Data auditing method and device, equipment and medium
Through automatic matching and auditing data records and automatic generation of mismatch causes, the problem of large data audit workload in the existing technology is solved, automated auditing is realized, and work pressure is reduced.
Patent Information
- Application Number
- CN202411639480.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-15
- Publication Date
- 2025-04-15
AI Technical Summary
In the prior art, data audit work mainly relies on manual completion, especially when the amount of data is large, the workload is huge and it is difficult to meet daily operational needs.
A data audit method and device are provided, by obtaining the first data set and the second data set related thereto, automatically match and audit data records, generate audit results, and automatically generate mismatch causes in case of mismatch.
Automatic data audit is realized, the audit workload is reduced, the audit pressure is reduced, and the need to manually check the causes of mismatch is avoided, and work efficiency is improved.
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Figure CN119829611A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of data processing technology, and in particular to a data audit method, device, equipment and medium. Background Art
[0002] In many business scenarios, there is a data audit process. For example, in the operation process of telecommunications operators, billing data for users and settlement data for upstream suppliers are generated. At the end of each month, billing data and settlement data are audited to ensure data accuracy.
[0003] At present, in some technologies, data auditing is done manually. When the amount of data is large, the auditing workload is also large, which is difficult to meet the needs of daily operations. Summary of the invention
[0004] In view of this, the present disclosure provides a data auditing method, a data auditing device, an electronic device and a computer-readable storage medium, which can realize automatic auditing of data and reduce the auditing workload.
[0005] In a first aspect, the present disclosure provides a data audit method, the method comprising:
[0006] Acquire a first data set and a second data set that is correlated with the first data set;
[0007] searching, in the second data set, for a second data record matching the first data record according to a first record feature of the first data record in the first data set;
[0008] Auditing the first data record and the second data record according to a preset audit logic to obtain an audit result indicating whether the first data record and the second data record match; and
[0009] When the audit result is mismatch, a mismatch reason between the first data record and the second data record is generated according to overall record features of the first data record and the second data record.
[0010] In a second aspect, the present disclosure provides a data auditing device, the device comprising:
[0011] A data set acquisition module, used to acquire a first data set and a second data set related to the first data set;
[0012] a data record search module, configured to search, in the second data set, for a second data record matching the first data record according to a first record feature of the first data record in the first data set;
[0013] an audit module, configured to audit the first data record and the second data record according to a preset audit logic, and obtain an audit result indicating whether the first data record and the second data record match; and
[0014] A reason matching module is used to generate a mismatch reason between the first data record and the second data record according to the overall record characteristics of the first data record and the second data record when the audit result is a mismatch.
[0015] In a third aspect, the present disclosure provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the above method by executing the computer instructions.
[0016] In a fourth aspect, the present disclosure provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the above method.
[0017] In the technical solutions of some embodiments of the present disclosure, on the one hand, based on the first record feature of the first data record, the first data record and the second data record can be automatically matched, and according to the pre-set audit logic, the first data record and the second data record can be automatically audited, and the corresponding audit results can be obtained. In this way, the auditor does not need to manually audit the data records in the first data set and the second data set, thereby greatly reducing the audit workload. On the other hand, in the case where the audit results of the first data record and the second data record are mismatched, the reason for the mismatch between the first data record and the second data record can be automatically generated according to the overall record features of the first data record and the second data record. In this way, the problem of auditors manually troubleshooting the reasons for the mismatch is avoided, further improving the audit workload. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In order to more clearly illustrate the specific embodiments of the present disclosure or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0019] Figure 1 is a schematic diagram of one of the application scenarios of the present disclosure;
[0020] Figure 2 It is a flowchart of a data audit method provided by an embodiment of the present disclosure;
[0021] Figure 3 It is a module schematic diagram of a data audit device provided by an embodiment of the present disclosure;
[0022] Figure 4 It is a schematic diagram of the structure of an electronic device provided by some embodiments of the present disclosure. DETAILED DESCRIPTION
[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.
