Data checking method, device and equipment and storage medium

Abstract

The application discloses a data checking method, device and equipment and a storage medium, and relates to the technical field of data checking. The method comprises the following steps: obtaining old service data for a target service in an old service database and new service data for the target service in a new service database; storing the old service data in a first offline big data hive table to obtain an old service data hive table, and storing the new service data in a second hive table to obtain a new service data hive table; and comparing the old service data hive table and the new service data hive table by using a difference query language (HQL) to obtain a full-amount data checking result of the new service data. The full-amount data checking of the new service data in the new service database is automatically completed, the technical problem that the service data of the new service database is checked by manual testing when the old service is switched to the new service, and the efficiency is low is solved, and the data checking efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of data verification technology, and in particular to a data verification method, apparatus, device, and storage medium. Background Technology

[0002] In related technologies, when switching from an old service system to a new service system for banking services, manual testing is usually used to verify the service data in the new service database, which is inefficient. Summary of the Invention

[0003] The main objective of this invention is to provide a data verification method, apparatus, device, and storage medium, which aims to solve the technical problem of low efficiency in manually verifying service data in the new service database during the switching between old and new services.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] In a first aspect, the present invention provides a data verification method, the method comprising:

[0006] Retrieve old service data for the target service from the old service database and new service data for the target service from the new service database;

[0007] Store the old service data into the first offline big data Hive table to obtain the old service data Hive table, and store the new service data into the second Hive table to obtain the new service data Hive table;

[0008] Using the difference query language HQL, the old service data Hive table and the new service data Hive table are compared to obtain the full data verification results of the new service data.

[0009] Optionally, using the difference query language HQL, the old service data Hive table and the new service data Hive table are compared to obtain the full data verification results, including:

[0010] Using the HQL (Hash Language) differential query language, the old service data Hive table and the new service data Hive table are compared to obtain the full old service differential data and the full new service differential data.

[0011] Store all the old service difference data into the third Hive table to obtain the old service difference data Hive table, and store all the new service difference data into the fourth Hive table to obtain the new service difference data Hive table.

[0012] Based on the full set of old service difference data Hive table and the full set of new service difference data Hive table, obtain the full data verification results.

[0013] Optionally, before retrieving the old service data for the target service from the old service database and the new service data for the target service from the new service database, the method further includes:

[0014] Receive real-time service requests and send them to the old service database and the new service database;

[0015] Receive the first return result returned by the old service database and the second return result returned by the new service database; the first return result is obtained by the old service database executing the real-time service request, and the second return result is obtained by the new service database executing the real-time service request.

[0016] The first and second returned results are compared to obtain the real-time service verification result.

[0017] Optionally, after receiving the first return result from the old service database and the second return result from the new service database, the method further includes:

[0018] Based on the service gateway, the first result is returned to the user, so that the old service database can provide services to the user.

[0019] Optionally, before comparing the first and second returned results to obtain the real-time service verification result, the method further includes:

[0020] Based on the message queue Kafka, the first returned result is processed to obtain the processed first returned result, and the second returned result is processed to obtain the processed second returned result.

[0021] The first and second returned results are compared to obtain the real-time service verification results, including:

[0022] The first and second returned results after processing are compared to obtain the real-time service verification result.

[0023] Optionally, the first and second returned results are compared to obtain the real-time service verification result, including:

[0024] The first and second returned results are compared to obtain real-time difference data;

[0025] Based on preset allowable difference conditions, real-time difference data is filtered to obtain abnormal real-time difference data, and the real-time service verification result is obtained.

[0026] Optionally, after filtering the real-time difference data based on preset allowable difference conditions to obtain abnormal real-time difference data and the real-time service verification result, the method further includes:

[0027] Anomaly warnings are generated based on real-time discrepancies in the data.

[0028] Secondly, the present invention also provides a data verification device, the device comprising:

[0029] The data acquisition module is used to acquire old service data for the target service from the old service database and new service data for the target service from the new service database.

[0030] The data storage module is used to store old service data into the first Hive table to obtain the old service data Hive table, and to store new service data into the second Hive table to obtain the new service data Hive table;

[0031] The full comparison module uses the difference query language HQL to compare the old service data Hive table with the new service data Hive table to obtain the full data verification result.

[0032] Thirdly, the present invention also provides a data verification device, the device comprising: a memory, a processor, and a data verification program stored in the memory and executable on the processor, the data verification program being configured to implement the steps of any of the above data verification methods.

[0033] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of the data verification method as described above.

