Data processing method and related equipment

Abstract

The invention discloses a data processing method and related equipment, and relates to the technical field of data processing, and the method comprises the steps: determining to-be-processed first commodity information data, determining a first data type corresponding to a data processing end, adjusting the first commodity information data, and obtaining second commodity information data, and sending the second commodity information data to a data processing end, receiving target data sent by the data processing end, and adjusting the target data based on a second data type corresponding to the local database to obtain the adjusted target data so as to update the local database. Based on the commodity information data, the local database in the uniform format is constructed, when the data processing end needs to process the data, the data in the local database is converted into the data needing to be processed by the data processing end, so that the data processing end can directly obtain the data from the data management end and process the data, different data processing ends are compatible, and the data processing efficiency is improved. Unified processing and management of multi-source heterogeneous data are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a data processing method and related equipment. BACKGROUND

[0002] In the existing overseas purchasing business, commodity information is scattered in multiple heterogeneous systems such as records, PDCs, and supply orders, forming data islands. Buyers need to repeatedly enter 30+ fields such as brand, specification, and material across systems, which is time-consuming and prone to errors. The data consistency is difficult to guarantee, which seriously restricts the purchasing efficiency and business digital transformation. SUMMARY

[0003] The main purpose of the present application is to provide a data processing method and related equipment, aiming to solve the technical problem of how to uniformly manage and process multi-source heterogeneous data.

[0004] To achieve the above purpose, the present application provides a data processing method applied to a data management end, which comprises: In response to a commodity information reading instruction sent by a data processing end, determining first commodity information data to be processed in a local database, and determining a first data type corresponding to the data processing end; Based on the first data type, adjusting the first commodity information data to obtain second commodity information data; Sending the second commodity information data to the data processing end, so that the data processing end processes the second commodity information data based on a preset data processing strategy to obtain target data; Receiving target data sent by the data processing end, adjusting the target data based on a second data type corresponding to the local database to obtain adjusted target data; Based on the adjusted target data, updating the local database.

[0005] In an embodiment, the step of adjusting the first commodity information data based on the first data type to obtain second commodity information data further comprises: Determining a first mapping relationship between a first field name corresponding to the first data type and a first field, and a second mapping relationship between a second field name corresponding to the first commodity information data and a second field; Based on semantic analysis, determining the similarity between the first field name and the second field name; Based on the similarity, the first mapping relationship and the second mapping relationship, determining a third mapping relationship between the first field and the second field; Based on the third mapping relationship, adjusting the first commodity information data to obtain second commodity information data.

[0006] In an embodiment, before the step of responding to the commodity information processing instruction sent by the data processing end, the method further comprises: In response to the data processing instruction, a plurality of first commodities are obtained, and a plurality of source heterogeneous data corresponding to the first commodities are obtained; Based on a preset AI intelligent analysis strategy, a preset AI intelligent analysis model is used to analyze the plurality of source heterogeneous data to obtain structured commodity information corresponding to the first commodities; Based on the structured commodity information, a local database is constructed.

[0007] In an embodiment, before the step of using a preset AI intelligent analysis model to analyze the plurality of source heterogeneous data to obtain structured commodity information corresponding to the first commodities, the method further comprises: Obtaining sample data corresponding to a first data processing result; Using a current AI intelligent analysis model to process the sample data to obtain a second data processing result; Judging whether the first data processing result and the second data processing result are consistent; If the first data processing result and the second data processing result are inconsistent, adjusting the parameters of the current AI intelligent analysis model, and based on the current AI intelligent analysis model after adjusting the parameters, returning to the step of using the current AI intelligent analysis model to process the sample data to obtain a second data processing result until the first data processing result and the second data processing result are consistent, and obtaining a preset AI intelligent analysis model.

[0008] In an embodiment, after the step of constructing a local database based on the structured commodity information, the method further comprises: Real-time obtaining market data corresponding to the first commodities, and obtaining business rules corresponding to the first commodities; Based on the business rules, the market data, and a preset data verification strategy, verifying the accuracy of the data in the local database to obtain a data verification result; Based on the data verification result, updating the local database.

[0009] To achieve the above purpose, the present application also proposes a data processing method applied to a data processing end, the data processing method comprising: In response to a commodity information processing instruction, determining first commodity information data to be processed in a data management end, and determining a second data type corresponding to the data management end; generate a commodity information reading instruction based on the first commodity information data, and send the commodity information reading instruction to the data management terminal, so that the data management terminal adjusts a structure of the first commodity information data based on the commodity information reading instruction, obtains second commodity information data, and sends the second commodity information data to the data processing terminal; receive the second commodity information data sent by the data management terminal, process the second commodity information data based on a preset data processing strategy, and obtain target data; send the target data to the data management terminal, so that the data management terminal updates a local database of the data management terminal based on the target data.

[0010] In addition, to achieve the above-mentioned purpose, the application further provides a data processing device applied to a data management terminal, which comprises: a first determination module, configured to determine first commodity information data to be processed in a local database and a first data type corresponding to the data processing terminal in response to a commodity information reading instruction sent by the data processing terminal; an adjustment module, configured to adjust the first commodity information data based on the first data type, and obtain second commodity information data; a first sending module, configured to send the second commodity information data to the data processing terminal, so that the data processing terminal processes the second commodity information data based on a preset data processing strategy, and obtains target data; a receiving module, configured to receive target data sent by the data processing terminal, adjust the target data based on a second data type corresponding to the local database, and obtain adjusted target data; an updating module, configured to update the local database based on the adjusted target data.

