Fuel Cell Fault Diagnosis Method and Device Based on Bidirectional Long Short-Term Memory Network

Abstract

The present invention discloses a fuel cell fault diagnosis method and device based on a bidirectional long short-term memory network in the field of fuel cells. The method comprises: obtaining time step data of target operating parameters of the fuel cell and preprocessing the time step data; constructing a fault diagnosis model based on a bidirectional LSTM and training the fault diagnosis model using the preprocessed time step data; and using the fault diagnosis model finally obtained from the training to perform fuel cell fault diagnosis. The present invention utilizes a bidirectional LSTM to process time series data, fully considering the before-after correlation of the data, to achieve accurate fault detection of each module of the fuel cell system and improve diagnostic accuracy. At the same time, through the lightweight design of the model, the computing resource usage is reduced, and the deployment and maintenance costs are reduced, making the solution suitable for scenarios with limited resources such as on-board controllers.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel cells, and in particular to a fuel cell fault diagnosis method and device based on a bidirectional long short-term memory network. Background Art

[0002] Fuel cells, as clean and efficient energy conversion devices, have broad application potential in transportation, stationary power plants, and portable devices. Fuel cell systems can be susceptible to various faults during operation, which can lead to decreased stack performance, cell aging, and even safety issues. To ensure the safety and stability of fuel cell systems, research on fault diagnosis technology is gaining increasing attention. Fault diagnosis technology can promptly detect system faults and implement appropriate control measures to prevent more serious consequences.

[0003] Existing fuel cell fault diagnosis methods fall into three main categories: those based on physical / chemical detection techniques, model-driven methods, and data-driven methods. Physical / chemical detection techniques rely directly on actual measurements, which are accurate but require a large number of sensors. Model-driven methods offer advantages in accurately simulating specific fault mechanisms, but they require high model accuracy. Data-driven methods can handle complex, multi-dimensional fault modes and are highly adaptable, but they rely on large amounts of high-quality data. With advances in artificial intelligence and big data technologies, data-driven methods are increasingly being used in fuel cell fault diagnosis. These methods can analyze massive amounts of historical and real-time data to identify complex fault patterns and trends, further improving diagnostic accuracy and real-time performance. Furthermore, the integration of multiple diagnostic techniques (such as combining data-driven and model-driven methods) is also becoming a growing trend in fault diagnosis. By comprehensively analyzing information from multiple sources, a more comprehensive and accurate assessment of system status can be achieved.

[0004] For example, patent publication number CN117388702A describes a neural network-based fuel cell fault diagnosis method. While capable of providing relatively accurate fault warnings, its Transformer-based model requires more time and computing resources to train than other neural network models, placing greater demands on hardware. This model is unsuitable for deployment on edge devices with limited computing resources (such as vehicle-mounted controllers), resulting in low diagnostic efficiency and unsuitable for scenarios requiring long-term real-time performance. Regarding fault types, the invention only diagnoses two types of water management faults: membrane dryness and flooding, which presents certain limitations. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a fuel cell fault diagnosis method and device based on a bidirectional long short-term memory network. Compared with the existing data-driven method, the model has fewer parameters, shorter training time, stronger real-time reasoning, and is easy to deploy in vehicle controllers with limited computing resources.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, the present invention provides a fuel cell fault diagnosis method based on a bidirectional long short-term memory network, comprising:

[0008] Acquiring time step data of target operating parameters of the fuel cell and preprocessing the time step data;

[0009] Constructing a fault diagnosis model based on a bidirectional LSTM and training the fault diagnosis model using the preprocessed time step data;

[0010] The fault diagnosis model finally obtained through training is used to perform fault diagnosis on the fuel cell.

[0011] Optionally, the target operating parameters include:

[0012] Stack current , current density , Excess air coefficient , the pressure of hydrogen entering the fuel cell and temperature , hydrogen pressure out of the stack and temperature , the pressure of air entering the stack and temperature , Air out of the stack pressure and temperature , the temperature of cooling water entering and leaving the stack and , minimum cell voltage of the stack , average voltage of stack cells , mean square error of stack cell voltage , stack impedance and cathode side pressure drop .

