Digital-twin-based whole-station stress early-warning method and system for flexible photovoltaic support

Abstract

A digital-twin-based whole-station stress early-warning method and system for a flexible photovoltaic support. The method comprises: collecting real-time stress data of a single photovoltaic array, properties of a photovoltaic support, and meteorological prediction data of a whole photovoltaic power station, performing data cleaning and preprocessing, and extracting features; establishing a digital twin model in view of the real-time stress data of the single photovoltaic array, the meteorological prediction data and the properties of the support, which real-time stress data, meteorological prediction data and properties have been subjected to data cleaning and preprocessing; training the digital twin model by using historical data, and inputting the real-time stress data of the single photovoltaic array into the trained digital twin model, such that the change trend of stress on the flexible support of the whole photovoltaic power station is predicted; and on the basis of a prediction result, analyzing whether the flexible support of the whole photovoltaic power station has potential safety hazards. A digital twin model is established by means of a neural network method, and has relatively high prediction accuracy and stability; and potential safety hazards can be found in a timely manner, thereby improving the safety and stability of a photovoltaic power station.

Description

Photovoltaic flexible support station stress early warning method and system based on digital twin Technical Field

[0001] The present invention belongs to the technical field of stress early warning for a photovoltaic flexible support station, and in particular relates to a method and system for stress early warning for a photovoltaic flexible support station based on digital twins. Background Art

[0002] With the rapid development of the photovoltaic industry, the scale of photovoltaic power plants continues to expand. As a key structure supporting photovoltaic panels, the safety and stability of flexible supports are crucial to the operation of the entire power plant. However, traditional stress monitoring methods typically require comprehensive monitoring of the entire power plant, which is costly and inefficient. Therefore, there is an urgent need for an efficient and low-cost stress warning technology for flexible supports in photovoltaic power plants.

[0003] Summary of the Invention

[0004] The purpose of the present invention is to provide a photovoltaic flexible support station stress warning method and system based on digital twins to solve the problem that traditional stress monitoring methods usually require comprehensive monitoring of the entire power station, which is high in cost and low in efficiency.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a photovoltaic flexible support station stress early warning method based on digital twins, comprising:

[0007] Collect real-time stress data of a single photovoltaic array, photovoltaic bracket properties, and meteorological forecast data for the entire photovoltaic power station, perform data cleaning and preprocessing, and extract features;

[0008] A digital twin model is built by combining the real-time stress data of a single photovoltaic array, meteorological forecast data, and bracket properties after data cleaning and preprocessing.

[0009] Use historical data to train the digital twin model, input the real-time stress data of a single photovoltaic array into the trained digital twin model, and predict the stress change trend of the flexible bracket of the entire photovoltaic power station;

[0010] Based on the prediction results, analyze whether there are potential safety hazards in the flexible bracket of the entire photovoltaic power station.

[0011] Furthermore, data collection and preprocessing: real-time stress data of a single photovoltaic array is collected through stress monitoring equipment installed in the photovoltaic power station; at the same time, wind speed and snow accumulation weather forecast data of the entire photovoltaic power station, as well as the strength, stiffness and seismic properties of the photovoltaic bracket are collected; the data is cleaned and preprocessed to extract characteristics related to stress changes in the flexible bracket.

[0012] Furthermore, a digital twin model is established: a neural network method is used to construct a digital twin model: the real-time stress data of a single photovoltaic array, meteorological forecast data, and the strength, stiffness, and seismic properties of the bracket are used as input, and the output is the stress change trend of the flexible bracket of the entire photovoltaic power station. The digital twin model is established by adjusting the number of layers, number of neurons, and parameters of the neural network, and using activation functions and optimizers.

[0013] Further, specific, modeling process is as follows:

[0014] 1) Input layer: The input data includes the real-time stress data S of a single photovoltaic array, the wind speed data W of the entire photovoltaic power station, the snow accumulation data S, and the structure, material properties, and boundary condition attribute data B of the bracket;

[0015] 2) Hidden layer: The hidden layer is implemented using a multilayer perceptron (MLP), where each layer uses a nonlinear activation function. The number of hidden layers and neurons is adjusted according to the complexity of the problem and the size of the data.

