Data generation method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a data generation method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining first data; searching N second data of which the matching degree with the first data is greater than a first threshold value from a first database; searching data of which the matching degree with the position information of the first article is greater than a second threshold value from the N pieces of second data to obtain target data; and generating structured data based on the target data and the article information of the first article. Through the data association method and device, the problem of low efficiency of associating existing data and article information of a system in related technologies is solved, and the effect of improving the efficiency of data association is achieved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data, and specifically, to a data generation method and device, a storage medium, and an electronic device. Background Art

[0002] In the current digital age, enterprises and organizations are faced with the challenge of processing and managing massive amounts of data, especially in scenarios involving information related to users and items.

[0003] Data association and processing methods in related technologies, such as manual entry and simple keyword-based search, can no longer meet the growing demand for data quality and processing efficiency. When processing the ownership relationship between users and items, the data usually contains a large amount of unstructured file information, such as user identity information, item location information, etc. The formats of this information are different and the content is complex. Directly using these raw data for matching and association is not only time-consuming and labor-intensive, but also prone to errors, resulting in data inconsistency and association failure. Summary of the invention

[0004] The embodiments of the present application provide a data generation method and device, a storage medium, and an electronic device to at least solve the problem of low efficiency in associating existing system data with item information in the related art.

[0005] According to an embodiment of the present application, a data generation method is provided, comprising: acquiring first data, wherein the first data includes user information of a target user and location information of a first item, and the first item belongs to the target user; searching a first database for N second data whose matching degree with the first data is greater than a first threshold, wherein the N second data include location information of M second items, identity information of the first user to which the M second items belong, and item information of the M second items, and both M and N are natural numbers greater than or equal to 1; searching the N second data for data whose matching degree with the location information of the first item is greater than a second threshold to obtain target data; generating structured data based on the target data and the item information of the first item, wherein the structured data is used to associate the target data with the item information of the first item.

[0006] In an exemplary embodiment, before searching the first database for N second data whose matching degree with the first data is greater than a first threshold, the method further includes: extracting file information from the acquired target file, wherein the file information includes user information of K second users, item information of P third items, and attribute information of the target file, wherein both K and P are natural numbers greater than or equal to 1, and the K second users include the first user, and the P third items include M second items; performing file processing operations on the file information to obtain target file information, wherein the file processing operations include, in sequence: text cleaning operations, text standardization operations, and text verification operations, and the text verification operations are used to verify the information integrity and logical integrity of the target file information; extracting text key information from the target file information, wherein the text key information includes user information of the K second users, item information of the P third items, association information between each third item and the user to which it belongs, and ownership certification information of each third item; and storing the text key information in the first database.

[0007] In an exemplary embodiment, searching from a first database for N second data whose matching degree with the first data is greater than a first threshold includes: searching from the first database for Q third data whose matching degree with the user information of the target user is greater than a third threshold, wherein Q is a natural number greater than or equal to 1; and when the third threshold is greater than or equal to the first threshold, determining the Q third data as the N second data.

[0008] In an exemplary embodiment, data whose matching degree with the location information of the first object is greater than a second threshold is searched from the N second data to obtain target data, including: calculating the matching degree between the location information of the N second objects and the location information of the first object to obtain N location matching degrees; comparing the N location matching degrees with the second threshold, and determining the location information and item information of the second object corresponding to the target location matching degree greater than the second threshold as the target data.

[0009] In an exemplary embodiment, generating structured data based on the target data and the item information of the first item includes: setting a first item identifier of the first item and a second item identifier of the second item included in the target data; converting the first item identifier and the second item identifier into structured formats respectively to obtain first structure data and second structure data; and when structural verification of the first structure data and the second structure data passes, associating the first structure data and the second structure data to obtain the structured data.

[0010] In an exemplary embodiment, when the structure verification of the above-mentioned first structure data and the above-mentioned second structure data passes, the above-mentioned first structure data and the above-mentioned second structure data are associated to obtain the above-mentioned structured data, and the above-mentioned method also includes: displaying the above-mentioned structured data through a first page, and performing a verification operation on the above-mentioned structured data on the above-mentioned first page, wherein the above-mentioned verification operation includes a data integrity verification; filling the execution result of the above-mentioned verification operation into the second page; or, marking the data that fails the above-mentioned verification operation on the above-mentioned first page to remind that there is an abnormality in the above-mentioned structured data.

