Structured management method, device and equipment for unstructured data

Abstract

The embodiment of the invention discloses a structured management method, device and equipment for unstructured data, and the method comprises the steps: obtaining the unstructured data generated in an aircraft assembly process, and extracting static data and dynamic data from the unstructured data; wherein the updating frequency of the dynamic data is greater than the updating frequency of the static data; target constraint conditions corresponding to the static data and the dynamic data are determined, a static data table is determined according to the static data and the target constraint conditions, and a dynamic data table is determined according to the dynamic data and the target constraint conditions; and constructing a target database according to the static data table and the dynamic data table to realize structured management of the unstructured data. According to the technical scheme, the unstructured data is managed through the target database, structured management of the unstructured data is achieved, the data sharing efficiency is improved, and a basis is provided for effective utilization of the multi-source assembly data.

Description

Technical Field

[0001] The present invention relates to the technical field of data conversion, and in particular to a structured management method, device and equipment for unstructured data. Background Art

[0002] With the rapid development of computer technology, more and more aircraft manufacturers are adopting information systems to achieve integrated and computerized manufacturing. However, the application of information systems in aircraft assembly generates a large amount of unstructured data from multiple sources. Unstructured data, characterized by irregular or incomplete data structures and lacking predefined data models, is difficult to represent using two-dimensional database logical tables, making it difficult to effectively manage.

[0003] In the prior art, unstructured data, such as text files, images, locations, sensor data, audio, and video, is typically stored in files. The storage path of the file corresponding to the unstructured data can then be obtained and stored in a field in a data table to achieve indirect storage of the unstructured data.

[0004] However, existing technologies often prevent direct access to the content of unstructured data; it requires access through specialized software, resulting in inefficient data sharing. Furthermore, because each piece of unstructured data is stored independently in its own file, users cannot intuitively understand the relationships between them, hindering the effective utilization of the data's value. Summary of the Invention

[0005] The present invention provides a structured management method, device and equipment for unstructured data, which realizes structured management of unstructured data, improves data sharing efficiency and provides a basis for effective utilization of multi-source assembly data.

[0006] In a first aspect, an embodiment of the present invention provides a method for structured management of unstructured data, comprising:

[0007] Acquire unstructured data generated during the aircraft assembly process and extract static and dynamic data from the unstructured data;

[0008] Among them, the update frequency of dynamic data is greater than that of static data;

[0009] Determine target constraints corresponding to each static data and dynamic data, and determine a static data table based on each static data and target constraints, and determine a dynamic data table based on each dynamic data and target constraints;

[0010] Build the target database based on static data tables and dynamic data tables to achieve structured management of unstructured data.

[0011] Optionally, extracting static data and dynamic data from unstructured data includes: extracting target data from unstructured data using a natural language processing algorithm; wherein the target data is entity data, attribute data or relational data; obtaining an update frequency corresponding to each target data, and dividing each target data into static data and dynamic data according to each update frequency and a preset standard frequency.

[0012] Optionally, each target data is divided into static data and dynamic data according to each update frequency and a preset standard frequency, including: determining whether the update frequency is greater than the preset standard frequency; if so, dividing the target data into dynamic data; if not, dividing the target data into static data.

[0013] Optionally, the target constraints include model constraints and data constraints, and the model constraints include unique identification constraints, foreign key constraints, and uniqueness constraints; determining the target constraints corresponding to each static data and dynamic data includes: determining the static constraints corresponding to each static data, and the dynamic constraints corresponding to each dynamic data; wherein, the static constraints are conditions for constraining each static data, and the dynamic constraints are conditions for constraining each dynamic data, and the unique identification constraints corresponding to the static data and the dynamic data are the same.

[0014] Optionally, before determining the target constraint conditions corresponding to each static data and dynamic data, the method further includes: determining the field names corresponding to each static data and dynamic data according to a preset naming rule.

[0015] Optionally, a static data table is determined based on each static data and target constraint condition, and a dynamic data table is determined based on each dynamic data and target constraint condition, including: determining a static data table based on the field name corresponding to the static data and the static constraint condition, and determining a dynamic data table based on the field name corresponding to the dynamic data and the dynamic constraint condition.

