A method and system for monitoring the operation status of an intelligent coffee machine

Abstract

The present invention relates to the field of data processing technology, and more specifically, to a method and system for monitoring the operating status of an intelligent coffee machine, the method comprising: obtaining the temperature of two components in a coffee extraction process, taking the temperature difference of the two components at any moment, the temperature difference of any component at this moment and the previous moment, and a one-dimensional vector composed of the temperature of any component at this moment as a feature vector at this moment, the two components being a boiler and a brewing head; taking the feature vectors of all moments in the historical extraction process as an initial space, adding the feature vector of the current moment in the current extraction process to the initial space, clustering the initial space after adding the new feature vector based on a preset neighborhood radius and a preset minimum number of sampling points using a DBSCAN algorithm, and identifying outliers based on the clustering results to monitor abnormal states. The present invention can accurately monitor the operating status of a coffee machine.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to a method and system for monitoring the operating status of an intelligent coffee machine. Background Art

[0002] In smart coffee machines, temperature control during the coffee making process is crucial to ensure the taste and quality of the coffee. Therefore, smart coffee machines are usually equipped with temperature sensors to monitor the temperature of the coffee water in real time and ensure that it is maintained within the ideal extraction temperature range. When the coffee machine fails or operates abnormally, the heating system may fail, causing the coffee water temperature to exceed the predetermined extraction temperature range. Therefore, it is crucial to accurately monitor the operating status of the coffee machine.

[0003] In the related technology, for example, a patent application document with publication number CN117113265A discloses an intelligent monitoring and management method and system for food processing equipment, the method comprising: obtaining temperature time series data, pressure time series data and flow time series data; obtaining the food release gas influencing factor in each time window according to the temperature characteristics in each time window; obtaining the heating equipment pressure adjustment sequence in each time window in combination with the pressure time series data; further obtaining the pressure relief valve differential pressure sequence in each time window according to the flow time series data; obtaining the pressure relief valve differential pressure balance index based on the time window; obtaining the abnormal factor based on the time window; thereby completing the abnormal detection of food processing equipment.

[0004] However, although the above scheme avoids the defect that the COF anomaly detection algorithm performs poorly on data sets with relatively uniform distribution, due to the high computational complexity of the COF anomaly detection algorithm, when using the algorithm to obtain abnormal data, the selection of the neighborhood is more sensitive and has poor adaptability to data distribution, which may cause deviations in the anomaly detection results and affect the processing process. Summary of the invention

[0005] In order to solve the problem that the accuracy of abnormal detection results is low and thus the operating status of the equipment cannot be accurately monitored, the present invention provides an intelligent coffee machine operating status monitoring method and system.

[0006] According to a first aspect of the present invention, there is provided a method for monitoring the operating status of an intelligent coffee machine, comprising:

[0007] Obtain the temperatures of two components during the coffee extraction process, and use the temperature difference of the two components at any moment, the temperature difference of any component at this moment and the previous moment, and the one-dimensional vector formed by the temperature of any component at this moment as the feature vector at this moment. The two components are the boiler and the brewing head;

[0008] The feature vectors of all moments in the historical extraction process are used as the initial space, and the feature vector of the current moment in the current extraction process is added to the initial space. Based on the preset neighborhood radius and the preset minimum number of sampling points, the DBSCAN algorithm is used to cluster the initial space after adding the new feature vector, and outliers are identified based on the clustering results to monitor abnormal conditions;

[0009] In the clustering process, the distance between the feature vectors at any two moments , For the The feature vector at time With The feature vector at time The cosine similarity of is a natural constant, is a natural exponential function; the neighborhood radius is the data with the largest difference from the adjacent data in the distance sequence of the feature vectors at adjacent moments in the historical extraction process; the minimum number of sampling points is the average number of neighboring vectors of the feature vectors at all moments in the historical extraction process; among them, the distance between the neighbor vector of any feature vector and the feature vector is less than the neighborhood radius.

