Data verification method and device, electronic equipment, storage medium and program product

Abstract

A data verification method, apparatus, electronic device, storage medium, and program product are provided. The method includes: receiving data to be verified; extracting verification rules from a pre-built data consistency model; verifying the data to be verified using the extracted verification rules; and importing the data into a simulation software system and verifying the imported data using the verification rules.

Description

Data verification method, device, electronic device, storage medium and program product Technical Field

[0001] The present disclosure generally relates to the field of digital technology, and more particularly, to a data verification method, apparatus, electronic device, storage medium, and program product. Background Art

[0002] Digitalization plays a vital role in modern factories, enabling transparent management of production orders, machines, and materials. Digital software systems provide users with valuable data-added services and standardize operational processes. However, certain factors in the factory environment can hinder the use of these software systems.

[0003] 1) Network limitations: Some areas within the factory may have limited or no network connectivity, such as corners where wireless signals are weak or non-existent.

[0004] 2) Intranet access only: The factory's software system can be designed to be accessible only via the intranet. External network access may be restricted due to security or infrastructure constraints. In supply chain systems, it is common for suppliers to be unable to access the factory's internal systems.

[0005] 3) Server Maintenance: Regular server maintenance or downtime may occur in a factory environment. During these maintenance windows, digital systems may be inaccessible, disrupting normal workflow.

[0006] To address these issues and ensure unimpeded production progress, users often choose to record offline data using Excel files and then import the data into the system in batches. When users encounter network limitations or system unavailability, Excel files provide a familiar and versatile tool for recording data, enabling them to continue working without relying on digital software systems.

[0007] To better understand the context and problem, the following description will be based on the Good Receiver System (GR), which manages goods receipt records, data synchronization with SAP, incoming material inspection, and material transfer to the main warehouse. Assume that a router in the warehouse is broken, making wireless communication unstable. Users might choose to enter data in Excel, as shown in the Excel spreadsheet in Table 1 below.

[0008] Table 1

[0009] In this hypothetical scenario, a user receives a batch of goods from a supplier and enters the records into Excel, including Purchase Order, Order Item, Quantity, Received At, and Received By. The batch is then sent to another worker for quality inspection, and the results are entered into Excel, including Scrap Quantity, Tested At, and Tested By.

[0010] However, manually entering data into Excel spreadsheets or copying data from various sources can be a laborious and error-prone process. For example, in Table 1 above, some common errors that may occur include:

[0011] 1) Wrong column order: Users may accidentally change the order of columns, resulting in a mismatch between the expected data and its associated columns.

[0012] a. The data in purchase order A2 (i.e., column A, row 2, etc.) and order item B2 are exchanged. In factories, purchase orders should follow a fixed format, such as starting with "800" and having a length of 8. Order items, on the other hand, always have a length of 2.

[0013] b. Data is exchanged between the receiving time D3 and the testing time G3. The testing time of the goods should be later than the receiving time of the goods.

[0014] 2) Value Error: There may be some type error.

[0015] c. "Zhang Sann" in E4. This name does not exist in the database. Referring to other records, we know it should be "Zhang San".

[0016] d. Quantity C5 and scrap quantity F5. The scrap quantity F5 is 30, which is greater than the value of quantity C5 and is definitely incorrect.

[0017] e. The scrap quantity F9 is -1, which is obviously wrong.

[0018] f. Incorrect quantity.

[0019] For example, the error 40 in C6 is not obvious because the record is correct. However, after importing the data into the software system, it is found that the total quantity of the purchase order exceeds the total purchase quantity in the system.

[0020] 3) Duplicate error. The above example is just a short list, but for the actual situation, there may be hundreds of records.

[0021] g. You can see that lines 7 and 8 are exactly the same. You can predefine whether to allow duplicate lines in the file.

[0022] Currently, when importing data into software systems, the main data validation method used is to set validation rules in Excel templates, software system codes, and database table schemas.

[0023] The process of one common method of importing data from an Excel worksheet is as follows.

[0024] First, an Excel template is prepared in advance by the end user or software supplier.

[0025] The software vendor then provides the end user with a graphical user interface (UI) to upload the file. The file is parsed and pre-processed. The software then performs some business operations within the system, inserting and updating the data into the database.

[0026] However, the above method has the following problems.