[0016] Optionally, the data constraint includes a data type and a data length. The data types include integer, floating point, and timestamp.

[0017] In a second aspect, an embodiment of the present invention further provides a structured management device for unstructured data, comprising:

[0018] A data extraction module is used to obtain unstructured data generated during the aircraft assembly process and extract static data and dynamic data from the unstructured data; wherein the update frequency of dynamic data is greater than the update frequency of static data;

[0019] A data table determination module is used to determine target constraints corresponding to each static data and dynamic data, and determine a static data table based on each static data and target constraints, and determine a dynamic data table based on each dynamic data and target constraints;

[0020] The database construction module is used to construct a target database based on static data tables and dynamic data tables to achieve structured management of unstructured data.

[0021] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0022] at least one processor; and

[0023] a memory communicatively connected to at least one processor; wherein,

[0024] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the structured management method for unstructured data provided by any embodiment of the present invention.

[0025] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing computer instructions, which are used to enable a processor to implement the structured management method for unstructured data of any embodiment of the present invention when executed.

[0026] The technical solution provided by the embodiment of the present invention obtains unstructured data generated during the aircraft assembly process and extracts static data and dynamic data from the unstructured data; wherein the update frequency of the dynamic data is greater than the update frequency of the static data; determines the target constraint conditions corresponding to each static data and dynamic data, and determines a static data table based on each static data and target constraint condition, and determines a dynamic data table based on each dynamic data and target constraint condition; constructs a target database based on the static data table and the dynamic data table to achieve technical means of structured management of unstructured data, realizes structured management of unstructured data, improves data sharing efficiency, and provides a basis for effective utilization of multi-source assembly data.

[0027] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0029] Figure 1 It is a flowchart of a method for structured management of unstructured data provided in Embodiment 1 of the present invention;

[0030] Figure 2 It is a flowchart of another method for structured management of unstructured data provided in Embodiment 2 of the present invention;

[0031] Figure 3 It is a schematic structural diagram of a device for structured management of unstructured data provided in Embodiment 3 of the present invention;

[0032] Figure 4 It is a schematic structural diagram of an electronic device for a method for structured management of unstructured data provided in Embodiment 4 of the present invention. Detailed implementation manners

[0033] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0035] Embodiment 1

[0036] Figure 1The flowchart of a structured management method for unstructured data provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of managing unstructured data generated during the aircraft assembly process. This method can be executed by a structured management device for unstructured data, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device.

[0037] As Figure 1 shown, a structured management method for unstructured data disclosed in this embodiment includes:

[0038] S110. Obtain the unstructured data generated during the aircraft assembly process, and extract static data and dynamic data from the unstructured data.

[0039] In this embodiment, the update frequency of the dynamic data is greater than that of the static data. Specifically, the static data can be data with a relatively long update time scale, such as equipment name, theoretical position of the reference point, workshop name, digital model type, product features, product type, and historical record data, etc. The static data can be data with a relatively short update time scale, such as real-time measurement data, real-time drive quantity, real-time sensor data, alarm status, and deviation value, etc.

[0040] In this step, specifically, the unstructured data generated during the docking process of the major aircraft sections can be obtained. The major aircraft sections can include the front fuselage, rear fuselage, pylon, rear pressure bulkhead, straight section, middle wing, flap, and moving mechanism, etc. Among them, the unstructured data can be data from multiple process links, such as design data, production data, and quality inspection data, etc. Then, techniques such as natural language processing, regular expressions, data modeling, or optical character recognition can be used to extract static data and dynamic data from the unstructured data.

[0041] Optionally, since there may be problems such as redundancy, inconsistency, and errors in the unstructured data, before extracting static data and dynamic data from the unstructured data, data cleaning, deduplication, and integration processing can be performed on the unstructured data to ensure the consistency and accuracy of the data.

[0042] S120. Determine the target constraint conditions corresponding to each static data and dynamic data, and determine the static data table according to each static data and the target constraint conditions, and determine the dynamic data table according to each dynamic data and the target constraint conditions.