[0016] (Hidden_1=f(W1\times[S,W,S,B]+b1))

[0017] (Hidden_2=f(W2\times Hidden_1+b2))

[0018] 3) Output layer: The output layer is a linear layer that outputs the predicted stress change trend F of the flexible support of the entire photovoltaic power station;

[0019] F=W3\times Hidden_2+b3

[0020] 4) Backpropagation: During the training process, the predicted value is compared with the actual value, the error is calculated, and then the weights and biases are updated using the backpropagation algorithm;

[0021] 5) Optimizer: Select an optimizer to reduce the prediction error and optimize the model parameters;

[0022] 6) Training loop: Repeat the following steps until the model converges or reaches the preset number of iterations:

[0023] Input data into the neural network

[0024] Forward propagation

[0025] Calculation error

[0026] Backpropagation

[0027] Use the optimizer to update weights and biases

[0028] 7) Prediction: After the model is trained, new real-time data is input and the prediction result is obtained through forward propagation;

[0029] 8) Early warning analysis: Based on the prediction results, analyze whether there are potential safety hazards in the flexible brackets of the entire photovoltaic power station and take corresponding measures in a timely manner.

[0030] Furthermore, historical data is used to train the digital twin model:

[0031] During the training process, the backpropagation algorithm is used to calculate the error and update the weights and biases of the model; the optimizer is used to continuously reduce the prediction error and optimize the parameters of the model, and the training and optimization process is repeated until the model converges or reaches the preset number of iterations.

[0032] Furthermore, based on the prediction results, analyze whether there are potential safety hazards in the flexible bracket of the entire photovoltaic power station:

[0033] Based on the prediction results, if an abnormal situation or potential safety risk is found, an early warning message will be sent. The early warning message includes the name, location, severity of the abnormal part and the corresponding response measures.

[0034] Furthermore, after implementing the early warning analysis, the operating status and meteorological conditions of the photovoltaic power station are continuously monitored to collect the latest data; the digital twin model is regularly updated to adapt to changes in the environment and operating status; and the accuracy of the model is verified by comparing actual measured values ​​and predicted values.

[0035] In a second aspect, the present invention provides a photovoltaic flexible support station stress warning system based on digital twins, comprising:

[0036] The data acquisition module is used to collect real-time stress data of a single photovoltaic array, photovoltaic support properties, and meteorological forecast data for the entire photovoltaic power station, perform data cleaning and preprocessing, and extract features;

[0037] A modeling module is used to build a digital twin model by combining real-time stress data of a single photovoltaic array, meteorological forecast data, and bracket properties after data cleaning and preprocessing;

[0038] The training and prediction module is used to train the digital twin model using historical data. The real-time stress data of a single photovoltaic array is input into the trained digital twin model to predict the stress change trend of the flexible bracket of the entire photovoltaic power station.

[0039] The judgment module is used to analyze whether there are potential safety hazards in the flexible bracket of the entire photovoltaic power station based on the prediction results.

[0040] In the third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements the steps of a digital twin-based photovoltaic flexible support station stress warning method.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a photovoltaic flexible support station stress warning method based on digital twins.

[0042] Compared with the prior art, the present invention has the following technical effects:

[0043] The purpose of this invention is to provide a digital twin-based photovoltaic flexible support stress early warning technology for the entire station. By monitoring the stress data of the flexible supports in a single array of a photovoltaic power station, this technology can predict stress changes in the flexible supports of the entire station, achieving cost savings and providing early warning for the entire station. This technology uses a neural network approach to build a digital twin model. Combined with meteorological forecast data such as wind speed and snow accumulation, this technology can accurately predict stress trends of the flexible supports in real time, improving the safety and stability of the power station.

[0044] By monitoring the real-time stress data of a single photovoltaic array and predicting the stress change trend of the entire photovoltaic power station, low-cost and efficient early warning is achieved; the establishment of the digital twin model utilizes the neural network method, which has high prediction accuracy and stability; it can timely detect potential safety hazards and improve the safety and stability of the photovoltaic power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] FIG1 is a flow chart of the present invention.