[0010] The present invention combines the various temperature characteristics of different components of the coffee machine at any time to construct a corresponding feature vector. This vector can not only reflect the temperature state of each component, but also capture the dynamic change characteristics of the temperature of different components, thereby more comprehensively presenting the temperature change characteristics of the coffee machine and providing accurate data support for the identification of abnormal data. In addition, when using the DBSCAN algorithm for anomaly detection, the present invention can reasonably set the neighborhood radius and the minimum number of sampling points according to the temperature change characteristics at each moment, ensure the accuracy of parameter setting, reduce the difficulty of parameter adjustment, thereby improving the accuracy of anomaly detection results and realizing accurate monitoring of the coffee machine status.

[0011] Preferably, the method for obtaining the feature vector includes:

[0012] The temperature difference between the boiler at any moment and the previous moment is taken as the first difference, the temperature difference between the brewing head at this moment and the previous moment is taken as the second difference, and the temperature difference between the boiler and the brewing head at this moment is taken as the third difference;

[0013] The temperature of the boiler and the brewing head at that moment, the first difference, the second difference and the third difference are randomly arranged or combined to form a feature vector at that moment.

[0014] The present invention combines multiple temperature characteristics of different components at any time to construct a vector at the corresponding time, which can fully reflect the temperature change characteristics at the corresponding time.

[0015] Preferably, the method for obtaining the neighborhood radius includes:

[0016] Calculate the absolute value of the difference between any data in the distance sequence and the previous data, and the absolute value of the difference between any data in the distance sequence and the next data, and sum them up to obtain the difference between any data in the distance sequence and the adjacent data;

[0017] The value of the data with the largest difference from the adjacent data in the distance sequence is taken as the neighborhood radius.

[0018] The present invention uses the distance between normal data and abnormal data with a greater possibility as the neighborhood radius of the DBSCAN algorithm, which can ensure the accuracy of the abnormal data detection result.

[0019] Preferably, the method for obtaining the feature vectors at all times during the history extraction process includes:

[0020] Obtain the intact temperature sequences of the boiler and the brewing head during the historical extraction process, and construct the feature vectors at each moment in the historical extraction process based on the two intact temperature sequences;

[0021] Among them, the intact temperature series does not contain abnormal data.

[0022] Preferably, the method for obtaining the minimum number of sampling points further includes:

[0023] The average number is rounded using the rounding function to obtain the minimum number of sampling points.

[0024] The present invention can ensure that the minimum number of sampling points is an integer.

[0025] Preferably, the minimum number of sampling points satisfies the following relationship:

[0026] ;

[0027] In the formula, is the minimum number of sampling points; The first The number of neighbor vectors of the eigenvector at each moment; is the number of moments included in the history extraction process; for the summation symbol; The round-up symbol.

[0028] The present invention utilizes the temperature during the historical extraction process, which can reflect the temperature conditions of coffee during real-time extraction to a certain extent, calculates the minimum number of sampling points of the DBSCAN algorithm, and ensures the accuracy of the determined minimum number of sampling points.

[0029] Preferably, after identifying outliers based on the clustering results, the method includes:

[0030] The extraction time of the outlier is obtained, and the temperature at the time corresponding to the outlier is replaced by the average temperature of the adjacent times of the extraction time.

[0031] According to a second aspect of the present invention, there is provided an intelligent coffee machine operation status monitoring system, the system comprising a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the first aspect of the present invention.

[0032] The present invention has the following effects:

[0033] 1. The present invention can comprehensively present the temperature change characteristics of the coffee machine at each extraction moment by constructing the feature vector at each moment in the coffee extraction process, thereby providing an accurate data basis for the identification of abnormal data.

[0034] 2. When the present invention uses the DBSCAN algorithm for anomaly detection, it can ensure the accuracy of the neighborhood radius and the minimum number of sampling points of the DBSCAN algorithm, thereby ensuring the accuracy of the anomaly detection results and realizing accurate monitoring of the operating status of the coffee machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0036] Figure 1 The figure is a schematic diagram of the steps of a method for monitoring the operating status of an intelligent coffee machine according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0038] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] Reference Figure 1 , a method for monitoring the operation status of an intelligent coffee machine, comprising steps S1-S2, specifically as follows:

[0040] S1: Obtain the temperatures of two components during the coffee extraction process, and use the temperature difference between the two components at any moment, the temperature difference between any component at this moment and the previous moment, and the one-dimensional vector formed by the temperature of any component at this moment as the feature vector at this moment. The two components are the boiler and the brewing head.