[0011] In an embodiment, the adjustment module further comprises: a first determination unit, configured to determine a first mapping relationship between a first field name corresponding to the first data type and a first field, and a second mapping relationship between a second field name corresponding to the first commodity information data and a second field; a second determination unit, configured to determine a similarity between the first field name and the second field name based on semantic analysis; a third determination unit, configured to determine a third mapping relationship between the first field and the second field based on the similarity, the first mapping relationship and the second mapping relationship; An adjusting unit is configured to adjust the first commodity information data based on the third mapping relationship to obtain second commodity information data.

[0012] In an embodiment, the data processing apparatus further comprises a data acquisition module, and the data acquisition module further comprises: A first acquisition unit is configured to acquire a plurality of groups of first commodities and acquire a plurality of source heterogeneous data corresponding to the first commodities in response to a data processing instruction. An analysis unit is configured to analyze the plurality of source heterogeneous data using a preset AI intelligent analysis model based on a preset AI intelligent analysis strategy to obtain structured commodity information corresponding to the first commodities. A construction unit is configured to construct a local database based on the structured commodity information.

[0013] In an embodiment, the data acquisition module further comprises: A second acquisition unit is configured to acquire sample data, and the sample data corresponds to a first data processing result. A data processing unit is configured to process the sample data using a current AI intelligent analysis model to obtain a second data processing result. A judgment unit is configured to judge whether the first data processing result and the second data processing result are consistent. An iterative training unit is configured to, if the first data processing result and the second data processing result are inconsistent, adjust parameters of the current AI intelligent analysis model, return to the step of processing the sample data using the current AI intelligent analysis model based on the current AI intelligent analysis model after the parameters are adjusted to obtain the second data processing result, until the first data processing result and the second data processing result are consistent, and obtain a preset AI intelligent analysis model.

[0014] In an embodiment, the data acquisition module further comprises: A third acquisition unit is configured to acquire market data corresponding to the first commodities and acquire business rules corresponding to the first commodities in real time. A verification unit is configured to verify the accuracy of data in the local database based on the business rules, the market data, and a preset data verification strategy to obtain a data verification result. An updating unit is configured to update the local database based on the data verification result.

[0015] In addition, to achieve the above object, the application further provides a data processing apparatus applied to a data processing end, and the data processing apparatus comprises: The second determining module is configured to determine first commodity information data to be processed in the data management end and determine a second data type corresponding to the data management end in response to the commodity information processing instruction; The generating module is configured to generate a commodity information reading instruction based on the first commodity information data, and send the commodity information reading instruction to the data management end, so that the data management end adjusts a structure of the first commodity information data based on the commodity information reading instruction to obtain second commodity information data, and sends the second commodity information data to the data processing end; The data processing module is configured to receive the second commodity information data sent by the data management end, process the second commodity information data based on a preset data processing strategy to obtain target data. The second sending module is configured to send the target data to the data management end, so that the data management end updates a local database of the data management end based on the target data.

[0016] In addition, to achieve the above-mentioned purpose, the present application further provides a data processing device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the data processing method as described above.

[0017] In addition, to achieve the above-mentioned purpose, the present application further provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the data processing method as described above.

[0018] In addition, to achieve the above-mentioned purpose, the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps of the data processing method as described above.

[0019] The one or more technical solutions provided by the present application have at least the following technical effects: The application provides a data processing method and a related device, relates to the technical field of data processing, and solves the problems that in related technologies, in an existing overseas purchasing business, commodity information is scattered in multiple sets of heterogeneous systems such as a record, a PDC and a supply order, data islands are formed, a buyer needs to repeatedly input 30+ fields such as a brand, a specification and a material across systems, manual operation is time-consuming and error-prone, data consistency is difficult to guarantee, and the purchasing efficiency is seriously restricted compared with a business digital transformation. In the application, first, in response to a commodity information reading instruction sent by a data processing end, first commodity information data to be processed in a local database is determined, and a first data type corresponding to the data processing end is determined. Then, based on the first data type, the first commodity information data is adjusted to obtain second commodity information data. Further, the second commodity information data is sent to the data processing end, so that the data processing end processes the second commodity information data based on a preset data processing strategy to obtain target data. Further, the target data sent by the data processing end is received, the target data is adjusted based on a second data type corresponding to the local database to obtain adjusted target data. Finally, the local database is updated based on the adjusted target data.

[0020] It can be understood that the application constructs a local database in a unified format based on commodity information data. When the data processing end needs to process data, the data in the local database is converted into data required to be processed by the data processing end, so that the data processing end can directly obtain data from the data management end and process the data, different data processing ends are compatible, and unified processing and management of multi-source heterogeneous data are realized. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings incorporated in and forming a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application.

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the application or the related art, the accompanying drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0023] Figure 1 A flowchart provided by the data processing method embodiment one of the application; Figure 2 A flowchart provided by the data processing method embodiment two of the application; Figure 3 A flowchart provided by the data processing method embodiment three of the application; Figure 4 A module structure diagram of the data processing device in the embodiment of the application. Figure 5 A device structure schematic diagram of a hardware running environment involved in a data processing method in an embodiment of the present application.

[0024] The object implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0025] It should be understood that the specific embodiments described herein are merely intended to explain the technical solutions of the present application, and are not intended to limit the present application.

[0026] In order to better understand the technical solutions of the present application, the specific embodiments will be described in detail below with reference to the drawings and the accompanying drawings.