[0043] In this embodiment, the target constraint conditions can be the conditions for constraining various static data and dynamic data. The target constraint conditions can include model constraint conditions and data constraint conditions. The model constraint conditions can include unique identifier constraints, foreign key constraints, uniqueness constraints, auto-increment constraints, and not-null constraints, etc. The data constraint conditions can include data types and data lengths, etc. The data types can include integer types, floating-point types, and timestamp types, etc. The data length can be used to reflect the storage space occupied by the data.

[0044] Specifically, the model constraint conditions and data constraint conditions corresponding to each static data can be determined, and a static data table can be determined based on each static data and its corresponding model constraint conditions and data constraint conditions. The model constraint conditions and data constraint conditions corresponding to each dynamic data can be determined, and a dynamic data table can be determined based on each dynamic data and its corresponding model constraint conditions and data constraint conditions.

[0045] Exemplarily, a data model set can be determined based on the static and dynamic data. The above data model set can be as follows:

[0046]

[0047] Among them, SD_FieldName is the field name corresponding to the static data or dynamic data, SD_Index is the data index, SD_Constraint is the model constraint, SD_DataType is the data type, and SD_DataLength is the data length.

[0048] Then, a model constraint set (i.e., the model constraint conditions) corresponding to the data model set can be constructed. The model constraint types can include unique identifier constraints, auto-increment constraints, uniqueness constraints, and not-null constraints corresponding to a single data model, as well as foreign key constraints between multiple data models. The specific expression of the model constraint set can be as follows:

[0049]

[0050] Among them, SD_PRIMARY is the unique identifier constraint, SD_FOREIGN is the foreign key constraint, SD_UNIQUE is the uniqueness constraint, SD_NOTNULL is the not-null constraint, and SD_INCREMENT is the auto-increment constraint.

[0051] After that, the data types corresponding to each static data and dynamic data can be determined, and the data length can be determined according to the data types. The static and dynamic data type set (i.e., the data constraint conditions) can be as follows:

[0052]

[0053] Among them, SD_VARCHAR is of string type, SD_INT is of integer type, and SD_TIMESTAMP is of timestamp type.

[0054] Finally, each data model can be mapped to each data table, and the static data or dynamic data in each data model can be mapped into individual fields in the data table. The data types of the corresponding data table fields can be mapped according to the data types of the dynamic data and static data. The primary key fields in each data table are mapped according to the unique identifier fields in each data model. Optionally, other constraints in the model constraint set also represent the constraint requirements of the data table fields. The foreign key relationships between multiple data models can be mapped into the association relationships between data tables through foreign keys.

[0055] S130. Construct a target database based on the static data table and the dynamic data table to achieve the structured management of unstructured data.

[0056] In this embodiment, the target database may include multiple static data tables and multiple dynamic data tables. Different static data tables (or different dynamic data tables) may correspond to different target devices. The target device may be a device for realizing aircraft assembly, such as a locator, a sensor, a laser tracker, etc.

[0057] Exemplarily, a target database can be constructed according to the locator static data table, the locator dynamic data table, the sensor static data table, and the sensor dynamic data table.

[0058] The advantage of such a setting is that, compared with the prior art of storing unstructured data in the form of files, the technical solution of this embodiment extracts dynamic data and static data from unstructured data, and constructs a target database according to the target constraint conditions, static data, and dynamic data, realizing the conversion of unstructured data into structured data, and providing a basis for expanding the application value of multi-source assembly data. Secondly, by storing static data in the static data table and dynamic data in the dynamic data table, it avoids modifying the static data table while modifying the dynamic data, and improves the data update efficiency.

