Data auditing method and device, equipment and storage medium

Abstract

The invention provides a data auditing method and device, equipment and a storage medium, and relates to the technical field of data processing. The method comprises the steps of obtaining multiple pieces of to-be-audited data uploaded by target equipment and a state identifier corresponding to each piece of to-be-audited data; wherein the target device is a device managed by the CMDB, and the to-be-audited data are all IT asset data and CI data currently contained in the target device. And when the state identifier indicates that the target device has uploaded all the to-be-audited data, obtaining target data corresponding to the to-be-audited data from the CMDB according to each to-be-audited data. And further performing data auditing on the to-be-audited data according to each piece of to-be-audited data and the corresponding target data to obtain an auditing result. According to the method, the accuracy of the data auditing result is improved.

Description

Technical Field

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

[0002] Currently, enterprises or organizations store a large amount of data in business systems so that they can be shared among business departments. Before using this data in the business system, data auditing is required to ensure that the data used by each business department is complete and accurate.

[0003] The existing data auditing method is to manually compare the data in the actual environment with the data in the business system after the business system collects the data in the actual environment, find out the inconsistencies, record them, and generate corresponding audit reports. Then, based on the audit results, the data in the business system is updated to ensure that it is consistent with the actual environment.

[0004] However, the existing technology has the technical problem of inaccurate data audit results. Summary of the invention

[0005] The present application provides a data auditing method, device, equipment and storage medium to solve the technical problem of inaccurate data auditing results in the prior art.

[0006] In a first aspect, the present application provides a data audit method, comprising:

[0007] Acquire multiple data to be audited uploaded by the target device and the status identifier corresponding to each data to be audited, wherein the target device is a device managed by the CMDB, and the data to be audited is all IT asset data and all CI data currently contained in the target device;

[0008] When the status indicator indicates that the target device has uploaded all the data to be audited, according to each data to be audited, the target data corresponding to the data to be audited is obtained from the CMDB;

[0009] According to each data to be audited and the corresponding target data, data audit is performed on the data to be audited to obtain audit results.

[0010] Optionally, performing data audit on the data to be audited and obtaining audit results according to each data to be audited and the corresponding target data includes:

[0011] Splitting all the data to be audited into multiple data groups according to the identification of each data to be audited and / or the time when the data to be audited reaches the CMDB;

[0012] According to the to-be-audited data contained in each data group and the target data corresponding to each to-be-audited data, an audit subtask corresponding to the data group is generated, and the audit subtask is used to perform data audit on the to-be-audited data contained in the data group;

[0013] Execute the audit subtasks corresponding to each data group in turn to obtain the audit results.

[0014] Optionally, the method further comprises:

[0015] If the target data corresponding to the data to be audited does not exist in the CMDB, the data to be audited is determined as the target data and stored in the CMDB.

[0016] Optionally, the method further comprises:

[0017] The CMDB is updated according to the audit results.

[0018] Optionally, updating the CMDB according to the audit result includes:

[0019] According to the audit result, determine the updated data that needs to be stored in the CMDB and the corresponding target storage location from all the data to be audited;

[0020] Store the updated data and the corresponding target storage location in the target topic in Kafka;

[0021] According to the updated data stored in the target subject and the corresponding target storage location, the updated data is stored in the target storage location in the CMDB.

[0022] Optionally, the method further comprises:

[0023] Acquire variable data uploaded by the target device, where the variable data is IT asset data and / or CI data of the target device that has changed at the current moment;

[0024] The data stored in the CMDB is updated according to the variable data.

[0025] In a second aspect, the present application provides a data auditing device, comprising:

[0026] An acquisition module is used to acquire multiple data to be audited uploaded by a target device and a status identifier corresponding to each data to be audited, wherein the target device is a device managed by CMDB, and the data to be audited is all IT asset data and all CI data currently contained in the target device;

[0027] The acquisition module is further configured to acquire target data corresponding to each data to be audited from the CMDB when the status identifier indicates that the target device has uploaded all the data to be audited;

[0028] The processing module is used to perform data audit on the data to be audited and obtain the audit result according to each data to be audited and the corresponding target data.