[0034] This invention provides a data verification method, apparatus, device, and storage medium. By acquiring service data for a target service from an old service database and a new service database, and storing it in an old service data Hive table and a new service data Hive table respectively, the full old service data and the full new service data in the two Hive tables are compared using the difference query language HQL to obtain the full data verification result of the new service data in the new service database.

[0035] Therefore, this invention constructs offline big data Hive tables based on old service data for the target service in the old service database and new service data for the target service in the new service database. The data differences between the two Hive tables are automatically compared using the difference query language HQL. When switching from the old service system to the new service system, the full data verification of the new service data in the new service database is automatically completed. Compared with data verification through manual testing, the efficiency of data verification is improved. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of the data verification device of the present invention;

[0038] Figure 2 This is a flowchart illustrating the first embodiment of the data verification method of the present invention;

[0039] Figure 3 This is a flowchart illustrating the second embodiment of the data verification method of the present invention;

[0040] Figure 4 Here is an example of a real-time anomaly detail chart;

[0041] Figure 5 This is a schematic diagram of the modules of the first embodiment of the data verification device of the present invention.

[0042] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0044] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0045] In this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that an apparatus or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an apparatus or system. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the apparatus or system that includes that element.

[0046] In view of the inefficiency of manually verifying service data in the new service database during the switch between old and new services, this invention provides a data verification method, the overall idea of ​​which is as follows:

[0047] The method includes: obtaining old service data for the target service from the old service database and new service data for the target service from the new service database; storing the old service data into a first offline big data Hive table to obtain an old service data Hive table, and storing the new service data into a second Hive table to obtain a new service data Hive table; using the differential query language HQL, comparing the old service data Hive table and the new service data Hive table to obtain the full data verification result of the new service data.

[0048] This invention provides a data verification method that constructs offline big data Hive tables based on old service data for the target service in the old service database and new service data for the target service in the new service database. The method automatically compares the data differences between the two Hive tables using the difference query language HQL. When switching from the old service system to the new service system, the method automatically performs full data verification on the new service data in the new service database. Compared with data verification through manual testing, this method improves the efficiency of data verification.

[0049] The data verification method, apparatus, equipment, and storage medium used in the technical implementation of this invention are described in detail below:

[0050] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of the data verification device in the hardware operating environment involved in the embodiments of this application.

[0051] like Figure 1 As shown, the device may include: a processor 1001, such as a CPU; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include electronic devices such as smartphones, tablets, and PDAs (Personal Digital Assistants). Optionally, the user interface 1003 may also be a display screen or an input unit such as a keyboard. The memory 1005 may be high-speed RAM or non-volatile memory, such as disk storage. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0052] It is understood that the device may also include a network interface 1004, which may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). Optionally, the device may also include RF (Radio Frequency) circuitry, sensors, audio circuitry, a Wi-Fi module, etc.

[0053] Those skilled in the art will understand that Figure 1 The device structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0054] The data verification method, apparatus, device, and storage medium of the present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0055] Based on, but not limited to, the above hardware structure, refer to Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the data verification method of the present invention.

[0056] This embodiment provides a data verification method, which may include:

[0057] Step S400: Obtain the old service data for the target service from the old service database and the new service data for the target service from the new service database.

[0058] In this embodiment, the executing entity can be, for example, Figure 1 The data verification device shown can be a physical server including an independent host, or a virtual server hosted in a host cluster.

[0059] It is understandable that the target service can be a business service determined based on actual banking business needs. The old service data refers to all service data related to the target service in the old service database, and the new service data refers to all service data related to the target service in the new service database. The old service data in the old service database can provide the corresponding business service to the user. During the actual switch between the old and new service systems, the old service data corresponding to the target service in the old service database is first migrated to the new service database, and then the system switches to using the new service data in the new service database to provide the corresponding business service to the user. Therefore, before actually switching from the old service database to the new service database to provide business services to the user, it is necessary to verify the new service data in the new service database to ensure that it can provide the corresponding business service to the user.

[0060] Step S500: Store the old service data into the first offline big data hive table to obtain the old service data hive table, and store the new service data into the second hive table to obtain the new service data hive table.

[0061] In this embodiment, the underlying storage of the new service database and the old service database are often different. Therefore, in order to provide a data comparison method with good commonality, big data offline analysis technology can be used to extract the old service data and the new service data from the different underlying storage of the Hive component into the Hive table for data comparison, thereby verifying the new service data in the new service database.

[0062] Step S600: Using the difference query language HQL, compare the old service data Hive table and the new service data Hive table to obtain the full data verification result of the new service data.

[0063] In this embodiment, the Hive component can use the HQL (Hashtag Language) to compare two Hive tables and obtain full difference data. Based on this full difference data, the Hive component analyzes and obtains the full data verification results of the new service data. The full difference data can be the difference data between the old service data and the new service data.