[0027] 1) Scattered validation rules: Validation rules for imported data are scattered across three locations: the Excel template, the front-end (UI), and the back-end code. This makes consistency verification difficult during the data import process.

[0028] 2) Verification code and business code are mixed together. Verification code is intertwined with business logic code, resulting in high code complexity and potential risks.

[0029] 3) Some inconsistent data can only be detected after the data is imported into the software system. For example, in a receiving system, when a batch of goods is imported, not only are new records inserted into the database, but the totals in another table are also updated. There is a possibility that the updated totals exceed the predetermined value.

[0030] Summary of the Invention

[0031] A brief overview of the present invention is provided below to provide a basic understanding of certain aspects of the present invention. It should be understood that this overview is not an exhaustive overview of the present invention. It is not intended to identify key or important aspects of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is simply to present certain concepts in a simplified form as a prelude to the more detailed description discussed later.

[0032] In view of this, the present disclosure proposes a data verification method. By constructing a data consistency model that can describe information such as data type, data format, business logic rules, etc., data verification rules can be automatically extracted from the data consistency model and the data can be verified, thereby avoiding the import of erroneous data into the software system.

[0033] According to one aspect of the present disclosure, a data verification method is provided, comprising:

[0034] Receive data to be verified;

[0035] Extract validation rules from pre-built data consistency models;

[0036] Verifying the data to be verified using the extracted verification rules; and

[0037] The data is imported into the simulation software system, and the imported data is verified using the verification rules.

[0038] In this way, validation rules can be automatically extracted from a pre-built data consistency model. This centralizes the rule extraction process, eliminating the need to set validation rules in multiple locations. This simplifies rule management and reduces maintenance workload.

[0039] Optionally, in an example of the above aspect, the data consistency model is constructed based on the elements and predefined rules of the data model in the database of the software system, and the data consistency model describes the data type, data format and business logic rules of each element.

[0040] This allows for customization based on user needs, providing the flexibility to define and combine additional rules beyond data formats, data ranges, and relationships. This customization allows for setting specific validation rules based on specific business needs, enabling more comprehensive and customized data validation.

[0041] Optionally, in an example of the above aspect, the receiving the data to be verified includes:

[0042] Generate Excel file templates using pre-built file import models;

[0043] Receive data;

[0044] The received data is used to generate an Excel file using the file template, and the generated Excel file is used as the data to be verified.

[0045] In this way, the input data can be converted into the common Excel format, and preliminary verification of the data can be performed during the process of generating the Excel file.

[0046] Optionally, in an example of the above aspect, the file import model is constructed based on the data consistency model, and the file import model describes the elements to be imported into the software system and the corresponding reference fields in the data consistency model.

[0047] In this way, the data consistency model can be used to build a file import model, and a file template in Excel format can be generated based on the file import model, thereby converting the input data into an Excel file.

[0048] Optionally, in an example of the above aspect, the software system and the database are cloned in a sandbox to implement the simulated software system.

[0049] In this way, there is no need to import data into the real software system for data verification. Instead, a simulated software system is built and the data is imported into the simulated software system for verification. Only the data that passes the verification will be imported into the real software system. This ensures that all inconsistent data are detected (for example, inconsistent data that can only be discovered after importing into the software system) and avoids importing inconsistent data into the software system.

[0050] According to another aspect of the present disclosure, there is provided a data verification device, comprising:

[0051] a receiving unit, configured to receive data to be verified;

[0052] a validation rule extraction unit, configured to extract validation rules from a pre-built data consistency model;

[0053] a first verification unit configured to verify the data to be verified using the extracted verification rules; and

[0054] The second verification unit is configured to import the data into the simulation software system and verify the imported data using the verification rules.

[0055] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory coupled to the at least one processor, the memory being used to store instructions that, when executed by the at least one processor, enable the processor to execute the method described above.

[0056] According to another aspect of the present disclosure, a non-transitory machine-readable storage medium is provided, which stores executable instructions. When the instructions are executed, the machine is caused to perform the method described above.

[0057] According to another aspect of the present disclosure, a computer program product is provided, which is tangibly stored on a computer-readable medium and includes computer-executable instructions that, when executed, cause at least one processor to perform the method described above.

