Data acquisition method and system for electric energy storage box

Abstract

The invention relates to the field of data processing, in particular to a data acquisition method and system for an electric energy storage box, and the method comprises the steps: carrying out the box separation of energy storage box data of each dimension, obtaining a data range, and constructing an item set according to the data range; calculating a frequent coefficient according to the item set; and calculating a weight coefficient of the energy storage data according to the frequent coefficient, and filtering the energy storage data according to the weight coefficient to obtain a filtering value. According to the method, the limitation of weighted filtering on burst noise processing is improved, and the effect of improving the data filtering accuracy of the energy storage box is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a data acquisition method and system for an electric energy storage box. Background Art

[0002] Energy storage tanks are key components for ensuring the proper operation of new energy vehicles. Their purpose is to promptly detect potential faults and optimize charging and discharging strategies. Data collection from these tanks is crucial. For example, patent application publication number CN115689841A discloses an integrated charging, swapping, storage, and transportation platform for new energy vehicles, including the installation of energy storage tanks and data collection. This not only facilitates fault warning and predictive protection, but also effectively prevents adverse impacts on vehicle performance caused by sudden failures.

[0003] However, due to factors such as environmental interference, the collected energy storage tank data often contains noise, affecting the accuracy of real-time monitoring and safety management. Therefore, reducing the impact of environmental interference on data collection and ensuring data accuracy and reliability have become key technical issues that need to be addressed. A weighted moving average (WMIF) algorithm is commonly used to denoise the collected energy storage tank data. WMIF is a sliding window-based filtering method that smooths the data by assigning different weights to the data points within the window and performing a weighted average.

[0004] However, in the complex automobile operating environment, there are certain bursts of data in the data collected by the energy storage box. The weighted moving filtering algorithm has limited processing capabilities for burst noise (such as transient sensor failures and electromagnetic interference). If the weight distribution of the data is inappropriate, the filtered data may still contain obvious outliers, which in turn affects the monitoring of the status of the energy storage box. Summary of the Invention

[0005] In order to solve the technical problem of how to improve the data filtering effect of an energy storage box, the present application provides a data collection method and system for an electric energy storage box.

[0006] In a first aspect, the present application provides a data collection method for an electric energy storage box, which adopts the following technical solution: A data collection method for an electric energy storage box comprises the steps of: binning the energy storage box data of each dimension to obtain a data range, and constructing an item set according to the data range; Calculate the frequent coefficient based on the item set; calculate the weight coefficient of the energy storage data based on the frequent coefficient, and filter the energy storage data based on the weight coefficient to obtain the filtered value; the calculation formula of the filtered value is: Where, is the filtered value of the energy storage box data collected in real time, The energy storage box data collected in real time Energy storage box data at the moment, for The frequent coefficient of the corresponding item set, is the size of the filter window.

[0007] The beneficial effects are as follows: the frequency coefficient of each item set is calculated using the data within all itemsets, the filter weight of each data item is calculated based on the frequency coefficient of the item set corresponding to the energy storage tank data, and the collected energy storage tank data is filtered using the filter weight. The frequency coefficient of the data item set corresponding to the data can effectively identify data points or abnormal patterns affected by noise, and then dynamically adjust the weight of the energy storage tank data during the filtering process. It can adaptively respond to sudden noise and abnormal data in the operating environment, improve data accuracy and system robustness, improve the limitations of weighted filtering in dealing with sudden noise, reduce noise in energy storage tank data collection, and make energy storage tank data collection more accurate, thereby better monitoring the energy storage tank status.

[0008] Optionally, the energy storage tank data of each dimension are binned, comprising the steps of: for any dimension, obtaining a frequency line graph of the energy storage tank data, where the horizontal axis of the frequency line graph is the energy storage tank data and the vertical axis is the frequency; smoothing the frequency line graph to obtain a smooth curve, calculating the absolute value of the slope of each energy storage tank data on the smooth curve, sorting the energy storage tank data in descending order according to the absolute value of the slope to form a sequence, using the energy storage tank data corresponding to the maximum value in the sequence as the data binning point, and binning the data of this dimension according to the data binning point.