[0027] The main solution of the embodiment of the present application is: In the present embodiment, for the convenience of description, the following data processing device is taken as the execution subject.

[0028] Due to the related art: in the existing overseas purchasing business, commodity data is scattered in multiple heterogeneous systems such as record, PDC, supply order, etc., forming a data island. Buyers need to repeatedly enter 30+ fields such as brand, specification, material, etc. across systems, which is time-consuming and prone to errors, and the data consistency is difficult to guarantee, which seriously restricts the purchasing efficiency and business digital transformation.

[0029] The present application provides a solution, which makes: first, in response to the commodity information reading instruction sent by the data processing end, the first commodity information data to be processed in the local database is determined, and the first data type corresponding to the data processing end is determined, then based on the first data type, the first commodity information data is adjusted to obtain the second commodity information data, further, the second commodity information data is sent to the data processing end, so that the data processing end processes the second commodity information data based on the preset data processing strategy to obtain the target data, further, the target data sent by the data processing end is received, based on the second data type corresponding to the local database, the target data is adjusted to obtain the adjusted target data, and finally, based on the adjusted target data, the local database is updated.

[0030] It can be understood that the present application constructs a local database with a unified format based on commodity information data, when the data processing end needs to process data, the data in the local database is converted into the data required by the data processing end, so that the data processing end can directly obtain data from the data management end and process it, compatible with different data processing ends, realizing the unified processing and management of multi-source heterogeneous data.

[0031] It should be noted that the execution subject of the embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, or an electronic device or a data processing device capable of realizing the above functions. The following will take a data processing device as an example to describe the embodiment and the following embodiments.

[0032] Based on this, the embodiment of the application provides a data processing method applied to a data management end, referring to Figure 1 , Figure 1 The figure is a flowchart of the first embodiment of the data processing method of the application.

[0033] In the embodiment, the data processing method comprises steps S10-S50: Step S10, in response to a product information reading instruction sent by a data processing end, determining first product information data to be processed in a local database, and determining a first data type corresponding to the data processing end; It should be noted that the data processing end refers to a system or module responsible for specific data processing. It can be a server, an application program or a data analysis platform, which can receive data and execute a preset processing strategy.

[0034] It should be noted that the product information reading instruction refers to an instruction sent by the data processing end, which is used to request the local database to provide specific product information data. The instruction usually contains query conditions such as product ID, time range, etc.

[0035] It should be noted that the local database refers to a database storing product information, which is located in the local environment of the data processing system. It can be a relational database (such as MySQL, Oracle) or a non-relational database (such as MongoDB).

[0036] It should be noted that the first product information data refers to the original product information data read from the local database, which has not been adjusted or formatted.

[0037] It should be noted that the first data type refers to the storage format or data type of the first product information data in the local database. For example, the inventory quantity can be stored as an integer type, the price can be stored as a floating point type, and the date can be stored as a date time type.

[0038] In the embodiment, the system first receives a product information reading instruction from the data processing end. According to the instruction, the system finds and extracts the product information data to be processed (first product information data) in the local database. At the same time, the system determines the storage format (first data type) of these data in the local database, so as to adjust according to the requirements of the data processing end in the subsequent steps.

[0039] adjusting the first commodity information data based on the first data type to obtain second commodity information data; It should be noted that in this application, adjustment refers to operations such as formatting, conversion, cleaning, or supplementing data to meet the requirements of the data processing end.

[0040] It should be noted that the second commodity information data refers to the adjusted commodity information data, the format and content of which have been adapted to the needs of the data processing end.

[0041] In this embodiment, the system adjusts the first commodity information data according to the first data type. The purpose of adjustment is to convert the data into a format that the data processing end can accept. For example: if the data processing end needs to receive JSON format data, and the data in the local database is in table form, the system will convert the table data into JSON format. If the data processing end needs the date and time format to be "YYYY-MM-DD", and the local database stores "DD-MM-YYYY", the system will convert the date format. If there are missing values or abnormal values in the data, the system will clean or supplement the data. After adjustment, the second commodity information data is obtained, which is ready to be sent to the data processing end.

[0042] Specifically, the step of adjusting the first commodity information data based on the first data type to obtain second commodity information data further comprises steps S21-S24: Step S21, determining a first mapping relationship between a first field name corresponding to the first data type and a first field, and determining a second mapping relationship between a second field name corresponding to the first commodity information data and a second field; It should be noted that the first data type refers to the storage format or data type of the commodity information data in the local database, which usually includes field name and field data type (such as integer, string, date, etc.).

[0043] It should be noted that the first field name refers to the name of the field in the local database, such as "product ID", "inventory quantity", "price", etc.

[0044] It should be noted that the first field refers to the field content corresponding to the first field name, i.e. the actual stored data.

[0045] It should be noted that the first mapping relationship refers to the correspondence between the first field name and the first field, i.e. the mapping of the field name to the field content.

[0046] It should be noted that the second field name refers to the field name expected to be received by the data processing end, which may be different from the field name in the local database.

[0047] It should be noted that the second field refers to the field content corresponding to the second field name, i.e. the data after adjustment needs to be sent to the data processing end.

[0048] It should be noted that the second mapping relationship refers to the mapping relationship between the field name and the field content in the first commodity information data.