[0013] Optionally, the fault diagnosis model includes a bidirectional LSTM module, a first fully connected layer, a PointNet encoder, a second fully connected layer, an average pooling layer, a projection layer, and a classification layer connected in sequence;

[0014] The bidirectional LSTM module performs forward feature processing and reverse feature processing on the input time step data to obtain a bidirectional hidden state :

[0015]

[0016] Where, is the time step The hidden state obtained by forward feature processing and reverse feature processing, , is the total number of time steps;

[0017] The first fully connected layer is used to store the bidirectional hidden state Mapping is performed to obtain the feature vector :

[0018]

[0019] Where, is the weight matrix and bias of the first fully connected layer;

[0020] The PointNet encoder uses a multi-layer perceptron to Mapping is performed to obtain the feature vector :

[0021]

[0022] Where, is a multi-layer perceptron;

[0023] The second fully connected layer performs a feature vector Mapping is performed to obtain the feature vector :

[0024]

[0025] Where, is the weight matrix and bias of the second fully connected layer;

[0026] The average pooling layer performs the feature vector Perform global average pooling to obtain global features :

[0027]

[0028] Where, is global average pooling;

[0029] The projection layer is used to calculate the global features Mapping to obtain global features :

[0030]

[0031] Where, is the weight matrix and bias of the projection layer;

[0032] The classification layer uses the softmax activation function to classify the global features Mapping is performed to obtain the probability distribution of each fault category and output the final probability distribution of the fault category :

[0033]

[0034] Where, is the softmax activation function.

[0035] Optionally, the bidirectional LSTM module is based on the input time step data of the current time step and the hidden state of the previous time step , the forgetting state is calculated through the forgetting gate, input gate, memory cell and output gate respectively , input status , unit status And the output status :

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] Where, is the sigmoid activation function, for activation function, is the weight vector, is the bias, is the time step time step data, is the Hadamard product of the matrix, is an intermediate variable;

[0042] According to the unit status and the output status Calculate the hidden state at the current time step :

[0043] .

[0044] Optionally, the training of the fault diagnosis model using the preprocessed time step data includes:

[0045] Initialize the model parameters of the fault diagnosis model and repeat the following steps until the model parameters converge or the preset number of iterations is reached:

[0046] Inputting the time step data into the fault diagnosis model to obtain the probability distribution of the corresponding fault category;

[0047] Calculate the loss using a cross entropy loss function based on the probability distribution of the fault class and the true label of the fault class;

[0048] The gradient of the loss with respect to the model parameters is calculated by the back-propagation algorithm, and the model parameters are updated according to the gradient by the Adam optimizer.

[0049] Optionally, the cross entropy loss function for:

[0050]

[0051] Where, Fault category output by the fault diagnosis model The probability distribution of is the total number of fault categories, Fault type The true label value of .

[0052] In a second aspect, the present invention provides a fuel cell fault diagnosis device based on a bidirectional long short-term memory network, comprising:

[0053] a data acquisition and processing module configured to acquire time step data of target operating parameters of the fuel cell and preprocess the time step data;

[0054] A model building and training module is configured to build a fault diagnosis model based on a bidirectional LSTM and train the fault diagnosis model using the preprocessed time step data;

[0055] The battery fault diagnosis module is configured to perform fault diagnosis on the fuel cell using the fault diagnosis model finally obtained through training.

[0056] In a third aspect, the present invention provides an electronic device, including a processor and a storage medium;

[0057] The storage medium is used to store instructions;

[0058] The processor is configured to operate according to the instructions to execute the steps of the above method.

[0059] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0060] In a fifth aspect, the present invention provides a computer program product, comprising a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The present invention provides a fuel cell fault diagnosis method and device based on a bidirectional long short-term memory (LSTM) network, enabling efficient and accurate fault detection. By utilizing a bidirectional LSTM to process time series data, the method fully considers the contextual correlation of the data, enabling precise fault detection for each module of the fuel cell system (hydrogen supply, air supply, and water management), improving diagnostic accuracy. The method also features multiple fault type identification: It can identify common fuel cell fault conditions, such as membrane dryness, flooding, hydrogen starvation, and oxygen starvation, providing comprehensive information for system maintenance and enhancing system reliability. It also provides real-time monitoring and early warning capabilities: Using a bidirectional LSTM to analyze real-time data from the fuel cell system, it can promptly detect abnormal conditions and trigger early warnings, effectively preventing major failures and extending the system's operational life. The method also features a lightweight design: The lightweight model reduces computing resource usage, lowering deployment and maintenance costs, making the solution suitable for resource-limited scenarios, such as on-board controllers. In summary, the present invention can significantly improve the fault detection capabilities and operational efficiency of fuel cell systems, promoting the popularization of fuel cell vehicles and other fuel cell applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 1 is a flow chart of a fuel cell fault diagnosis method based on a bidirectional long short-term memory network provided by an embodiment of the present invention;

[0064] Figure 2 is a schematic structural diagram of a fault diagnosis model provided by an embodiment of the present invention;

[0065] Figure 3 This is a flow chart of training a fault diagnosis model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0067] Example 1:

[0068] like Figure 1As shown, an embodiment of the present invention provides a fuel cell fault diagnosis method based on a bidirectional long short-term memory network, comprising the following steps:

[0069] Step S1: Acquire time step data of target operating parameters of the fuel cell and preprocess the time step data.