[0029] Optionally, the processing module further includes: a splitting module and a generating module;

[0030] The splitting module is used to split all the data to be audited into multiple data groups according to the identification of each data to be audited and / or the time when the data to be audited reaches the CMDB;

[0031] The generation module is used to generate an audit subtask corresponding to each data group according to the data to be audited contained in each data group and the target data corresponding to each data to be audited, and the audit subtask is used to perform data audit on the data to be audited contained in the data group;

[0032] The processing module is also used to execute the audit subtask corresponding to each data group in sequence to obtain the audit result.

[0033] Optionally, the data auditing device further includes: a storage module, configured to determine the data to be audited as target data and store it in the CMDB if target data corresponding to the data to be audited does not exist in the CMDB.

[0034] Optionally, the data auditing device further includes: an updating module, configured to update the CMDB according to the auditing result.

[0035] Optionally, the updating module further includes: a determining module;

[0036] The determination module is used to determine the updated data that needs to be stored in the CMDB and the corresponding target storage location from all the data to be audited according to the audit result;

[0037] The storage module is also used to store the updated data and the corresponding target storage location in the target topic in Kafka; based on the updated data stored in the target topic and the corresponding target storage location, the updated data is stored in the target storage location in the CMDB.

[0038] Optionally, the acquisition module is further used to acquire variable data uploaded by the target device, where the variable data is IT asset data and / or CI data of the target device that has changed at the current moment;

[0039] The updating module is further used to update the data stored in the CMDB according to the variable data.

[0040] In a third aspect, the present application provides a data auditing device, comprising:

[0041] Processor and memory;

[0042] Wherein, the memory stores computer-executable instructions;

[0043] The processor executes the computer-executable instructions stored in the memory to implement the data audit method as described in the first aspect and various possible implementations of the first aspect.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium, which stores computer execution instructions. When the computer execution instructions are executed by a processor, they are used to implement the data audit method as described in the first aspect and various possible implementation methods of the first aspect.

[0045] In a fifth aspect, the present application provides a computer program product, which includes a computer program, which, when executed by a processor, is used to implement the data audit method as described in the first aspect and various possible implementation methods of the first aspect.

[0046] The data audit method, device, equipment and storage medium provided by the present application obtain multiple data to be audited uploaded by the target device and the status identifier corresponding to each data to be audited. Among them, the target device is a device managed by CMDB, and the data to be audited is all IT asset data and CI data currently contained in the target device. When the status identifier indicates that the target device has uploaded all the data to be audited, the target data corresponding to the data to be audited is obtained from CMDB according to each data to be audited. Further, according to each data to be audited and the corresponding target data, the data to be audited is audited to obtain the audit results. CMDB is not just a simple database, it usually includes a complete set of functions and tools to support configuration management and other IT service management processes. Therefore, the data audit function provided by CMDB can be directly used to perform data audit on the data to be audited uploaded by the target device and the target data in CMDB within CMDB. At the same time, CMDB usually uses automated tools to implement regular data collection, auditing and updating, avoiding errors caused by human operations and improving the accuracy of data audit results. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0048] Figure 1 Data auditing method process provided in this application embodiment Figure 1 ;

[0049] Figure 2 A system architecture diagram of the data audit method provided in the embodiment of the present application;

[0050] Figure 3 A service call interaction diagram of the data audit method provided in an embodiment of the present application;

[0051] Figure 4 Data auditing method process provided in this application embodiment Figure 2 ;

[0052] Figure 5 A data push flow chart of the data audit method provided in the embodiment of the present application;

[0053] Figure 6 A schematic diagram of the structure of the data audit device provided for this application;

[0054] Figure 7 This is a hardware structure diagram of the data auditing device provided for this application.

[0055] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0056] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0057] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein, for example.

[0058] In the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0059] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0060] Terminology explanation:

[0061] Configuration Management Database (CMDB) provides a centralized view to record all information technology (IT) asset data and configuration item (CI) data within an enterprise or organization, including hardware, software, network settings, cloud resources, etc. With accurate and complete CMDB data, an enterprise or organization can manage its IT infrastructure more efficiently, improve service quality, reduce operational risks, and meet various compliance requirements.

[0062] The accuracy and completeness of data are the basis for helping organizations to carry out IT management and business operations. Incorrect data may lead to wrong decisions, thus affecting business operations. Therefore, it is necessary to timely discover and correct errors in data through data auditing to reduce system failures and service terminations caused by data errors.