[0049] In this embodiment, the system first determines the mapping relationship (first mapping relationship) between the field name and the field content of the first data type in the local database. At the same time, the mapping relationship (second mapping relationship) between the field name and the field content in the first commodity information data is determined. The purpose of this step is to clarify the field difference between the local database and the target data processing end, and to provide a basis for subsequent field mapping and adjustment.

[0050] Step S22, based on semantic analysis, determining the similarity between the first field name and the second field name; It should be noted that semantic analysis refers to a natural language processing technology for analyzing the semantic meaning of field names to determine whether they have similar or identical meanings.

[0051] It should be noted that the similarity refers to the semantic similarity between the field names, which is usually represented by a numerical value (such as between 0 and 1, 1 indicating complete identity).

[0052] In this embodiment, the system compares the first field name in the local database and the second field name expected by the data processing end through semantic analysis technology. For example, the local field name is "inventory quantity", and the field name expected by the data processing end is "inventory". Through semantic analysis, the system can determine that the two field names are similar in semantics, and thus calculate the similarity between them.

[0053] Step S23, based on the similarity, the first mapping relationship and the second mapping relationship, determining a third mapping relationship between the first field and the second field; It should be noted that the third mapping relationship refers to the mapping relationship between the first field and the second field, i.e. the corresponding relationship between the local field content and the adjusted field content.

[0054] In this embodiment, the system determines the mapping relationship (third mapping relationship) between the local field and the target field according to the similarity calculated in step S22, in combination with the first mapping relationship and the second mapping relationship. For example, if the similarity between "inventory quantity" and "inventory amount" is high, the system maps the "inventory quantity" field in the local database to the "inventory amount" field in the data processing end.

[0055] In step S24, the first commodity information data is adjusted based on the third mapping relationship to obtain second commodity information data.

[0056] It should be noted that adjustment refers to adjusting the field name and content of the first commodity information data according to the third mapping relationship, so as to meet the requirements of the data processing end.

[0057] In this embodiment, the system adjusts the first commodity information data according to the third mapping relationship to generate the second commodity information data. This may include operations such as renaming of field names and format conversion of field contents. For example, if the field name in the local database is "inventory quantity" and the field name expected by the data processing end is "inventory amount", the system will adjust the field name from "inventory quantity" to "inventory amount", and format the corresponding field content (such as converting an integer to a floating point number), and finally generate the second commodity information data that meets the requirements of the data processing end.

[0058] It can be understood that by determining the field mapping relationship, analyzing the similarity of the field names, and adjusting the data according to the similarity and the mapping relationship, the system can efficiently convert the data in the local database into the format required by the data processing end, and significantly improve the efficiency and accuracy of data processing.

[0059] In step S30, the second commodity information data is sent to the data processing end, so that the data processing end processes the second commodity information data based on a preset data processing strategy to obtain target data. It should be noted that the preset data processing strategy refers to the processing logic defined in advance by the data processing end, which is used to perform specific operations on the input data. For example, calculating the average value, sorting, classification, etc.

[0060] It should be noted that the target data refers to the result data generated by the data processing end after processing the second commodity information data according to the preset strategy.

[0061] In this embodiment, the system sends the adjusted second commodity information data to the data processing end. After receiving the data, the data processing end processes the data according to its preset processing strategy. For example, if the task of the data processing end is to calculate the average inventory level of the commodity, it will receive the second commodity information data, calculate the average inventory of each commodity, and generate target data. If the task of the data processing end is to classify the commodities, it will process the data according to the characteristics of the commodities (such as price, category, etc.), and generate the classification results as target data. After processing, the data processing end returns the target data to the local system.

[0062] Step S40, receiving the target data sent by the data processing end, adjusting the target data based on the second data type corresponding to the local database, to obtain adjusted target data; It should be noted that the second data type refers to the format or data type of the target data expected to be stored by the local database. This may be different from the format of the target data generated by the data processing end.

[0063] It should be noted that the adjusted target data refers to the target data after format adjustment, which is adapted to the storage requirements of the local database.

[0064] In this embodiment, the local system receives the target data returned by the data processing end. Since the target data generated by the data processing end may not be consistent with the storage format of the local database, the system needs to adjust the target data according to the second data type of the local database. For example, if the target data returned by the data processing end is in floating-point format (such as average inventory of 10.5), and the local database needs to store integer format, the system will round the floating-point number to an integer. If the data returned by the data processing end is in string format, and the local database needs to store date and time format, the system will convert the string to date and time format. After adjustment, the adjusted target data adapted to the local database is obtained.

[0065] Step S50, updating the local database based on the adjusted target data.

[0066] It should be noted that updating refers to storing the adjusted target data into the local database, replacing or supplementing the existing data.

[0067] In this embodiment, the system writes the adjusted target data into the local database, completing the data update operation. This step ensures that the data in the local database is up-to-date and meets the system's storage requirements. For example, if the target data is the average inventory level of a product, the system will update these data into the corresponding field of the local database. If the target data is the classification result of a product, the system will store the classification information into the classification field of the local database. After the update is completed, the data in the local database will reflect the latest processing results, providing support for subsequent business operations.