[0009] The beneficial effects are: binning divides continuous data into discrete intervals, reducing the data's dimensionality and complexity, facilitating subsequent analysis and processing. Using the absolute value of the slope as the basis for binning better captures data trends and characteristic points, thereby more accurately reflecting the inherent patterns of energy storage tank data. Binning reduces the amount of data and the computing resources required to calculate the frequency coefficient and weight coefficient, thereby speeding up the entire data processing process.

[0010] Optionally, a spline regression algorithm is used to smooth the frequency line graph to obtain a smooth curve.

[0011] The beneficial effect is that the spline regression algorithm can effectively smooth the noise and fluctuations in the frequency line graph, making the smooth curve smoother and better reflecting the overall trend of the data.

[0012] Optionally, the expression for the itemset is: Where, is the item set consisting of the energy storage tank data at any moment, is the data range corresponding to the voltage data of the energy storage box at that moment, is the data range corresponding to the energy storage box current data at that moment, is the data range corresponding to the energy storage box temperature data at that moment, The data range corresponding to the acceleration data of the energy storage box at this moment is axis, axis, The vector and magnitude of the acceleration in the three directions of the axis.

[0013] The beneficial effect is that the itemset encompasses data ranges across multiple dimensions, including the energy storage tank's voltage, current, temperature, and acceleration. This fully reflects the tank's operating status under different operating conditions, providing richer information for subsequent analysis and processing. This achieves a joint representation of multidimensional data, capturing the inherent correlations between individual data points. Furthermore, by binning continuous data, the original continuous data is converted into discrete categories (data ranges). This not only reduces the impact of random fluctuations in the data but also facilitates the calculation of frequent coefficients using the Apriori association rule mining algorithm, making subsequent analysis more efficient and accurate.

[0014] Optionally, the method for calculating the frequent coefficient based on the item set is: construct subsets of the item set, the subset containing one element is the first subset, the subset containing two elements is the second subset, and similarly, all subsets are divided; obtain the support of each subset of the item set and the support of each item set through the Apriori association rule mining algorithm, and calculate the frequent coefficient of each item set through the support of the subset, the weight of the subset and the support of the item set.

[0015] Beneficial Effects: By introducing weights for different subsets and itemsets, the weights of different subsets in the frequency coefficient calculation can be flexibly adjusted according to actual needs, making the frequency coefficient more reflective of the actual importance of the data. By comprehensively considering the support of different subsets and the overall support of the itemset, the frequency of the itemset can be comprehensively assessed, avoiding inaccurate frequency coefficient calculations caused by interference from a single factor.

[0016] Optionally, when the subsets of the item set include the first subset, the second subset, and the third subset, the expression of the frequent coefficient is: ; ; ; ; ;in, is the frequent coefficient of any item set, For this concentration The support of the first subset, represents the total number of the first subset, For this concentration The support of the second subset, represents the total number of the second subset, For this concentration The support of the third subset, represents the total number of the third subset, is the support of the item set, is the weight of the first subset, is the weight of the second subset, is the weight of the third subset, is the weight of the item set; is the information entropy of the first subset of all item sets, is the information entropy of the second subset of all item sets, is the information entropy of the third subset of all item sets, is the information entropy of all item sets.

[0017] Optionally, when the subsets of the itemset include the first subset and the second subset, the expression of the frequent coefficient is: ; ; ; ;in, is the frequent coefficient of any item set, For this concentration The support of the first subset, represents the total number of the first subset, For this concentration The support of the second subset, represents the total number of the second subset, is the support of the item set, is the weight of the first subset, is the weight of the second subset, is the weight of the itemset.