[0059] The technical solution of this embodiment obtains unstructured data generated during the aircraft assembly process, and extracts static data and dynamic data from the unstructured data; among them, the update frequency of the dynamic data is greater than that of the static data; determines the target constraint conditions corresponding to each static data and dynamic data, determines the static data table according to each static data and the target constraint conditions, and determines the dynamic data table according to each dynamic data and the target constraint conditions; constructs a target database according to the static data table and the dynamic data table to achieve the structured management of unstructured data. This technical means solves the problem that the content of unstructured data usually cannot be directly obtained through the existing technology and must be opened and browsed through the corresponding software, resulting in low data sharing efficiency, and improves the data sharing efficiency. Secondly, by using the target database to manage unstructured data, the structured management of unstructured data is realized, providing a basis for the effective utilization of multi-source assembly data.

[0060] Embodiment 2

[0061] Figure 2 FIG. is a flowchart of another method for structured management of unstructured data provided according to Embodiment 2 of the present invention. This embodiment is a further optimization and extension based on the above embodiments and can be combined with each optional technical solution in the above embodiments.

[0062] As Figure 2 shown, a method for structured management of unstructured data disclosed in this embodiment includes:

[0063] S210. Obtain unstructured data generated during the aircraft assembly process, and extract target data from the unstructured data by using a natural language processing algorithm.

[0064] In this embodiment, the target data may be entity data, attribute data, or relationship data. The relationship data can be used to represent the relationship between entities. Taking a locator as an example, the locator can be used as entity data, the locator number can be used as attribute data, and the relationship between the locator and the sensor can be used as relationship data.

[0065] In this step, specifically, before extracting the target data from the unstructured data, obtain the source, format, content, and quality of the unstructured data. Then, the device to which the unstructured data belongs, such as a locator, a laser tracker, and a sensor, can be determined according to the data source. Determine the method for extracting static data and dynamic data from the unstructured data according to the data format. The data format may include text and pictures, etc. Determine whether the data is qualified according to the data quality, so as to filter out unqualified data.

[0066] Exemplarily, if the data format of the unstructured data is a picture, deep learning techniques (such as a convolutional neural network model or an image recognition model) can be used to convert the picture into a feature vector, and then the feature vector is input into a natural language processing model for processing to obtain the target data. If the data format of the unstructured data is text, the target data can be directly extracted using natural language processing algorithms.

[0067] S220. Obtain the update frequency corresponding to each target data, and divide each target data into static data and dynamic data according to each update frequency and a preset standard frequency.

[0068] In an alternative implementation manner of this embodiment, dividing each target data into static data and dynamic data according to each update frequency and a preset standard frequency includes: determining whether the update frequency is greater than the preset standard frequency; if so, dividing the target data into dynamic data; if not, dividing the target data into static data.

[0069] Taking the attitude adjustment and docking process in the aircraft section assembly and docking as an example, the unstructured data involved in the attitude adjustment and docking process can be obtained, such as the locator data information. Based on the analysis of the attitude adjustment and docking process, a comprehensive analysis is performed on the characteristics and content of the unstructured data (such as the data source, format, content, quality, etc.) to understand the basic characteristics of the data. Then, static data such as the locator number, locator name, affiliated equipment number, affiliated tooling number, active axis, and first use time can be extracted from the locator data information, as well as dynamic data such as the process file number, actual position, actual speed, driving quantity, time stamp, and flight number.

[0070] S230. Determine the field names corresponding to each static data and dynamic data according to a preset naming rule.

[0071] In this embodiment, the preset naming rule can be the naming specification of the database table fields preset. In practical applications, the field names determined according to the preset naming rule need to be able to clearly describe the corresponding content of each data.

[0072] Continuing with the above example, the locator number can be named POSTN_ID, the locator name can be named POSTN_NAME, the equipment number can be named EQUIP_ID, the tooling number can be named TOOL_ID, the driving shaft can be named DRIVER_AXIS, and the first use time can be named FIRST_TIME. The process document number can be named AO_ID, the actual location can be named ACTUAL_LOCATION, the actual speed can be named ACTUAL_SPEED, the driving amount can be named MOVEMENT_SIZE, the timestamp can be named TIMESTAMP, and the sortie order can be named SORTIE_ORDER. The above equipment number can be the number of the equipment to which the locator belongs, and the above tooling number can be the number of the tooling to which the locator belongs.