[0068] The present application provides a data processing method and related equipment, which relates to the technical field of data processing. Compared with related technologies, in the existing overseas purchasing business, product information is scattered in multiple heterogeneous systems such as records, PDC, and supply lists, forming a data island. Buyers need to repeatedly enter 30+ fields such as brand, specification, and material across systems, which is time-consuming and prone to errors. The data consistency is difficult to guarantee, which seriously restricts the purchasing efficiency and business digital transformation. In the present application, first, in response to the product information reading instruction sent by the data processing end, the first product information data to be processed in the local database is determined, and the first data type corresponding to the data processing end is determined. Then, based on the first data type, the first product information data is adjusted to obtain second product information data. Further, the second product information data is sent to the data processing end for processing based on a preset data processing strategy to obtain target data. Further, the target data sent by the data processing end is received, and the target data is adjusted based on the second data type corresponding to the local database to obtain adjusted target data. Finally, the local database is updated based on the adjusted target data.

[0069] It can be understood that the present application constructs a local database with a unified format based on product information data. When the data processing end needs to process data, the data in the local database is converted into the data that the data processing end needs to process, so that the data processing end can directly obtain data from the data management end and process it. Different data processing ends are compatible, realizing unified processing and management of multi-source heterogeneous data.

[0070] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar content as the above embodiment one can refer to the above introduction, and the following will not be repeated. On this basis, please refer to Figure 2 , the step of responding to the product information processing instruction sent by the data processing end further includes steps A10-A30: Step A10, in response to the data processing instruction, a plurality of first products are obtained, and a plurality of source heterogeneous data corresponding to the first products are obtained; It should be noted that the data processing instruction refers to the instruction that triggers the data processing flow, which is usually initiated by a system user or an automated task, instructing the system to start processing data.

[0071] It should be noted that the first commodity refers to the commodity data that needs to be processed, and these commodities may be obtained from different data sources. Each set of commodity data may contain multiple instances of commodities.

[0072] It should be noted that multi-source heterogeneous data refers to data from multiple different sources, which may differ in format, structure, and type. For example, data may come from different databases, file systems, API interfaces, etc., and formats may be JSON, XML, CSV, relational database tables, etc.

[0073] In this embodiment, the system first responds to the data processing instruction and then obtains multiple sets of first commodity data. At the same time, the system also obtains multi-source heterogeneous data corresponding to these first commodities. This means that the system needs to extract data related to commodities from different data sources and integrate these data together. For example: the first set of commodity data may come from a database of an e-commerce platform, containing commodity name, price, and inventory information. The second set of commodity data may come from a supplier's CSV file, containing commodity supplier information and production date. The third set of commodity data may come from an API interface, containing commodity user reviews and sales data.

[0074] Step A20, based on the preset AI intelligent analysis strategy, using the preset AI intelligent analysis model to analyze the multi-source heterogeneous data, obtaining the structured commodity information corresponding to the first commodity; It should be noted that the preset AI intelligent analysis strategy refers to the rules and methods of using artificial intelligence (AI) technology to analyze data defined in advance. These strategies usually include data cleaning, feature extraction, format conversion, etc.

[0075] It should be noted that the preset AI intelligent analysis model refers to the pre-trained AI model used to analyze and process multi-source heterogeneous data. These models may be based on machine learning algorithms (such as classification, clustering) or deep learning algorithms (such as neural networks).

[0076] It should be noted that the structured commodity information refers to the data after analysis and arrangement, which has a unified format and structure, facilitating subsequent processing and storage. For example, structured commodity information may be a table, where each row represents a commodity and each column represents an attribute (such as name, price, inventory, etc.).

[0077] In this embodiment, the system uses a preset AI intelligent analysis strategy and model to analyze the multi-source heterogeneous data obtained in step A10. The AI intelligent analysis model automatically identifies and extracts key information from the data and converts it into structured product information. For example: if the data contains text descriptions, the AI model may use natural language processing (NLP) techniques to extract key information such as product specifications, functions, etc. If the data contains images, the AI model may use computer vision techniques to identify image content such as product appearance features. If the data contains different formats of date and time information, the AI model will convert it into a standard date and time format. After analysis, structured product information is obtained, which has a unified format and structure, facilitating subsequent processing and storage.

[0078] Specifically, before the step of using a preset AI intelligent analysis model to analyze the multi-source heterogeneous data to obtain structured product information corresponding to the first product, the system further includes steps A21-A24: Step A21: Obtain sample data, which corresponds to a first data processing result. It should be noted that sample data refers to a data set used to train and verify the AI intelligent analysis model. These data are usually extracted from actual business scenarios and are representative.

[0079] It should be noted that the first data processing result refers to the result obtained by manually processing or known correct processing method of sample data, which is used as the target or reference standard for model training.

[0080] In this embodiment, the system obtains a set of sample data, which has been processed and has a clear processing result (first data processing result). These results are usually manually annotated or generated through known correct processing logic and used as the standard for model training and verification.

[0081] For example: the sample data may be a set of product description texts, and the first data processing result is the extracted product name, price, and other key information in these texts. The sample data may be a set of images, and the first data processing result is the identified product features in the images.

[0082] Step A22: Process the sample data using the current AI intelligent analysis model to obtain a second data processing result. It should be noted that the current AI intelligent analysis model refers to the AI model being trained or optimized, which is used to process sample data and generate results.

[0083] It should be noted that the second data processing result refers to the result generated by the current AI intelligent analysis model after processing the sample data.

[0084] In this embodiment, the system uses the current AI intelligent analysis model to process the sample data and generate a second data processing result. The purpose of this step is to observe the model's ability to process sample data through actual operation.

[0085] For example, if the sample data is a product description text, the current AI model will try to extract the product name, price, etc. from it and generate a second data processing result. If the sample data is an image, the current AI model will try to identify the product features in the image and generate a second data processing result.

