Road area system high-entropy energy output prediction method, device and equipment based on traffic flow, medium and product

Abstract

The invention discloses a road area system high-entropy energy output prediction method and device based on traffic flow, equipment, a medium and a product, and relates to the field of high-entropy energy output prediction, and the method comprises the steps: carrying out the data processing of the current traffic flow and traffic network of a road area system, and obtaining a traffic flow feature vector; on the basis of the traffic flow feature vector, an improved Transform model is adopted to predict the traffic flow; constructing a coupling output model of high-entropy energy and traffic flow; and predicting the high-entropy energy output of the road domain system by adopting a coupling output model based on the predicted traffic flow. According to the method, the traffic flow and the high-entropy energy are combined, the high-entropy energy output prediction of the road domain system is realized, and the high-entropy energy potential of the road domain system is quantified.

Description

Technical Field

[0001] The present application relates to the field of high entropy energy output prediction, and in particular to a method, device, equipment, medium and product for predicting high entropy energy output of a road system based on traffic flow. Background Art

[0002] High entropy energy refers to disordered energy that is widely distributed in nature. Due to the frequent movement of cars and pedestrians, a large amount of high entropy energy is contained in the mechanical vibration of road infrastructure. The widely distributed high entropy energy matches the dispersed distribution characteristics of the road system load and becomes a new energy supply method for the road system. Traffic flow is an important factor affecting the high entropy energy characteristics of the road system. However, the current high entropy energy output characteristics mostly focus on laboratory measurements, and the coupling relationship between high entropy energy output and traffic flow has not been established, and the high entropy energy output potential of the road system has not been fully tapped. Summary of the invention

[0003] The purpose of this application is to provide a method, device, equipment, medium and product for predicting high-entropy energy output of a road system based on traffic flow, which can combine traffic flow with high-entropy energy, realize high-entropy energy output prediction of the road system, and quantify the high-entropy energy potential of the road system.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for predicting high entropy energy output of a road system based on traffic flow, comprising:

[0006] Process the current traffic flow and traffic network of the road system to obtain the traffic flow feature vector;

[0007] Based on the traffic flow feature vector, an improved Transformer model is used to predict traffic flow;

[0008] Construct a coupling output model of high entropy energy and traffic flow;

[0009] Based on the predicted traffic flow, a coupled output model is used to predict the high entropy energy output of the road system.

[0010] In a second aspect, the present application provides a high entropy energy output prediction device for a road system based on traffic flow, comprising:

[0011] A data processing module is used to process the current traffic flow and traffic network of the road system to obtain a traffic flow feature vector;

[0012] A traffic flow prediction module, used to predict traffic flow using an improved Transformer model based on the traffic flow feature vector;

[0013] The coupled output model building module is used to build a coupled output model of high entropy energy and traffic flow;

[0014] The high entropy energy output prediction module is used to predict the high entropy energy output of the road system based on the predicted traffic flow and using a coupled output model.

[0015] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned high-entropy energy output prediction method for a road system based on traffic flow.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for predicting high-entropy energy output of a road system based on traffic flow.

[0017] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned traffic flow-based road system high-entropy energy output prediction method.

[0018] According to the specific embodiments provided in this application, this application has the following technical effects:

[0019] The present application provides a method, device, equipment, medium and product for predicting high-entropy energy output of a road system based on traffic flow. Traffic flow is predicted by an improved Transformer model. A coupling relationship between high-entropy energy output and traffic flow is established by constructing a coupling output model. The predicted traffic flow is combined with the coupling output model to realize the prediction of high-entropy energy output of the road system and quantify the output potential of high-entropy energy of the road system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments 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 drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A schematic diagram of a flow chart of a method for predicting high entropy energy output of a road system based on traffic flow provided in one embodiment of the present application;

[0022] Figure 2 A schematic diagram of the principle framework of a method for predicting high entropy energy output of a road system based on traffic flow provided in one embodiment of the present application;

[0023] Figure 3 This is a schematic diagram of the tunnel airflow type high entropy energy structure;

[0024] Figure 4 This is a schematic diagram of the vibration piezoelectric high entropy energy structure;

[0025] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0028] In an exemplary embodiment, Figure 1-Figure 2 As shown, a method for predicting high entropy energy output of a road system based on traffic flow is provided. The method is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In an embodiment of the present application, the method is applied to a server as an example for explanation, including the following steps S1 to S4.