[0058] According to the technical solution disclosed in the present invention, verification rules can be automatically extracted from a pre-built data consistency model, and the data can be verified in two rounds using the verification rules. The first round uses the extracted verification rules to directly verify, and the second round verifies the data imported into the simulated software system. Only the data that passes the verification will be imported into the real software system. This can ensure that all inconsistent data are detected (for example, inconsistent data that can only be discovered after being imported into the software system) and avoid importing inconsistent data into the software system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The above and other purposes, features and advantages of the present invention will be more easily understood by referring to the following description of the embodiments of the present invention in conjunction with the accompanying drawings. The components in the accompanying drawings are only for illustrating the principles of the present invention. In the accompanying drawings, the same or similar technical features or components will be represented by the same or similar reference numerals. In the accompanying drawings:

[0060] FIG1 is a flow chart of an exemplary process of a data verification method according to an embodiment of the present invention;

[0061] FIG2 is a flowchart of an exemplary process of step S102 in FIG1 ;

[0062] FIG3 is a block diagram of an exemplary configuration of a data verification device according to another embodiment of the present disclosure;

[0063] FIG4 is a block diagram illustrating an exemplary configuration of the receiving unit 302 in FIG3 ;

[0064] FIG5 shows a block diagram of an electronic device according to an embodiment of the present disclosure.

[0065] The accompanying drawings are numerals as follows:

[0066] 100: Data Verification Method S102, S104, S106, S108, S1022, S1024, S1026: Steps

[0067] 300: Data verification device 302: Receiving unit

[0068] 304: Verification rule extraction unit 306: First verification unit

[0069] 308: Second verification unit 3022: File template generation subunit

[0070] 3024: Data receiving subunit 3026: File generating subunit

[0071] 500: Electronic device 502: Processor

[0072] 504: Memory DETAILED DESCRIPTION

[0073] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that discussing these embodiments is intended only to enable those skilled in the art to better understand and implement the subject matter described herein, and is not intended to limit the scope of protection, applicability, or examples set forth in the claims. The functions and arrangements of the elements discussed may be changed without departing from the scope of protection of this disclosure. Various examples may omit, replace, or add various processes or components as needed. For example, the described method may be performed in an order different from the described order, and various steps may be added, omitted, or combined. In addition, features described relative to some examples may also be combined in other examples.

[0074] As used herein, the term "including" and its variations are open terms meaning "including but not limited to". The term "based on" means "based at least in part on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment". The term "another embodiment" means "at least one other embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other definitions may be included below, whether explicit or implicit. Unless the context clearly indicates otherwise, the definition of a term is consistent throughout the specification.

[0075] In view of this, the present disclosure proposes a data verification method. By constructing a data consistency model that can describe information such as data type, data format, business logic rules, etc., data verification rules can be automatically extracted from the data consistency model and the data can be verified, thereby avoiding the import of erroneous data into the software system.

[0076] Given that all data will ultimately be stored in a database, extracting validation rules from the data model associated with the software system is the best approach. The database table schema can provide some data validation rules, but this is not sufficient. A data consistency model can be constructed based on a data model similar to the one in the software system's database, adding more rules based on the software system's business logic (these can be called predefined rules for the software system). This is described in detail below.

[0077] The following describes a method and apparatus for verifying data according to an embodiment of the present disclosure in conjunction with the accompanying drawings.

[0078] FIG1 is a flowchart of an exemplary process of a data verification method 100 according to an embodiment of the present invention.

[0079] First, in step S102, data to be verified is received.

[0080] The data verification method according to the present disclosure can be used to verify data to be imported into a software system. In the present disclosure, a receiving system is used as a specific example of a software system to illustrate the method according to an embodiment of the present disclosure. Those skilled in the art will understand that the method according to the present disclosure is not limited to any specific software system, but can be used for any software system to verify the data to be imported into the software system before importing. In addition, the data verification method according to the present disclosure is not limited to data to be imported into a software system, but can also be applied to consistency verification of error-prone data in general scenarios.

[0081] The received data to be verified may be data input through a graphical user interface, or data in a file format, such as Excel format, or data input in any other manner.

[0082] Next, in step S104 , verification rules are extracted from the pre-built data consistency model.