[0063] After collecting data in the actual environment, the existing technology compares the data in the actual environment with the data in the business system through manual comparison, finds out the inconsistencies and records them, and generates corresponding audit reports. Then, based on the audit results, the data in the business system is updated to ensure that it is consistent with the actual environment.

[0064] Since manual auditing is highly dependent on the experience and skills of auditors, different auditors may have different auditing methods, and manual methods are difficult to effectively handle and verify complex CI relationships and dependencies, which may lead to omissions or errors in some relationship information, affecting the consistency and reliability of data auditing. Moreover, manual comparisons are usually performed periodically and cannot reflect changes in the actual environment in real time, which may lead to insufficient timeliness of data in the business system. These problems will make the data audit results inaccurate.

[0065] In view of the above technical problems, the inventor found in the process of studying data auditing in business systems that the existing business systems lack process management of data auditing and do not have a special data auditing task processing process, which easily makes the data auditing process chaotic. Therefore, the inventor combined the characteristics of CMDB with standardized data models and automatic discovery and update of data, considered storing all data of an enterprise or organization in a systematic tool CMDB, and used the automated tools in CMDB to achieve regular data collection, auditing and updating, avoiding errors caused by human operations and improving the accuracy of data auditing results.

[0066] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0067] Figure 1 Data auditing method process provided in this application embodiment Figure 1 .like Figure 1 As shown, a data audit method provided in an embodiment of the present application includes:

[0068] S101, obtaining a plurality of data to be audited uploaded by a target device and a status identifier corresponding to each data to be audited.

[0069] The target devices are devices managed by the CMDB. For example, these devices may be Infrastructure as a Service (IaaS), Platform as a Service (PaaS), or assets (such as hardware devices, software, etc.).

[0070] The data to be audited is all IT asset data and all CI data currently contained in the target device. It can be understood that the data to be audited is all surviving data audit in the target device at the current moment, and does not include deleted data.

[0071] In addition, each audited data has a corresponding status identifier, which is used to describe the current status of the audited data, and can help the audit system or personnel quickly understand the current status of each audited data. The status identifier can be a mark, label or code to indicate the processing status or audit status of the data.

[0072] In a possible implementation, the timed triggered resource synchronization task can be started by calling the CMDB application programming interface, that is, the target device starts a complete upload operation for all the current data to be audited, and pushes the data to be audited to the CMDB.

[0073] It is understandable that uploading a large amount of data at one time may cause network congestion and affect the normal progress of other network activities. When the target device uploads too much data to be audited, batch uploading can be used to balance the network load and better utilize network bandwidth.

[0074] S102: When the status indicator indicates that the target device has uploaded all the data to be audited, the target data corresponding to each data to be audited is obtained from the CMDB.

[0075] Among them, the target data is stored in the CMDB instance database. The CMDB instance database provides a centralized repository for recording and managing the organization's IT asset data and CI data, and is the core component of the CMDB.

[0076] In a possible implementation, if the target data corresponding to the data to be audited does not exist in the CMDB, the data to be audited is determined as the target data and stored in the CMDB instance database.

[0077] S103: According to each data to be audited and the corresponding target data, perform data audit on the data to be audited and obtain the audit result.

[0078] Specifically, the specific steps of this step are as follows:

[0079] Step a1: split the data to be audited into multiple data groups according to the identification of the data to be audited and / or the time when the data to be audited reaches the CMDB.

[0080] It is worth noting that the CMDB is also provided with a database for storing the audit data uploaded by the target device. Exemplarily, the Mongo database (Mongo Database, MongoDB) can be used to store the audit data uploaded by the target device. MongoDB has very high write performance and is suitable for processing a large amount of pushed data. In addition, MongoDB supports batch write operations and can efficiently process the push of large-scale data.

[0081] Specifically, after receiving the data to be audited pushed by the target device, the CMDB splits the data to be audited into multiple data groups according to the data storage rules corresponding to the database used to store the data to be audited, and maps them into corresponding field information and stores them in the database.

[0082] This embodiment divides the data to be audited into multiple data groups according to the identification of the data to be audited and / or the time when the data to be audited reaches the CMDB.

[0083] Exemplarily, the product type and push date corresponding to the data to be audited are determined, the data to be audited is split into multiple data groups according to the push date, and stored in MongoDB in the format of "product type + push date".

[0084] Step a2: Generate an audit subtask corresponding to each data group according to the data to be audited contained in each data group and the target data corresponding to each data to be audited.