[0011] In an exemplary embodiment, after displaying the structured data through a first page, the method further includes: setting a trigger component for the structured data in the first page; and when storing the structured data in a target system, setting a storage tag for the structured data in the target system through the trigger component.

[0012] According to another embodiment of the present application, a data generating device is provided, including: an acquisition module, used to acquire first data, wherein the first data includes user information of a target user and location information of a first item, and the first item belongs to the target user; a first search module, used to search a first database for N second data whose matching degree with the first data is greater than a first threshold, wherein the N second data include location information of M second items, identity information of users to which the M second items belong, and item information of the M second items, and both M and N are natural numbers greater than or equal to 1; a second search module, used to search the N second data for data whose matching degree with the location information of the first item is greater than a second threshold, to obtain target data; a generation module, used to generate structured data based on the target data and the item information of the first item, wherein the structured data is used to associate the target data with the item information of the first item.

[0013] According to another embodiment of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0014] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any one of the above method embodiments when running.

[0015] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0016] Through this application, the first data including the target user information and the first item location information is obtained, and based on the comparison between the matching degree and the first threshold, N second data with a matching degree greater than the first threshold with the first data are found from the first database as the basis for further processing, and then the item location information in the second data is carefully matched, and the data with a high degree of consistency with the first item location is accurately identified by calculating the position matching degree and comparing it with the second threshold, and the target data is obtained, and then structured data is generated based on the target data and the item information of the first item. Therefore, the problem of low efficiency in associating the existing system data with the item information in the related technology can be solved, and the effect of improving the efficiency of data association can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of a hardware environment of a data generation method according to an embodiment of the present application;

[0018] Figure 2 is a flow chart of a data generation method according to an embodiment of the present application;

[0019] Figure 3 is a flow chart of a method for generating real estate data according to an embodiment of the present application;

[0020] Figure 4 It is a structural block diagram of a data generating device according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0023] The method embodiments provided in the embodiments of the present application can be executed in a server device or a similar computing device. Taking running on a server device as an example, Figure 1 Schematic diagram of the hardware environment of a data generation method in an embodiment of the present application. Figure 1 As shown, the server device may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the server device may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above server device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0024] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a data generation method in an embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the server device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0025] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the server device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0026] In this embodiment, a data generation method is provided. Figure 2 is a flow chart of a method according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0027] Step S202, obtaining first data, wherein the first data includes user information of a target user and location information of a first item, and the first item belongs to the target user;

[0028] Optionally, the user information in this embodiment includes multi-dimensional data that identifies and describes the user's identity, contact information, financial status, credit record, etc., including but not limited to the user's name, ID number, contact information (such as telephone, email), address, occupation, income information, credit score, loan record, etc.

[0029] Optionally, the first object in this embodiment includes but is not limited to a house, a car, jewelry and the like.

[0030] Optionally, the location information in this embodiment is used to locate the physical location of the first item. For real estate, it may refer to a specific street address, longitude and latitude coordinates, the city where it is located, etc.; for items in logistics, it may be a warehouse number, shelf location, geographical location in current transportation, etc.; in an asset management scenario, it may involve the specific place or location where the asset is stored.

[0031] Step S204, searching the first database for N second data whose matching degree with the first data is greater than a first threshold, wherein the N second data include location information of M second objects, identity information of the first users to which the M second objects belong, and object information of the M second objects, and both M and N are natural numbers greater than or equal to 1;

[0032] Optionally, in this embodiment, the matching degree is used to determine the degree of matching between the data in the first database and the first data, and the matching degree includes but is not limited to being calculated in the following ways: a string similarity algorithm, for example, a Levenshtein distance algorithm or a Jaro-Winkler similarity algorithm; using a machine learning model to identify and match key features; converting user information and item information into feature vectors (for example, converting file information through a word embedding model), and then using a vector distance (such as Euclidean distance, Manhattan distance) or a similarity metric (such as cosine similarity) to compare the feature vectors to obtain a matching degree.

[0033] Optionally, the identity information in this embodiment includes, but is not limited to, the identity card of the target user and the user's unique ID.

[0034] Optionally, the item information in this embodiment includes but is not limited to basic attribute information, location information, representation information, status information, and value information. For example, in a bank's personal loan system, the item information may include but is not limited to the real estate registration certificate number, real estate registration certificate number, mortgage period, real estate type (residential, commercial, etc.), building area, floor, room number and other information.