[0024] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.
[0025] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "some embodiments" or "the embodiment" should be understood as "at least some embodiments". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below.
[0026] Herein, unless explicitly stated, executing a step “in response to A” does not mean executing the step immediately after “A” but may include one or more intermediate steps.
[0027] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition, use, storage or deletion of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.
[0028] It is understandable that before using the technical solutions disclosed in the various embodiments of the present disclosure, the types, scopes of use, usage scenarios, etc. of the information involved in the present disclosure should be informed to relevant users and their authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations. The relevant users may include any type of right holders, such as individuals, enterprises, and groups.
[0029] For example, in response to receiving an active request from a user, a prompt message is sent to the relevant user to clearly prompt the relevant user that the operation requested to be performed will require obtaining and using the information of the relevant user, so that the relevant user can independently choose whether to provide information to software or hardware such as an electronic device, application, server or storage medium that executes the operation of the technical solution of the present disclosure based on the prompt message.
[0030] As an optional but non-limiting implementation, in response to receiving an active request from a relevant user, a prompt message is sent to the relevant user, for example, in the form of a pop-up window, in which the prompt message may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to select "agree" or "disagree" to provide information to the electronic device.
[0031] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.
[0032] First, one of the application scenarios of the present disclosure is described. Figure 1 . Communication operators, upper-level operators and general operators can be operators of different levels in the same company. For example, a communication operator can be a city-level operator, an upper-level operator can be a provincial-level operator, and a general operator can be the highest-level operator (i.e., the headquarters). A third-party operator can be an operator that belongs to a different company from the communication operator. Communication operators can purchase products from upper-level operators, general operators, and third-party operators and sell them to users to make a profit. Products can be traffic packages, network bandwidth, etc.
[0033] based on Figure 1 In the scenario shown, during the operation process, the communication operator will generate billing data for users and settlement data for upper-level operators, general operators and third-party operators, and the billing data and settlement data can be associated through associated fields (such as product ID). For example, the billing data may include data records as shown in Table 1:
[0034] Table 1 Data records in billing data
[0035] Product ID Billing amount Product Type Sales Type User ID Billing method P00001 30 Data Plan A Try the product U 001 Monthly Billing P00002 5 Data Plan B Official product U 002 Pay-per-view
[0036] The settlement data may include data records as shown in Table 2:
[0037] Table 2 Data records in billing data
[0038] Product ID Settlement amount Product Type Purchase time Selling operators Settlement Method P00001 25 Data Plan A 20240603 Upper level operator Monthly Billing P00002 3 Data Plan B 20240701 Total Operator Annual settlement
[0039] Combine Table 1 and Table 2. For each data record in the billing data, a corresponding data record can be found in the settlement data based on the associated field (such as product ID).
[0040] In order to ensure profit income, at the end of each month, the communication operator will audit the billing data and settlement data to determine whether the billing amount of the same product is greater than the settlement amount. If the billing amount is not greater than the settlement amount, the corresponding reason needs to be given.
[0041] At present, this data auditing work is done manually. Due to the large amount of data, the auditing workload is also relatively large. In addition, the short auditing time often makes the auditing staff face relatively large auditing pressure. In view of this, the present disclosure provides a data auditing method, which can realize automatic auditing of data, reduce the auditing workload, and reduce the auditing pressure of the auditing staff. The data auditing method can be applied to a data auditing engine, or to an electronic device running a data auditing engine. The electronic device can include but is not limited to a tablet computer, a laptop computer, a desktop computer, etc. Figure 2 , which is a flow chart of a data audit method provided in one embodiment of the present disclosure. Figure 2 In the data audit method, the following steps are included:
[0042] Step S201: Acquire a first data set and a second data set that is correlated with the first data set.