[0029] in:

[0030] S1: Process the current traffic flow and traffic network of the road system to obtain the traffic flow feature vector.

[0031] S2: Based on the traffic flow feature vector, an improved Transformer model is used to predict traffic flow.

[0032] S3: Constructing a coupling output model of high entropy energy and traffic flow. The coupling output model includes a tunnel airflow type high entropy energy output model and a vibration piezoelectric type high entropy energy output model.

[0033] S4: Based on the predicted traffic flow, a coupled output model is used to predict the high entropy energy output of the road system.

[0034] In a specific embodiment, step S1 specifically includes:

[0035] S11: Use a fully connected layer to convert the current traffic flow into a first feature vector.

[0036] pass is used to represent the traffic flow of node N at time t, C represents the dimension of traffic flow, and when C = 2, it means that the traffic flow includes two dimensions: inflow and outflow. Therefore, the traffic flow in the time scale T can be expressed as

[0037] The traffic flow X is converted into the first feature vector through the fully connected layer Here, d represents the embedding dimension of the data.

[0038] S12: Determine a second feature vector based on distances between different nodes in the traffic network.

[0039] The traffic network can be represented as G = {ν, ε, A}, where ν = {ν1, ..., ν N} represents N nodes in the traffic network, represents the set of edges in the transportation network, and A represents the adjacency matrix of G in the transportation network.

[0040] For the structure of the traffic network, the graph Laplacian eigenvector is used to describe the distance between different nodes as the second eigenvector

[0041] S13: Convert the current traffic flow into traffic flow with a weekly index and a daily index respectively through a weekly cycle function and a daily cycle function to obtain a third eigenvector and a fourth eigenvector.

[0042] Define the weekly cycle function w(t) and daily cycle function d(t), respectively convert the traffic flow at time t into the traffic flow of weekly index and daily index, and obtain the time characteristic input of weekly cycle and daily cycle Determined as the third eigenvector and the fourth eigenvector.

[0043] S14: Summing the first eigenvector, the second eigenvector, the third eigenvector and the fourth eigenvector to obtain a traffic flow eigenvector.

[0044] The feature vectors obtained in step S11-step S13 are added to obtain the traffic flow feature vector X emb :

[0045] X emb =X data +X spe +X w +X d .

[0046] In a specific embodiment, the improved Transformer model in step S2 includes a multi-head attention mechanism module; the multi-head attention mechanism module includes a spatial self-attention module and a temporal self-attention module; the spatial self-attention module introduces a short-distance graph mask matrix and a long-distance graph mask matrix to capture the short-distance and long-distance spatial correlation in traffic flow.

[0047] Specifically, the multi-head attention mechanism module models the spatiotemporal correlation of complex dynamics based on the self-attention mechanism, which includes a spatial self-attention module and a temporal self-attention module.

[0048] Spatial self-attention module: Calculate the Q, K, and V matrices in the self-attention mechanism at each time t:

[0049]

[0050] In the formula, is a learnable parameter. Calculate the attention score at each time t Used to reflect the spatial dependency between spatial nodes.

[0051]

[0052] The spatial dependencies between spatial nodes are different at different times, i.e. dynamic. Finally, the output of the spatial self-attention module is obtained by multiplying the attention score and the V matrix.

[0053]

[0054] The above expression allows each node to interact with all nodes in the space, but there are some nodes that are far away but have similar features and the features are not obvious. Therefore, the short-distance graph mask matrix M is introduced geo and the long distance graph mask matrix M sem To capture the short-distance and long-distance spatial correlation in traffic flow. The short-distance map mask matrix M geo When the distance between two nodes is less than the threshold M, the weight is 1, otherwise it is 0. The long distance graph mask matrix M sem The DTW method is used to calculate the similarity of historical traffic flow between nodes, and k nodes with similar traffic flow are set as virtual neighbors for each node. The weight between the node and the virtual node is 1, otherwise it is 0.