[0018] In a second aspect, the present application provides a data acquisition system for an electric energy storage box, which adopts the following technical solution: A data acquisition system for an electric energy storage box comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the data acquisition method for the electric energy storage box is implemented.

[0019] The beneficial effect is that the data collection method of the electric energy storage box is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0020] This application has the following technical effects: 1. By constructing item sets from the energy storage tank data and the vibration and temperature data at the energy storage tank, we achieve a joint expression of multidimensional data and can capture the inherent correlations between the individual data. Furthermore, by binning the continuous data, the original continuous data is converted into discrete categories (data ranges). This not only reduces the impact of random fluctuations in the data but also facilitates the calculation of frequent coefficients using the Apriori association rule mining algorithm, making subsequent analysis more efficient and accurate.

[0021] 2. Calculate the frequency coefficient of each item set using the data within all itemsets. This frequency coefficient can effectively identify data points affected by noise or abnormal patterns, and dynamically adjust the weight of the energy storage tank data during the filtering process. The frequency coefficient of the item set corresponding to the energy storage tank data is used to calculate the filter weight of each data point. The collected energy storage tank data is filtered using this filter weight, enabling adaptive response to sudden noise and abnormal data in the operating environment, improving data accuracy and system robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the detailed description below with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present application will become readily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, and the same or corresponding numbers represent the same or corresponding parts.

[0023] Figure 1 This is a method flow chart of a data collection method for an electric energy storage box in an embodiment of the present application.

[0024] Figure 2 This is a flow chart of a method for binning energy storage box data of various dimensions in a data collection method for an electric energy storage box according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0026] It should be understood that when the terms "first," "second," etc. are used in the claims, specification, and drawings of this application, they are only used to distinguish different objects, rather than to describe a specific order. The terms "comprise" and "comprising" used in the specification and claims of this application indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0027] The present application discloses a data collection method for an electric energy storage box, referring to Figure 1 , including steps S1 to S3, specifically as follows: S1: Bin the energy storage tank data of each dimension to obtain the data range, and construct the item set based on the data range.

[0028] When the car is running, the energy storage box will move synchronously with the car. When the car is bumpy, the energy storage box will also bump synchronously. At the same time, the temperature of the energy storage box will change with the operation of the car. The bumps and temperature changes of the energy storage box will affect the collection of energy storage box data. Since the data related to the energy storage box is continuous, it brings inconvenience to the subsequent application of the Apriori association rule mining algorithm. Therefore, this application divides the data of each dimension into boxes and constructs item sets based on the boxed data. Specifically, refer to Figure 2 , binning the energy storage box data of each dimension includes steps S10 and S11: S10: For any dimension, a frequency line graph of the energy storage box data is obtained, where the horizontal axis of the frequency line graph is the energy storage box data and the vertical axis is the frequency.

[0029] S11: Smoothing the frequency line graph to obtain a smooth curve, calculating the absolute value of the slope of each energy storage tank data on the smooth curve, sorting the energy storage tank data in descending order according to the absolute value of the slope to form a sequence, using the energy storage tank data corresponding to the maximum value in the sequence as the data binning point, and binning the dimension data according to the data binning point.

[0030] Obtain a frequency line graph of any dimension data and smooth the frequency line graph using a spline regression algorithm. Alternatively, you can use other existing algorithms that can be applied to the application scenario of this application to smooth the frequency line graph, which will not be elaborated here.

[0031] Calculate the absolute value of the slope of each point on the smooth curve, and construct a slope absolute value sequence according to the order of the energy storage box data corresponding to the absolute value of the slope. Use the data value corresponding to the maximum value in the slope absolute value sequence as the data binning point, and bin the dimension data through the data binning point.

[0032] For example, the original slope is: {1,2,3,2,2,2,1,0,-1,-2,-1}; the energy storage box data corresponding to the slope is: {1,2,3,4,5,6,7,8,9,10,11}; the absolute value sequence of the slope is: {1,2,3,2,2,2,1,0,1,2,1}; the absolute value maximum of the slope is 3 and the last 2 in the absolute value sequence, and the resulting binning results are {1,2,3}, {4,5,6,7,8,9,10}, {11}.