[0086] Step A23, determining whether the first data processing result and the second data processing result are consistent; It should be noted that consistency determination refers to comparing the first data processing result (known correct result) and the second data processing result (model generated result) to determine whether they are the same or close enough.

[0087] In this embodiment, the system compares the first data processing result and the second data processing result to determine whether they are consistent. This step is a key link in the model training process, which is used to evaluate the accuracy and reliability of the model.

[0088] For example, if the first data processing result is the product name "iPhone14" and the second data processing result is also "iPhone14", it is considered consistent. If the first data processing result is the product price "6999 yuan" and the second data processing result is "6998 yuan", it may need to be determined whether to accept this small difference according to business requirements.

[0089] Step A24, if the first data processing result and the second data processing result are inconsistent, adjust the parameters of the current AI intelligent analysis model, based on the adjusted parameters of the current AI intelligent analysis model, return to the step of using the current AI intelligent analysis model to process the sample data to obtain the second data processing result, until the first data processing result and the second data processing result are consistent, and obtain the preset AI intelligent analysis model.

[0090] It should be noted that adjusting parameters means adjusting the parameters of the model (such as learning rate, weight, etc.) according to the difference between the output result of the model and the target result, in order to optimize the performance of the model.

[0091] It should be noted that the preset AI intelligent analysis model refers to an AI model that has been trained and optimized and can accurately process sample data and generate consistent results with the target result.

[0092] In this embodiment, if the first data processing result is inconsistent with the second data processing result, the system will adjust the parameters of the current AI intelligent analysis model. The purpose of adjusting the parameters is to enable the model to generate output closer to the target result when processing sample data next time.

[0093] After adjusting the parameters, the system will use the current AI intelligent analysis model to process the sample data again, generate the second data processing result, and repeat the consistency judgment of step A23. This process will continue to circulate until the first data processing result is consistent with the second data processing result.

[0094] For example, if the model makes mistakes when extracting product names, the system may adjust the text analysis parameters of the model to make it more accurate in identifying key information. If the model deviates when identifying image features, the system may adjust the neural network weights of the model to improve the accuracy of identification. When the model generates a result consistent with the target result, it is considered that the model has been trained and the preset AI intelligent analysis model has been obtained. This model can be used for subsequent actual data processing tasks.

[0095] It can be understood that by obtaining sample data, using the model to process data, judging the consistency of the results, and adjusting the parameters of the model, the system can continuously optimize the AI intelligent analysis model until it can accurately process sample data. This iterative optimization method ensures the accuracy and reliability of the model in actual application.

[0096] Step A30, based on the structured product information, a local database is constructed.

[0097] It should be noted that the local database refers to a database that stores structured product information and is located in the local environment of the system. It can be a relational database (such as MySQL, PostgreSQL) or a non-relational database (such as MongoDB).

[0098] It should be noted that construction means storing structured product information into the local database, creating corresponding data tables and fields, and inserting data into the database.

[0099] In this embodiment, the system stores the structured product information obtained in step A20 into the local database. This step includes: creating data tables (creating corresponding data tables and fields according to the format and content of structured product information), data insertion (inserting structured product information into data tables to ensure data integrity and consistency), and data indexing (creating indexes for data tables to improve query efficiency).

[0100] For example, if the structured commodity information contains fields such as commodity name, price, inventory, supplier, etc., the system will create a commodity table in the local database and use these fields as columns of the table. Then, each piece of commodity information is inserted into the database as a row of the table.

[0101] It can be understood that by using AI intelligent analysis strategy and model, the system can efficiently process data from different sources, extract key information, and convert it into a unified format, which can significantly improve the efficiency and accuracy of data processing.

[0102] Specifically, after the step of constructing a local database based on the structured commodity information, the steps A40-A60 are further included: Step A40, real-time acquisition of market data corresponding to the first commodity, and acquisition of business rules corresponding to the first commodity; It should be noted that market data refers to real-time market information related to the first commodity, such as market price, sales trend, inventory level, competitor information, etc. These data usually come from external data sources such as market analysis platforms, e-commerce platforms or supplier systems.

[0103] Business rules refer to business logic and rules related to the first commodity, such as price strategy, inventory management rules, promotion rules, etc. These rules are usually defined internally by the enterprise to guide data verification and updating.

[0104] In this embodiment, the system acquires real-time market data related to the first commodity, which may include current market price, sales volume, inventory changes, etc. At the same time, the system acquires business rules related to the first commodity, which define the standards and logic of data verification. For example: market data may show that the current market price of a certain commodity is 100 yuan, while the price recorded in the local database is 95 yuan. Business rules may stipulate that the fluctuation range of commodity price should not exceed 5%, otherwise the price in the database needs to be updated.

[0105] Step A50, based on the business rules, market data and preset data verification strategy, verifying the accuracy of the data in the local database to obtain a data verification result; It should be noted that the preset data verification strategy refers to the rules and methods defined in advance for verifying the accuracy of the data. These strategies usually include data format verification, data range verification, data consistency verification, etc.

[0106] It should be noted that the data verification result refers to the result obtained after verifying the data in the local database, which usually contains information such as whether the data is accurate and which data needs to be updated.

[0107] It should be noted that the system checks the data in the local database based on business rules, market data, and preset data verification strategies. The verification process may include the following aspects: Data format verification: Check if the data conforms to the predefined format. For example, is the date format correct, and is the price a numerical type?

[0108] Data range verification: Check if the data is within a reasonable range. For example, is the product price within the allowed fluctuation range?