[0073] S240. Determine the static constraint conditions corresponding to each static data and the dynamic constraint conditions corresponding to each dynamic data.

[0074] In this embodiment, the static constraint condition can be the condition for constraining each static data, the dynamic constraint condition can be the condition for constraining each dynamic data, and the unique identifier constraints for the static data and the dynamic data are the same. The constraint types corresponding to the static constraint condition and the dynamic constraint condition can include unique identifier constraint, foreign key constraint, uniqueness constraint, auto-increment constraint, not-null constraint, data type constraint, data length constraint, etc.

[0075] Exemplarily, based on the locator static data, a static constraint model set (i.e., static constraint condition) that only includes a primary key constraint, a not-null constraint, and a foreign key constraint can be constructed. The static constraint model set is as follows:

[0076]

[0077] Based on the locator dynamic data, a dynamic constraint model set (i.e., dynamic constraint condition) that only includes a primary key constraint, a not-null constraint, and a foreign key constraint can be constructed. The dynamic constraint model set is as follows:

[0078]

[0079] Then, based on the locator static data, a static data type set can be constructed, which includes three types: string, integer, and timestamp. The static data type set can be shown as follows:

[0080]

[0081] Based on the locator dynamic data, a dynamic data type set can be constructed, which includes three types: string, integer, and timestamp. The dynamic data type set can be shown as follows:

[0082]

[0083] Optionally, the same fields in the static data and the dynamic data can be used as index fields (i.e., unique identifier fields), and data queries can be performed based on these index fields, which improves the efficiency of data query, modification, and deletion. Specifically, the primary key of the static data or the keyword of the dynamic data can be used as the index field. For example, POSTN_GUID can be used as the index field for locator data. Then, the index field can be used as one of the constraint conditions to determine the static data table and the dynamic data table.

[0084] The advantage of this setting is that since the GUID globally unique identifier is a binary identifier generated by an algorithm and can guarantee uniqueness globally, each row record in the table can be uniquely identified through the POSTN_GUID field (i.e., the unique identifier field), avoiding duplicate data during data synchronization and ensuring data consistency and security.

[0085] S250. Determine the static data table according to the field names corresponding to the static data and the static constraint conditions, and determine the dynamic data table according to the field names corresponding to the dynamic data and the dynamic constraint conditions.

[0086] Exemplarily, Table 1 is a locator static data table provided according to an embodiment of the present invention. As shown in Table 1, the primary key of the locator static data table is POSTN_GUID, and the foreign keys are EQUIP_Id and TOOL_ID. Among them, the data types of POSTN_GUID, EQUIP_Id, and TOOL_ID are all VARCHAR, and the data lengths are all 32.

[0087] Table 1

[0088]

[0089]

[0090] Table 2 is a locator dynamic data table provided according to an embodiment of the present invention. As shown in Table 2, the primary key of the locator static data table is POSTN_GUID, and the foreign keys are EQUIP_Id and TOOL_ID. Among them, the data types of POSTN_GUID, EQUIP_Id, and TOOL_ID are all VARCHAR, and the data lengths are all 32.

[0091] Table 2

[0092]

[0093]

[0094] The advantage of such a setting is that by setting the index field, the correlation between the static data and the dynamic data corresponding to the same target device is improved. Secondly, by setting foreign keys such as the process file number, the assembly data under the same process can be managed in an associated manner, thus realizing the structured associated management of a large amount of multi-source unstructured data.

[0095] S260. Construct a target database based on the static data table and the dynamic data table to realize the structured management of unstructured data.

[0096] The technical solution of this embodiment obtains the unstructured data generated during the aircraft assembly process, extracts the target data from the unstructured data by using natural language processing algorithms; obtains the update frequency corresponding to each target data, and divides each target data into static data and dynamic data according to each update frequency and a preset standard frequency; determines the field names corresponding to each static data and dynamic data according to a preset naming rule; determines the static data table according to the field names corresponding to the static data and the static constraint conditions, and determines the dynamic data table according to the field names corresponding to the dynamic data and the dynamic constraint conditions; constructs a target database according to the static data table and the dynamic data table to realize the structured management of unstructured data. By this technical means, it solves the problem that the content of unstructured data usually cannot be directly obtained through the existing technology and must be opened and browsed through the corresponding software, resulting in low data sharing efficiency, and improves the data sharing efficiency. Secondly, by storing the unstructured data in the dynamic data table and the static data table respectively, it avoids modifying the static data table while modifying the dynamic data, improves the data management efficiency, and reduces the aircraft assembly cost.