[0033] During the driving process of the car, or during the factory test of the energy storage box, the energy storage box data (current data and voltage data) in the working state of the energy storage box is collected, the temperature data of the energy storage box is collected through the temperature sensor, and the vibration data (acceleration data) of the energy storage box is collected through the accelerometer. The energy storage box at each moment is calculated ( axis, axis, The vector sum modulus of the acceleration in the three directions (axis) is used as the acceleration data of the energy storage box. The current data, voltage data, acceleration data, and temperature data of the energy storage box in the working state are binned.

[0034] In one embodiment, the expression of the itemset is: Where, is the item set consisting of the energy storage tank data at any moment, is the data range corresponding to the voltage data of the energy storage box at that moment, is the data range corresponding to the energy storage box current data at that moment, is the data range corresponding to the energy storage box temperature data at that moment, The data range corresponding to the acceleration data of the energy storage box at this moment is axis, axis, The vector and magnitude of the acceleration in the three directions of the axis.

[0035] For example, if the data binning points are 10, 20, 32, and 50, then the data range from 0 to 10 is 1, the data range from 11 to 20 is 2, the data range from 21 to 32 is 3, the data range from 33 to 50 is 4, and the data range greater than 50 is 5. Data 8 and 9 both correspond to the data range 1 from 0 to 10, and data 11 and 20 correspond to the data range 2. This method is used to bin current data, voltage data, temperature data, and acceleration data. A collection of energy storage tank data item sets is constructed based on the data ranges to which the energy storage tank data, vibration data, and temperature data at each moment belong. That is, the data range corresponding to each dimension of data at each moment constitutes an item set.

[0036] and They represent the external environment status indicators during the use of the energy storage box. When the external environment of the energy storage box is in different states, the energy storage box data (that is, voltage and current ) will also be interfered with to varying degrees, i.e. 、 and 、 There is a certain correlation between them. When the correlation changes, the energy storage box data may be noise data. Perform the subsequent steps for the itemset.

[0037] In other embodiments, in order to simplify the calculation, the item set can also be set to or , I will not go into details here.

[0038] S2: Calculate the frequent coefficient based on the item set.

[0039] Normally, the energy storage tank data at each moment has a certain intrinsic correlation with the environmental state of the energy storage tank. When the new data does not match this correlation, the energy storage tank data may be interfered with by noise. In order to better reduce the interference of noise data on energy storage tank data collection, this application calculates the frequency coefficient of each item set through the data in all item sets.

[0040] The method for calculating the frequent coefficient based on the item set is: Construct subsets of the item set. The subset containing one element is the first subset, and the subset containing two elements is the second subset. Similarly, all subsets are divided. The support of each subset of the item set and the support of each item set are obtained through the Apriori association rule mining algorithm. The frequent coefficient of each item set is calculated by the support of the subset, the weight of the subset and the support of the item set.

[0041] In one embodiment, when the subsets of the item set include three categories: the first subset, the second subset, and the third subset, the expression of the frequent coefficient is: ;in, is the frequent coefficient of any item set, For this concentration The support of the first subset, represents the total number of the first subset, For this concentration The support of the second subset, represents the total number of the second subset, For this concentration The support of the third subset, represents the total number of the third subset, is the support of the item set, is the weight of the first subset, is the weight of the second subset, is the weight of the third subset, is the weight of the itemset.