[0043] Specifically, the first data set and the second data set may respectively include multiple data records similar to those shown in Table 1 and Table 2. The correlation between the first data set and the second data set means that when auditing the data in the first data set, the data in the second data set needs to be used.
[0044] In this embodiment, the communication operator's billing data may be referred to as a first data set, and the communication operator's settlement data may be referred to as a second data set.
[0045] The data audit engine can provide a channel for data import. Based on the data import channel, the data audit engine can obtain the first data set and the second data set submitted by the auditor in the form of Excel, txt text, etc. For example, the data audit engine can provide a data import interface. After the auditor imports the Excel or txt text in the data import interface, the data audit engine can use the data in the Excel or txt text as the first data set and the second data set obtained. For another example, the data audit engine can provide a specified data storage path. After the auditor puts the Excel or txt text into the specified data storage path, the data audit engine can use the data in the Excel or txt text as the first data set and the second data set obtained.
[0046] Step S202: searching the second data set for a second data record that matches the first data record according to a first record feature of the first data record in the first data set.
[0047] Specifically, in combination with the relevant descriptions of Table 1 and Table 2, the first record feature can be an associated feature (also called an associated field) in the first data set and the second data set, such as a product ID. Based on the first record feature, a second data record matching the first data record can be found in the second data set.
[0048] Step S203: audit the first data record and the second data record according to a preset audit logic to obtain an audit result indicating whether the first data record and the second data record match.
[0049] Specifically, the audit logic may specify a condition for the first data record and the second data record to match. In this embodiment, the audit logic may be that the billing amount in the first data record is greater than or equal to the settlement amount in the second data record. In this way, when the first data record and the second data record are audited according to the audit logic, if the billing amount in the first data record is greater than or equal to the settlement amount in the second data record, it indicates that the first data record and the second data record match. If the billing amount in the first data record is less than the settlement amount in the second data record, it indicates that the first data record and the second data record do not match.
[0050] It should be noted that, in specific implementation, the audit logic can be pre-configured in the data audit engine by the audit staff as needed. The above-mentioned audit logic "the billing amount in the first data record is greater than or equal to the settlement amount in the second data record" is only an exemplary description and should not constitute a limitation on the present disclosure. For example, the audit logic can also be that the billing amount in the first data record is greater than the settlement amount in the second data record, and the difference between the billing amount and the settlement amount is not less than 10.
[0051] Specifically, the data audit engine can provide a configuration channel for the audit logic. Based on the audit logic configuration channel, the data audit engine can obtain the audit logic configured by the auditor. For example, the data audit engine can provide a configuration interface, and use the content configured by the auditor in the configuration interface as the audit logic. For another example, the data audit engine can provide a configuration file under a specified storage path, and use the content configured by the auditor in the configuration file as the audit logic.
[0052] Step S204: when the audit result is mismatch, a mismatch reason between the first data record and the second data record is generated according to the overall record characteristics of the first data record and the second data record.
[0053] Specifically, in some scenarios, the cause of the mismatch between the first data record and the second data record may be determined by comprehensively judging the overall record features of the first data record and the second data record.
[0054] For example, suppose that in the billing data, the billing method of product A is annual billing, and in the settlement data, the settlement method of product A is monthly settlement. Then in the billing data, if the billing of the product has been completed in January of the year, the billing amount for the product should be 0 from the 2nd to the 12th month of the year. However, in the settlement data, because product A is settled on a monthly basis, there will be a settlement amount for the product in each month of the year. This results in a mismatch between the first data record and the second data record of product A during the 2nd to the 12th month of the year when the first data record and the second data record are audited (i.e., the billing amount is less than the settlement amount), and the reason for the mismatch between the first data record and the second data record is that product A is a one-time billing product, and it is not a billing month, so the billing is not billed.