[0083] In the data verification method according to the present disclosure, a data consistency model is constructed based on the elements of the data model in the software system's database and predefined rules. Corresponding data consistency models can be constructed for different data models in the database. The predefined rules of the software system may include data rules for the data model in the database, as well as data rules required by the software system's business logic. The data consistency model can be constructed while designing the software system, or after the software system is designed.

[0084] Specifically, the data consistency model describes information such as the data format, data range, and business logic rules of each element.

[0085] For example, a data consistency model may describe the following rules:

[0086] Data type: integer, string, date and time, etc.

[0087] Data format

[0088] Range: If it is an integer, you can assign [minimum value, maximum value] to the field.

[0089] Start and end value: If it is a string, you can assign a fixed value to the start and end points; or you can assign a regular expression to filter out erroneous data.

[0090] Business logic rules. These refer to the relationship between two or more columns.

[0091] It is understandable that different software systems may have different data elements and corresponding business logic rules. Those skilled in the art can build a corresponding data consistency model based on the data model of a specific software system without being limited to the above information.

[0092] The following two code snippets are two data consistency models for purchase order records and receipt records built for the receipt system, written in Django language.

[0093] Purchase order record: class PurchaseOrderRecord(models.Model,ConsistentModel): purchase_order=models.CharField(length=8,starts_with="800") purchase_item=models.CharField(lenght=2) order_quantity=models.FloatField(value>=0)

[0094] Receipt record: class GoodReceiveRecord(models.Model,ConsistentModel): order=models.ForeignKey(PurchaseOrder,on_delete=models.CASCADE) received_quantity=models.FloatField(value>=0) received_at=models.DateTimeField(auto_now_add=True) received_by=models.CharField(max_length=40,default="",blank=True) scrape_quantity=models.FloatField(value>=0) checked_at=models.DateTimeField(auto_now_add=True) checked_by=models.CharField(max_length=40,default="",blank=True) class Meta: business_logic_rules=[ "scrap_quantity<=received_quantity", "checked_at<=received_at", "sum(received_quantity)<=order.quantity group_by order", ]

[0095] It is understood that the data consistency model shown above is only a specific example, and any other method can be used to construct the model. The fields (elements) included in the data consistency model here are simplified, and the actual data consistency model may include more fields.

[0096] Validation rules for different fields can be extracted from the data consistency model, including simple rules and business logic rules.

[0097] For example, in the data consistency model of the purchase order record shown above, the following rules can be extracted for each field:

[0098] For purchase order purchase_order, the data type is string, the length must be equal to 8, the value must start with "800", and the value cannot be empty.

[0099] For the purchase item purchase_item, the data type is a string, the length must be equal to 2, and the value cannot be empty.

[0100] For the order data order_quantity, the data type is floating point, the value must be greater than or equal to 0, and the value cannot be empty.

[0101] These are simple rules, which refer to the fields themselves, including data types, formats, value ranges, etc. Using these rules, you can eliminate some problems such as incorrect output, incorrect column order, etc.

[0102] In the data consistency model of the above receipt record, in addition to defining the type and format of each field, it also defines business logic rules business_logic_rules, including:

[0103] The scrap quantity scrap_quantity is less than or equal to the received quantity received_quantity.

[0104] The checking time checked_at is after the receiving time received_at.

[0105] The sum of the received quantities is less than or equal to the order quantity.

[0106] Business logic rules refer to the relationship between two or more fields, or even the relationship between fields in different tables.

[0107] Through the operation in step S104 , verification rules for verifying the data to be verified can be extracted from the data consistency model.

[0108] Next, in step S106 , the data to be verified is verified using the extracted verification rules.

[0109] This step is a validation operation performed before the received data is imported into the software system. Validation rules can be used to directly check the received data. For example, the purchase_order should begin with "800", the test time should be later than the reception time, and so on. If errors are found in the data, appropriate processing can be performed. Preferably, according to the method disclosed herein, when an error is detected in certain data, a prompt message indicating the error can be output, including [row number, column number, and rule not met], allowing the user to take appropriate action based on the prompt message. The method disclosed herein does not limit how to handle erroneous data and will not be described in detail here.

[0110] Next, in step S108, the data is imported into the simulation software system, and the imported data is verified using the verification rules.

[0111] As mentioned above, some inconsistent data can only be detected after it is imported into the software system. For example, the purchase order quantity may be correctly recorded in a table, but after importing it into the software system, it may be found that the total number of purchase orders exceeds the total number of goods in the software system.