[0035] Step S206, searching for data whose matching degree with the location information of the first object is greater than a second threshold from the N second data to obtain target data;

[0036] Step S208: generating structured data based on the target data and the item information of the first item, wherein the structured data is used to associate the target data with the item information of the first item.

[0037] Optionally, the structured data in this embodiment includes but is not limited to the following formats: Extensible Markup Language (XML) files, JavaScript Object Notation (JSON), and table formats in a database.

[0038] Optionally, the method in this embodiment includes but is not limited to application in the following fields: bank real estate mortgage loan system, e-commerce platform user behavior analysis, logistics tracking and management, social media friend recommendation, and medical and health data management. For example, when a bank handles real estate mortgage loan business, the real estate information provided by the user can be matched with the database within the bank or the real estate registration center. Through the set matching threshold, the user's loan information and real estate information are automatically associated to generate structured loan and real estate association data for loan approval and collateral management; the e-commerce platform can collect user shopping records, browsing history and location information, and match them with user portraits and product information in the database to generate structured data for analyzing user behavior preferences. , optimize recommendation algorithms and improve customer experience; in the logistics industry, the barcode and location information of the goods can be matched with the user information of the shipper and consignee at multiple levels to generate structured transportation records and cargo status information, which is convenient for cargo tracking and logistics network optimization; social media platforms can recommend friends or content that may be of interest to users based on user interest tags, location information and information of other users in the social network through multi-level matching and similarity calculation, thereby improving user engagement and satisfaction; hospitals or health management systems can match and associate patients' personal information (such as name, ID number), medical records (such as diagnosis results, drug information) with location information (such as hospital and department) to generate structured medical records, which is convenient for case analysis and health data management.

[0039] Through the above steps, after obtaining the user information of the target user and the location information of the first item, N records whose matching degree with the above information is greater than the first threshold are searched from the first database, and then the records whose matching degree with the first item location information is greater than the second threshold are screened out from the N records to obtain the target data. Finally, based on the target data and the item information of the first item, structured data for association is generated, which can solve the problem of low efficiency in associating the existing data and item information of the system in related technologies, thereby achieving the effect of improving the efficiency of data association. The generated structured data is convenient for the system to understand and process, which is helpful for subsequent data management and analysis.

[0040] In an exemplary embodiment, before searching the first database for N second data whose matching degree with the first data is greater than a first threshold, the method further includes: extracting file information from the acquired target file, wherein the file information includes user information of K second users, item information of P third items, and attribute information of the target file, wherein both K and P are natural numbers greater than or equal to 1, and the K second users include the first user, and the P third items include M second items; performing file processing operations on the file information to obtain target file information, wherein the file processing operations include, in sequence: text cleaning operations, text standardization operations, and text verification operations, and the text verification operations are used to verify the information integrity and logical integrity of the file information; extracting text key information from the target file information, wherein the text key information includes user information of the K second users, item information of the P third items, association information between each third item and the user to which it belongs, and ownership certification information of each third item; and storing the text key information in the first database.

[0041] Optionally, the file information in this embodiment includes but is not limited to basic information, number, obligor corresponding to the file, and identity information. For example, in a bank's personal loan system, the file obtained by the system may be a paper real estate registration certificate. At this time, the file information includes the real estate registration certificate number, real estate registration certificate number, mortgage period, and real estate type (residential, commercial, etc.).

[0042] Optionally, in this embodiment, extracting file information from the target file includes but is not limited to being achieved by the following methods: Optical Character Recognition (OCR for short), extracting text information from paper documents (such as scans or photos); Intelligent Character Recognition (ICR for short), recognizing text, understanding information in tables and graphics, and converting this information into structured data; Natural Language Processing (NLP for short), extracting semantic information from unstructured text data, identifying entities (such as user information, item information), relationships and key information.

[0043] Optionally, the text verification operation in this embodiment includes but is not limited to: integrity verification, logic verification, format verification, range verification, and uniqueness verification, for example, confirming whether all necessary field information is complete, whether the name, address, ID number in the user information and the name, serial number, location information in the item information are complete; checking whether the logical relationship between the fields is correct, and whether the location information of the item matches the known geographical area; verifying whether the field data meets the preset format requirements, such as format verification of telephone number and ID number, verification of time and date format, verification of digital format of currency amount, etc.; determining whether the numerical field is within a reasonable range, such as whether the values ​​of item value, area, etc. are reasonable, whether the user age is within the adult range, and whether the date is within the validity period; checking whether key identifiers (such as user ID, item serial number) are unique in the database.