[0109] Data consistency verification: Check if the local data is consistent with the market data. For example, is the local inventory consistent with the market sales data?

[0110] Step A60, based on the data verification result, update the local database.

[0111] In this embodiment, the system updates the data in the local database according to the data verification result obtained in step A50. This may include: updating the data in the local database according to the verification result. For example, adjust the product price or inventory quantity. If the verification result shows that some data is missing in the local database, the system will supplement these data from the market data. If the verification result shows that some data is no longer valid or has become obsolete, the system will delete it from the local database.

[0112] For example: If the verification result shows that the product price in the local database is lower than the market minimum price, the system will update the price to the latest price in the market data. If the verification result shows that the inventory quantity of a certain product in the local database is inconsistent with the market sales data, the system will adjust the inventory quantity according to the market data.

[0113] It can be understood that this mechanism can ensure that the data in the local database is always up-to-date and accurate, thereby supporting the business decision and operation of the enterprise. This technology can significantly improve the efficiency and reliability of data management.

[0114] Based on the first and second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as the above-mentioned embodiments one and two can refer to the above introduction, and the following will not be repeated. On this basis, the present embodiment provides a data processing method applied to a data processing end, which refers to Figure 3 , Figure 3 The flowchart of the third embodiment of the data processing method of the present application.

[0115] In this embodiment, the data processing method includes steps B10~B40: Step B10, in response to the commodity information processing instruction, determining first commodity information data to be processed in the data management end, and determining a second data type corresponding to the data management end; It should be noted that the commodity information processing instruction refers to an instruction that triggers the entire processing flow, which is usually initiated by a system user or an automated task, indicating the system to start processing specific commodity information.

[0116] It should be noted that the data management end refers to a system or module responsible for storing and managing commodity information data, which usually includes a local database.

[0117] It should be noted that the first commodity information data refers to the original commodity information data to be processed in the data management end.

[0118] It should be noted that the second data type refers to the storage format or data type of commodity information data in the data management end, such as field name, data format, etc.

[0119] In this embodiment, the system responds to the commodity information processing instruction to determine the first commodity information data to be processed from the data management end, and identifies the storage format (second data type) of these data. The purpose of this step is to clarify the data to be processed and its structure, preparing for subsequent steps.

[0120] Step B20, based on the first commodity information data, generating a commodity information reading instruction, and sending the commodity information reading instruction to the data management end, so that the data management end adjusts the structure of the first commodity information data based on the commodity information reading instruction to obtain second commodity information data, and sends the second commodity information data to the data processing end; It should be noted that the commodity information reading instruction refers to an instruction generated by the data processing end, which is used to instruct the data management end to read and adjust the first commodity information data.

[0121] It should be noted that the second commodity information data refers to the commodity information data after the structure is adjusted by the data management end, which is usually to adapt to the requirements of the data processing end.

[0122] In this embodiment, the data processing end generates a commodity information reading instruction based on the first commodity information data and sends it to the data management end. After receiving the instruction, the data management end adjusts the structure of the first commodity information data according to the instruction requirements to generate the second commodity information data. The adjustment may include field format conversion, data cleaning, supplementing missing information, etc.

[0123] Step B30, receiving the second commodity information data sent by the data management end, processing the second commodity information data based on a preset data processing strategy to obtain target data; It should be noted that the preset data processing strategy refers to the processing logic defined in advance by the data processing end, which is used to perform specific operations on the input data, such as data analysis, calculation, sorting, etc.

[0124] The target data refers to the result data generated by the data processing end after processing the second commodity information data according to the preset strategy.

[0125] In this embodiment, the data processing end receives the second commodity information data sent by the data management end, and processes these data according to the preset data processing strategy, generating target data. For example: if the preset strategy is to calculate the average sales price of commodities, the data processing end will process the second commodity information data to calculate the average price of each commodity, generating target data. If the preset strategy is to classify commodities, the data processing end will classify the data according to the characteristics of the commodities (such as price range, category, etc.), and generate the classification result as target data. After processing, the data processing end sends the target data back to the data management end.

[0126] Step B40, sending the target data to the data management end, so that the data management end updates the local database of the data management end based on the target data.

[0127] In this embodiment, the data processing end sends the target data back to the data management end. After receiving the target data, the data management end updates the local database according to these data. This step ensures that the data in the local database is the latest, and reflects the processing result of the data processing end.

[0128] For example: if the target data is the average sales price of commodities, the data management end will update these prices to the field of the corresponding commodities in the local database. If the target data is the classification result of commodities, the data management end will store the classification information in the classification field of the local database. After updating, the data in the local database will reflect the latest processing result, providing support for subsequent business operations.

[0129] It can be understood that by generating reading instructions, adjusting data structures, processing data and updating databases, the system can efficiently process commodity information, ensuring the accuracy and timeliness of the data. This mechanism can significantly improve the efficiency and reliability of data management.

[0130] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the data processing method of the present application. Based on this technical concept, more forms of simple transformation are within the protection scope of the present application.

[0131] The present application also provides a data processing device, please refer to Figure 4 applied to the data management end, the data processing device comprises: The first determining module 10 is configured to determine first commodity information data to be processed in the local database in response to a commodity information reading instruction sent by the data processing end, and determine a first data type corresponding to the data processing end. The adjusting module 20 is configured to adjust the first commodity information data to obtain second commodity information data based on the first data type. The first sending module 30 is configured to send the second commodity information data to the data processing end, so that the data processing end processes the second commodity information data based on a preset data processing strategy to obtain target data. The receiving module 40 is configured to receive target data sent by the data processing end, and adjust the target data based on a second data type corresponding to the local database to obtain adjusted target data. The updating module 50 is configured to update the local database based on the adjusted target data.

