Data integration method and device, equipment and storage medium

Abstract

The invention discloses a data processing method and device and a processor. Creating data processing tasks for the plurality of to-be-processed data processing items, and obtaining target data format requirements of the to-be-processed data processing items; converting each data item in the data processing task into an initial statement conforming to a target language format one by one, and creating a format conversion statement for the initial statement based on a target data format requirement; and if the format conversion statement conforms to the statement proofreading standard, performing data conversion on the corresponding initial statement based on the format conversion statement to obtain a target statement. Compared with the difficulty and the problem of manually integrating various medical data formats, the automatic data processing method provided by the invention has the advantages that the data is quickly and accurately converted and integrated by creating the data processing task, performing the language format and generating the initial statement and then generating the format conversion statement according to the format requirement and verifying the format conversion statement; and the data integration efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data integration method, device, equipment and storage medium. Background Art

[0002] In clinical research scenarios, medical data processing is the core step of the entire research project and the key to speed control. Medical data not only has the 4V characteristics of big data (volume, velocity, variety, and value), but also has unique characteristics such as time series and incompleteness. These characteristics make medical data processing complex and challenging.

[0003] Time series means that medical data is updated continuously over time. For example, patients' physiological parameters and laboratory test results are continuously recorded over time. Incompleteness is reflected in missing or incomplete data due to patient behavior, incomplete records in hospital information systems, or equipment failures. These characteristics make the collection, integration, and analysis of medical data more complex and difficult.

[0004] However, in the medical field, there are medical data with various data formats. These medical data have different data formats because they come from different systems, equipment or standards. For example, electronic health record systems, laboratory management systems and imaging systems may use different data formats and structures. When integrating medical data in these different data formats, manual operations are usually required. This manual integration process easily leads to problems such as increased error rates in data integration results and low integration efficiency. Summary of the invention

[0005] Based on the above problems, the present application provides a data integration method, device, equipment and storage medium, the purpose of which is to reduce the error rate of medical data in different data formats during the integration process, while improving the integration efficiency.

[0006] The embodiments of the present application disclose the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a data integration method, the method comprising:

[0008] Creating a data processing task for a data processing project to be processed, and obtaining target data format requirements; the data processing project to be processed includes multiple data items, each of which has the same or different data formats;

[0009] Convert each of the data items in the data processing task into an initial statement conforming to the target language format;

[0010] Creating a corresponding format conversion statement for each of the initial statements based on the target data format requirement;

[0011] If the format conversion statement meets the statement proofreading standard, data conversion is performed on the initial statement corresponding to the format conversion statement to obtain a target statement;

[0012] According to the data integration requirements, the target statements of the data processing task are integrated to obtain integrated data.

[0013] Optionally, the sentence proofreading standard includes: sentence editing proofreading standard and sentence content proofreading standard;

[0014] The sentence editing and proofreading standards include: sentence editing including sentence highlighting, sentence formatting and sentence real-time saving;

[0015] The statement content proofreading standard includes: the specific content of the statement shall not include at least a database deletion field, a table deletion field, and a batch data clearing field.

[0016] Optionally, after creating data processing tasks for the multiple data processing projects to be processed, the method further includes:

[0017] Query the source identification of the data processing project to be processed;

[0018] Determining a source database of the data processing item to be processed based on the source identifier;

[0019] Identify whether the data processing item to be processed exceeds the data scope of the source database;

[0020] If it does not exceed the limit, the subsequent steps are executed in sequence; otherwise, the data integration task is terminated and an error message is recorded.

[0021] Optionally, after creating data processing tasks for the multiple data processing projects to be processed, the method further includes:

[0022] Obtaining a key input by a user and verifying the key input by the user;

[0023] If the verification passes, the subsequent steps are executed in sequence; otherwise, the data integration task is terminated and the error information is recorded.

[0024] Optionally, the method further comprises:

[0025] Define the first output data table and the column names of the first output data table;

[0026] Parsing the integrated data into field values ​​corresponding to the column names of the first output data table;

[0027] Fill the parsed field values ​​into the first output data table.

[0028] Optionally, the method further comprises:

[0029] If a data format conversion request is received, data processing is performed on the data in the first output data table based on the data format conversion rule in the data format conversion request to obtain processed data;

[0030] defining a second output data table and column names of the second output data table based on the processed data;

[0031] Filling the processed data into the second output data table;

[0032] generating a data quality control report based on the second output data table filled with the processed data;

[0033] The data format conversion rules include defining a data aggregation index field, defining a data group cycle identification field and a same group data field.

[0034] Optionally, the method further comprises:

[0035] querying the target database at fixed intervals whether a new data processing project to be processed is added, and if so, taking the new data processing project to be processed as the data processing project to be processed, so as to execute the step of creating a data processing task for the data processing project to be processed and subsequent steps; or,

[0036] When the number of newly added data processing items to be processed in the target database is equal to the processing threshold, the new data processing items to be processed are used as the data processing items to be processed to execute the step of creating data processing tasks for the data processing items to be processed and subsequent steps.

[0037] In a second aspect, an embodiment of the present application provides a data integration device, the device comprising:

[0038] A data processing task creation unit, used to create a data processing task for a data processing project to be processed; the data processing project to be processed includes a plurality of data items, each of which has the same or different data formats;

[0039] A target data format requirement obtaining unit, used for obtaining target data format requirements;

[0040] A first data conversion unit, used for converting each of the data items in the data processing task into an initial statement conforming to the target language format;

[0041] A format conversion statement creation unit, configured to create a corresponding format conversion statement for each of the initial statements based on the target data format requirement;

[0042] A second data conversion unit, configured to perform data conversion on the corresponding initial statement based on the format conversion statement to obtain a target statement if the format conversion statement meets the statement proofreading standard;

[0043] The integration unit is used to integrate the target statements of the data processing task according to the data integration requirements to obtain integrated data.