[0041] It should be noted that in the process of extracting coffee using a coffee machine, water temperature plays a vital role, and the water temperature is controlled by the coffee machine boiler. During the extraction process, water flows out through the brewing head, and the quality of the finally extracted coffee is closely related to the water temperature in the boiler and the water temperature flowing out of the brewing head. Therefore, the present invention monitors the operating state of the coffee machine by collecting the temperature of the boiler and the brewing head of the coffee machine.

[0042] The characteristic vector refers to a one-dimensional vector composed of multiple elements that can reflect the temperature characteristics. It should be noted that the temperature change amplitude and trend of the boiler and the brewing head are different under different working conditions of the coffee machine, resulting in different changes in the response of the temperature sensor. Therefore, in order to capture this change and identify the abnormality of the temperature sensor, the present invention constructs the characteristic vector at each moment.

[0043] Optionally, a temperature sensor may be used to obtain the temperature of the boiler and the brew head during the coffee extraction process. It should be noted that the temperature of the boiler and the brew head correspond one to one, that is, the sampling time of the temperature of the boiler and the brew head is the same.

[0044] In an exemplary embodiment of the present invention, the construction of a feature vector at any time can be achieved by the following steps:

[0045] The temperature difference between the boiler at any moment and the previous moment is taken as the first difference, the temperature difference between the brewing head at this moment and the previous moment is taken as the second difference, and the temperature difference between the boiler and the brewing head at this moment is taken as the third difference; the temperature, the first difference, the second difference and the third difference of the boiler and the brewing head at this moment are randomly arranged or combined to form a feature vector at this moment.

[0046] For example, the temperature of the boiler at any time can be expressed as , the brew head at the corresponding time is recorded as , then the first difference is The second difference is The third difference is . Then, the tuple { , , , , }randomly arranged to form a one-dimensional vector, and the first The feature vector at that moment.

[0047] Optionally, you can also pass a tuple { , , , , } are combined by functions, such as combining the tuple into { }, and the one-dimensional vector formed by the combined tuple is used as the first Of course, a suitable method can be selected to determine the feature vector according to specific circumstances, and this embodiment does not specifically limit the method for determining the feature vector.

[0048] in, , It reflects the temperature value of the boiler and brewing head at that moment. When the response of the temperature sensor changes and the collected temperature is inaccurate, as well as The existence of will make the eigenvector appear in different forms.

[0049] It reflects the temperature difference between the boiler and the brewing head at that moment. When water flows from the boiler of the coffee machine to the brewing head, there will be a certain amount of heat loss, resulting in a temperature difference between the boiler and the brewing head. However, the structure of the coffee machine is fixed, and the heat loss is relatively stable to a certain extent. Therefore, the temperature difference between the boiler and the brewing head should also remain relatively constant. When the temperature sensor of the boiler or the brewing head responds abnormally, the temperature difference will also change, so this value can be used to measure the accuracy of the temperature of the boiler and the brewing head at that moment.

[0050] , It reflects the temperature change of the boiler and the brewing head at that moment. Normally, the temperature of the boiler and the brewing head of the coffee machine will show a stable change. When the response of the temperature sensor is abnormal, the stable change trend of the temperature change will be destroyed. Therefore, the temperature change of the boiler and the brewing head at that moment can be used to measure the accuracy of the temperature of the boiler and the brewing head at that moment.

[0051] S2: The feature vectors of all moments in the historical extraction process are used as the initial space, and the feature vector of the current moment in the current extraction process is added to the initial space. Based on the preset neighborhood radius and the preset minimum number of sampling points, the DBSCAN algorithm is used to cluster the initial space after adding the new feature vector, and outliers are identified based on the clustering results to monitor abnormal conditions.