[0044] Optionally, in this embodiment, extracting key text information from the target file information includes but is not limited to the following methods: using regular expressions to match file information in a specific format; identifying and extracting keywords or phrases in the file; based on NLP technology, automatically identifying entities in the text, such as names of people, places, organizations, item models, etc.; or designing specific patterns or templates to extract information that conforms to a specific structure from the file, such as extracting the purchase date and price of an item from a contract text; training deep learning models, such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Transformer to understand the context of the text and extract key information and relationships.

[0045] Through the above steps, file information is extracted from the target file and file processing operations are performed, and the extracted text key information is stored in the first database. The data quality is improved through preprocessing, and errors and inconsistencies in subsequent processing are reduced. The key information is stored in the database, which provides a rich and reliable data source for subsequent data retrieval and matching.

[0046] In an exemplary embodiment, searching from a first database for N second data whose matching degree with the first data is greater than a first threshold includes: searching from the first database for Q third data whose matching degree with the user information of the target user is greater than a third threshold, wherein Q is a natural number greater than or equal to 1; and when the third threshold is greater than or equal to the first threshold, determining the Q third data as the N second data.

[0047] Optionally, the matching degree in this embodiment includes but is not limited to being calculated in the following ways: a string similarity algorithm, such as a Levenshtein distance algorithm or a Jaro-Winkler similarity algorithm; using a machine learning model to identify and match key features; converting user information and item information into feature vectors (for example, converting user information and item information through a word embedding model), and then using vector distance (such as Euclidean distance, Manhattan distance) or a similarity metric (such as cosine similarity) to compare the feature vectors to obtain a matching degree.

[0048] For example, the similarity algorithm of the Jaro-Winkler similarity database is used to calculate the name matching degree between the obligor in the full real estate certificate and the personal loan system (corresponding to the above-mentioned first database), and the data in the personal loan system with a matching degree greater than 70% (corresponding to the above-mentioned third threshold) is determined as the second data, wherein the set first threshold is 60%. Since in this example, the third threshold is greater than the first threshold, the data with a matching degree greater than 70% can be determined as the second data.

[0049] Through the above steps, the first database is searched for records whose matching degree with the target user information is greater than the third threshold, and when the third threshold is not lower than the first threshold, the above records are determined to be N second data. Through threshold control, the accuracy and reliability of user information matching are ensured, and the targetedness of data search is improved.

[0050] In an exemplary embodiment, data whose matching degree with the location information of the first object is greater than a second threshold is searched from the N second data to obtain target data, including: calculating the matching degree between the location information of the N second objects and the location information of the first object to obtain N location matching degrees; comparing the N location matching degrees with the second threshold, and determining the location information and item information of the second object corresponding to the target location matching degree greater than the second threshold as the target data.

[0051] Optionally, the location matching degree in this embodiment is calculated for the location information of the second item and the location information of the first item, and is used to indicate the matching degree between the location information of the second item and the location information of the first item, including but not limited to calculation by the following methods: calculating the actual distance between two points or using a proximity algorithm of a Geographic Information System (GIS) to determine the matching degree. For example, using the Haversine formula to calculate the distance between longitude and latitude coordinates, or performing matching analysis of spatial data through a GIS system.

[0052] Through the above steps, the matching degree between the location information of N second objects and the location information of the first object is calculated, and the records whose matching degree exceeds the second threshold are determined as target data. This can more accurately filter out relevant records from a large amount of data, ensure the accurate association of location information, and reduce location matching errors.

[0053] In an exemplary embodiment, generating structured data based on the target data and the item information of the first item includes: setting a first item identifier of the first item and a second item identifier of the second item included in the target data; converting the first item identifier and the second item identifier into structured formats respectively to obtain first structure data and second structure data; and when structural verification of the first structure data and the second structure data passes, associating the first structure data and the second structure data to obtain the structured data.

[0054] Optionally, the item identifier in this embodiment is an identifier used to uniquely identify the item, including but not limited to a serial number, a barcode / QR code, a Radio Frequency Identification (RFID) tag, a real estate certificate number, and a vehicle identification number (VIN).