[0046] FIG2 is a diagram showing the system structure of the present invention. DETAILED DESCRIPTION

[0047] The present invention will be further described below with reference to the accompanying drawings:

[0048] Referring to Figures 1 and 2, the present invention provides a digital twin-based early warning method for flexible support stress in a photovoltaic power station. By monitoring the stress data of the flexible supports in a single array of a photovoltaic power station, the stress changes of the entire photovoltaic power station can be predicted, thereby achieving cost savings and early warning. The technology includes the following steps:

[0049] Collect real-time stress data of a single photovoltaic array, photovoltaic bracket properties, and meteorological forecast data for the entire photovoltaic power station, perform data cleaning and preprocessing, and extract features;

[0050] A digital twin model is built by combining the real-time stress data of a single photovoltaic array, meteorological forecast data, and bracket properties after data cleaning and preprocessing.

[0051] Use historical data to train the digital twin model, input the real-time stress data of a single photovoltaic array into the trained digital twin model, and predict the stress change trend of the flexible bracket of the entire photovoltaic power station;

[0052] Based on the prediction results, analyze whether there are potential safety hazards in the flexible bracket of the entire photovoltaic power station.

[0053] Specifically:

[0054] 1. Data collection and preprocessing: Collect real-time stress data of a single photovoltaic array, and meteorological forecast data such as wind speed and snow accumulation for the entire photovoltaic power station. Perform data cleaning and preprocessing to extract necessary features.

[0055] 2. Establish a digital twin model: Using neural network methods, combine the real-time stress data of a single photovoltaic array, meteorological forecast data, and the strength, stiffness, and seismic resistance of the bracket to establish a digital twin model.

[0056] 3. Training and optimizing models: Use historical data to train the digital twin model, and continuously adjust the model parameters through backpropagation algorithms and optimizers to improve prediction accuracy.

[0057] 4. Predict stress changes throughout the entire photovoltaic power station: Input the real-time stress data of a single photovoltaic array into the trained digital twin model to predict the stress change trend of the flexible brackets of the entire photovoltaic power station.

[0058] 5. Early warning analysis: Based on the prediction results, analyze whether there are potential safety hazards in the flexible bracket of the entire photovoltaic power station and send early warning information to management personnel in a timely manner.

[0059] 6. Continuous monitoring and updating: Continuously monitor the operating status and meteorological conditions of the photovoltaic power station, collect the latest data, and regularly update the digital twin model to adapt to changes in the environment and operating status.

[0060] Example

[0061] 1. Data Collection and Preprocessing: Real-time stress data for individual photovoltaic arrays is collected using stress monitoring equipment installed at the photovoltaic power station. Weather forecast data, such as wind speed and snow accumulation, is also collected for the entire photovoltaic power station. The data is cleaned and preprocessed to extract features related to stress changes in the flexible support.

[0062] 2. Establish a digital twin model: A digital twin model is constructed using a neural network approach. Real-time stress data for a single photovoltaic array, weather forecast data, and properties of the support, such as strength, stiffness, and seismic resistance, are used as input. The output is the stress trend of the flexible support for the entire photovoltaic power station. By adjusting parameters such as the number of neural network layers and neurons, and selecting appropriate activation functions and optimizers, a digital twin model with high predictive accuracy and stability is established.

[0063] 3. Model training and optimization: The digital twin model is trained using historical data. During training, the backpropagation algorithm calculates the error and updates the model's weights and biases. Optimizers such as Adam are used to continuously reduce the prediction error and optimize the model parameters. The training and optimization process is repeated until the model converges or the preset number of iterations is reached.

[0064] 4. Predict stress changes throughout the entire PV power station: Based on the trained digital twin model, the real-time stress data of a single PV array is input into the model to obtain the predicted results of the stress change trend of the flexible brackets of the entire PV power station.

[0065] 5. Early Warning Analysis: Based on the prediction results, the system analyzes the flexible supports of the entire photovoltaic power station for potential safety hazards. If any abnormalities or potential safety risks are detected, a timely early warning message is sent to management. The warning message can include the name, location, severity, and corresponding countermeasures and recommendations.

[0066] 6. Continuous Monitoring and Updates: After conducting early warning analysis, continuously monitor the operating status and meteorological conditions of the PV power plant and collect the latest data. Regularly update the digital twin model to adapt to changes in the environment and operating conditions. Verify the model's accuracy by comparing actual measurements with predicted values. If significant errors or performance degradation are detected, adjust and optimize the model promptly.

[0067] More specifically, the detailed modeling process is as follows:

[0068] 1. Input layer: The input data includes the real-time stress data of a single photovoltaic array (S), the wind speed data of the entire photovoltaic power station (W), snow accumulation data (S), and the structure, material properties, boundary conditions and other attribute data of the bracket (B).