[0064] As a specific implementation, step S600 may include: using the difference query language HQL to compare the old service data Hive table and the new service data Hive table to obtain full old service difference data and full new service difference data; storing the full old service difference data into a third Hive table to obtain a full old service difference data Hive table, and storing the full new service difference data into a fourth Hive table to obtain a full new service difference data Hive table; obtaining the full data verification result based on the full old service difference data Hive table and the full new service difference data Hive table.

[0065] In this embodiment, the full set of difference data includes both the full set of old service difference data and the full set of new service difference data, each stored in a separate Hive table. Using the Hive to MySQL export function provided by the big data offline analysis technology, the compared full set of difference data can be stored in the data storage component provided by the big data offline analysis technology. The data analysis component provided by the big data offline analysis technology can then examine and analyze the full set of old service difference data and the full set of new service difference data in the Hive table to obtain the full set of data verification results. The data storage component and data analysis component can be selected according to actual practical needs. The data storage component can include HDFS, HBase, and Cassandra components, while the data analysis component can include Flink, Spark, and Mahout components. The full set of data verification results can also be displayed to the user through a visual interface.

[0066] This embodiment provides a data verification method. Based on the old service data for the target service in the old service database and the new service data for the target service in the new service database, an offline big data Hive table is constructed. The data differences between the two Hive tables are automatically compared using the difference query language HQL. When switching from the old service system to the new service system, the new service data in the new service database is automatically verified to complete a full data verification. Compared with data verification through manual testing, the efficiency of data verification is improved.

[0067] Furthermore, referring to Figure 3 and Figure 4 , Figure 3 This is a flowchart illustrating the second embodiment of the data verification method of the present invention. Figure 4 Here is an example of a real-time anomaly detail chart.

[0068] Based on the above embodiment one, this embodiment provides a data verification method. Before step S400, the method may further include:

[0069] Step S100: Receive a real-time service request and send the real-time service request to the old service database and the new service database.

[0070] In this embodiment, during the switching process between old and new services, real-time data verification can also be performed based on real-time service requests sent by the user.

[0071] Step S200: Receive the first return result returned by the old service database and the second return result returned by the new service database.

[0072] The first returned result is obtained by executing a real-time service request on the old service database, and the second returned result is obtained by executing a real-time service request on the new service database.

[0073] In this embodiment, real-time data verification is implemented based on the return data from real-time service requests executed using both the old and new service databases. The returned results may include real-time returned data and / or a real-time result identifier. The real-time returned data can be query data, and the real-time result identifier can be a business logic execution result identifier.

[0074] As a specific implementation, after step S200, the method may further include: returning the first return result to the user terminal based on the service gateway, so that the old service database can provide services to the user terminal.

[0075] In this embodiment, to avoid impacting user-end business needs, during the service switchover process, the old service database can be used to provide business services to users, and after the switchover, the new service database can be used to provide business services to users. This embodiment can be based on gateway technology, configuring gateway routing, and based on the corresponding gateway routing, flexibly forwarding real-time service requests to the old service database and / or the new service database according to the actual service switchover situation, so as to ensure that the service switchover process does not affect user-end business needs.

[0076] In practical use, before service data migration, based on the gateway route corresponding to the old service database, only real-time service requests are forwarded to the old service database, which then provides business services to the user. After service data is migrated to the new service database, real-time service requests are forwarded to both the old and new service databases through their respective gateway routes. Real-time data verification is performed by comparing the returned data from the old and new service databases. Then, through the gateway route corresponding to the old server, the returned data from the old service database is returned to the user, which then provides business services to the user. After the switch between old and new services, real-time user requests are sent to the new service database through the gateway route corresponding to the new service database, which then provides business services to the user. This achieves a seamless transition between old and new services without affecting business transactions.

[0077] Step S300: Compare the first return result and the second return result to obtain the real-time service verification result.

[0078] In this embodiment, if there is no difference between the first and second returned results, it indicates that the real-time service verification result of the new service database is normal; if there is a difference between the first and second returned results, it indicates that the real-time service verification result of the new service database is abnormal. The real-time service verification result can include real-time data verification and real-time business logic verification. By comparing the real-time returned data in the returned results, real-time data verification of the new service data in the new service database can be achieved; by comparing the real-time result identifiers in the returned results, real-time business logic verification of the business logic of the new service system can be achieved.