[0052] In an exemplary embodiment of the present invention, the determination of the feature vector at any time during the history extraction process can be achieved by the following steps:

[0053] The intact temperature sequences of the boiler and the brewing head during the historical extraction process are obtained, and the feature vectors of each moment in the historical extraction process are constructed based on the two intact temperature sequences; wherein the intact temperature sequence does not contain abnormal data.

[0054] Among them, the intact temperature sequence refers to a temperature sequence without abnormal data. It should be noted that when the temperature of the boiler and the brewing head in the historical extraction process is abnormal data, the temperature is removed to obtain the intact temperature sequence corresponding to the historical extraction period, and then the feature vector of each moment in the historical extraction process can be constructed based on the intact temperature sequence. Among them, the construction method of the feature vector has been described in detail in step S1, and this embodiment will not be repeated here.

[0055] Furthermore, after the initial space is obtained based on the feature vectors at all times in the historical extraction process, the feature vector at the current time in the current extraction process can be added to the initial space, and then the DBSCAN algorithm is used to cluster the initial space after the new feature vector is added.

[0056] It should be noted that DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm based on spatial density. In the process of clustering using the algorithm, the algorithm defines clustering by neighborhood radius and minimum number of sampling points, that is, when the number of data points in the neighborhood of any data point is greater than or equal to the minimum number of sampling points, the data point is regarded as a core point; when the number of data points in the neighborhood of any data point is less than the minimum number of sampling points, the data point is regarded as a boundary point; when any data point is neither a core point nor in the neighborhood of any core point, the data point is an outlier. However, the traditional DBSCAN algorithm is difficult to adjust parameters, and the neighborhood size and the minimum number of sampling points have a great influence on the clustering results. Therefore, the present invention improves the traditional DBSCAN algorithm and changes the determination method of the neighborhood radius and the minimum number of sampling points in the algorithm, that is, combining the data characteristics of the temperature during the extraction process, setting the neighborhood radius and the minimum number of sampling points of appropriate size to reduce the difficulty of adjusting the parameters of the algorithm and realize accurate identification of outliers.

[0057] Specifically, in the process of clustering the initial space after adding new feature vectors using the DBSCAN algorithm, the distance between the feature vectors at any two moments satisfies the following relationship:

[0058] ;

[0059] In the formula, For the The eigenvector at the moment The distance between the feature vectors at each moment; For the The feature vector at time With The feature vector at time The cosine similarity of is a natural exponential function, that is, with a natural constant An exponential function with base ; is a natural constant.

[0060] in, It reflects the similarity between the feature vectors at two corresponding moments. The larger the value, the more similar the feature vectors at two corresponding moments are, and the smaller the distance between the feature vectors at two corresponding moments is. It should be noted that the calculation process of the cosine similarity between vectors is a prior art, and this embodiment will not be described in detail here.

[0061] Optional, due to The value range is Therefore, the present invention reduces The distance between feature vectors is limited to .

[0062] Furthermore, after determining the distance between any two eigenvectors in the initial space after adding the new eigenvector, the neighborhood radius and the minimum sampling point data of the DBSCAN algorithm can be used to identify outliers.

[0063] The neighborhood radius of the DBSCAN algorithm is the data with the largest difference from the adjacent data within the distance sequence of the feature vectors at adjacent moments in the historical extraction process. It should be noted that since the boiler and the brewing head have different structures, the temperature during the extraction process will also show different temperature change characteristics. Therefore, in order to better identify abnormal temperatures, the present invention combines the feature vectors at each moment in the historical extraction process to determine the neighborhood radius of the algorithm.

[0064] The minimum number of sampling points of the DBSCAN algorithm is the average number of neighbor vectors of the feature vectors at all times in the historical extraction process. It should be noted that since the temperature in the historical extraction process can reflect the temperature of the coffee during real-time extraction to a certain extent, the present invention uses this feature to determine the minimum number of sampling points based on the overall level of the neighbor vectors of each feature vector in the historical extraction process, thereby ensuring the accuracy of the determined minimum number of sampling points.