[0112] However, the internal operations of a software system can be very complex. For data import operations, values ​​in multiple tables need to be inserted and updated. If incorrect data is imported into the software system, many problems will arise.

[0113] In the method according to the present disclosure, user data is imported into a simulated software system rather than an actual software system, and then the imported data is verified.

[0114] In a specific example, a sandbox can be introduced, the software system and database can be cloned in the sandbox, and then the data to be verified can be imported into the sandbox for verification to check whether there is inconsistent data.

[0115] This process may take a long time, but it is necessary and effective. By doing this, you can detect inconsistent data that may appear only after importing it into the software system. Only data that has been verified in the simulated software system will be imported into the real software system, avoiding the import of incorrect data into the software system.

[0116] FIG2 is a flowchart of an exemplary process of step S102 (ie, receiving data to be imported into the software system) in FIG1 .

[0117] Files in Excel format are a common format for data imported into software systems. In a specific embodiment shown in FIG2 , data input by a user, for example, through a graphical user interface can be converted into files in Excel format for subsequent verification operations.

[0118] As shown in FIG. 2 , first in step S1022 , a file template in Excel format is generated using a pre-built file import model.

[0119] Here, the file import model is constructed based on the data consistency model, and the file import model describes the elements (also called fields or columns in Excel files) to be imported into the software system and the corresponding reference fields in the data consistency model.

[0120] Specifically, the file import model describes the name of each column to be included in the Excel template and the corresponding reference fields. Since all reference fields have been configured with rules in the data consistency model, preliminary verification of the input data can be performed when the Excel file is generated using the file import model.

[0121] Below is a specific example of a file import model. class GRImportModel(FileImportModel): purchase_order=PurchaseOrderRecord.purchase_order purchase_item=PurchaseOrderRecord.purchase_item received_quantity=GoodReceiveRecord.received_quantity received_at=GoodReceiveRecord.received_at received_by=GoodReceiveRecord.received_by scrape_quantity=GoodReceiveRecord.scrap_quantity checked_at=GoodReceiveRecord.checked_at checked_by=GoodReceiveRecord.checked_by class Meta: file_rules={"duplicated_rows":False}

[0122] In this file import model, the names of the columns in the Excel template are defined and reference the corresponding fields in the "PurchaseOrderRecord" and "GoodReceiveRecord" data consistency models. In addition, the file import model defines a new validation rule, "file_rules," which disallows duplicate rows in the Excel file.

[0123] Using the file import model, you can generate a file template in Excel format.

[0124] Specifically, the template can include two types of information:

[0125] 1) Column headers

[0126] 2) Data Validation in Excel

[0127] Here, based on the validation rules in the data consistency model referenced by the file import model, you can use Excel's built-in data validation features to preset some data validation rules for the input data. Due to the limitations of Excel's built-in data validation features, this step can only perform preliminary data validation, but cannot implement all the validation rules in the data consistency model. For example, business logic rules cannot be verified in this step.

[0128] Next, in step S1024, data is received.

[0129] Next, in step S1026, an Excel file is generated from the received data using a file template.

[0130] Specifically, users can input data into an Excel template to generate an Excel file. Since the template file already contains some data validation rules, some errors can be eliminated at this stage.

[0131] After the Excel file is generated, the Excel file is used as the data to be verified and the extracted verification rules are used to perform verification operations. The subsequent verification operations are the same as the data verification process in the above embodiment and will not be repeated here.

[0132] The method disclosed herein can verify whether received data contains inconsistent data. If the verification passes, i.e., no inconsistent data is detected, the received data is imported into the software system. If inconsistent data is detected, a prompt message can be provided to the user for appropriate processing, and the inconsistent data will not be updated to the software system, thereby not affecting the status of the software system. Those skilled in the art can perform appropriate processing based on the verification results, which will not be described in detail here.

[0133] FIG3 is a block diagram illustrating an exemplary configuration of a data verification apparatus 300 according to another embodiment of the present disclosure.

[0134] The data verification device 300 according to an embodiment of the present disclosure includes: a receiving unit 302 , a verification rule extraction unit 304 , a first verification unit 306 and a second verification unit 308 .