[0070] Specifically in this embodiment, in order to efficiently and accurately diagnose and warn of faults in the hydrogen supply module, air supply module, and water management module of the fuel cell system, the target operating parameters are selected including:

[0071] Stack current , current density , Excess air coefficient , the pressure of hydrogen entering the fuel cell and temperature , hydrogen pressure out of the stack and temperature , the pressure of air entering the stack and temperature , Air out of the stack pressure and temperature , the temperature of cooling water entering and leaving the stack and , minimum cell voltage of the stack , average voltage of stack cells , mean square error of stack cell voltage , stack impedance and cathode side pressure drop .

[0072] In other optional implementations, the staff can adjust and select other operating parameters as needed, which will not be described in detail in this implementation.

[0073] Preprocessing of time-step data generally includes missing value processing and normalization. Missing value processing ensures data integrity and accuracy by addressing missing values in the data. Common methods include deletion, filling, and interpolation. Normalization converts data of different magnitudes or distributions to the same magnitude or distribution range to facilitate subsequent data analysis and model training.

[0074] Step S2: construct a fault diagnosis model based on bidirectional LSTM, and use the preprocessed time step data to train the fault diagnosis model.

[0075] Bidirectional LSTM (BiLSTM) is a special recurrent neural network (RNN) architecture that extends the capabilities of RNNs, enabling them to leverage both previous and future information in a sequence for prediction. A bidirectional LSTM consists of an input layer, a hidden layer, and an output layer. The input layer takes the input state sequence as input, and the hidden layer contains recurrent connections, allowing the network to maintain a memory of past states. The output layer produces predicted future states. Compared to LSTM, bidirectional LSTM plays a significant role in fault warning, especially in the analysis and prediction of time series data. By capturing contextual information of the sequence from both forward and backward directions, it can better detect potential fault trends in the system, thereby improving the accuracy and response speed of fault warnings. In this embodiment, constructing a fault diagnosis model based on bidirectional LSTM can improve the accuracy of fault diagnosis.

[0076] like Figure 2 As shown, specifically in this embodiment, the fault diagnosis model includes a bidirectional LSTM module, a first fully connected layer, a PointNet encoder, a second fully connected layer, an average pooling layer, a projection layer and a classification layer connected in sequence.

[0077] (1) The bidirectional LSTM module performs forward feature processing and reverse feature processing on the input time step data to obtain a bidirectional hidden state :

[0078]

[0079] Where, is the time step The hidden state obtained by forward feature processing and reverse feature processing, , is the total number of time steps;

[0080] The bidirectional LSTM module is based on the input time step data of the current time step and the hidden state of the previous time step. , the forgetting state is calculated through the forgetting gate, input gate, memory cell and output gate respectively , input status , unit status And the output status :

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] Where, is the sigmoid activation function, for activation function, is the weight vector, is the bias, is the time step time step data, is the Hadamard product of the matrix, is an intermediate variable;

[0087] According to the unit status and output status Calculate the hidden state at the current time step :

[0088] .

[0089] (2) The first fully connected layer has two hidden states. Mapping is performed to obtain the feature vector :

[0090]

[0091] Where, are the weight matrix and bias of the first fully connected layer.

[0092] (3) PointNet encoder uses a multi-layer perceptron to encode feature vectors Mapping is performed to obtain the feature vector :

[0093]

[0094] Where, It is a multi-layer perceptron.

[0095] (4) The second fully connected layer pairs the feature vector Mapping is performed to obtain the feature vector :

[0096]

[0097] Where, is the weight matrix and bias of the second fully connected layer.

[0098] (5) Average pooling layer for feature vector Perform global average pooling to obtain global features :

[0099]

[0100] Where, is global average pooling.

[0101] (6) Projection layer for global features Mapping to obtain global features :

[0102]

[0103] Where, are the weight matrix and bias of the projection layer.