[0055] Modify the previous formula and introduce the graph mask matrix into the self-attention mechanism as follows:

[0056]

[0057] Temporal Self-Attention Module: Temporal self-attention is similar to the spatial self-attention mentioned above. Temporal self-attention models long-term dependencies across the entire time scale.

[0058]

[0059] Temporal self-attention can capture the dynamic temporal dependencies of different nodes in the current traffic flow.

[0060] After obtaining the three different types of attention outputs, the heterogeneous attention needs to be fused into a multi-head self-attention module to reduce the computational complexity of the model.

[0061]

[0062] In the formula, is the output of different types of modules, h geo 、h sem 、h t is the number of different types of module heads, W o is a learnable projection matrix, and the output is a fully connected feedforward network to obtain the output

[0063] Use a 1×1 convolutional block to form a skip connection and output X o Add together to get the final hidden layer d sk is the jump dimension. hid Transformed into traffic flow using two 1D convolutions

[0064]

[0065] In a specific embodiment, step S3 specifically includes:

[0066] S31: Modeling of high entropy energy output of tunnel airflow type.

[0067] like Figure 3 As shown in the figure, the airflow generated by the running of vehicles in the tunnel is an important factor in generating tunnel wind energy. Different traffic flows also generate different airflows, a phenomenon known as the piston effect. To capture the tiny wind energy in the tunnel, it is necessary to model the airflow velocity under different traffic flows in the tunnel.

[0068] There will be a pressure jump in front of the moving vehicle, which will transfer kinetic energy to the air. At the same time, the pressure difference between the tunnel openings can be understood as a resistance, which will obtain kinetic energy from the air. In a stable state, the kinetic energy transferred should be equal, and the following air speed equation can be written:

[0069]

[0070] Where n is the number of vehicles, A v is the cross-sectional area of ​​the vehicle in the direction of travel, c X is the aerodynamic drag coefficient, ρ is the air density, v v is the vehicle speed, v a is the air velocity, λ is the Darcy coefficient, whose value depends on the roughness of the wall, L is the tunnel length, D h is the equivalent diameter of the tunnel, A t is the tunnel cross-sectional area, and Δt is the sampling time period.

[0071] This is a quadratic equation for air velocity, and the corresponding solution can be found:

[0072]

[0073] In the formula, B(T I ,v v ) is the coefficient reflecting the vehicles in the tunnel, T I Represents the traffic density, whose value is the traffic flow during the sampling period, m t is the correction factor.

[0074] Considering that the air resistance will change when multiple vehicles are traveling in a row in the tunnel, X To make corrections:

[0075]

[0076] Where N is the number of lanes and d0 is the fitting parameter.

[0077] Under different air flow rates, the airflow-driven high-entropy energy collection device has different output performance. By fitting the above data, the output power of the airflow-driven high-entropy energy under different air flow rates can be obtained. The expression of the tunnel airflow-driven high-entropy energy output model is:

[0078]

[0079] The air velocity v a and traffic density T I The coupling relationship between them can be obtained by the following formula:

[0080]

[0081] Where v0 is the zero flow velocity of traffic flow, C a Represents the traffic capacity of the road. Combining the above formula, we can get the tunnel airflow type high entropy energy output and traffic density T I The coupling relationship model between them.

[0082] S32: Modeling of vibration piezoelectric high entropy energy output.

[0083] like Figure 4 As shown in FIG. 1 , the vibration piezoelectric high entropy energy harvesting device uses piezoelectric materials to convert mechanical strain into voltage through the piezoelectric effect. The IEEE piezoelectric standard gives various parameters of the piezoelectric effect, and according to the standard, the electric displacement vector D can be obtained.

[0084] D=dT+ε T E

[0085] Where d is the piezoelectric charge constant, T is the stress, and ε T is the dielectric constant under constant stress, and E is the external electric field strength.