[0085] It is worth noting that when the data to be audited is being uploaded to the CMDB, the corresponding task status is "data push". After all the data to be audited is uploaded to the corresponding database in the CMDB, the task status of these data to be audited is updated to "to be audited", so that the tasks that need to be audited can be queried according to the task status.

[0086] In addition, after all the data to be audited are uploaded to the corresponding database in the CMDB, it is necessary to determine whether there are audit tasks for these data to be audited. If not, it is necessary to add a new data audit task record in the data audit task table to facilitate the execution of subsequent data audit tasks. If so, the target data corresponding to the data to be audited is obtained from the CMDB instance database, and the audit subtask corresponding to the data is generated according to the data to be audited contained in each data group and the target data corresponding to each data to be audited. It can be understood that an audit subtask is generated according to the data to be audited contained in each data group and its corresponding target data.

[0087] In a possible implementation, the data audit task is automatically executed at a preset time point or time interval, that is, the data audit task is triggered regularly to ensure that the target data in the CMDB is always up-to-date and accurate. The scheduled task can be managed by the CMDB task scheduler or special scheduling software, and there is no specific limitation on this.

[0088] Step a3: Execute the audit subtasks corresponding to each data group in turn to obtain the audit results.

[0089] Specifically, by repeatedly calling the data audit comparison model, the audit subtask is audited and the audit result is obtained. Further, the newly added, updated, and deleted data are determined according to the audit result to update the CMDB instance database.

[0090] In a possible implementation, the specific method of updating the CMDB instance database is as follows: according to the audit results, the updated data that needs to be stored in the CMDB instance database and the corresponding target storage location in the CMDB instance database are determined from all the data to be audited. The updated data and the target storage location are stored in the target topic of Kafka, and according to the updated data and the target storage location stored in the target topic, the updated data is stored in the target storage location in the CMDB instance database.

[0091] Kafka is a distributed message queue system, and a topic is a logical partition in Kafka for classifying and storing data. Storing the updated data and the target storage location in a specific topic of Kafka, namely the target topic, means that the updated data is sent to the Kafka message queue and waits to be consumed.

[0092] It is worth noting that after all audit subtasks have completed the data audit, the task status corresponding to the data contained therein is updated, such as updating the task status to complete the audit.

[0093] It is understandable that the data to be audited uploaded by the target device is all the IT asset data and CI data currently contained in the target device, and when the user operates through the interface of the target device, the IT asset data and CI data in the target device will change. The IT asset data and CI data of the target device that have changed at the current moment are regarded as variable data, so the variable data must be inconsistent with the data stored in the CMDB. It is only necessary to obtain the variable data uploaded by the target device through the CMDB and update the data stored in the CMDB according to the variable data.

[0094] The data audit method provided by the present application obtains multiple data to be audited uploaded by the target device in batches and the status identifier corresponding to each data to be audited, and splits the data to be audited into multiple data groups and stores them in the corresponding database of CMDB. When the status identifier indicates that the target device has uploaded all the data to be audited, the target data corresponding to the data to be audited is obtained from CMDB according to each data to be audited, and then the audit subtask corresponding to the data group is generated according to the data to be audited and the target data. Next, the audit subtask corresponding to each data group is executed in turn to obtain the audit result. It can be seen that CMDB not only has strict data management standards, that is, each type of data has a corresponding storage location, but also can automatically execute the pre-designed data audit process. Therefore, by conducting data audits in CMDB, it is possible to effectively avoid the occurrence of data confusion and errors caused by human operations, thereby improving the accuracy of data audit results.

[0095] Next, a specific embodiment is used to Figure 1 The data audit method shown is further explained.

[0096] Figure 2 The system architecture diagram of the data audit method provided in the embodiment of the present application is as follows: Figure 2 As shown, the data audit method includes functions such as full data / incremental data push task triggering, full data / incremental data receiving, data audit task triggering, and data audit execution.

[0097] Specifically, Figure 3 The service call interaction diagram of the data audit method provided in the embodiment of the present application is as follows: Figure 3 As shown in the figure, the middleware used for data auditing includes Kafka (used for receiving and pushing data to be audited), MongoDB (used for caching the full data pushed by the target device), Structured Query Language (MySQL) database (used for saving existing data and data auditing tasks in CMDB), and Remote Dictionary Server (Redis) database (used for caching historical data during CMDB auditing, that is, the required CMDB data).

