is determined according to the proportional risk model, and the state transition probability distribution of the root node in the Bayesian network model is updated according to the target state transition probability distribution.

5. The method according to claim 4, characterized in that The determining the target state transition probability distribution of the root node according to the proportional risk model includes: Obtaining a state transition probability distribution of the external factor; Determine the impact value of the external factor on the root node failure probability under different states according to the proportional risk model and the probability distribution of the external factor; The target state transition probability distribution of the root node is determined according to the state transition probability distribution of the external factors and each of the influence values.

6. The method according to claim 1, characterized in that The predicting the probability distribution of the leaf nodes at the target time according to the updated Bayesian network model includes: Iterate the probability distribution prediction process of the leaf nodes multiple times until the number of iterations reaches a target number, and the target number is determined according to the target time; The probability distribution prediction process of the i-th leaf node includes: Obtaining a first probability distribution of each of the root nodes and a state transition probability distribution of each of the root nodes; If the root node is affected by external factors, the state transition probability distribution of the root node is determined according to the first probability distribution of the external factors and the updated Bayesian network model, wherein when i is equal to 1, the first probability distribution is the probability distribution at the initial moment, and when i is greater than 1, the first probability distribution is the target probability distribution solved in the probability distribution prediction process of the leaf node for the i-1th time; Determining a target probability distribution of each of the root nodes according to the first probability distribution of each of the root nodes and the state transition probability distribution of each of the root nodes; If the root node is affected by an external factor, determining a target probability distribution of the external factor according to a first probability distribution of the external factor and a state transition probability distribution of the external factor; The probability distribution of the leaf nodes is determined according to the target probability distribution of each of the root nodes and the conditional probability distribution between each of the nodes.

7. A reliability assessment device for a numerically controlled machine tool, characterized in that: The device comprises: An initial module, used to determine a Bayesian network model corresponding to the CNC machine tool according to the fault logic relationship of each component in the CNC machine tool, wherein the Bayesian network model includes a leaf node, multiple intermediate nodes, multiple root nodes, state transition probability distribution of each of the root nodes, and conditional probability distribution between each node; An updating module, configured to determine a corresponding proportional risk model according to the root node and the external factor if the root node is affected by an external factor, so as to update the state transition probability distribution of the root node in the Bayesian network model according to the proportional risk model; A prediction module, used to predict the probability distribution of the leaf nodes at a target time according to the updated Bayesian network model, wherein the probability distribution includes the failure probability and normal probability of each of the nodes; An evaluation module is used to evaluate the reliability of the CNC machine tool according to the probability distribution of the leaf nodes.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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