[0086] When piezoelectric materials are buried in roads and subjected to traffic loads, polarization occurs on the vertical surface. Assuming that the external electric field strength of the traffic road surface is 0, the electric displacement vector of the vertical surface can be obtained:

[0087] D=d 33 T

[0088] The electrical energy density ω of a material can be expressed as:

[0089]

[0090] The total electrical energy of the material can be obtained by integrating the electrical energy density:

[0091] W = ∫ V ωdV

[0092] Assuming that the piezoelectric material is a uniform material, the output power of the vibration piezoelectric high entropy energy can be obtained. The expression of the vibration piezoelectric high entropy energy output model is:

[0093]

[0094] In the formula, E 33 is the electric field strength in the piezoelectric material, A is the surface area of ​​the piezoelectric material, h is the thickness of the piezoelectric material, and t p The time required for vehicles at different speeds to pass through the piezoelectric material.

[0095] Assume that the average vehicle weight is m c , the expression of the vibration piezoelectric high entropy energy output model is:

[0096]

[0097] Where, T I is the traffic density, which is the number of traffic flows in the sampling period, d 33is the piezoelectric charge constant perpendicular to the device surface, L is the length of the collection device, v v is the vehicle speed, which is similar to v in the tunnel airflow type high entropy energy harvesting device. v The calculation formula is the same and will not be repeated in this paragraph.

[0098] In a specific embodiment, step S4 specifically includes:

[0099] The traffic flow predicted by the improved Transformer model in step S2 is combined with the coupled output model constructed in step S3 to obtain high entropy energy output prediction information of the road system.

[0100] The high-entropy energy output prediction method for a road system based on traffic flow provided in the present application takes into account the coupling relationship between high-entropy energy output characteristics and traffic flow, and models the high-entropy energy output characteristics of the road area based on traffic flow, which can fully tap the high-entropy energy output potential of the road system, provide a basis for subsequent high-entropy energy output prediction, and improve the prediction accuracy of the model; combining the graph mask matrix based on long-distance and short-distance modeling with the spatial self-attention mechanism can effectively extract the coupling characteristics between adjacent node pairs and node pairs with farther nodes but similar traffic flow, and deeply tap the spatiotemporal correlation of traffic flow; finally, combining the output data of the multi-head attention module with the coupled output model, realizes the prediction of road high-entropy energy output, and quantifies the high-entropy energy output potential of the road area.

[0101] Based on the same inventive concept, the embodiment of the present application also provides a device for implementing the above-mentioned method for predicting the high entropy energy output of a road system based on traffic flow. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the device for predicting the high entropy energy output of a road system based on traffic flow provided below can be referred to the limitations of the method for predicting the high entropy energy output of a road system based on traffic flow in the above text, and will not be repeated here.

[0102] In an exemplary embodiment, a high entropy energy output prediction device for a road system based on traffic flow is provided, comprising:

[0103] The data processing module is used to process the current traffic flow and traffic road network of the road system to obtain the traffic flow feature vector.

[0104] The traffic flow prediction module is used to predict the traffic flow based on the traffic flow feature vector by using an improved Transformer model.

[0105] The coupled output model construction module is used to construct the coupled output model of high entropy energy and traffic flow.

[0106] The high entropy energy output prediction module is used to predict the high entropy energy output of the road system based on the predicted traffic flow and using a coupled output model.

[0107] 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 in the above-mentioned method embodiments when executing the computer program. The computer device may be a server or a terminal, and its internal structure diagram may be as shown in FIG. Figure 5 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. Among them, 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. Among them, 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 to be processed. 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 high-entropy energy output prediction method for a road system based on traffic flow is implemented.

[0108] Those skilled in the art will understand that Figure 5 The 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. 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 in the above-mentioned method embodiments when executing the computer program.

[0109] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0110] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0111] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0112] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods 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 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. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0113] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. 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, etc., but is not limited thereto.

[0114] 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 specification.