[0097] In a preferred implementation manner of the embodiment of the present invention, the unstructured data generated during the aircraft automated docking process can be analyzed from aspects such as data source, format, content, and quality, so as to determine the extraction method of dynamic and static data and eliminate unqualified data. Then, natural language processing algorithms can be used to extract dynamic and static data from the unstructured data, and determine the set of data models corresponding to each static data. The specific process of determining the set of data models is as follows: first, determine the field names corresponding to the dynamic and static data, then design the data index, then determine the model constraint conditions, and finally determine the data constraint conditions.

[0098] Finally, a target database can be constructed according to the set of data models. The construction process of the target database can be as follows: map the data table according to the data model, map the data table fields according to each dynamic and static data, map the primary key field according to the unique identifier field, map the data table field type according to the dynamic and static data types, and map the relationship between the data tables according to the foreign key relationship between the data models.

[0099] Embodiment III

[0100] Figure 3 FIG. 2 is a schematic structural diagram of a structured management device for unstructured data provided in Embodiment 3 of the present invention. This embodiment is applicable to the situation of structured management of unstructured data. The structured management device for unstructured data can be implemented in the form of hardware and / or software and can be configured in an electronic device.

[0101] As Figure 3 shown, the structured management device for unstructured data disclosed in this embodiment includes:

[0102] A data extraction module 31, configured to obtain unstructured data generated during the aircraft assembly process, and extract static data and dynamic data from the unstructured data; wherein, the update frequency of the dynamic data is greater than that of the static data;

[0103] A data table determination module 32, configured to determine target constraint conditions corresponding to each static data and dynamic data, and determine a static data table according to each static data and the target constraint conditions, and determine a dynamic data table according to each dynamic data and the target constraint conditions;

[0104] Among them, the target constraint conditions include model constraint conditions and data constraint conditions. The model constraint conditions include unique identifier constraint, foreign key constraint, and uniqueness constraint; the data constraint conditions include data type and data length, and the data type includes integer type, floating point type, and timestamp type;

[0105] A database construction module 33, configured to construct a target database according to the static data table and the dynamic data table to implement structured management of unstructured data.

[0106] In the technical solution of this embodiment, through the mutual cooperation of the data extraction module, the data table determination module, and the database construction module, the problem that the content of unstructured data usually cannot be directly obtained through the prior art and must be opened and browsed through the corresponding software, resulting in low data sharing efficiency, is solved. The structured management of unstructured data is realized, the data sharing efficiency is improved, and a basis for the effective utilization of multi-source assembly data is provided.

[0107] Optionally, the data extraction module 31 includes:

[0108] A target data extraction unit, which extracts target data from unstructured data by using a natural language processing algorithm; wherein, the target data is entity data, attribute data, or relationship data;

[0109] A data classification unit, which obtains the update frequency corresponding to each target data, and divides each target data into static data and dynamic data according to each update frequency and a preset standard frequency;

[0110] A data division unit determines whether the update frequency is greater than a preset standard frequency; if so, the target data is divided into dynamic data; if not, the target data is divided into static data.

[0111] Optionally, the data table determination module 32 includes:

[0112] A constraint condition determination unit is configured to determine static constraint conditions corresponding to each static data and dynamic constraint conditions corresponding to each dynamic data;

[0113] Among them, the static constraint condition is a condition for constraining each static data, the dynamic constraint condition is a condition for constraining each dynamic data, and the unique identifier constraints corresponding to the static data and the dynamic data are the same;

[0114] A data table determination unit is configured to determine a static data table according to the field names and static constraint conditions corresponding to the static data, and determine a dynamic data table according to the field names and dynamic constraint conditions corresponding to the dynamic data.