[0098] In addition, the distributed task scheduling and computing framework PowerJob is used to regularly trigger the push of audited data, regularly trigger data audit tasks, and regularly trigger the clearing of historical records saved to MongoDB. The configuration management database task CMDB-TASK is mainly used for data push, full data reception, data audit tasks, data audit and other functions. The CMDB service is mainly used for logical processing of incremental data and variable data after push and data storage.

[0099] Explanatory note: full data refers to the set of all surviving data in the target device at the current moment, excluding deleted data; variable data refers to the set of data that has changed in the target device within a certain period of time, including deleted data; incremental data refers to the set of data generated in real time by users through interface operations (creation, deletion, modification) of the target device.

[0100] In one possible implementation, Figure 4 Data auditing method process provided in this application embodiment Figure 2 ,like Figure 4 As shown in the figure, the data audit process specifically includes the following steps:

[0101] It is worth noting that the execution entity of the embodiment of the present application is CMDB.

[0102] Step b1: Receive the data to be audited uploaded by the target device and save the data to be audited to MongoDB.

[0103] In one possible implementation, Figure 5 The data push flow chart of the data audit method provided in the embodiment of the present application is as follows: Figure 5 As shown in the figure, the specific process of the target device uploading the data to be audited is as follows:

[0104] Step c1: CMDB periodically triggers the push of audited data.

[0105] Exemplarily, the CMDB periodically triggers the application programming interface (API) of data synchronization, ie, data synchronization starts.

[0106] Step c2: The target device queries the data to be audited that is pushed to the CMDB.

[0107] Step c3: The target device pushes the audited data to the CMDB in batches.

[0108] The audit data can be divided into batches according to the time when it is uploaded to the target device, or according to the identifier of the audit data. Furthermore, each batch of audit data carries a status flag indicating whether the audit data has been pushed, such as a resource push flag ResourcePushFlag.

[0109] For example, if the target device needs to push 1000 audit data, and the audit data is divided into 10 batches, that is, each batch includes 100 audit data. Each batch of audit data carries a ResourcePushFlag, where the ResourcePushFlag of the first 9 batches is set to False, and the ResourcePushFlag of the 10th batch is set to True. During the push process, if CMDB detects that ResourcePushFlag is False, it indicates that the data push is not completed; if CMDB detects that ResourcePushFlag is True, it indicates that the data push is completed.

[0110] Step c4: CMDB saves the audited data to MongoDB according to the corresponding data storage rules;

[0111] Step c5, CMDB queries the status corresponding to ResourcePushFlag;

[0112] Step c6: Add a new data audit task record in the data audit task table through CMDB.

[0113] Explanatory, if ResourcePushFlag is False, a new data audit task record formed by this batch of data to be audited will be added to the data audit task table; if ResourcePushFlag is True, after the new data audit task record is added, the task status of the data to be audited will be updated to facilitate the execution of subsequent audit tasks.

[0114] Step b2: Use PowerJob to periodically scan data audit tasks to determine whether there are data audit tasks.

[0115] If it exists, execute step b3; if it does not exist, execute step b10.

[0116] Step b3: Update the task status of the data to be audited to data auditing;

[0117] Step b4: Determine whether MongoDB has the target data required for this data audit, and whether MySQL has the source data required for this data audit;

[0118] If it exists, execute step b5; if it does not exist, execute step b10.

[0119] Step b5: Cache the source data in MySQL to Redis;

[0120] Step b6: Call the data audit comparison model to perform data audit.

[0121] The data audit is performed by calling the data audit comparison model through the MapReduce in PowerJob. MapReduce is a programming model that is mainly used to process and generate large-scale data sets. The core idea is to decompose the task into two stages: Map and Reduce.

[0122] Explanatory, in the Map phase, the input data is decomposed into a series of key-value pairs, each Map task processes a subset of the input data and generates intermediate key-value pairs. In the Reduce phase, the intermediate key-value pairs are merged to generate the final output result, and the Reduce task processes all intermediate values ​​with the same key-value pair and generates the final result.