[0055] For another example, suppose that a communications operator purchases product B from a superior operator, but when selling product B to users, it sells product B as a trial product (i.e., the sales type is a trial product). In the case where no charges are made for trial products, the billing amount for product B in the billing data is 0, but the settlement amount for product B in the settlement data is not 0. In this case, when the first data record and the second data record of product B are audited, it is possible that the first data record and the second data record do not match (i.e., the billing amount is less than the settlement amount), and the reason for the mismatch between the first data record and the second data record is that product B is a trial product, so the billing is not billed.
[0056] In summary, in this embodiment, the auditor can pre-extract and configure a list of correspondences between one or more record features and mismatch reasons. In this way, when generating the mismatch reasons of the first data record and the second data record based on the overall record features of the first data record and the second data record, the pre-configured list of correspondences between the record features and the mismatch reasons can be obtained. If there is a target record feature that matches the overall record feature in the correspondence list, the mismatch reason corresponding to the target record feature can be used as the mismatch reason of the first data record and the second data record.
[0057] For example, the correspondence list can be shown in Table 3:
[0058] Table 3 Correspondence list
[0059]
[0060] After obtaining the overall record features of the first data record and the second data record, the corresponding relationship list can be traversed to find the target record features that match the overall record features. The so-called matching means that the features in the overall record features are the same as the target record features. For example, assuming that in the overall record features of the first data record and the second data record, the billing type in the billing data is annual billing, and the settlement type in the settlement data is monthly billing, then the record features of the first row in Table 3 can be used as the target record features that match the overall record features, and the reason for the mismatch between the first data record and the second data record can be located as: one-time billing, non-billing month, billing is not billed. For another example, assuming that in the overall record features of the first data record and the second data record, the sales type in the billing data is a trial product, then the record features of the second row in Table 3 can be used as the target record features that match the overall record features, and the reason for the mismatch between the first data record and the second data record can be located as: trial product, billing is not billed.
[0061] It should be noted that the correspondence list in Table 3 is only an exemplary description. In specific implementation, the data audit engine can provide a configuration channel for the correspondence list, and the auditor can flexibly configure the correspondence between the record characteristics and the mismatch reasons according to actual needs. By pre-configuring the correspondence list between the record characteristics and the mismatch reasons, the mismatch reasons between the first data record and the second data record can be automatically generated when the first data record and the second data record do not match, without the need for the auditor to manually check and locate the mismatch reasons, which can greatly reduce the audit workload.
[0062] Further, if there is no target record feature matching the overall record feature in the correspondence list, it indicates that there is a mismatch reason that has not been counted in the correspondence list, that is, the mismatch reason between the first data record and the second data record cannot be automatically located. In this case, a prompt message can be displayed. After seeing the prompt message, the auditor can analyze the mismatch reason between the first data record and the second data record, and extract the new record feature corresponding to the mismatch reason from the overall record feature of the first data record and the second data record. The data audit engine can obtain the new record feature extracted from the overall record feature and the mismatch reason corresponding to the new record feature input by the auditor, and create a correspondence between the new record feature and the mismatch reason in the correspondence list. In this way, when the first data record and the second data record do not match each other next time, if the overall record feature of the first data record and the second data record matches the above-mentioned new record feature, the mismatch reason can be automatically located. It can be understood that as the number of iterations of the data audit increases, some mismatch reasons that have not been counted will be gradually added to the correspondence list. In this way, the record features and mismatch reasons in the correspondence list will become more and more complete, and the probability of being unable to locate the mismatch cause will become smaller and smaller, making the degree of automation of automatically locating the mismatch cause higher and higher, thereby further reducing the audit workload.
[0063] In summary, in the technical solutions of some embodiments of the present disclosure, on the one hand, based on the first record feature of the first data record, the first data record and the second data record can be automatically matched, and according to the pre-set audit logic, the first data record and the second data record can be automatically audited, and the corresponding audit results can be obtained. In this way, the auditor does not need to manually audit the data records in the first data set and the second data set, thereby greatly reducing the audit workload. On the other hand, in the case where the audit results of the first data record and the second data record are mismatched, the mismatch reason of the first data record and the second data record can be automatically generated according to the overall record characteristics of the first data record and the second data record. In this way, the problem of auditors manually troubleshooting the reasons for the mismatch is avoided, further improving the audit workload.