[0044] In a third aspect, an embodiment of the present application provides a data integration device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the data integration method as described above is implemented.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium. When the instructions are executed on a terminal device, the terminal device executes the data integration method as described above.

[0046] Compared with the prior art, this application has the following beneficial effects:

[0047] The present application provides a data integration method, device, equipment and storage medium. Specifically, when executing the data integration method provided in the embodiment of the present application, first, a data processing task is set up for the data items to be processed, and the required target data format requirements are identified. Then, the data items in the data processing task are converted one by one into multiple initial statements that conform to the target language format. Subsequently, a format conversion statement applicable to the initial statement is created based on the target data format requirements. Finally, after proofreading and confirming that the format conversion statement meets the standard, the initial statements are converted one by one into their corresponding target statements according to these rules, and the target statements of the data processing task are integrated according to the data integration requirements to obtain integrated data.

[0048] Compared with the manual integration of medical data in different data formats, the automated data integration method of the present application has the following advantages: First, by creating tasks for the data to be processed and obtaining the target data format requirements, data in different formats can be processed accurately and quickly, avoiding errors that may be caused by human intervention; secondly, the data is converted into initial statements that conform to the target language format, and then the format conversion statements are generated based on the target data format requirements, which can greatly simplify the integration process and improve the convenience and accuracy of the operation. In addition, by verifying whether the format conversion statement meets the statement proofreading standards, potential errors can be automatically identified and corrected to ensure the accuracy of data conversion. Most importantly, the present application can automatically convert and integrate all initial statement data, thereby effectively improving the integration efficiency, reducing the error rate, and saving time and resources in the data integration process. Therefore, the automated data integration method of the present application not only improves the quality and accuracy of medical data integration, but also greatly simplifies the entire data integration process, providing more efficient and reliable data support for medical research and clinical practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0050] Figure 1 A schematic diagram of medical data integration provided in an embodiment of the present application;

[0051] Figure 2 A flow chart of a data integration implementation method provided in an embodiment of the present application;

[0052] Figure 3 A flowchart of an implementation method of a data verification method provided in an embodiment of the present application;

[0053] Figure 4 A flowchart of an implementation method of an identity authentication method in a data integration process provided in an embodiment of the present application;

[0054] Figure 5 A flowchart of an implementation method of a method for constructing an output data table provided in an embodiment of the present application;

[0055] Figure 6 A flowchart of an implementation method of a method for generating a data quality control report provided in an embodiment of the present application;

[0056] Figure 7A schematic diagram of the structure of a data integration device provided in an embodiment of the present application;

[0057] Figure 8 A schematic diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] As described above, in clinical research scenarios, medical data processing is the core step and speed-control step of the entire research project. Medical data not only has the 4V characteristics of big data (volume, velocity, variety, and value), but also has characteristics such as timeliness and incompleteness. These characteristics make the collection, integration, and analysis of medical data more complex and challenging.

[0059] However, a major challenge in processing medical data is that data from different sources come in different formats. These data may come from different medical devices, electronic health record systems, laboratory management systems, etc. When integrating medical data in different data formats, it currently relies mainly on manual work. Although this method is feasible in some cases, it has the following problems:

[0060] High error rate: Manual data integration is prone to errors, such as data entry errors, missing data, etc. These problems will directly affect the accuracy and reliability of subsequent data analysis.

[0061] Low efficiency: Manual data processing takes a long time, especially when the amount of data is large. The process of manual integration and cleaning of data is very time-consuming and inefficient.

[0062] Based on this, the embodiment of the present application provides a data integration method, device, equipment and storage medium. First, a corresponding data processing task is created for the data project to be processed, and the format requirements of the data project are obtained. Then, the data in the data processing task is converted into multiple initial statements that conform to the target language format. According to the target data format requirements, a format conversion statement applicable to the initial statement is generated. Finally, it is verified whether the format conversion statement meets the statement proofreading standard. If it does, all the initial statements are processed one by one according to these format conversion statements, and all the target statements obtained after processing are integrated to realize the data conversion process and ensure the correctness and efficiency of data integration.

[0063] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0064] See also Figure 1 , which is a schematic diagram of medical data integration provided in an embodiment of the present application.

[0065] First, obtain the medical data processing projects to be integrated, and create medical data processing tasks for medical data processing projects (such as electronic health record systems, laboratory management systems, and imaging systems). Clarify the specific format requirements that the integrated data needs to meet. Suppose that patient information from different hospitals needs to be integrated. One hospital uses a comma-separated values ​​(CSV) file to save patient identification (ID), name, diagnosis and other data fields. Another hospital uses JavaScript Object Notation (JSON) format to store data. The target data format after integration requires "saving data in CSV file format."

[0066] Then, multiple different data items in the medical data processing task (such as data item 1, data item 2, ... data item n) are converted into multiple initial statements (such as initial statement 1, initial statement 2, ... initial statement n) that conform to the target language format, ensuring that the data fields in each initial statement are consistent with the target language format. The target language format can be a Structured Query Language (SQL) statement type. Data field

[0067] Then, according to the target data format requirements, corresponding format conversion statements are created for each initial statement. The role of these format conversion statements is to guide the initial statement on how to perform data conversion to generate the required target statement. In this way, format conversion statements serve as an important tool to guide and standardize the data conversion process, while ensuring data accuracy and consistency, and simplifying the entire data integration process.

[0068] Verify whether these format conversion statements meet the statement proofreading standards. If they do, you can use the format conversion statements that meet the statement proofreading standards to convert all the initial statements one by one to generate the final target statements. Then, according to the data integration requirements, the target statements of the data processing task are integrated to obtain integrated data. Statement proofreading standards include two aspects: one is the statement editing proofreading standard: including statement highlighting, formatting, and real-time saving; the other is the statement content proofreading standard: ensure that the statement content does not at least contain the deletion of database fields, deletion of table fields, and bulk data clearing fields.