[0042] For example, a subset of each item set is constructed. The first subset contains one element, the second subset contains two elements, and the third subset contains three elements. Each item set contains 4 first subsets, 6 second subsets, and 4 third subsets. The support of each subset of the item set and the support of each item set are obtained through the Apriori association rule mining algorithm. The frequency coefficient of each item set is calculated based on the support of each subset of the item set and the support of the item set. The calculation method of the frequency coefficient is as follows: ; ; ; ; ;in, is the frequent coefficient of any item set, For this concentration The support of the first subset, For this concentration The support of the second subset, For this concentration The support of the third subset, is the support of the item set, is the weight of the first subset, is the weight of the second subset, is the weight of the third subset, is the weight of the itemset, is the information entropy of the first subset of all item sets, is the information entropy of the second subset of all item sets, is the information entropy of the third subset of all item sets, is the information entropy of all item sets.

[0043] It is the mean of the support of the first subset of the item set. The larger the value, the more frequently the first subset of the item set appears in the set of items, and the larger the frequent coefficient of the item set. The smaller the value, the less frequently the first subset of the item set appears in the set of items, and the smaller the frequent coefficient of the item set. 、 and Similarly, I will not go into details here.

[0044] It is the stability of all first subsets of all item sets in the item set set. Usually, when the energy storage box operates normally, the data of the energy storage box is relatively stable. The larger the value is, the higher the consistency and certainty they show in normal data, which can better reflect whether the item set appears frequently. On the contrary, The smaller it is, the lower the consistency and certainty they show in normal data, and thus the less likely they are to reflect whether the item set appears frequently. As The weight of The bigger it is, The calculation of item set frequent coefficient plays a more important role; The smaller the time, The smaller the effect on the calculation of the frequent coefficient of the item set. 、 The principle is similar to that of w, so I will not go into details here.

[0045] In other embodiments, when the subset of the item set includes two categories, the first subset and the second subset, the expression of the frequent coefficient is: ;in, is the frequent coefficient of any item set, For this concentration The support of the first subset, represents the total number of the first subset, For this concentration The support of the second subset, represents the total number of the second subset, is the support of the item set, is the weight of the first subset, is the weight of the second subset, This embodiment is similar to the analysis in the previous embodiment when the subsets of the item set are three categories, and will not be described in detail here.

[0046] S3: Calculate a weight coefficient of the energy storage data according to the frequency coefficient, and filter the energy storage data according to the weight coefficient to obtain a filtered value.

[0047] Since the noise in the energy storage tank data is sporadic, the sporadic nature of the noise data may correspond to the frequency coefficient of the item set corresponding to the energy storage tank data. The energy storage tank data may be weighted by the frequency coefficient of the item set corresponding to the energy storage tank data to better remove burst noise. Therefore, the present application calculates the filtering weight of each data according to the frequency coefficient of the item set corresponding to the energy storage tank data, and filters the collected energy storage tank data by the filtering weight.

[0048] During the operation of the energy storage box, the above-mentioned relevant data are collected in real time, and the real-time data are formed into an item set through the above method. The frequent coefficient of the real-time data item set is calculated, and the filter weight of the energy storage box data (current or voltage) is calculated based on the frequent coefficient of the item set. The filter weight is used as the size of the filter window (in this example, , users can choose according to actual conditions Filtering the energy storage box data using the above method during energy storage box data collection can reduce sudden noise in the data and reduce the impact of sudden noise on the status monitoring of the energy storage box during data collection.

[0049] In one embodiment, the calculation formula of the filter value is: Where, is the filtered value of the energy storage box data collected in real time, The energy storage box data collected in real time Energy storage box data at the moment, for The frequent coefficient of the corresponding item set, is the size of the filter window.

[0050] The frequent coefficient of the corresponding item set The bigger, the The more common and stable the combination of elements in the corresponding item set is, the The more credible it is; The smaller, the The less common the combination of elements in the corresponding item set is, the The more likely it is a burst noise, the The less credible it is, the more As The weight of The bigger, The smaller the possibility of being noise, the The greater the weight; The smaller, The greater the possibility of being noise, the The smaller the weight.

[0051] An embodiment of the present application also discloses a data acquisition system for an electric energy storage box, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the data acquisition method for the electric energy storage box according to the present application is implemented.