[0055] Optionally, the structural verification in this embodiment includes but is not limited to association verification to ensure that the relationship between the item identifier and the user information or other item information is reasonable, such as checking whether the user legally owns the item; logical verification, checking the logical relationship between the item identifier and other data fields to ensure the rationality of the data.

[0056] Optionally, in this embodiment, associating the first structure data and the second structure data includes but is not limited to the following methods: database association based on identifiers, using SQL statements to associate these records; using data management middleware or services to send and receive data using the item identifier as a key to achieve real-time association between the first structure data and the second structure data.

[0057] Through the above steps, the identification of the first item and the second item is set, and then the above identification is converted into a structured format. When the structural data verification is passed, the first structure data and the second structure data are associated. Through identification and structuring, a clear data relationship is constructed to facilitate data management and subsequent operations.

[0058] In an exemplary embodiment, when the structure verification of the above-mentioned first structure data and the above-mentioned second structure data passes, the above-mentioned first structure data and the above-mentioned second structure data are associated to obtain the above-mentioned structured data, and the above-mentioned method also includes: displaying the above-mentioned structured data through a first page, and performing a verification operation on the above-mentioned structured data on the above-mentioned first page, wherein the above-mentioned verification operation includes a data integrity verification; filling the execution result of the above-mentioned verification operation into the second page; or, marking the data that fails the above-mentioned verification operation on the above-mentioned first page to remind that there is an abnormality in the above-mentioned structured data.

[0059] Optionally, the verification operation in this embodiment is mainly used to verify whether the data displayed on the first page meets the requirements of the field and whether the fields that must be filled in are filled with data.

[0060] Optionally, in this embodiment, the data that fails the verification operation is marked on the first page, and specific marking forms include but are not limited to highlighting, flashing display, and page pop-up message box prompt.

[0061] Optionally, the first page and the second page in this embodiment correspond to different databases. For example, in a scenario where a bank provides mortgage loans, the first page may be an operating interface for a branch. After the branch receives a mortgage request from a user, it processes the request, stores the processed structured data in the bank's database, and displays it on the bank's page. Since the above structured data needs to be synchronously stored in the head office's database, but the branch and head office databases cannot be directly associated, it is necessary to fill the execution results of the data verification operation displayed in the branch's operating interface into the second page to store the above data in the head office's database.

[0062] Through the above steps, the structured data is displayed on the first page, and a data integrity check is performed. The check result is filled in the second page or an exception is marked on the first page to remind the data anomaly. Through visual display and verification, the data review process is more intuitive and efficient. The exception marking makes it easy to quickly locate the problem and reduce errors in subsequent processing.

[0063] In an exemplary embodiment, after displaying the structured data through a first page, the method further includes: setting a trigger component for the structured data in the first page; and when storing the structured data in a target system, setting a storage tag for the structured data in the target system through the trigger component.

[0064] Optionally, the trigger component in this embodiment is a component used to store structured data in a database, including but not limited to the form of a button, a drop-down menu, and a check box.

[0065] Optionally, the storage tag in this embodiment is used to indicate that the structured data has been stored in the database, including but not limited to data status tags, such as "entered", "pending review", "review passed", "review failed"; operator tag: records the system user or operator information who performs the data storage operation, so as to facilitate tracking the person responsible for data entry; timestamp tag, records the specific time when the data is stored in the target system, such as "warehouse entry time: 2023-04-05 14:30:00"; data source tag, marks the source of the data, such as "source: ICR automatic identification" or "source: manual entry".

[0066] For example, in the scenario where a bank enters a user's real estate loan information, the real estate ownership certificate records to be stored need to be displayed in a list on the web page of the query list of electronic ledgers to be stored. An "enter" button (corresponding to the above-mentioned trigger component) is set behind each record. After the data is entered into the bank's personal loan system, you need to click the "enter" button to mark the record with an entry label (corresponding to the above-mentioned storage label). After the data is successfully "stored", it will not be displayed again on the web page of the query list of electronic ledgers to be stored.

[0067] Through the above steps, when the structured data is stored in the target system, the storage tag is set through the trigger component set in the first page, which simplifies the data storage process and improves the efficiency of the storage operation.