[0069] Those skilled in the art will understand that Figure 1 The framework diagram shown is only an example in which the embodiments of the present application can be implemented. The scope of application of the embodiments of the present application is not limited by any aspect of the framework.

[0070] To facilitate understanding of the present application, a data integration method provided in an embodiment of the present application is described below with reference to the accompanying drawings.

[0071] See also Figure 2 As shown in FIG. 1 , this figure is a flow chart of a data integration method provided in an embodiment of the present application, such as Figure 2 As shown, the method may include S201-S205:

[0072] S201: Create a data processing task for the data processing project to be processed, and obtain target data format requirements.

[0073] Creating data processing tasks for the data processing project to be processed and obtaining the target data format requirements means that before processing the data, the data processing process and the standardized form of the target data need to be clearly defined. Data processing projects contain multiple data items from different sources or systems, and these data items have the same or different data formats. Data items with the same data format may be easier to process and integrate, while data items with different data formats need to be format converted or normalized for unified processing.

[0074] By creating data processing tasks, you can determine how to process each data item, including steps such as cleaning, conversion, and integration, and prepare for subsequent analysis and application. Since the target data format is set by the user, obtaining the target data format requirements is to ensure that the final processing results meet the needs and expectations of the user. This means understanding the user's specific requirements for the presentation form, content structure, data type, etc. of the data so that data processing and conversion can be performed in accordance with these requirements. Therefore, creating data processing tasks and obtaining the target data format requirements is to ensure that the data processing project proceeds smoothly, the data accuracy is high, and the final results meet the user's expectations.

[0075] In one possible implementation, the target data format requirement includes a target data format defined by a user, and the target data format may include but is not limited to: text format, spreadsheet format, database format, CSV format, Health Level Seven (HL7) format, Digital Imaging and Communications in Medicine (DICOM) format, JSON, eXtensible Markup Language (XML) format, and International Classification of Diseases-Tenth Revision (ICD-10) coding format for medical diagnosis codes.

[0076] In one possible implementation, creating data processing tasks for the data processing project to be processed is a key step to ensure that the data can be cleaned, transformed, and analyzed effectively. First, the goals and scope of data processing need to be clearly defined, including determining the data source, data type, processing method, etc. Then, a comprehensive assessment and analysis of the data to be processed is conducted to understand the structure, quality, and characteristics of the data. Based on the results of the data analysis, a data integration process is designed. When formulating data processing tasks, it is necessary to clarify the specific operations and sequence of each step, and consider how to deal with abnormal situations and inconsistencies. In addition, it is necessary to determine the tools and techniques required for data processing, as well as to allocate resources and timetables.

[0077] In a possible implementation, the multiple data items included in the data processing project to be processed may refer to various data units or data records that need to be involved in the processing process. These data items may include but are not limited to the following categories:

[0078] Patient information: basic information of the patient, such as name, gender, age, contact information, etc.;

[0079] Diagnostic information: including patients’ medical records, diagnosis results, medical imaging reports, etc.;

[0080] Treatment information: including the treatment plan received by the patient, drug prescriptions, surgical records, etc.;

[0081] Laboratory test data: such as blood test results, imaging test results, physiological parameter monitoring data, etc.;

[0082] Medical expense information: including patient medical expenses, medical insurance reimbursement and other financial related data;

[0083] Timestamp information: records the time when each data item is generated, so as to track and analyze the time series characteristics of the data;

[0084] Medical equipment information: data related to medical equipment, such as equipment model, usage records, maintenance information, etc.

[0085] It should be noted that the user information (including but not limited to patient information, diagnosis information, treatment information, medical expense information, etc.) and data (including but not limited to laboratory test data, timestamp information, medical equipment information, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0086] See also Figure 3 , Figure 3 A flowchart of an implementation method of a data verification method provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the method may include:

[0087] A1: Query the source identifier of the data processing item to be processed.

[0088] Querying the source identification of the data processing project to be processed means determining the specific data source or source information involved in the data processing task. By querying the source identification, the transparency and traceability of the data processing process can be established, helping to ensure the accuracy and reliability of the data processing task.

[0089] Querying the source identity of a data processing project typically involves identifying the specific data source or source to which each data item or data set belongs. This can be done by looking at identifiers in the data itself, metadata information, or documents and records associated with the data. The source identity can be in various forms, such as a database name, data table name, file path, or any mark that can uniquely identify the source of the data. The process of querying the source identity can include checking the data storage location, viewing data attributes or metadata, and communicating with system administrators or data providers.

[0090] A2: Determine the source database of the data processing item to be processed based on the source identifier.

[0091] Determining the source database of the data processing project to be processed based on the source identification means confirming the specific database involved in the data processing task through the known source identification. This process aims to ensure that the database containing the required data can be accurately located and accessed for subsequent data integration, analysis or transformation operations.

[0092] Determining the source database of a pending data processing project requires first identifying key information provided in the source identifier, such as database name, connection string, table name, or file path, etc. Based on these identifiers, different approaches can be taken to locate the database.

[0093] Determining the source database of the data processing project to be processed based on the source identification also involves verifying the availability of the database and the accuracy of the data. This can include checking whether the database connection is normal, confirming that the database contains the required data tables or collections, and verifying whether the data structure is consistent with expectations. By accurately determining the source database of the data processing project, it can be ensured that the required data resources can be accurately accessed and utilized before further data processing.

[0094] A3: Identify whether the data processing item to be processed exceeds the data scope of the source database.

[0095] Identifying whether the data processing project to be processed exceeds the data scope of the source database means determining whether the data to be processed conforms to the definition and scale of the source database during the data processing process. This means checking whether the content, quantity or characteristics of the data to be processed are consistent with the existing data structure, constraints or expected scope in the source database to ensure that the data processing task is carried out within a reasonable range.