[0052] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0053] In this application, the aforementioned memory can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium can be part of, accessible to, or connected to the device.

[0054] Although this specification has shown and described a number of embodiments of the present application, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, variations, and alternatives without departing from the spirit and scope of the present application. It should be understood that in practicing the present application, various alternatives to the embodiments of the present application described herein may be employed.

[0055] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A data acquisition method for an electric energy storage box, characterized in that: Including steps: The energy storage tank data of each dimension is binned to obtain the data range, and the item set is constructed based on the data range; Calculate the frequent coefficient based on the item set; Calculate the weight coefficient of the energy storage data according to the frequency coefficient, and filter the energy storage data according to the weight coefficient to obtain a filtered value; The calculation formula of the filter value is: Where, is the filtered value of the energy storage box data collected in real time, The energy storage box data collected in real time Energy storage box data at the moment, for The frequent coefficient of the corresponding item set, is the size of the filter window.

2. The data acquisition method for an electric energy storage box according to claim 1, characterized in that: Binning the energy storage box data of each dimension includes the following steps: For any dimension, obtain a frequency line graph of the energy storage box data, where the horizontal axis is the energy storage box data and the vertical axis is the frequency; The frequency line graph is smoothed to obtain a smooth curve. The absolute value of the slope of each energy storage box data on the smooth curve is calculated. The energy storage box data corresponding to the absolute value of the slope is sorted from large to small to form a sequence. The energy storage box data corresponding to the maximum value in the sequence is used as the data binning point, and the dimension data is binned according to the data binning point.

3. The data acquisition method for an electric energy storage box according to claim 2, characterized in that: The frequency line graph is smoothed using the spline regression algorithm to obtain a smooth curve.

4. The data acquisition method for an electric energy storage box according to claim 1, characterized in that: The expression of the itemset is: Where, is the item set consisting of the energy storage tank data at any moment, is the data range corresponding to the voltage data of the energy storage box at that moment, is the data range corresponding to the energy storage box current data at that moment, is the data range corresponding to the energy storage box temperature data at that moment, The data range corresponding to the acceleration data of the energy storage box at this moment is axis, axis, The vector and magnitude of the acceleration in the three directions of the axis.

5. The data acquisition method for an electric energy storage box according to claim 1, characterized in that: The method for calculating the frequent coefficient based on the item set is: Construct subsets of the item set. The subset containing one element is the first subset, and the subset containing two elements is the second subset. Similarly, all subsets are divided; The support of each subset of the item set and the support of each item set are obtained through the Apriori association rule mining algorithm, and the frequent coefficient of each item set is calculated by the support of the subset, the weight of the subset and the support of the item set.

6. The data acquisition method for an electric energy storage box according to claim 5, characterized in that: When the subsets of the item set include the first subset, the second subset, and the third subset, the expression of the frequent coefficient is: ； ； ； ； ； in, is the frequent coefficient of any item set, For this concentration The support of the first subset, represents the total number of the first subset, For this concentration The support of the second subset, represents the total number of the second subset, For this concentration The support of the third subset, represents the total number of the third subset, is the support of the item set, is the weight of the first subset, is the weight of the second subset, is the weight of the third subset, is the weight of the item set; is the information entropy of the first subset of all item sets, is the information entropy of the second subset of all item sets, is the information entropy of the third subset of all item sets, is the information entropy of all item sets.

7. The data acquisition method for an electric energy storage box according to claim 5, characterized in that: When the subsets of the item set include the first subset and the second subset, the expression of the frequent coefficient is: ； ； ； ； in, is the frequent coefficient of any item set, For this concentration The support of the first subset, represents the total number of the first subset, For this concentration The support of the second subset, represents the total number of the second subset, is the support of the item set, is the weight of the first subset, is the weight of the second subset, is the weight of the itemset.

8. A data acquisition system for an electric energy storage box, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the data collection method for an electric energy storage box according to any one of claims 1 to 7 is implemented.

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