[0123] Furthermore, the specific process of data audit is as follows:

[0124] Step b61, obtaining source data in Redis, target data in MongoDB, and data types;

[0125] Step b62, converting the target data into a Map format;

[0126] Explanatory, the target data is converted into Map format in order to use the needs of MapReduce, which facilitates data segmentation, grouping, and sorting to simplify the data processing logic.

[0127] Step b63, traverse the source data and target data to perform data audit;

[0128] Step b64: determine updated and newly added data based on the data audit results;

[0129] Step b65, remove the audited data in the target data;

[0130] Step b66: Send the changed data (updated and newly added data) and data audit results to Kafka;

[0131] Step b67: Update the data in MySQL.

[0132] After the data audit is completed, continue to execute

[0133] Step b7, receiving data audit results;

[0134] Step b8: Determine whether all the target data in MongoDB has been audited;

[0135] If yes, go to step b9; if no, go to step b6.

[0136] Step b9: Summarize the final data audit results and update the data audit task table;

[0137] Step b10: After the data audit is completed, the task status of the target data in MongoDB is updated.

[0138] The data audit method provided by the present application triggers the task of pushing the data to be audited regularly through CMDB, and obtains multiple data to be audited uploaded by the target device in batches and the status identifier corresponding to each data to be audited. The data to be audited is split into multiple data groups and stored in the corresponding database of CMDB, and the corresponding data audit task record is added in the data audit task table. When the status identifier indicates that the target device has uploaded all the data to be audited, it is determined whether there is a data audit task by scanning the data audit task regularly. If it exists, the target data corresponding to the data to be audited is obtained from CMDB according to each data to be audited, and then the audit subtask corresponding to the data group is generated according to the data to be audited and the target data. Next, the audit subtask corresponding to each data group is executed in turn by calling the data audit comparison model, the audit results are obtained, and the CMDB is updated according to the audit results. It can be seen that CMDB not only has strict data management standards, that is, each type of data has a corresponding storage location, but also can automatically execute the pre-designed data audit process. Therefore, by performing data audits in CMDB, data confusion and errors caused by human operations can be effectively avoided, thereby improving the accuracy of data audit results.

[0139] Figure 6 This is a schematic diagram of the structure of the data audit device provided by this application. Figure 6 As shown, the data audit device 600 includes: an acquisition module 601 and a processing module 602;

[0140] The acquisition module 601 is used to acquire multiple data to be audited uploaded by the target device and the status identifier corresponding to each data to be audited. The target device is a device managed by the CMDB, and the data to be audited is all IT asset data and all CI data currently contained in the target device.

[0141] The acquisition module 601 is further used to acquire target data corresponding to each data to be audited from the CMDB when the status indicator indicates that the target device has uploaded all the data to be audited;

[0142] The processing module 602 is used to perform data audit on the audited data and obtain the audit result according to each audited data and the corresponding target data.

[0143] Optionally, the processing module 602 further includes: a splitting module 603 and a generating module 604;

[0144] A splitting module 603 is used to split all the data to be audited into multiple data groups according to the identification of each data to be audited and / or the time when the data to be audited reaches the CMDB;

[0145] A generation module 604 is used to generate an audit subtask corresponding to each data group according to the to-be-audited data contained in each data group and the target data corresponding to each to-be-audited data, and the audit subtask is used to perform data audit on the to-be-audited data contained in the data group;

[0146] The processing module 602 is also used to execute the audit subtask corresponding to each data group in sequence to obtain the audit result.

[0147] Optionally, the data auditing device 600 further includes: a storage module 605, which is used to determine the data to be audited as the target data and store it in the CMDB if the target data corresponding to the data to be audited does not exist in the CMDB.

[0148] Optionally, the data auditing device 600 further includes: an updating module 606, configured to update the CMDB according to the auditing result.

[0149] Optionally, the updating module 606 further includes: a determining module 607;

[0150] The determination module 607 is used to determine the updated data to be stored in the CMDB and the corresponding target storage location from all the data to be audited according to the audit results;

[0151] The storage module 605 is also used to store the updated data and the corresponding target storage location in the target topic in Kafka; based on the updated data stored in the target topic and the corresponding target storage location, the updated data is stored in the target storage location in the CMDB.

[0152] Optionally, the acquisition module 601 is further used to acquire variable data uploaded by the target device, where the variable data is IT asset data and / or CI data of the target device that has changed at the current moment;

[0153] The updating module 606 is further used to update the data stored in the CMDB according to the variable data.