[0115] Optionally, the device further includes a field naming module, which is specifically configured to:

[0116] Determine the field names corresponding to each static data and dynamic data according to a preset naming rule.

[0117] The unstructured data structured management device provided by the embodiments of the present invention can execute the unstructured data structured management method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. The content not described in detail in this embodiment can be referred to the description in any method embodiment of this application.

[0118] Embodiment 4

[0119] Figure 4 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0120] As Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0121] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0122] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the structured management method for unstructured data.

[0123] In some embodiments, the structured management method for unstructured data can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the structured management method for unstructured data described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the structured management method for unstructured data in any other appropriate manner (e.g., by means of firmware).

[0124] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing device, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on the remote machine or server.

[0126] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0127] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0128] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0129] A computing system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0130] It should be understood that various forms of the processes shown above can be used, reordering, adding, or deleting steps. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0131] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A structured management method for unstructured data, characterized in that The method includes: Obtaining unstructured data generated during aircraft assembly, and extracting static data and dynamic data from the unstructured data; Among them, the update frequency of the dynamic data is greater than that of the static data; Determining the target constraint conditions corresponding to each of the static data and dynamic data, determining a static data table according to each of the static data and the target constraint conditions, and determining a dynamic data table according to each of the dynamic data and the target constraint conditions; Constructing a target database according to the static data table and the dynamic data table to realize the structured management of unstructured data.

2. The method according to claim 1, characterized in that, Extracting static data and dynamic data from the unstructured data includes: Using a natural language processing algorithm to extract target data from the unstructured data; Among them, the target data is entity data, attribute data or relationship data; Obtaining the update frequency corresponding to each of the target data, and dividing each of the target data into static data and dynamic data according to each of the update frequencies and a preset standard frequency.

3. The method according to claim 2, wherein The dividing each of the target data into static data and dynamic data according to each of the update frequencies and the preset standard frequency includes: Judging whether the update frequency is greater than the preset standard frequency; If so, dividing the target data into dynamic data; If not, dividing the target data into static data.

4. The method according to claim 1, characterized in that, The target constraint conditions include model constraint conditions and data constraint conditions, and the model constraint conditions include unique identifier constraint, foreign key constraint and uniqueness constraint; The determining the target constraint conditions corresponding to each of the static data and dynamic data includes: Determining the static constraint conditions corresponding to each of the static data, and the dynamic constraint conditions corresponding to each of the dynamic data; Among them, the static constraint conditions are the conditions for constraining each of the static data, the dynamic constraint conditions are the conditions for constraining each of the dynamic data, and the unique identifier constraints corresponding to the static data and the dynamic data are the same.

5. The method according to claim 4, characterized in that, Before determining the target constraint conditions corresponding to each of the static data and dynamic data, it further includes: Determining the field names corresponding to each of the static data and dynamic data according to a preset naming rule.

6. The method according to claim 5, wherein The determining a static data table according to each of the static data and the target constraint conditions, and determining a dynamic data table according to each of the dynamic data and the target constraint conditions includes: Determining a static data table according to the field names corresponding to the static data and the static constraint conditions, and determining a dynamic data table according to the field names corresponding to the dynamic data and the dynamic constraint conditions.

7. The method according to claim 4, wherein The data constraint conditions include data type and data length, and the data types include integer type, floating point type and timestamp type.

8. A structured management device for unstructured data, characterized in that, The device includes: A data extraction module, configured to obtain unstructured data generated during aircraft assembly, and extract static data and dynamic data from the unstructured data; among them, the update frequency of the dynamic data is greater than that of the static data; A data table determination module, configured to determine the target constraint conditions corresponding to each of the static data and dynamic data, determine a static data table according to each of the static data and the target constraint conditions, and determine a dynamic data table according to each of the dynamic data and the target constraint conditions; A database construction module, configured to construct a target database according to the static data table and the dynamic data table, so as to implement structured management of unstructured data.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the method for structured management of unstructured data according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for structured management of unstructured data according to any one of claims 1-7 is implemented.

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