[0135] The receiving unit 302 is configured to receive data to be imported into the software system for verification.

[0136] The validation rule extraction unit 304 is configured to extract validation rules from a pre-built data consistency model.

[0137] The first verification unit 306 is configured to verify the data to be verified using the extracted verification rules.

[0138] The second verification unit 308 is configured to import the data into the simulation software system and verify the imported data using the verification rules.

[0139] The data consistency model is constructed based on the elements and predefined rules of the data model in the database of the software system, and the data consistency model describes the data type, data format and business logic rules of each element.

[0140] FIG4 is a block diagram illustrating an exemplary configuration of the receiving unit 302 in FIG3 .

[0141] As shown in FIG. 4 , the receiving unit 302 includes a file template generating subunit 3022 , a data receiving subunit 3024 and a file generating subunit 3026 .

[0142] The file template generating subunit 3022 is configured to generate a file template in Excel format using a pre-built file import model.

[0143] The data receiving subunit 3024 is configured to receive data.

[0144] The file generating subunit 3026 is configured to generate an Excel file from the received data using the file template, and use the generated Excel file as the data to be verified.

[0145] The file import model is constructed based on the data consistency model, and the file import model describes the elements to be imported into the software system and the corresponding reference fields in the data consistency model.

[0146] The simulated software system is implemented by cloning the software system and the database in a sandbox.

[0147] The details of the operations and functions of the various parts of the data verification device 300 may be the same as or similar to the relevant parts of the embodiment of the data verification method 100 of the present disclosure described in conjunction with Figures 1-2, and will not be described in detail here.

[0148] It should be noted that the structures of the data verification device 300 and its constituent units shown in Figures 3 and 4 are merely exemplary, and those skilled in the art may modify the structural block diagrams shown in Figures 3 and 4 as needed.

[0149] The data verification method and apparatus according to the embodiments of the present disclosure have at least one of the following technical advantages.

[0150] 1) According to the data validation method and apparatus of the present disclosure, validation rules can be automatically extracted from a pre-built data consistency model. This centralizes the rule extraction process, eliminating the need to set validation rules in multiple locations. This simplifies rule management and reduces maintenance workload.

[0151] 2) The data validation method and apparatus according to the disclosed embodiments can be customized to meet specific user needs, providing flexibility for users to define and incorporate additional rules beyond data formats, data ranges, and relationships. This customization allows for the establishment of specific validation rules based on specific business needs, enabling more comprehensive and customized data validation.

[0152] 3) According to the data verification method and device of the embodiment of the present disclosure, two rounds of verification are performed on the data. The first round uses the extracted verification rules to directly verify, and the second round verifies the data imported into the simulated software system. Only the data that passes the verification will be imported into the real software system. This can ensure that all inconsistent data are detected (for example, inconsistent data that can only be discovered after being imported into the software system) and avoid importing inconsistent data into the software system.

[0153] 4) The data verification method and apparatus according to the embodiments of the present disclosure can not only be used to verify the data to be imported, but the data consistency model and verification method therein can also solve general data consistency verification, thereby improving the data quality and integrity in various data management tasks.

[0154] The method and apparatus according to the embodiments of the present disclosure are described above with reference to Figures 1 to 4. Each unit of the data verification apparatus described above can be implemented using hardware, software, or a combination of hardware and software.

[0155] 5 shows a block diagram of an electronic device 500 according to an embodiment of the present disclosure. According to one embodiment, the electronic device 500 may include at least one processor 502 that executes at least one computer-readable instruction stored or encoded in a computer-readable storage medium (ie, memory 504).

[0156] It should be understood that the computer executable instructions stored in the memory 504, when executed, cause the at least one processor 502 to perform the various operations and functions described above in conjunction with Figures 1-4 in various embodiments of the present disclosure.

[0157] According to one embodiment, a non-transitory machine-readable medium is provided. The non-transitory machine-readable medium may have machine-executable instructions, which, when executed by a machine, enable the machine to perform various operations and functions described above in conjunction with Figures 1-4 in various embodiments of the present disclosure.

[0158] According to one embodiment, a computer program is provided, including computer-executable instructions, which, when executed, enable at least one processor to perform the various operations and functions described above in conjunction with Figures 1-4 in various embodiments of the present disclosure.