[0104] (7) The classification layer uses the softmax activation function to Mapping is performed to obtain the probability distribution of each fault category and output the final probability distribution of the fault category :

[0105]

[0106] Where, is the softmax activation function.

[0107] This implementation combines a bidirectional LSTM with a PointNet to effectively extract both local and global features from time series. The bidirectional LSTM processes the dependencies between the previous and next steps in the time series, while the PointNet captures the overall features across different time steps. This approach can more accurately identify faults in fuel cell systems, providing diagnosis and early warning.

[0108] like Figure 3 As shown, specifically in this embodiment, using the preprocessed time step data to train the fault diagnosis model includes:

[0109] Step S01: Initialize the model parameters of the fault diagnosis model, and repeat steps S02 to S04 until the model parameters converge or the preset number of iterations is reached:

[0110] Step S02: input the time step data into the fault diagnosis model to obtain the probability distribution of the corresponding fault category;

[0111] Step S03: Calculate the loss using a cross entropy loss function based on the probability distribution of the fault category and the true label of the fault category;

[0112] Cross Entropy Loss Function for:

[0113]

[0114] Where, Fault category output by the fault diagnosis model The probability distribution of is the total number of fault categories, Fault type The true label value of .

[0115] Specifically in this embodiment, the present invention uses the target operating parameters of the hydrogen supply module, air supply module and water management module to diagnose four fault states (membrane dryness, flooding, hydrogen starvation, oxygen starvation) and normal state of the fuel cell, so The value is 5.

[0116] Step S04: Calculate the gradient of the loss with respect to the model parameters through the back-propagation algorithm, and update the model parameters according to the gradient through the Adam optimizer;

[0117]

[0118]

[0119] Where, are model parameters, is the learning rate, For loss.

[0120] Step S3: Using the fault diagnosis model finally obtained through training to perform fault diagnosis on the fuel cell.

[0121] In summary, the fuel cell fault diagnosis method based on a bidirectional long short-term memory (LSTM) network provided by the present invention collects relevant sensor and controller data from the fuel cell system and filters the collected raw data to ensure that the characteristic data is suitable for input into the model for fault detection. A trained bidirectional LSTM neural network model is used to analyze the preprocessed data to identify potential system faults. Based on the model output, it determines whether the system is normal or a fault has been detected. If the model determines that the system is normal, it outputs a "system normal" status message. If the model detects a fault, it outputs the specific fault type, including membrane dryness, water flooding, hydrogen starvation, and oxygen starvation.

[0122] Example 2:

[0123] An embodiment of the present invention provides a fuel cell fault diagnosis device based on a bidirectional long short-term memory network, comprising:

[0124] a data acquisition and processing module configured to acquire time step data of target operating parameters of the fuel cell and preprocess the time step data;

[0125] A model building and training module is configured to build a fault diagnosis model based on a bidirectional LSTM and train the fault diagnosis model using the preprocessed time step data;

[0126] The battery fault diagnosis module is configured to perform fault diagnosis on the fuel cell using the fault diagnosis model finally obtained through training.

[0127] Example 3:

[0128] An embodiment of the present invention provides an electronic device, including a processor and a storage medium;

[0129] The storage medium is used to store instructions;

[0130] The processor is configured to operate according to the instructions to execute the steps of the above method.

[0131] Example 4:

[0132] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0133] Embodiment 5:

[0134] An embodiment of the present invention provides a computer program product, including a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0135] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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.

[0136] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, 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 flowcharts and / or block diagrams. 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.

[0137] 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.

[0138] 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 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0139] 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 fuel cell fault diagnosis method based on a bidirectional long short-term memory network, characterized in that: include: Acquiring time step data of target operating parameters of the fuel cell and preprocessing the time step data; Constructing a fault diagnosis model based on a bidirectional LSTM and training the fault diagnosis model using the preprocessed time step data; Performing fault diagnosis on the fuel cell using the fault diagnosis model finally obtained through training; The fault diagnosis model includes a bidirectional LSTM module, a first fully connected layer, a PointNet encoder, a second fully connected layer, an average pooling layer, a projection layer, and a classification layer connected in sequence; The bidirectional LSTM module performs forward feature processing and reverse feature processing on the input time step data to obtain a bidirectional hidden state : ； Where, is the time step The hidden state obtained by forward feature processing and reverse feature processing, , is the total number of time steps; The first fully connected layer is used to store the bidirectional hidden state Mapping is performed to obtain the feature vector : ； Where, is the weight matrix and bias of the first fully connected layer; The PointNet encoder uses a multi-layer perceptron to Mapping is performed to obtain the feature vector : ； Where, is a multi-layer perceptron; The second fully connected layer performs a feature vector Mapping is performed to obtain the feature vector : ； Where, is the weight matrix and bias of the second fully connected layer; The average pooling layer performs the feature vector Perform global average pooling to obtain global features : ； Where, is global average pooling; The projection layer is used to calculate the global features Mapping to obtain global features : ； Where, is the weight matrix and bias of the projection layer; The classification layer uses the softmax activation function to classify the global features Mapping is performed to obtain the probability distribution of each fault category and output the final probability distribution of the fault category : ； Where, is the softmax activation function.