[0096] To identify whether the data to be processed exceeds the data range of the source database, the following steps can be taken: First, check whether the information contained in the data item or data set is consistent with the field type, length, and constraints defined in the database schema. Second, verify whether the data complies with the validity rules in the database, such as unique constraints, foreign key associations, etc. Third, confirm whether the amount of data is within the range that the database can accommodate to avoid performance problems or database crashes caused by entering too much data. Finally, check whether the data complies with the business logic requirements in the database, such as date ranges, value ranges, or other specified conditions.

[0097] By identifying whether the data to be processed exceeds the data range of the source database, invalid or abnormal data can be prevented from being processed, ensuring the consistency and integrity of the data and avoiding negative impacts on the database. This process helps ensure that data processing tasks are carried out within a controlled range and minimizes potential data errors or problems.

[0098] A41: If the data processing items to be processed do not exceed the data range of the source database, the subsequent steps S202-S205 are executed in sequence.

[0099] After confirming that the data meets the database definition and size, the subsequent processing steps S202-S205 will be carried out in accordance with the predefined data processing flow sequence. This means that the data processing tasks can be carried out step by step according to the designed process to ensure that the data is effectively integrated, converted or analyzed.

[0100] A42: If the data processing items to be processed exceed the data range of the source database, the subsequent steps S202-S205 are terminated and an error message is recorded.

[0101] If the data item to be processed exceeds the data range of the source database, the subsequent steps S202-S205 will be terminated and error information will be recorded according to the instruction. This means that the data processing flow will stop immediately when it is found that the data exceeds the definition or scale of the source database, and no further data processing, conversion or loading operations will be performed. Instead, detailed information about data errors or anomalies will be recorded for subsequent analysis and processing.

[0102] The purpose of terminating the subsequent steps S202-S205 is to avoid processing invalid or unexpected data that is beyond the scope of the database, thereby preventing situations that may cause data corruption or system errors. By immediately stopping the data integration task, the potential negative impact on the database or other systems can be reduced, and the security and stability of the data processing process can be ensured.

[0103] It is also important to record error information because it helps track the root cause of the problem, investigate the cause of the data anomaly, and develop appropriate corrective actions. Recording error information also helps to establish a logging and audit trail mechanism so that problems that arise during data processing can be better managed in the future and improve the quality and reliability of data integration tasks.

[0104] See also Figure 4 , Figure 4 A flowchart of an implementation method of an identity authentication method in a data integration process provided in an embodiment of the present application, such as Figure 4 As shown, the method may include:

[0105] B1: Obtain the key input by the user, and verify the key input by the user.

[0106] Obtaining a key from the user and validating it is a security measure used to confirm the user's identity or authority. This means that the system requires the user to provide a specific key (usually a password, access token, or other credential) and validates the key to determine whether the user has legitimate access rights or identity.

[0107] In order to obtain the key input by the user, the system will prompt the user to enter the required key information in a specific interface or input box and confirm the submission. Subsequently, the system will verify the key provided by the user through a specific authentication algorithm or process. The key is compared with the known key stored in the system in advance, or encryption technology is applied to verify the validity of the key.

[0108] During the verification process, the system checks whether the key provided by the user correctly matches the credentials stored in the system or meets the preset security criteria. If the provided key passes the verification, the user will be authorized to continue to access system resources or perform corresponding operations. On the contrary, if the key fails to pass the verification, the system may deny the user's access request and record relevant error information for further processing.

[0109] By obtaining the key entered by the user and verifying it, the system can effectively protect data and systems from unauthorized access or potential security risks, ensuring that only authenticated users can access system resources and perform operations, thereby enhancing the security and credibility of the overall system.

[0110] B21: If the verification is successful, the subsequent steps S202-S205 are executed in sequence.

[0111] If the verification is successful, the subsequent steps S202-S205 are executed in sequence, which means that after the user identity verification is successful, the system will gradually perform the subsequent operations or processing steps according to the pre-defined process sequence. This means that the system recognizes the identity of the user and allows him to continue to perform subsequent tasks or operations according to the prescribed process, ensuring that data processing or other related activities can be carried out as expected.

[0112] B22: If the verification fails, the subsequent steps S202-S205 are terminated and the error information is recorded.

[0113] If the identity verification fails, it is a common security measure to terminate the subsequent steps S202-S205 and record the error information. This means that when there is a problem or failure in the system to verify the user's identity, the subsequent data processing flow will be stopped immediately and the relevant error information will be recorded for further investigation and processing.

[0114] Terminating the operations of subsequent steps S202-S205 is to prevent unauthorized or unclearly identified users from continuing to access or process sensitive data, thereby avoiding potential security threats or data leakage risks. Logging error information helps system administrators or relevant personnel understand what went wrong, so that they can quickly take appropriate corrective measures and further enhance the security and reliability of the system.

[0115] In this way, the system can promptly identify and handle authentication failures, effectively protecting data and system resources from unauthorized access or operations. This strict security measure helps ensure the integrity and security of data, while improving the overall security level of the system, enabling the system to better respond to potential security challenges and risks.

[0116] S202: Convert each of the data items in the data processing task into an initial statement conforming to the target language format.

[0117] Converting each data item in the data processing task into an initial statement in the target language format one by one means processing and converting the original data according to the rules according to the requirements of a specific language or format to generate output that conforms to the target language format.

[0118] In a possible implementation, the target language format may be a SQL language format, and the conversion process will involve mapping different data items to corresponding database tables, fields, and associated conditions to facilitate data query and analysis.

[0119] Exemplarily, for patient information data items, a "Patients" table can be created, and data items such as name, gender, and age can be converted into INSERT or UPDATE statements to store patient information in the database. Diagnostic information data items can be converted into SELECT statements to retrieve the patient's diagnosis results from the database. Laboratory test data can be inserted into the corresponding laboratory data table through INSERT statements. Timestamp information can be used to determine the creation or modification time of the data record, and date and time functions are used in the database for processing. Medical expense information can be converted into an SQL query statement to calculate the total cost. By converting data items one by one and generating initial statements that conform to the SQL statement type, medical data can be easily managed, queried, and analyzed in the database to support decision making and business process optimization.