[0065] In an exemplary embodiment of the present invention, the neighborhood radius may be determined by the following steps:

[0066] Calculate the absolute value of the difference between any data in the distance sequence and the previous data, as well as the absolute value of the difference between any data in the distance sequence and the next data, and sum them up to obtain the difference between any data in the distance sequence and the adjacent data; take the value of the data with the largest difference from the adjacent data in the distance sequence as the neighborhood radius.

[0067] For example, the distance sequence can be written as , where , , are the feature vectors at the first moment and the second moment in the history extraction process, respectively. The eigenvector at the moment The eigenvector at the moment The eigenvector at the moment The distance between the feature vectors at the moment, is the number of moments included in the historical extraction process; for Differences from adjacent data.

[0068] Optional, when When the difference with adjacent data is small, it means that the temperature change characteristics of multiple consecutive moments are relatively consistent, which further indicates that the The eigenvector at the moment It is highly likely that the distance between the feature vectors at each moment is the distance between normal data.

[0069] when When the difference with adjacent data is large, it means that The moment to The temperature variation characteristics at each moment are The moment to The temperature variation characteristics at each moment and the The moment to The temperature change characteristics at each moment are quite different, while when the coffee machine is in normal operation, the temperature changes are relatively consistent. The eigenvector at the moment The distance between the feature vectors at each moment is more likely to be the distance between normal data and abnormal data.

[0070] Optional, by selecting The neighborhood radius is determined by the data with the largest difference from the adjacent data, so that the neighborhood radius is close to the distance between normal data and abnormal data, ensuring the accuracy of the neighborhood radius setting.

[0071] Optionally, the difference between any data and adjacent data in the distance sequence may also be expressed in other ways, such as by the ratio of any data to adjacent data instead of by the absolute value of the difference.

[0072] In another embodiment, the intact temperature sequences of the boiler and the brewing head in multiple historical extraction processes may be collected, and then multiple neighborhood radii may be determined based on the feature vectors at each moment in each historical extraction process, and the average value of all neighborhood radii may be used as the neighborhood radius of the DBSCAN algorithm, thereby reducing the impact of data uncertainty.

[0073] In an exemplary embodiment of the present invention, the method for obtaining the minimum number of sampling points further includes:

[0074] The average number is rounded using the rounding function to obtain the minimum number of sampling points.

[0075] Specifically, the minimum number of sampling points of the DBSCAN algorithm satisfies the following relationship:

[0076] ;

[0077] In the formula, is the minimum number of sampling points; The first The number of neighbor vectors of the eigenvector at each moment; is the number of moments included in the history extraction process; for the summation symbol; The round-up symbol.

[0078] In another embodiment, the average number of feature vectors at all moments in the history extraction process may be rounded down by rounding down.

[0079] Furthermore, the feature vectors in the initial space can be clustered based on the distance between the feature vectors in the initial space after the new feature vector is added, the neighborhood radius of the DBSCAN algorithm, and the minimum number of sampling points, so as to identify outliers based on the clustering results. It should be noted that the process of clustering based on the distance between data points, the neighborhood radius, and the minimum number of sampling points, and identifying outliers based on the clustering results is a prior art, and this embodiment will not be described in detail here.

[0080] In an exemplary embodiment of the present invention, after any outlier is determined, the following steps may be performed:

[0081] The extraction time of the outlier is obtained, and the temperature at the time corresponding to the outlier is replaced by the average temperature of the adjacent times of the extraction time.

[0082] Specifically, when any outlier corresponds to an extraction moment, the temperature of the boiler at the extraction moment is replaced by the average value of the boiler at adjacent moments at the extraction moment, and the temperature of the brewing head at the extraction moment is replaced by the average value of the brewing head at adjacent moments at the extraction moment.

[0083] Optionally, when an outlier is detected, an alarm may be sounded to remind the staff that the operating status of the coffee machine is abnormal at the corresponding moment so that corresponding measures can be taken, thereby monitoring the operating status of the coffee machine in real time.