[0064] Furthermore, in some embodiments, after obtaining the audit result and the mismatch reason, the method of the present disclosure may further include:
[0065] Based on the audit results and mismatch reasons, an audit report for the first data set is generated according to the specified audit dimensions.
[0066] For example, according to the billing method, the audit dimension can be divided into monthly billing and per-time billing; according to the audit result, the audit dimension can be divided into matching and non-matching; according to the sales type, the audit dimension can be divided into trial products and formal products. When generating an audit report according to the audit dimension corresponding to the billing method, an audit report can be generated for the first data record of monthly billing, and an audit report can be generated for the first data record of per-time billing. When generating an audit report according to the audit dimension corresponding to the audit result, an audit report can be generated for the first data record with a matching audit result, and an audit report can be generated for the first data record with a non-matching audit result. And so on.
[0067] Furthermore, if an audit report is generated according to the audit dimension corresponding to the billing method + audit result + sales type, 8 audit reports can be generated, that is, one audit report is generated for the first data record that satisfies monthly billing + audit result is a match + trial product, and one audit report is generated for the first data record that satisfies per-time billing + audit result is a match + trial product. And so on.
[0068] Generating audit reports according to the specified audit dimensions can make it convenient for auditors to view audit reports by category. The data audit engine can also provide a configuration channel for audit dimensions, so that auditors can configure the audit dimensions of the generated audit reports according to actual needs.
[0069] In some embodiments, the second data set may be divided by operator. For example, the settlement data with the upper-level operator is placed in the second data set A, the settlement data with the general operator is placed in the second data set B, and the settlement data with the third-party operator is placed in the second data set B. This means that there may be multiple second data sets that are related to the first data set. If the second data record that matches the first data record is searched for, each second data set is traversed in turn, the data processing amount will inevitably increase, and the time consumption of data search will also increase. In view of this, the present disclosure provides a method for searching for second data records in multiple second data sets, which can reduce the data processing amount and reduce the time consumption of data search.
[0070] Specifically, when there are multiple second data sets, searching the second data set for a second data record matching the first data record according to the first record feature of the first data record in the first data set in step S202 may include:
[0071] searching, according to a second record feature of the first data record, a target second data set matching the first data record in a plurality of second data sets;
[0072] According to the first record feature, a second data record matching the first data record is searched in the target second data set.
[0073] Among them, the second record feature can be a supplier identifier in the first data record. The supplier identifiers of the upper-level operator, the general operator, and the third-party operator may be different. In this way, based on the supplier identifier, the corresponding target second data set can be first found in multiple second data sets, and then, according to the first record feature, the second data record matching the first data record can be found in the target second data set. In this way, when searching for the second data record, there is no need to traverse each second data set, thereby achieving the purpose of reducing the amount of data processing and reducing the time consumption of data search.
[0074] Furthermore, in some embodiments, when the second data set is divided by operator, the first data set and each second data set may have their own corresponding audit logic. For example, the audit logic between the first data set and the second data set A may be "the billing amount in the first data record is greater than the settlement amount in the second data record, and the difference between the billing amount and the settlement amount is not less than 10", and the audit logic between the first data set and the second data set B may be "the billing amount in the first data record is greater than the settlement amount in the second data record, and the difference between the billing amount and the settlement amount is not less than 20". In this way, the audit logic can be configured according to the operator, thereby improving the flexibility of data auditing. Based on such a solution implementation, the audit of the first data record and the second data record according to the preset audit logic in step S203 may include:
[0075] According to the second record feature, searching for a target audit logic between the first data set and the target second data set in a plurality of pre-configured audit logics;
[0076] According to the target audit logic, the first data record and the second data record are audited.