[0120] S203: Creating a corresponding format conversion statement for each of the initial statements based on the target data format requirement.

[0121] Creating a corresponding format conversion statement for each initial statement based on the target data format requirements means designing and generating necessary conversion logic for each data item according to the specific data format and specifications (i.e., the target data format requirements) to ensure that the original data can be converted into a form that meets the target data format requirements.

[0122] For example, for a certain data item 1, data item 1 includes user 1, user 2 and user 2, user 1 includes data row 1, data row 2 and data row 3; user 2 includes data row 4 and data row 5; user 3 includes data row 6, data row 7 and data row 8. For the target data format requirement of "saving data in CSV file format", the format conversion statement of data item 1 can be "save data row 1, data row 2 and data row 3 included in user 1 in CSV file format respectively; save data row 4 and data row 5 included in user 2 in CSV file format respectively; save data row 6, data row 7 and data row 8 included in user 3 in CSV file format respectively".

[0123] In a possible implementation, the language format of the format conversion statement is consistent with the language format of the initial statement, both being the target language format.

[0124] In a possible implementation, the initial statement and the format conversion statement corresponding to the initial statement can be grouped into a conversion group. In this conversion group, the initial statement describes the operation object of the format conversion statement, and the format conversion statement guides how to convert the initial statement according to a specific rule or method to achieve the target data format requirement. By grouping them into a conversion group, it is helpful to link the operation object with the specific conversion steps, making the entire data processing flow more coherent and clear.

[0125] In this conversion group, a statement conversion execution order can also be set for each format conversion statement. The statement conversion execution order is used to indicate the order in which the various format conversion statements should be executed when the initial statement executes step S204. Such a sequence setting can ensure the logical correctness and consistency in the data conversion process, avoid the situation of data disorder or incorrect conversion, and also help to improve data processing efficiency and accuracy. Therefore, setting the statement conversion execution order in the conversion group can effectively manage the data processing process and ensure the correct conversion and display of data.

[0126] S204: If the format conversion statement meets the statement proofreading standard, data conversion is performed on the initial statement corresponding to the format conversion statement to obtain a target statement.

[0127] When performing data conversion based on format conversion statements, you first need to ensure that the given format conversion statement meets the statement proofing standard, that is, the format conversion statement meets the predefined constraints. Once it is confirmed that the format conversion statement meets the standard, the system can convert the source statement (i.e., the initial statement) into the target statement according to these rules and conversion operations. If the format conversion statement does not meet the statement proofing standard, the format conversion statement needs to be adjusted based on the statement proofing standard to make the format conversion statement meet the statement proofing standard.

[0128] Regarding how to perform data conversion based on format conversion statements that meet statement proofreading standards, the following steps are usually involved: First, the system parses the initial statement and applies the given format conversion rules (i.e., format conversion statements), and performs data reconstruction, replacement, merging, and other operations according to the rules to generate a new, format-converted target statement, and records relevant information during the conversion process when necessary for further analysis or tracking.

[0129] Through this process, the system can effectively convert data according to given rules and standards, ensuring the correctness, consistency and integrity of the data, while improving the efficiency and accuracy of data processing. This format conversion method based on standardization helps ensure seamless conversion of data between different formats to meet the needs of data exchange or integration.

[0130] In a possible implementation, the sentence proofreading standard includes: a sentence editing proofreading standard and a sentence content proofreading standard.

[0131] The sentence editing and proofreading standards include: sentence editing includes sentence highlighting, sentence formatting and real-time saving of sentences, that is, when editing a sentence, text editing operations such as syntax highlighting, typesetting formatting and real-time saving can be performed, but other data operations such as changes or data processing cannot be performed.

[0132] The statement content proofreading standard includes: the specific content of the statement shall not include at least the fields of deleting database, deleting table and clearing data in batches, that is, when proofreading the statement content, care should be taken to ensure that the statement does not contain critical commands that may cause serious data loss or data clearing, such as fields such as deleting database, deleting table or clearing data in batches.

[0133] Following these two standards helps ensure the safety and accuracy of sentence editing and content proofreading.

[0134] S205: Integrate the target statements of the data processing task according to the data integration requirement to obtain integrated data.

[0135] Integrating the target statements in a data processing task means comprehensively processing and integrating multiple target statements generated in the task according to the data integration requirements. This means combining or integrating the target statements in a prescribed manner to meet specific data integration goals. Such an integration process may involve merging, filtering, sorting, or other operations to ensure that the final integration results meet the project requirements and can be effectively applied to subsequent data processing processes or analysis.

[0136] See also Figure 5 , Figure 5A flowchart of an implementation method of a method for constructing an output data table provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the method may include C1-C3:

[0137] C1: Define the first output data table and the column names of the first output data table.

[0138] During the data processing process, a new data table needs to be created to store the integrated data. First, a specific data table needs to be defined to store the processed data or results. This data table can contain multiple rows and columns, each row represents a data record, and each column corresponds to different attributes or information in the data record. When defining the first output data table, it is necessary to clarify the structure of the table, including column names, data types, and possible constraints. Column names refer to giving each column a descriptive name to better understand the data content stored in the column. By defining the column names of the first output data table, clear identification can be established for each piece of information in the data table, making the data processing and analysis process more efficient and reliable. By reasonably defining the first output data table and its column names, a reliable foundation can be provided for subsequent data integration, analysis, and reporting, ensuring the accuracy, consistency, and comprehensibility of the data, thereby supporting effective decision-making and business development.

[0139] C2: Parsing the integrated data into field values ​​corresponding to the column names of the first output data table.