[0115] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for predicting high entropy energy output of a road system based on traffic flow, characterized in that: include: Process the current traffic flow and traffic network of the road system to obtain the traffic flow feature vector; Based on the traffic flow feature vector, an improved Transformer model is used to predict traffic flow; Construct a coupling output model of high entropy energy and traffic flow; Based on the predicted traffic flow, a coupled output model is used to predict the high entropy energy output of the road system.

2. The method for predicting high entropy energy output of a road system based on traffic flow according to claim 1 is characterized in that: The current traffic flow and traffic network of the road system are processed to obtain the traffic flow feature vector, including: Using a fully connected layer to convert the current traffic flow into a first feature vector; Determining a second eigenvector based on distances between different nodes in the traffic network; The current traffic flow is converted into the traffic flow of the weekly index and the daily index by using the weekly cycle function and the daily cycle function respectively, so as to obtain the third eigenvector and the fourth eigenvector; The first eigenvector, the second eigenvector, the third eigenvector and the fourth eigenvector are summed to obtain a traffic flow feature vector.

3. The method for predicting high entropy energy output of a road system based on traffic flow according to claim 1 is characterized in that: The improved Transformer model includes a multi-head attention mechanism module; the multi-head attention mechanism module includes a spatial self-attention module and a temporal self-attention module; the spatial self-attention module introduces a short-distance graph mask matrix and a long-distance graph mask matrix to capture the short-distance and long-distance spatial correlation in traffic flow.

4. The method for predicting high entropy energy output of a road system based on traffic flow according to claim 3 is characterized in that: The coupled output model includes a tunnel airflow type high entropy energy output model and a vibration piezoelectric type high entropy energy output model.

5. The method for predicting high entropy energy output of a road system based on traffic flow according to claim 4 is characterized in that: The expression of the tunnel airflow type high entropy energy output model is: in, is the output power of tunnel airflow type high entropy energy, v a (T I ,v v ) is the vehicle speed v v , the traffic density is T I The air velocity and traffic density T I is the traffic flow in the sampling period, B(T I ,v v ) is the vehicle speed v v , the traffic density is T I The coefficient of the vehicle in the tunnel at that time, λ is the Darcy coefficient, A t is the tunnel cross-sectional area, D h is the equivalent diameter of the tunnel, c′ X is the corrected aerodynamic drag coefficient, m t is the correction coefficient, v0 is the zero flow speed of traffic flow, C a The traffic capacity of the road.

6. The method for predicting high entropy energy output of a road system based on traffic flow according to claim 4 is characterized in that: When the piezoelectric material is a uniform material, the expression of the vibration piezoelectric high entropy energy output model is: When the average vehicle weight is m c When , the expression of the vibration piezoelectric high entropy energy output model is: in, is the output power of the vibration piezoelectric high entropy energy source, D is the electric displacement vector, E 33 is the electric field strength in the piezoelectric material, A is the surface area of ​​the piezoelectric material, h is the thickness of the piezoelectric material, and t p T is the time required for vehicles at different speeds to pass through the piezoelectric material; I is the traffic density, d 33 is the piezoelectric charge constant perpendicular to the surface of the vibration piezoelectric high entropy energy harvesting device, g is the gravity constant, L is the layout length of the vibration piezoelectric high entropy energy harvesting device, ε T is the dielectric constant under constant stress, v v is the vehicle speed.

7. A high entropy energy output prediction device for a road system based on traffic flow, characterized in that: include: A data processing module is used to process the current traffic flow and traffic network of the road system to obtain a traffic flow feature vector; A traffic flow prediction module, used to predict traffic flow using an improved Transformer model based on the traffic flow feature vector; The coupled output model building module is used to build a coupled output model of high entropy energy and traffic flow; The high entropy energy output prediction module is used to predict the high entropy energy output of the road system based on the predicted traffic flow and using a coupled output model.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the high-entropy energy output prediction method for a road system based on traffic flow as described in any one of claims 1-6.

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 method for predicting high entropy energy output of a road system based on traffic flow described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting high entropy energy output of a road system based on traffic flow described in any one of claims 1 to 6 is implemented.

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