[0077] Similarly, there is a corresponding relationship list between the first data set and each second data set, which includes corresponding record features and mismatch reasons;
[0078] Generating a mismatch reason between the first data record and the second data record according to overall record characteristics of the first data record and the second data record, including:
[0079] According to the second record feature, searching a target correspondence list between the first data set and the target second data set in a pre-configured correspondence list of multiple record features and mismatch reasons;
[0080] In the target correspondence list, the mismatch reason corresponding to the overall record feature is searched as the mismatch reason between the first data record and the second data record.
[0081] This completes the entire description of the data auditing method disclosed in the present invention.
[0082] Corresponding to the data auditing method, the present disclosure also provides a data auditing device. Figure 3 , which is a module diagram of a data auditing device provided in one embodiment of the present disclosure. Figure 3 The data audit device includes:
[0083] A data set acquisition module 301 is used to acquire a first data set and a second data set that is correlated with the first data set;
[0084] A data record search module 302, configured to search, in a second data set, for a second data record matching the first data record according to a first record feature of the first data record in the first data set;
[0085] An audit module 303, configured to audit the first data record and the second data record according to a preset audit logic, and obtain an audit result indicating whether the first data record and the second data record match; and
[0086] The reason matching module 304 is used to generate a mismatch reason between the first data record and the second data record according to the overall record characteristics of the first data record and the second data record when the audit result is mismatch.
[0087] In some embodiments, the cause matching module 304 is specifically configured to:
[0088] Get a list of pre-configured correspondences between record features and mismatch reasons;
[0089] If there is a target record feature matching the overall record feature in the correspondence list, the mismatch reason corresponding to the target record feature is used as the mismatch reason between the first data record and the second data record.
[0090] In some embodiments, if there is no target record feature matching the overall record feature in the correspondence list, the reason matching module 304 is further configured to:
[0091] Obtain the new record features extracted from the overall record features and the mismatch reasons corresponding to the new record features input by the auditor;
[0092] In the correspondence list, create a correspondence between the new record feature and the mismatch reason.
[0093] In some embodiments, after obtaining the audit results and mismatch reasons, the reason matching module 304 is further used to:
[0094] Based on the audit results and mismatch reasons, an audit report for the first data set is generated according to the specified audit dimensions.
[0095] In some embodiments, when there are multiple second data sets, the data record search module 302 is specifically used to:
[0096] searching, according to a second record feature of the first data record, a target second data set matching the first data record in a plurality of second data sets;
[0097] According to the first record feature, a second data record matching the first data record is searched in the target second data set.
[0098] In some embodiments, the first data set and each second data set have respective corresponding audit logics; the audit module 303 is specifically used for:
[0099] According to the second record feature, searching for a target audit logic between the first data set and the target second data set in a plurality of pre-configured audit logics;
[0100] According to the target audit logic, the first data record and the second data record are audited.
[0101] In some embodiments, the first data set and each second data set have a corresponding relationship list of record features and mismatch reasons; the reason matching module 304 is specifically used to:
[0102] According to the second record feature, searching a target correspondence list between the first data set and the target second data set in a pre-configured correspondence list of multiple record features and mismatch reasons;
[0103] In the target correspondence list, the mismatch reason corresponding to the overall record feature is searched as the mismatch reason between the first data record and the second data record.
[0104] The data auditing device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.
[0105] The data auditing device disclosed in the present invention has the same beneficial effects as the above-mentioned data auditing method, which will not be described in detail here.
[0106] The present disclosure also provides an electronic device having the above Figure 3 The data auditing device shown.
[0107] Combined with reference Figure 4 , is a schematic diagram of the structure of an electronic device provided by some embodiments of the present disclosure. Figure 4 As shown, the electronic device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.
[0108] The processor 10 may be a first PCIe device, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.
[0109] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.
[0110] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0111] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.