[0084] The present invention also provides an intelligent coffee machine operation status monitoring system, the system includes a memory and a processor, and a computer program is stored in the memory, the computer program integrates the function of an intelligent coffee machine operation status monitoring method, when the computer program is executed, the operation status of the coffee machine can be accurately monitored through an intelligent coffee machine operation status monitoring method.

[0085] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0086] Although this specification has shown and described a number of embodiments of the present invention, 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, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for monitoring the operating status of an intelligent coffee machine, characterized in that: include: Obtain the temperatures of two components during the coffee extraction process, and use the temperature difference of the two components at any moment, the temperature difference of any component at this moment and the previous moment, and the one-dimensional vector formed by the temperature of any component at this moment as the feature vector at this moment, wherein the two components are the boiler and the brewing head; The feature vectors of all moments in the historical extraction process are used as the initial space, and the feature vector of the current moment in the current extraction process is added to the initial space. Based on the preset neighborhood radius and the preset minimum number of sampling points, the DBSCAN algorithm is used to cluster the initial space after adding the new feature vector, and outliers are identified based on the clustering results to monitor abnormal conditions; In the clustering process, the distance between the feature vectors at any two moments , For the The feature vector at time With The feature vector at time The cosine similarity of is a natural constant, is a natural exponential function; the neighborhood radius is the data with the largest difference from the adjacent data in the distance sequence of the feature vectors at adjacent moments in the historical extraction process; the minimum number of sampling points is the average number of neighboring vectors of the feature vectors at all moments in the historical extraction process; wherein the distance between the neighboring vector of any feature vector and the any feature vector is less than the neighborhood radius.

2. The method for monitoring the operation status of an intelligent coffee machine according to claim 1, characterized in that: The method for obtaining the feature vector comprises: The temperature difference between the boiler at any moment and the previous moment is taken as the first difference, the temperature difference between the brewing head at this moment and the previous moment is taken as the second difference, and the temperature difference between the boiler and the brewing head at this moment is taken as the third difference; The temperatures of the boiler and the brewing head at that moment, the first difference, the second difference, and the third difference are randomly arranged or combined to form a feature vector at that moment.

3. The method for monitoring the operation status of an intelligent coffee machine according to claim 1, characterized in that: The method for obtaining the neighborhood radius includes: Calculate the absolute value of the difference between any data in the distance sequence and the previous data, and the absolute value of the difference between any data in the distance sequence and the next data, and sum them up to obtain the difference between any data in the distance sequence and the adjacent data; The value of the data with the largest difference from the adjacent data in the distance sequence is used as the neighborhood radius.

4. The method for monitoring the operation status of an intelligent coffee machine according to claim 1, characterized in that: The method for obtaining the feature vectors at all times during the history extraction process includes: Obtain the intact temperature sequences of the boiler and the brewing head during the historical extraction process, and construct the feature vectors at each moment in the historical extraction process based on the two intact temperature sequences; The intact temperature sequence does not contain abnormal data.

5. The method for monitoring the operation status of an intelligent coffee machine according to claim 1, characterized in that: The method for obtaining the minimum number of sampling points also includes: The average number is rounded using a rounding function to obtain the minimum number of sampling points.

6. The method for monitoring the operation status of an intelligent coffee machine according to claim 5, characterized in that: The minimum number of sampling points satisfies the following relationship: ； In the formula, is the minimum number of sampling points; The first The number of neighbor vectors of the eigenvector at each moment; is the number of moments included in the history extraction process; for the summation symbol; The round-up symbol.

7. The method for monitoring the operation status of an intelligent coffee machine according to claim 1, characterized in that: After identifying outliers based on the clustering results, the method includes: The extraction time of the outlier is obtained, and the temperature at the time corresponding to the outlier is replaced by the average value of the temperatures at the adjacent times of the extraction time.

8. An intelligent coffee machine operation status monitoring system, characterized in that: The intelligent coffee machine operation status monitoring system comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

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