[0068] The above method is explained below with a specific example:

[0069] Figure 3 is a flow chart of a method for generating real estate data according to an embodiment of the present application. The branch personal loan system needs to process the real estate registration certificate provided by the user, such as Figure 3 As shown, the process includes the following steps:

[0070] Step S302, using ICR technology to identify key information in the real estate certificate, and storing the key information in the real estate information database, wherein the key information includes user information of the user to whom the real estate (corresponding to the first item) belongs, real estate registration certificate number, real estate registration certificate number, mortgage period, location (corresponding to the location information of the first item), etc.;

[0071] Step S304, extracting user information and location from the key information to obtain first data;

[0072] Step S306, using a database similarity algorithm, searching for N second data whose matching degree with the first data is greater than 80% (corresponding to the first threshold) from the user information database of the bank's personal loan system (corresponding to the first database), where N is a natural number greater than or equal to 1;

[0073] Step S308, searching for data that completely matches the location from the location information of the N first real estates to obtain target data, wherein the N first real estates are the real estates in the N second data;

[0074] Step S310, generating structured data based on the target data and the location, and displaying the structured data on the "web page for querying the list of electronic ledgers to be stored";

[0075] Step S312, performing a storage operation on the above structured data, and entering the structured data into the branch loan information database.

[0076] It should be noted that, through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0077] In the present embodiment, a data generating device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0078] Figure 4 is a structural block diagram of a data generating device according to an embodiment of the present application, such as Figure 4 As shown, the device comprises:

[0079] An acquisition module 402 is used to acquire first data, wherein the first data includes user information of a target user and location information of a first item, and the first item belongs to the target user;

[0080] A first search module 404 is used to search the first database for N second data whose matching degree with the first data is greater than a first threshold, wherein the N second data include location information of M second objects, identity information of users to which the M second objects belong, and item information of the M second objects, and both M and N are natural numbers greater than or equal to 1;

[0081] A second search module 406 is used to search for data whose matching degree with the location information of the first object is greater than a second threshold from the N second data to obtain target data;

[0082] The generating module 408 is used to generate structured data based on the target data and the item information of the first item, wherein the structured data is used to associate the target data with the item information of the first item.

[0083] In an exemplary embodiment, the first search module 404 further includes: a first extraction unit, used to extract file information from the acquired target file, wherein the file information includes user information of K second users, item information of P third items, and attribute information of the target file, wherein both K and P are natural numbers greater than or equal to 1, and the K second users include the first user, and the P third items include M second items; a first execution unit, used to perform file processing operations on the file information to obtain target file information, wherein the file processing operations include: text cleaning operations, text standardization operations, and text verification operations in sequence, and the text verification operations are used to verify the information integrity and logical integrity of the target file information; a second extraction unit, used to extract text key information from the target file information, wherein the text key information includes user information of the K second users, item information of the P third items, association information between each third item and the user to which it belongs, and ownership certification information of each third item; a first storage unit, used to store the text key information in the first database.

[0084] In an exemplary embodiment, the first search module 404 also includes: a first search unit, used to search from the first database for Q third data whose matching degree with the user information of the target user is greater than a third threshold, wherein Q is a natural number greater than or equal to 1; and a first determination unit, used to determine the Q third data as the N second data when the third threshold is greater than or equal to the first threshold.

[0085] In an exemplary embodiment, the second search module 406 also includes: a first calculation unit, used to calculate the matching degree between the location information of the N second items and the location information of the first item, to obtain N location matching degrees; a first comparison unit, used to compare the N location matching degrees and a second threshold, and determine the location information and item information of the second item corresponding to the target location matching degree greater than the second threshold as the target data.

[0086] In an exemplary embodiment, the generation module 408 further includes: a first setting unit, used to set a first item identifier of the first item and a second item identifier of the second item included in the target data; a first conversion unit, used to convert the first item identifier and the second item identifier into a structured format, respectively, to obtain first structure data and second structure data; a first association unit, used to associate the first structure data and the second structure data to obtain the structured data when the structure verification of the first structure data and the second structure data passes.

[0087] In an exemplary embodiment, the generation module 408 further includes: a first display unit, used to display the structured data through a first page, and perform a verification operation on the structured data on the first page, wherein the verification operation includes a data integrity verification; a first filling unit, used to fill the execution result of the verification operation into the second page; or, marking the data that has not passed the verification operation on the first page to remind that there is an abnormality in the structured data.