[0140] Parsing the integrated data into field values ​​corresponding to the column names of the first output data table means matching the processed and integrated data with the columns in the first output data table according to specific rules or mapping relationships. This process involves matching specific information or values ​​in the integrated data with the target column names to ensure that each field value is correctly filled into the corresponding column. Through this parsing process, the results of multi-source data integration are effectively converted into a form that conforms to the target data table structure. This correspondence is established to ensure that the data is imported into the specified column in the correct manner, thereby facilitating subsequent data analysis, report generation, or other data processing operations.

[0141] C3: Fill the parsed field values ​​into the first output data table.

[0142] Filling the parsed field values ​​into the first output data table means inserting the matched and converted data into the corresponding columns of the first output data table one by one according to the predetermined rules or mapping relationships. This step is a crucial link in the data processing process, ensuring that the integrated data is correctly archived and stored for subsequent analysis and application. By filling the parsed field values ​​into the first output data table, the processing results can be effectively recorded and saved so that they can be easily retrieved and used in the future. This operation helps to build a structured data storage method so that data can be easily queried, filtered and analyzed. Only when the parsed field values ​​are successfully filled into the first output data table and meet the format and constraints of the table can the integrity and accuracy of the data be ensured, providing a data basis for further data processing.

[0143] In a possible implementation, the method further includes:

[0144] The first output data table of the data processing project to be processed is stored in the source database of the data processing project to be processed. When the data quality control report of the data processing project to be processed shows that the quality of the second output data table is passed, the user can obtain the required data from the first output data of the source database.

[0145] See also Figure 6 , Figure 6 A flow chart of an implementation method of a method for generating a data quality control report provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the method may include D1-D4:

[0146] D1: If a data format conversion request is received, data in the first output data table is processed based on a data format conversion rule in the data format conversion request to obtain processed data.

[0147] When a data format conversion request is received, the data in the first output data table is processed according to the data format conversion rules in the request to generate converted processed data. This means that according to the guiding principles defined in the data format conversion rules, the information in the first output data table is operated accordingly to meet the new target data format requirements.

[0148] In this process, data format conversion rules are crucial, including the following key elements: first, define the data aggregation index field, which helps to identify and organize related data records so that they can be correctly aggregated or associated; second, define the data group loop identification field, which is used to define the loop structure between data groups and help the system effectively handle the hierarchical relationship and loop structure of data; finally, the same group data field is used to indicate the association or common characteristics between each data item in the same group of data, so as to facilitate data grouping and integration. By following these rules, the accuracy, completeness and reliability of data conversion can be ensured.

[0149] It should be noted that defining the data aggregation index field is to determine whether the final data is aggregated by one visit or the same patient, that is, to define the file row identifier. Defining the data group loop identifier field and the same group data field is to handle the corresponding relationship of data when looping by column. The loop identifier field is defined separately to define the file column header and handle the column header duplication problem.

[0150] D2: defining a second output data table and column names of the second output data table based on the processed data.

[0151] Defining the second output data table and its column names based on the processed data means creating another new data table based on the processed data results, while clearly defining the columns contained in the data table and their respective names. This process involves determining the structure of the data table based on the characteristics and requirements of the processed data, including determining the different information contents that need to be displayed or stored and how to organize this information. Defining the column names of the second output data table is crucial because they represent the various fields or attributes in the data table and can accurately describe the data type or information meaning stored in each column. By reasonably defining the second output data table and column names, you can ensure that the structure of the data table is clear and complete, and meets the needs of data analysis or reporting. In addition, appropriate column naming also helps to improve the readability and comprehensibility of the data, making the data more convenient and effective in subsequent processing and application. Therefore, when defining the second output data table and column names based on the processed data, you need to fully consider the purpose, structure and relationship with other data tables of the data table to ensure that the final generated data table can meet business needs and provide a good foundation for further data processing and analysis.

[0152] D3: Fill the processed data into the second output data table.

[0153] Filling the processed data into the second output data table means inserting the processed data into the corresponding columns one by one according to the defined second output data table structure. This key step involves converting the data into a format that can be recognized and stored by the database or spreadsheet system, and importing it into the specified data table in an effective way. In this process, it is crucial to ensure the accuracy and completeness of the data filling, because it directly affects the quality of subsequent data analysis and reporting. By filling the processed data into the second output data table, effective management and archiving of the data is achieved, so that the data can be easily queried, retrieved and used. Such operations help to organize and integrate data, make information easier to access and use, and provide strong support for business decisions.

[0154] D4: Generate a data quality control report based on the second output data table filled with the processed data.

[0155] The process of generating a data QC report based on the second output data table filled with processed data involves converting each table into a temporary CSV file, then calling the QC method to read these CSV files and generate the corresponding QC report. This means that the second output data table is checked, evaluated and analyzed to ensure that the data in it meets specific standards and quality requirements. By exporting the data table as a CSV file, the calling of the QC method and data processing operations can be easily performed. In the process of generating the QC report, the system reads the data in these CSV files and applies various QC techniques and algorithms, including standards for verifying data integrity, consistency, accuracy, etc. By executing the QC procedure, potential data quality issues and anomalies can be identified, thereby providing assurance for the reliability of the data.

[0156] In one possible implementation, the generated quality control report is rendered on a visual interface, forming a detailed quality control review result of a single data table, providing users with a comprehensive view of data quality. Such a process not only helps to discover and solve data quality problems, but also improves the efficiency and reliability of data management, ensuring that data plays an accurate and effective role in business applications and decision-making.

[0157] In a possible implementation, the method further includes:

[0158] In the operation of querying whether new data items to be processed are added to the target database at fixed intervals, the system will regularly check the database to determine whether there are new data items to be processed. If there are new data items to be processed, the system will treat the new data items to be processed as the data items to be processed to perform all the steps of the above steps S201-S205.