[0159] According to one embodiment, a computer program product is provided, including computer-executable instructions, which, when executed, enable at least one processor to perform the various operations and functions described above in conjunction with Figures 1-4 in various embodiments of the present disclosure.

[0160] It should be understood that the various embodiments in this specification are described in a progressive manner, and reference can be made to the identical or similar parts of the various embodiments. Each embodiment focuses on the differences from the other embodiments. For example, the aforementioned apparatus embodiments, electronic device embodiments, and machine-readable storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant details, reference can be made to the descriptions of the method embodiments.

[0161] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0162] Not all steps and units in the above processes and system structure diagrams are required, and some steps or units may be omitted according to actual needs. The device structures described in the above embodiments can be physical structures or logical structures, that is, some units may be implemented by the same physical entity, or some units may be implemented by multiple physical entities, or may be implemented by certain components in multiple independent devices.

[0163] The specific embodiments described above in conjunction with the accompanying drawings describe exemplary embodiments, but do not represent all embodiments that can be implemented or fall within the scope of protection of the claims. The term "exemplary" used throughout this specification means "used as an example, instance or illustration" and does not mean "preferred" or "having advantages" over other embodiments. For the purpose of providing an understanding of the described technology, the specific embodiments include specific details. However, these technologies can be implemented without these specific details. In some instances, in order to avoid obscuring the concepts of the described embodiments, well-known structures and devices are shown in block diagram form.

[0164] The foregoing description of the present disclosure is provided to enable any person skilled in the art to implement or use the present disclosure. Various modifications to the present disclosure will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the present disclosure. Therefore, the present disclosure is not limited to the examples and designs described herein, but is intended to be consistent with the widest range of principles and novel features disclosed herein.

[0165] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0166] Nouns and pronouns referring to persons in this patent application are not limited to a specific gender.

Claims

1. A data verification method (100), comprising: receiving data to be verified (S102); extracting verification rules from a pre-constructed data consistency model (S104); verifying the data to be verified by using the extracted verification rules (S106); and importing the data into a simulated software system and verifying the imported data by using the verification rules (S108).

2. The method (100) according to claim 1, wherein, the data consistency model is constructed based on elements of a data model in a database of the software system and predefined rules, and the data consistency model describes data types, data formats, and business logic rules of each element.

3. The method (100) according to claim 1 or 2, wherein, the receiving the data to be verified (S102) includes: generating an Excel format file template by using a pre-constructed file import model (S1022); receiving data (S1024); generating an Excel file from the received data by using the file template and using the generated Excel file as the data to be verified (S1026).

4. The method (100) according to claim 3, wherein, the file import model is constructed based on the data consistency model, and the file import model describes elements to be imported into the software system and corresponding reference fields in the data consistency model.

5. The method (100) according to claim 1 or 2, wherein, the simulated software system is implemented by cloning the software system and the database in a sandbox.

6. A data verification device (300), comprising: a receiving unit (302) configured to receive data to be verified; a verification rule extraction unit (304) configured to extract verification rules from a pre-constructed data consistency model; a first verification unit (306) configured to verify the data to be verified by using the extracted verification rules; and a second verification unit (308) configured to import the data into a simulated software system and verify the imported data by using the verification rules.

7. The device (300) according to claim 6, wherein, the data consistency model is constructed based on elements of a data model in a database of the software system and predefined rules, and the data consistency model describes data types, data formats, and business logic rules of each element.

8. The device (300) according to claim 6 or 7, wherein, the data receiving unit (302) includes: a file template generation subunit (3022) configured to generate an Excel format file template by using a pre-constructed file import model; a data receiving subunit (3024) configured to receive data; a file generation subunit (3026) configured to generate an Excel file from the received data by using the file template and using the generated Excel file as the data to be verified.

9. An electronic device (500), comprising: at least one processor (502); and A memory (504) coupled to the at least one processor (502), the memory for storing instructions that, when executed by the at least one processor (502), cause the processor (502) to perform the method according to any one of claims 1-5.

10. A non-transitory machine-readable storage medium storing executable instructions that, when executed, cause the machine to perform the method according to any one of claims 1-5.

11. A computer program product tangibly stored on a computer-readable medium and including computer-executable instructions that, when executed, cause at least one processor to perform the method according to any one of claims 1-5.

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