2. The fuel cell fault diagnosis method based on bidirectional long short-term memory network according to claim 1 is characterized in that: The target operating parameters include: Stack current , current density , Excess air coefficient , the pressure of hydrogen entering the fuel cell and temperature , hydrogen pressure out of the stack and temperature , the pressure of air entering the stack and temperature , Air out of the stack pressure and temperature , the temperature of cooling water entering and leaving the stack and , minimum cell voltage of the stack , average voltage of stack cells , mean square error of stack cell voltage , stack impedance and cathode side pressure drop .

3. The fuel cell fault diagnosis method based on bidirectional long short-term memory network according to claim 1 is characterized in that: The bidirectional LSTM module is based on the input time step data of the current time step and the hidden state of the previous time step , the forgetting state is calculated through the forgetting gate, input gate, memory cell and output gate respectively , input status , unit status And the output status : ； ； ； ； ； Where, is the sigmoid activation function, for activation function, is the weight vector, is the bias, is the time step time step data, is the Hadamard product of the matrix, is an intermediate variable; According to the unit status and the output status Calculate the hidden state at the current time step : 。 4. The fuel cell fault diagnosis method based on bidirectional long short-term memory network according to claim 1 is characterized in that: The training of the fault diagnosis model using the preprocessed time step data includes: Initialize the model parameters of the fault diagnosis model and repeat the following steps until the model parameters converge or the preset number of iterations is reached: Inputting the time step data into the fault diagnosis model to obtain the probability distribution of the corresponding fault category; Calculate the loss using a cross entropy loss function based on the probability distribution of the fault class and the true label of the fault class; The gradient of the loss with respect to the model parameters is calculated by the back-propagation algorithm, and the model parameters are updated according to the gradient by the Adam optimizer.

5. The fuel cell fault diagnosis method based on bidirectional long short-term memory network according to claim 4 is characterized in that: The cross entropy loss function for: ； Where, Fault category output by the fault diagnosis model The probability distribution of is the total number of fault categories, Fault type The true label value of .

6. A fuel cell fault diagnosis device based on a bidirectional long short-term memory network, characterized in that: include: a data acquisition and processing module configured to acquire time step data of target operating parameters of the fuel cell and preprocess the time step data; A model building and training module is configured to build a fault diagnosis model based on a bidirectional LSTM and train the fault diagnosis model using the preprocessed time step data; A battery fault diagnosis module is configured to perform fault diagnosis on the fuel cell using the fault diagnosis model finally obtained through training; The fault diagnosis model includes a bidirectional LSTM module, a first fully connected layer, a PointNet encoder, a second fully connected layer, an average pooling layer, a projection layer, and a classification layer connected in sequence; The bidirectional LSTM module performs forward feature processing and reverse feature processing on the input time step data to obtain a bidirectional hidden state : ； Where, is the time step The hidden state obtained by forward feature processing and reverse feature processing, , is the total number of time steps; The first fully connected layer is used to store the bidirectional hidden state Mapping is performed to obtain the feature vector : ； Where, is the weight matrix and bias of the first fully connected layer; The PointNet encoder uses a multi-layer perceptron to Mapping is performed to obtain the feature vector : ； Where, is a multi-layer perceptron; The second fully connected layer performs a feature vector Mapping is performed to obtain the feature vector : ； Where, is the weight matrix and bias of the second fully connected layer; The average pooling layer performs the feature vector Perform global average pooling to obtain global features : ； Where, is global average pooling; The projection layer is used to calculate the global features Mapping to obtain global features : ； Where, is the weight matrix and bias of the projection layer; The classification layer uses the softmax activation function to classify the global features Mapping is performed to obtain the probability distribution of each fault category and output the final probability distribution of the fault category : ； Where, is the softmax activation function.

7. An electronic device, 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 5.

8. 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 5 are implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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