[0159] Alternatively, when the number of newly added data items to be processed in the target database reaches the set processing threshold, the system will treat the new data items to be processed as data items to be processed to execute all the steps S201-S205. By setting the processing threshold, the system can intelligently decide when to execute all the steps S201-S205 according to the change in the amount of data to avoid frequent processing and reduce resource consumption. Once the set processing threshold is reached, the system will immediately execute all the steps S201-S205 on the new data to be processed to ensure that the data is effectively processed and recorded.

[0160] It should be noted that the target database refers to any database that stores the data processing items to be processed.

[0161] This automated data processing process enables the system to monitor data changes in the database in real time and automatically trigger corresponding processing steps according to the set conditions. Such a mechanism not only improves the efficiency and timeliness of data processing, but also ensures the accuracy and integrity of the data, providing reliable support for the organization's data management and analysis work.

[0162] Based on the description of S201-S205, in the data processing project, the embodiment of the present application first needs to create a data processing task, and clearly specifies the target data format requirement of the project. Next, the original data items in the task are converted into statements that meet the target language format one by one. Then, according to these target data format requirements, corresponding format conversion statements are created for each initial statement. Once it is confirmed that these format conversion statements meet the proofreading standard, they can be used to convert the initial statement, thereby generate the final target statement, so that the final target statement is integrated according to the data integration requirement. The present application, through automated processing steps and format proofreading, has ensured the accuracy and consistency of data conversion, while improving data integration accuracy and efficiency.

[0163] Based on a data integration method provided by the above method embodiment, the embodiment of the present application also provides a data integration device, which will be described below in conjunction with the accompanying drawings.

[0164] See also Figure 7 As shown in FIG. 1 , this figure is a schematic diagram of the structure of a data integration device provided in an embodiment of the present application. Figure 7 As shown, the data integration device includes:

[0165] The data processing task creation unit 701 is used to create a data processing task for a data processing project to be processed; the data processing project to be processed includes multiple data items, and each of the data items has the same or different data formats;

[0166] The target data format requirement obtaining unit 702 is used to obtain the target data format requirement;

[0167] A first data conversion unit 703, configured to convert each of the data items in the data processing task into an initial statement conforming to the target language format;

[0168] A format conversion statement creating unit 704, configured to create a corresponding format conversion statement for each of the initial statements based on the target data format requirement;

[0169] A second data conversion unit 705 is configured to perform data conversion on the initial statement corresponding to the format conversion statement to obtain a target statement if the format conversion statement meets the statement proofreading standard;

[0170] The integration unit 706 is used to integrate the target statements of the data processing task according to the data integration requirements to obtain integrated data.

[0171] As an example, the sentence proofreading standards include: sentence editing proofreading standards and sentence content proofreading standards;

[0172] The sentence editing and proofreading standards include: sentence editing including sentence highlighting, sentence formatting and sentence real-time saving;

[0173] The statement content proofreading standard includes: the specific content of the statement shall not include at least a database deletion field, a table deletion field, and a batch data clearing field.

[0174] As an example, the device further includes:

[0175] A source identification query unit, used to query the source identification of the data processing item to be processed;

[0176] A source database determining unit, configured to determine a source database of the data processing item to be processed based on the source identifier;

[0177] An identification unit, used to identify whether the data processing item to be processed exceeds the data range of the source database;

[0178] A first execution unit, configured to execute the subsequent steps in sequence if the number of steps exceeds the limit;

[0179] The first termination recording unit is used to terminate the data integration task and record error information otherwise.

[0180] As an example, the device further includes:

[0181] A key acquisition unit, used to acquire a key input by a user;

[0182] A key verification unit, used to verify the key input by the user;

[0183] The second execution unit is used to execute the subsequent steps in sequence if the verification passes;

[0184] The second termination recording unit is used to terminate the data integration task and record error information if the verification fails.

[0185] As an example, the device further includes:

[0186] A first output data table definition unit, used to define the first output data table and the column names of the first output data table;

[0187] A data parsing unit, used for parsing the integrated data into field values ​​corresponding to the column names of the first output data table;

[0188] The first filling unit is used to fill the parsed field value into the first output data table.

[0189] As an example, the device further includes:

[0190] a data processing unit, which, upon receiving a data format conversion request, is used to process the data in the first output data table based on the data format conversion rule in the data format conversion request to obtain processed data;

[0191] A second output data table definition unit, configured to define a second output data table and column names of the second output data table based on the processed data;

[0192] A second filling unit, used for filling the processed data into the second output data table;

[0193] a data quality control report generating unit, configured to generate a data quality control report based on the second output data table filled with the processed data;

[0194] The data format conversion rules include defining a data aggregation index field, defining a data group cycle identification field and a same group data field.

[0195] As an example, the device further includes:

[0196] A new project query unit is used to query whether new data processing projects to be processed are added to the target database at fixed intervals;

[0197] The third execution unit is used to execute all the data integration steps as described above for the new data processing items to be processed if a new data processing item to be processed is found in the target database at fixed intervals.

[0198] As an example, the device further includes:

[0199] The fourth execution unit is used to execute all the data integration steps as described above on the new data processing items to be processed when the number of the new data processing items to be processed in the target database is equal to the processing threshold.

[0200] As an example, the device further includes:

[0201] The rendering unit is used to render the data quality control report onto a visual interface to display the data in the data quality control report.

[0202] In addition, an embodiment of the present application further provides a data integration device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the data integration method described above is implemented.

[0203] In addition, an embodiment of the present application further provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a terminal device, the terminal device executes the data integration method as described above.