[0112] The electronic device further comprises a communication interface 30 for the electronic device to communicate with other devices or a communication network.
[0113] The embodiments of the present disclosure also provide a computer-readable storage medium. The above-mentioned method according to the embodiments of the present disclosure can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium and downloaded through a network, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.
[0114] A part of the present disclosure may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present disclosure through the operation of the computer. Those skilled in the art should understand that the existence of computer program instructions in computer-readable media includes, but is not limited to, source files, executable files, installation package files, etc., and accordingly, the way in which computer program instructions are executed by a computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.
[0115] Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations are all within the scope defined by the appended claims.
Claims
1. A data audit method, characterized in that: The method comprises: Acquire a first data set and a second data set that is correlated with the first data set; searching, in the second data set, for a second data record matching the first data record according to a first record feature of the first data record in the first data set; Auditing the first data record and the second data record according to a preset audit logic to obtain an audit result indicating whether the first data record and the second data record match; and When the audit result is mismatch, a mismatch reason between the first data record and the second data record is generated according to overall record features of the first data record and the second data record.
2. The method according to claim 1, characterized in that The generating, according to the overall record features of the first data record and the second data record, a mismatch reason between the first data record and the second data record, comprises: Get a list of pre-configured correspondences between record features and mismatch reasons; If there is a target record feature matching the overall record feature in the correspondence list, the mismatch reason corresponding to the target record feature is used as the mismatch reason between the first data record and the second data record.
3. The method according to claim 2, characterized in that If there is no target record feature matching the overall record feature in the correspondence list, the method further includes: Obtaining new record features extracted from the overall record features and mismatch reasons corresponding to the new record features input by the auditor; In the correspondence list, a correspondence between the new record feature and the mismatch reason is created.
4. The method according to claim 1, characterized in that: After obtaining the audit result and the mismatch reason, the method further includes: Based on the audit result and the mismatch reason, an audit report of the first data set is generated according to the specified audit dimension.
5. The method according to claim 1, characterized in that: In the case where there are multiple second data sets, searching the second data set for a second data record matching the first data record according to a first record feature of the first data record in the first data set includes: searching, according to a second record feature of the first data record, for a target second data set that matches the first data record in the plurality of second data sets; According to the first record feature, the second data record matching the first data record is searched in the target second data set.
6. The method according to claim 5, characterized in that The first data set and each of the second data sets have respective corresponding audit logics; The auditing the first data record and the second data record according to the preset audit logic includes: searching, according to the second record feature, a target audit logic between the first data set and the target second data set in a plurality of pre-configured audit logics; The first data record and the second data record are audited according to the target audit logic.
7. The method according to claim 5, characterized in that There is a corresponding relationship list between the first data set and each of the second data sets, which has corresponding record features and mismatch reasons; The generating, according to the overall record features of the first data record and the second data record, a mismatch reason between the first data record and the second data record, comprises: According to the second record feature, searching for a target correspondence list between the first data set and the target second data set in a pre-configured correspondence list of multiple record features and mismatch reasons; In the target correspondence list, the mismatch reason corresponding to the overall record feature is searched as the mismatch reason between the first data record and the second data record.
8. A data auditing device, characterized in that: The device comprises: A data set acquisition module, used to acquire a first data set and a second data set related to the first data set; a data record search module, configured to search, in the second data set, for a second data record matching the first data record according to a first record feature of the first data record in the first data set; an audit module, configured to audit the first data record and the second data record according to a preset audit logic, and obtain an audit result indicating whether the first data record and the second data record match; and A reason matching module is used to generate a mismatch reason between the first data record and the second data record according to the overall record characteristics of the first data record and the second data record when the audit result is a mismatch.
9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, wherein computer instructions are stored in the memory, and wherein the processor executes the data audit method according to any one of claims 1 to 7 by executing the computer instructions.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the data audit method according to any one of claims 1 to 7.