[0069] (Input=[S,W,S,B])

[0070] 2. Hidden layer: The hidden layer is implemented using a multi-layer perceptron (MLP), where each layer uses a nonlinear activation function (such as ReLU). The number of hidden layers and the number of neurons can be adjusted according to the complexity of the problem and the size of the data.

[0071] (Hidden_1=f(W1\times[S,W,S,B]+b1))

[0072] (Hidden_2=f(W2\times Hidden_1+b2))

[0073] 3. Output layer: The output layer is a linear layer used to output the predicted stress change trend (F) of the flexible bracket of the entire photovoltaic power station.

[0074] (F=W3\times Hidden_2+b3)

[0075] 4. Backpropagation: During the training process, the predicted values ​​are compared with the actual values, the error is calculated, and then the weights and biases are updated using the backpropagation algorithm.

[0076] 5. Optimizer: Choose a suitable optimizer (such as Adam) to reduce the prediction error and optimize the parameters of the model.

[0077] 6. Training loop: Repeat the following steps until the model converges or reaches the preset number of iterations:

[0078] Input data into the neural network

[0079] Forward propagation

[0080] Calculation error

[0081] Backpropagation

[0082] Use the optimizer to update weights and biases

[0083] 7. Prediction: Once the model is trained, new real-time data can be input and the prediction results can be obtained through forward propagation.

[0084] 8. Early Warning Analysis: Based on the prediction results, the flexible supports throughout the entire photovoltaic power station are analyzed for potential safety hazards, and appropriate measures are taken promptly. This process is a typical neural network training process, requiring considerable computing resources and expertise. Furthermore, attention must be paid to data preprocessing, model verification and adjustment, and continuous monitoring and updating to ensure model accuracy and reliability.

[0085] In another embodiment of the present invention, a photovoltaic flexible support whole-station stress warning system based on digital twin is provided, which can be used to implement the above-mentioned photovoltaic flexible support whole-station stress warning method based on digital twin. Specifically, the system includes:

[0086] The data acquisition module is used to collect real-time stress data of a single photovoltaic array, photovoltaic support properties, and meteorological forecast data for the entire photovoltaic power station, perform data cleaning and preprocessing, and extract features;

[0087] A modeling module is used to build a digital twin model by combining real-time stress data of a single photovoltaic array, meteorological forecast data, and bracket properties after data cleaning and preprocessing;

[0088] The training and prediction module is used to train the digital twin model using historical data. The real-time stress data of a single photovoltaic array is input into the trained digital twin model to predict the stress change trend of the flexible bracket of the entire photovoltaic power station.

[0089] The judgment module is used to analyze whether there are potential safety hazards in the flexible bracket of the entire photovoltaic power station based on the prediction results.

[0090] The module division in the embodiments of the present invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in various embodiments of the present invention may be integrated into a single processor, exist physically as separate modules, or two or more modules may be integrated into a single module. The integrated modules may be implemented in either hardware or software functional modules.

[0091] In another embodiment of the present invention, a computer device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the photovoltaic flexible support station stress warning method based on digital twins.

[0092] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the stress warning method for the entire photovoltaic flexible support station based on digital twins in the above embodiment.

[0093] It will be understood by those skilled in the art 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. 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.

[0094] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0095] 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 operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A photovoltaic flexible support station stress early warning method based on digital twins is characterized by: include: Collect real-time stress data of a single photovoltaic array, photovoltaic bracket properties, and meteorological forecast data for the entire photovoltaic power station, perform data cleaning and preprocessing, and extract features; A digital twin model is built by combining the real-time stress data of a single photovoltaic array, meteorological forecast data, and bracket properties after data cleaning and preprocessing. Use historical data to train the digital twin model, input the real-time stress data of a single photovoltaic array into the trained digital twin model, and predict the stress change trend of the flexible bracket of the entire photovoltaic power station; Based on the prediction results, analyze whether there are potential safety hazards in the flexible bracket of the entire photovoltaic power station.

2. The photovoltaic flexible support station stress early warning method based on digital twin according to claim 1 is characterized in that: Data collection and preprocessing: Real-time stress data of a single photovoltaic array is collected through stress monitoring equipment installed in the photovoltaic power station. At the same time, wind speed and snow accumulation weather forecast data for the entire photovoltaic power station, as well as the strength, stiffness and seismic properties of the photovoltaic brackets, are collected. The data is cleaned and preprocessed to extract characteristics related to stress changes in the flexible brackets.