[0088] In an exemplary embodiment, the generation module 408 further includes: a second setting unit for setting a trigger component for the structured data in the first page; and a third setting unit for setting a storage tag for the structured data in the target system through the trigger component when storing the structured data in the target system.

[0089] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0090] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0091] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0092] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0093] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0094] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0095] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0096] The embodiments of the present application also provide a computer program, which includes computer instructions, which are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of any one of the above method embodiments.

[0097] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0098] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0099] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A data generation method, characterized in that: The method comprises: Acquire first data, wherein the first data includes user information of a target user and location information of a first item, and the first item belongs to the target user; Searching for N second data whose matching degree with the first data is greater than a first threshold from the first database, wherein the N second data include location information of M second objects, identity information of first users to which the M second objects belong, and object information of the M second objects, and both M and N are natural numbers greater than or equal to 1; Searching for data whose matching degree with the location information of the first object is greater than a second threshold from the N second data to obtain target data; Structured data is generated based on the target data and the item information of the first item, wherein the structured data is used to associate the target data with the item information of the first item.

2. The method according to claim 1, characterized in that Before searching the first database for N second data whose matching degree with the first data is greater than a first threshold, the method further includes: Extracting file information from the acquired target file, wherein the file information includes user information of K second users, item information of P third items, and attribute information of the target file, wherein both K and P are natural numbers greater than or equal to 1, and the K second users include the first user, and the P third items include M second items; Performing a file processing operation on the file information to obtain target file information, wherein the file processing operation includes: a text cleaning operation, a text standardization operation, and a text verification operation in sequence, and the text verification operation is used to verify the information integrity and logical integrity of the target file information; Extracting text key information from the target file information, wherein the text key information includes user information of K second users, item information of P third items, association information between each third item and the user to which it belongs, and ownership certification information of each third item; The text key information is stored in the first database.

3. The method according to claim 1, characterized in that Searching, from a first database, N second data whose matching degree with the first data is greater than a first threshold, comprises: Searching the first database for Q third data whose matching degree with the user information of the target user is greater than a third threshold, where Q is a natural number greater than or equal to 1; When the third threshold is greater than or equal to the first threshold, the Q third data are determined as the N second data.

4. The method according to claim 1, characterized in that: Searching for data whose matching degree with the location information of the first object is greater than a second threshold from the N second data to obtain target data includes: Calculating the matching degrees between the N pieces of location information of the second objects and the location information of the first object to obtain N position matching degrees; The N position matching degrees are compared with a second threshold, and the position information and the item information of the second item corresponding to the target position matching degree greater than the second threshold are determined as the target data.

5. The method according to claim 1, characterized in that Generating structured data based on the target data and the item information of the first item includes: Setting a first item identifier of the first item and a second item identifier of a second item included in the target data; Converting the first item identifier and the second item identifier into a structured format respectively to obtain first structure data and second structure data; When the structure verification of the first structure data and the second structure data is passed, the first structure data and the second structure data are associated to obtain the structured data.

6. The method according to claim 5, characterized in that In the case where the structure verification of the first structure data and the second structure data is passed, after associating the first structure data and the second structure data to obtain the structured data, the method further includes: Displaying the structured data through a first page, and performing a verification operation on the structured data on the first page, wherein the verification operation includes a data integrity check; Fill the execution result of the verification operation into the second page; or, mark the data that fails the verification operation on the first page to remind that there is an abnormality in the structured data.

7. The method according to claim 6, characterized in that After displaying the structured data through the first page, the method further includes: Setting a trigger component of the structured data in the first page; When the structured data is stored in a target system, a storage tag of the structured data is set in the target system by the trigger component.

8. A data generating device, characterized in that: include: An acquisition module, configured to acquire first data, wherein the first data includes user information of a target user and location information of a first item, wherein the first item belongs to the target user; A first search module, configured to search, from a first database, N second data whose matching degree with the first data is greater than a first threshold, wherein the N second data include location information of M second objects, identity information of users to which the M second objects belong, and object information of the M second objects, and both M and N are natural numbers greater than or equal to 1; A second search module, configured to search for data whose matching degree with the location information of the first object is greater than a second threshold from the N second data, to obtain target data; A generating module is used to generate structured data based on the target data and the item information of the first item, wherein the structured data is used to associate the target data with the item information of the first item.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 7 when executed by a processor.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 7 are implemented.

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