[0204] When processing the data project to be processed, the embodiment of the present application first needs to create a data integration task and clarify the target data format requirements required for the project. Then, multiple original data items in the task are converted into multiple statements that meet the target language format. Then, according to these target data format requirements, corresponding format conversion statements are created for each initial statement. If these format conversion statements pass the verification of the proofreading standard, they can be used to convert the initial statement to generate the required target statement, and finally these target statements of the data processing task are integrated according to the data integration requirements. This automated data integration method of the present application is through creating a data processing task, performing data conversion and generating an initial statement, and then generating a format conversion statement and verifying according to the format requirements, and finally realizing the rapid and accurate conversion and integration of data, thereby improving the efficiency and accuracy of data integration.

[0205] See also Figure 8 , Figure 8 A schematic diagram of a computer device provided in an embodiment of the present application.

[0206] like Figure 8 As shown, the computer device 01 is in the form of a general computing device. The components of the computer device 01 may include but are not limited to: one or more processors or processing units 03, a system memory 08, and a bus 04 connecting different system components (including the system memory 08 and the processing unit 03).

[0207] Bus 04 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0208] The computer device 01 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 01, including volatile and non-volatile media, removable and non-removable media.

[0209] The system memory 08 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 09 and / or cache memory 10. The computer device 01 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 11 may be used to read and write non-removable, non-volatile magnetic media ( Figure 3 not shown, usually called a "hard drive"). Although Figure 3 Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 04 via one or more data medium interfaces. The memory 08 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0210] A program / utility 12 having a set (at least one) of program modules 13 may be stored, for example, in the memory 08, such program modules 13 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may include an implementation of a network environment. The program modules 13 generally perform the functions and / or methods of the embodiments described herein.

[0211] The computer device 01 may also communicate with one or more external devices 02 (e.g., keyboard, pointing device, display 07, etc.), one or more devices that enable a user to interact with the computer device 01, and / or any device that enables the computer device 01 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via input / output (I / O) interface 06. Furthermore, the computer device 01 may also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN) and / or public network, such as the Internet) via network adapter 05. Figure 3 As shown, the network adapter 05 communicates with other modules of the computer device 01 via the bus 04. It should be understood that although Figure 3 Not shown, other hardware and / or software modules may be used in conjunction with computer device 01, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0212] The processor unit 03 executes various functional applications and data processing by running programs stored in the system memory 08, such as implementing the data integration method provided in the embodiment of the present application.

[0213] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely schematic, in which the unit described as a separate component may or may not be physically separated, and the component prompted as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0214] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data integration method, characterized in that: The method comprises: Creating a data processing task for a data processing project to be processed, and obtaining target data format requirements; the data processing project to be processed includes multiple data items, each of which has the same or different data formats; Convert each of the data items in the data processing task into an initial statement conforming to the target language format; Creating a corresponding format conversion statement for each of the initial statements based on the target data format requirement; If the format conversion statement meets the statement proofreading standard, data conversion is performed on the initial statement corresponding to the format conversion statement to obtain a target statement; According to the data integration requirements, the target statements of the data processing task are integrated to obtain integrated data.

2. The method according to claim 1, characterized in that: The sentence proofreading standards include: sentence editing proofreading standards and sentence content proofreading standards; The sentence editing and proofreading standards include: sentence editing including sentence highlighting, sentence formatting and sentence real-time saving; The statement content proofreading standard includes: the specific content of the statement shall not include at least a database deletion field, a table deletion field, and a batch data clearing field.

3. The method according to claim 1, characterized in that After creating data processing tasks for the plurality of data processing items to be processed, the method further includes: Query the source identification of the data processing project to be processed; Determining a source database of the data processing item to be processed based on the source identifier; Identify whether the data processing item to be processed exceeds the data scope of the source database; If it does not exceed the limit, the subsequent steps are executed in sequence; otherwise, the data integration task is terminated and an error message is recorded.

4. The method according to claim 1, characterized in that: After creating data processing tasks for the plurality of data processing items to be processed, the method further includes: Obtaining a key input by a user and verifying the key input by the user; If the verification passes, the subsequent steps are executed in sequence; otherwise, the data integration task is terminated and the error information is recorded.

5. The method according to claim 1, characterized in that The method further comprises: Define the first output data table and the column names of the first output data table; Parsing the integrated data into field values ​​corresponding to the column names of the first output data table; Fill the parsed field values ​​into the first output data table.

6. The method according to claim 5, characterized in that The method further comprises: If a data format conversion request is received, data processing is performed on the data in the first output data table based on the data format conversion rule in the data format conversion request to obtain processed data; defining a second output data table and column names of the second output data table based on the processed data; Filling the processed data into the second output data table; generating a data quality control report based on the second output data table filled with the processed data; The data format conversion rules include defining a data aggregation index field, defining a data group cycle identification field and a same group data field.

7. The method according to claim 1, characterized in that The method further comprises: querying the target database at fixed intervals whether a new data processing project to be processed is added, and if so, taking the new data processing project to be processed as the data processing project to be processed, so as to execute the step of creating a data processing task for the data processing project to be processed and subsequent steps; or, When the number of newly added data processing items to be processed in the target database is equal to the processing threshold, the new data processing items to be processed are used as the data processing items to be processed to execute the step of creating data processing tasks for the data processing items to be processed and subsequent steps.

8. A data integration device, characterized in that: The device comprises: A data processing task creation unit, used to create a data processing task for a data processing project to be processed; the data processing project to be processed includes a plurality of data items, each of which has the same or different data formats; A target data format requirement obtaining unit, used for obtaining target data format requirements; A first data conversion unit, used for converting each of the data items in the data processing task into an initial statement conforming to the target language format; A format conversion statement creation unit, configured to create a corresponding format conversion statement for each of the initial statements based on the target data format requirement; A second data conversion unit, configured to perform data conversion on the corresponding initial statement based on the format conversion statement to obtain a target statement if the format conversion statement meets the statement proofreading standard; The integration unit is used to integrate the target statements of the data processing task according to the data integration requirements to obtain integrated data.

9. A data integration device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the data integration method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the data integration method according to any one of claims 1 to 7.

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