3. The photovoltaic flexible support station stress early warning method based on digital twin according to claim 2 is characterized in that: Establish a digital twin model: Use a neural network method to build a digital twin model: take the real-time stress data of a single photovoltaic array, meteorological forecast data, and the strength, stiffness, and seismic properties of the bracket as input, and output the stress change trend of the flexible bracket of the entire photovoltaic power station. By adjusting the number of layers, number of neurons, and parameters of the neural network, and using activation functions and optimizers, a digital twin model is established.

4. The photovoltaic flexible support station stress early warning method based on digital twin according to claim 3 is characterized in that: Specifically, the modeling process is as follows: 1) Input layer: The input data includes the real-time stress data S of a single photovoltaic array, the wind speed data W of the entire photovoltaic power station, the snow accumulation data S, and the structure, material properties, and boundary condition attribute data B of the bracket; 2) Hidden layer: The hidden layer is implemented using a multilayer perceptron (MLP), where each layer uses a nonlinear activation function. The number of hidden layers and neurons is adjusted according to the complexity of the problem and the size of the data. (Hidden_1=f(W1\times[S,W,S,B]+b1)) (Hidden_2=f(W2\times Hidden_1+b2)) 3) Output layer: The output layer is a linear layer that outputs the predicted stress change trend F of the flexible support of the entire photovoltaic power station; F=W3\times Hidden_2+b3 4) Backpropagation: During the training process, the predicted value is compared with the actual value, the error is calculated, and then the weights and biases are updated using the backpropagation algorithm; 5) Optimizer: Select an optimizer to reduce the prediction error and optimize the model parameters; 6) Training loop: Repeat the following steps until the model converges or reaches the preset number of iterations: Input data into the neural network Forward propagation Calculation error Backpropagation Use the optimizer to update weights and biases 7) Prediction: After the model is trained, new real-time data is input and the prediction result is obtained through forward propagation; 8) Early warning analysis: Based on the prediction results, analyze whether there are potential safety hazards in the flexible brackets of the entire photovoltaic power station and take corresponding measures in a timely manner.

5. The photovoltaic flexible support station stress early warning method based on digital twin according to claim 1 is characterized in that: Use historical data to train the digital twin model: During the training process, the backpropagation algorithm is used to calculate the error and update the weights and biases of the model; the optimizer is used to continuously reduce the prediction error and optimize the parameters of the model, and the training and optimization process is repeated until the model converges or reaches the preset number of iterations.

6. The photovoltaic flexible support station stress early warning method based on digital twin according to claim 1 is characterized in that: Based on the prediction results, analyze whether there are potential safety hazards in the flexible bracket of the entire photovoltaic power station: Based on the prediction results, if an abnormal situation or potential safety risk is found, an early warning message will be sent. The early warning message includes the name, location, severity of the abnormal part and the corresponding response measures.

7. The photovoltaic flexible support station stress early warning method based on digital twin according to claim 1 is characterized in that: After implementing early warning analysis, continuously monitor the operating status and meteorological conditions of the photovoltaic power station and collect the latest data; Update the digital twin model regularly to adapt to changes in the environment and operating status; verify the accuracy of the model by comparing actual measurements and predicted values.

8. The photovoltaic flexible support station stress warning system based on digital twin is characterized by: include: The data acquisition module is used to collect real-time stress data of a single photovoltaic array, photovoltaic support properties, and meteorological forecast data for the entire photovoltaic power station, perform data cleaning and preprocessing, and extract features; A modeling module is used to build a digital twin model by combining real-time stress data of a single photovoltaic array, meteorological forecast data, and bracket properties after data cleaning and preprocessing; The training and prediction module is used to train the digital twin model using historical data. The real-time stress data of a single photovoltaic array is input into the trained digital twin model to predict the stress change trend of the flexible bracket of the entire photovoltaic power station. The judgment module is used to analyze whether there are potential safety hazards in the flexible bracket of the entire photovoltaic power station based on the prediction results.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the photovoltaic flexible support station stress warning method based on digital twins as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the photovoltaic flexible support station stress warning method based on digital twins as described in any one of claims 1 to 7 are implemented.

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