[0154] The data auditing device provided in the embodiments of the present application may be used to execute the data auditing method in any of the above embodiments. The implementation principles and technical effects thereof are similar and will not be described in detail here.

[0155] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0156] Figure 7 The hardware structure diagram of the data auditing device provided for this application. Figure 7 As shown, the data auditing device 700 includes:

[0157] Processor 701 and memory 702;

[0158] Memory stores computer-executable instructions;

[0159] The processor executes the computer execution instructions stored in the memory 702, so that the data auditing device executes the data auditing method as described above.

[0160] It should be understood that the processor 701 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), or application-specific integrated circuits (ASIC). A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The memory 702 may include a high-speed random access memory (RAM), or may also include a non-volatile memory (NVM), such as at least one disk memory, or may be a USB flash drive, a mobile hard disk, a read-only memory, a disk, or an optical disk.

[0161] The data auditing device provided in the embodiment of the present application can be used to execute the data auditing method provided in any of the above method embodiments. Its implementation principles and technical effects are similar and will not be repeated here.

[0162] The embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the data audit method as described above.

[0163] The embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to implement the data audit method as described above.

[0164] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0165] It should be further noted that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flow chart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0166] It should be understood that the above-mentioned device embodiments are only illustrative, and the device of the present application can also be implemented in other ways. For example, the division of units / modules in the above-mentioned embodiments is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0167] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present application may be integrated into one unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0168] If the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. If not specifically stated, the processor may be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. If not specifically stated, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc.

[0169] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0170] In the above embodiments, the description of each embodiment has its own emphasis. For the part not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0171] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0172] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A data audit method, characterized in that: include: Acquire multiple data to be audited uploaded by the target device and the status identifier corresponding to each data to be audited, wherein the target device is a device managed by the configuration management database CMDB, and the data to be audited is all information technology IT asset data and all configuration item CI data currently contained in the target device; When the status indicator indicates that the target device has uploaded all the data to be audited, according to each data to be audited, the target data corresponding to the data to be audited is obtained from the CMDB; According to each data to be audited and the corresponding target data, data audit is performed on the data to be audited to obtain audit results.

2. The method according to claim 1, characterized in that According to each data to be audited and the corresponding target data, performing data audit on the data to be audited and obtaining the audit result includes: Splitting all the data to be audited into multiple data groups according to the identification of each data to be audited and / or the time when the data to be audited reaches the CMDB; According to the to-be-audited data contained in each data group and the target data corresponding to each to-be-audited data, an audit subtask corresponding to the data group is generated, and the audit subtask is used to perform data audit on the to-be-audited data contained in the data group; Execute the audit subtasks corresponding to each data group in turn to obtain the audit results.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: If the target data corresponding to the data to be audited does not exist in the CMDB, the data to be audited is determined as the target data and stored in the CMDB.

4. The method according to claim 1 or 2, characterized in that: The method further comprises: The CMDB is updated according to the audit results.

5. The method according to claim 4, characterized in that The updating of the CMDB according to the audit result includes: According to the audit result, determine the updated data that needs to be stored in the CMDB and the corresponding target storage location from all the data to be audited; Store the updated data and the corresponding target storage location in the target topic in Kafka; According to the updated data stored in the target subject and the corresponding target storage location, the updated data is stored in the target storage location in the CMDB.

6. The method according to claim 1 or 2, characterized in that: The method further comprises: Acquire variable data uploaded by the target device, where the variable data is IT asset data and / or CI data of the target device that has changed at the current moment; The data stored in the CMDB is updated according to the variable data.

7. A data auditing device, characterized in that: include: An acquisition module is used to acquire multiple data to be audited uploaded by a target device and a status identifier corresponding to each data to be audited, wherein the target device is a device managed by a configuration management database CMDB, and the data to be audited is all information technology IT asset data and all configuration item CI data currently contained in the target device; The acquisition module is further configured to acquire target data corresponding to each data to be audited from the CMDB when the status identifier indicates that the target device has uploaded all the data to be audited; The processing module is used to perform data audit on the data to be audited and obtain the audit result according to each data to be audited and the corresponding target data.

8. A data auditing device, characterized in that: Applied to a security platform, comprising: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the data audit method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the data audit method according to any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises a computer program, which is used to implement the data auditing method according to any one of claims 1 to 6 when executed by a processor.

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