[0079] For example, if the real-time service request is a transfer transaction request, which requires a series of business processes (including determining whether the transfer user has sufficient funds and whether the transferred user is correct), the return results of the old and new service systems can be used as transfer result identifiers. By comparing whether the return results of the old and new services for the same transfer transaction request are consistent, it can be determined whether the business logic of the old and new services for the transfer transaction is the same, and the service business logic of the new service database can be verified.

[0080] As one implementation, before step S300, the method may further include: processing the first returned result based on the message queue Kafka to obtain a processed first returned result, and processing the second returned result to obtain a processed second returned result; step S300 may include: comparing the processed first returned result and the processed second returned result to obtain the real-time service verification result.

[0081] In this embodiment, after the message queue Kafka receives the first and second return results, it can perform decoupling and peak reduction processing on the first and second return results.

[0082] As one specific implementation, step S300 may further include:

[0083] Step S310: Compare the first return result and the second return result to obtain real-time difference data.

[0084] Step S320: Based on the preset allowable difference conditions, filter the real-time difference data to obtain abnormal real-time difference data and obtain the real-time service verification result.

[0085] In this embodiment, a real-time data comparison service built on Flink technology can be used to quickly compare the first and second return results returned online by the old service database and the new service database using streaming computing capabilities, so as to obtain the real-time difference data between the real-time returned data in the first return result and the real-time returned data in the second return result.

[0086] The preset allowable difference conditions can be determined based on the actual target service. After filtering by the preset allowable difference conditions, if abnormal real-time difference data is found in the real-time difference data, it indicates that the real-time data verification result of the new service database is abnormal.

[0087] As one implementation, after step S320, the method may further include: generating an anomaly warning prompt based on the anomaly real-time difference data.

[0088] In this embodiment, real-time anomaly difference data can be directly pushed to the user terminal, generating various anomaly alerts to inform testers. Alternatively, the real-time anomaly difference data can be stored in the search engine Elasticsearch. Elasticsearch can then generate real-time anomaly details based on the anomaly difference data. After receiving the anomaly alert on the user terminal, testers can obtain the real-time anomaly details for anomaly analysis based on the unique identifier of the real-time service request (e.g., business transaction reference number). Figure 4 As shown, real-time anomaly details can include version comparison, total number of differences, total number of comparisons, and a list of differences.

[0089] It is understandable that the old service database can include service data corresponding to multiple business services, or it can be a service database corresponding to different business services. Similarly, the new service database can also include service data corresponding to multiple business services, or it can be a service database corresponding to different business services. Therefore, service data for different business services can be migrated and fully validated in batches according to business needs. Alternatively, after the service data migration, full data validation can be performed on a pre-set schedule during the service testing phase of the new service database. The pre-set schedule can be set according to actual needs. Preferably, a full data validation can be performed once a day after the end-of-day batch business processing. Thus, the service quality of the new service database can be validated daily based on real-time service validation combined with full data validation.

[0090] Firstly, this embodiment provides a data verification method that, by configuring gateway routing, flexibly switches between the old and new service databases to provide services to the user end during the switching process between old and new services, so as to achieve seamless switching between old and new services for the external user end without affecting the user end's business transactions.

[0091] Secondly, in this embodiment, during the switchover process between old and new services, the old service database and the new service database are connected online. Real-time service verification is performed using Flink technology. Based on actual business needs, real-time data verification of the new service database is performed by comparing the real-time returned data in the comparison results, and real-time business logic verification is performed by comparing the real-time result identifiers in the comparison results. This allows for comprehensive real-time service verification. Combined with full data verification, the service quality of the new service database can be ensured from multiple perspectives.

[0092] Thirdly, when performing real-time data verification, preset allowable difference conditions are set as a humanized difference ignoring mechanism to customize the ignoring of reasonable differences between new and old services and to ignore acceptable differences, thereby reducing the cost of repeated analysis.

[0093] Fourthly, after real-time data verification, anomaly alerts are generated based on abnormal real-time difference data, and anomaly difference details are generated to display the real-time service verification results. This allows testers to quickly discover abnormal issues and quickly analyze and resolve them based on the anomaly difference details.

[0094] Based on the same inventive concept, referring to Figure 5 , Figure 5 This is a schematic diagram of the modules of the first embodiment of the data verification device of the present invention.

[0095] This embodiment provides a data verification device, which may include:

[0096] Data acquisition module 10 is used to acquire old service data for the target service from the old service database and new service data for the target service from the new service database;

[0097] The data storage module 20 is used to store old service data into the first Hive table to obtain the old service data Hive table, and to store new service data into the second Hive table to obtain the new service data Hive table;

[0098] The full comparison module 30 is used to compare the old service data Hive table and the new service data Hive table using the difference query language HQL to obtain the full data verification result.

[0099] For more details on the specific implementation of the above-mentioned data verification device, please refer to the description of the specific implementation of the data verification method in Embodiment 1 or 2 above. For the sake of brevity, these details will not be repeated here.

[0100] Furthermore, embodiments of the present invention also propose a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the data verification method described above. Therefore, further details will not be repeated here. Additionally, the beneficial effects of employing the same method will also not be repeated. For technical details not disclosed in the embodiments of the computer-readable storage medium involved in this application, please refer to the description of the method embodiments of this application. As an example, program instructions can be deployed to execute on a single computing device, or on multiple computing devices located at one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0101] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A data verification method, characterized in that, The method includes: Retrieve old service data for the target service from the old service database and new service data for the target service from the new service database; The old service data is stored in the first offline big data Hive table to obtain the old service data Hive table, and the new service data is stored in the second Hive table to obtain the new service data Hive table; Using the difference query language HQL, the old service data Hive table and the new service data Hive table are compared to obtain the full data verification result of the new service data; Before obtaining the old service data for the target service from the old service database and the new service data for the target service from the new service database, the method further includes: Receive real-time service requests and send the real-time service requests to the old service database and the new service database; Receive a first return result returned by the old service database and a second return result returned by the new service database; the first return result is obtained by the old service database executing the real-time service request, and the second return result is obtained by the new service database executing the real-time service request; Based on the message queue Kafka, the first returned result is decoupled and subjected to peak reduction processing to obtain the processed first returned result. Similarly, the second returned result is decoupled and subjected to peak reduction processing to obtain the processed second returned result. The first returned result after processing and the second returned result after processing are compared to obtain the real-time service verification result; The method utilizes the difference query language HQL to compare the old service data Hive table and the new service data Hive table to obtain a full data verification result, including: Using the difference query language HQL, the old service data Hive table and the new service data Hive table are compared to obtain the full old service difference data and the full new service difference data; The full set of old service difference data is stored in the third Hive table to obtain the full set of old service difference data Hive table, and the full set of new service difference data is stored in the fourth Hive table to obtain the full set of new service difference data Hive table. The full data verification result is obtained based on the full old service difference data hive table and the full new service difference data hive table.

2. The method as described in claim 1, characterized in that, After receiving the first return result from the old service database and the second return result from the new service database, the method further includes: Based on the service gateway, the first returned result is returned to the user terminal so that the old service database can provide services to the user terminal.

3. The method as described in claim 1, characterized in that, The step of comparing the first returned result and the second returned result to obtain the real-time service verification result includes: The first returned result and the second returned result are compared to obtain real-time difference data; Based on preset allowable difference conditions, the real-time difference data is filtered to obtain abnormal real-time difference data, and the real-time service verification result is obtained.

4. The method as described in claim 3, characterized in that, After filtering the real-time difference data based on preset allowable difference conditions to obtain abnormal real-time difference data and obtaining the real-time service verification result, the method further includes: An anomaly warning is generated based on the real-time difference data.

5. A data verification device, characterized in that, The device includes: The data acquisition module is used to acquire old service data for the target service from the old service database and new service data for the target service from the new service database; The data storage module is used to store the old service data into a first Hive table to obtain an old service data Hive table, and to store the new service data into a second Hive table to obtain a new service data Hive table; The full comparison module is used to compare the old service data Hive table and the new service data Hive table using the difference query language HQL to obtain the full data verification result. The data acquisition module is also used to receive real-time service requests and send the real-time service requests to the old service database and the new service database; Receive a first return result returned by the old service database and a second return result returned by the new service database; the first return result is obtained by the old service database executing the real-time service request, and the second return result is obtained by the new service database executing the real-time service request; Based on the message queue Kafka, the first returned result is decoupled and subjected to peak reduction processing to obtain the processed first returned result. Similarly, the second returned result is decoupled and subjected to peak reduction processing to obtain the processed second returned result. The first returned result after processing and the second returned result after processing are compared to obtain the real-time service verification result; The full comparison module is used to compare the old service data Hive table and the new service data Hive table using the difference query language HQL to obtain full old service difference data and full new service difference data. The full set of old service difference data is stored in the third Hive table to obtain the full set of old service difference data Hive table, and the full set of new service difference data is stored in the fourth Hive table to obtain the full set of new service difference data Hive table. The full data verification result is obtained based on the full old service difference data hive table and the full new service difference data hive table.

6. A data verification device, characterized in that, The device includes: a memory, a processor, and a data verification program stored in the memory and executable on the processor, the data verification program being configured to implement the steps of the data verification method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the data verification method as described in any one of claims 1 to 4.

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