[0132] In an embodiment, the adjusting module further comprises: The first determining unit is configured to determine a first mapping relationship between a first field name corresponding to the first data type and a first field, and determine a second mapping relationship between a second field name corresponding to the first commodity information data and a second field. The second determining unit is configured to determine a similarity between the first field name and the second field name based on semantic analysis. The third determining unit is configured to determine a third mapping relationship between the first field and the second field based on the similarity, the first mapping relationship and the second mapping relationship. The adjusting unit is configured to adjust the first commodity information data to obtain second commodity information data based on the third mapping relationship.

[0133] In an embodiment, the data processing apparatus further comprises a data acquisition module, and the data acquisition module further comprises: The first acquisition unit is configured to acquire a plurality of first commodities and acquire a plurality of source heterogeneous data corresponding to the first commodities in response to a data processing instruction. The parsing unit is configured to parse the plurality of source heterogeneous data using a preset AI intelligent parsing model based on a preset AI intelligent parsing strategy to obtain structured commodity information corresponding to the first commodities. The construction unit is configured to construct a local database based on the structured commodity information.

[0134] In an embodiment, the data acquisition module further comprises: The second obtaining unit is configured to obtain sample data corresponding to the first data processing result. The data processing unit is configured to process the sample data using the current AI intelligent analysis model to obtain a second data processing result. The judging unit is configured to judge whether the first data processing result is consistent with the second data processing result. The iterative training unit is configured to, if the first data processing result is not consistent with the second data processing result, adjust parameters of the current AI intelligent analysis model, return to the step of processing the sample data using the current AI intelligent analysis model to obtain a second data processing result based on the current AI intelligent analysis model with the adjusted parameters, until the first data processing result is consistent with the second data processing result, and obtain a preset AI intelligent analysis model.

[0135] In an embodiment, the data obtaining module further includes: The third obtaining unit is configured to obtain market data corresponding to the first commodity in real time and obtain business rules corresponding to the first commodity. The verifying unit is configured to verify the accuracy of data in the local database based on the business rules, the market data, and a preset data verification strategy to obtain a data verification result. The updating unit is configured to update the local database based on the data verification result.

[0136] The application further provides a data processing device applied to a data processing end, and the data processing device includes: The second determining module is configured to determine first commodity information data to be processed in a data management end and determine a second data type corresponding to the data management end in response to a commodity information processing instruction. The generating module is configured to generate a commodity information reading instruction based on the first commodity information data, send the commodity information reading instruction to the data management end, and adjust the structure of the first commodity information data based on the commodity information reading instruction to obtain second commodity information data, and send the second commodity information data to the data processing end. The data processing module is configured to receive the second commodity information data sent by the data management end, process the second commodity information data based on a preset data processing strategy, and obtain target data. The second sending module is configured to send the target data to the data management end to update a local database of the data management end based on the target data.

[0137] The data processing apparatus provided in the present application adopts the data processing method in the above embodiments, and can solve the technical problem of data processing. Compared with the related art, the data processing apparatus provided in the present application has the same beneficial effects as the data processing method provided in the above embodiments, and other technical features in the data processing apparatus are the same as the features disclosed in the above embodiments, which will not be repeated here.

[0138] The present application provides a data processing device, comprising: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the data processing method in Embodiment One.

[0139] Reference will now be made to the drawings, and specific examples thereof will be illustrated. Figure 5 which shows a structural schematic diagram of a data processing device suitable for implementing the embodiments of the present application. The data processing device in the embodiments of the present application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable Media Player), vehicle terminals (such as vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 5 The data processing device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0140] As Figure 5As shown, the data processing device can include a processing apparatus 1001 (e.g., a central processor, a graphics processor, etc.) that can perform various appropriate actions and processes according to a program stored in a read only memory (ROM 1002) or a program loaded from a storage apparatus 1003 into a random access memory (RAM 1004). In the RAM 1004, various programs and data required for operation of the data processing device are also stored. The processing apparatus 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output interface (I / O interface 1006) is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input apparatus 1007 including, for example, a touch panel, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output apparatus 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage apparatus 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication apparatus 1009. The communication apparatus 1009 can allow the data processing device to perform wireless or wired communication with other devices to exchange data. Although the data processing device having various systems is shown in the figure, it should be understood that all the shown systems are not required to be implemented or possessed. More or less systems can be alternatively implemented or possessed.

[0141] In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication apparatus, or installed from the storage apparatus 1003, or installed from the ROM 1002. When the computer program is executed by the processing apparatus 1001, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.

[0142] The data processing device provided by the present disclosure adopts the data processing method in the above-mentioned embodiments, and can solve the technical problem of data processing. Compared with the related art, the data processing device provided by the present disclosure has the same beneficial effects as the data processing method provided by the above-mentioned embodiments, and other technical features in the data processing device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0143] It should be understood that portions of the application disclosed can be implemented in hardware, software, firmware, or combinations thereof. In the description of the embodiments above, specific features, structures, materials or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0144] The above description is merely illustrative of the application and is not intended to limit the scope of the application. Any modifications or equivalents of the application should be construed as falling within the scope of the application. The scope of the application should be determined by the appended claims.

[0145] The application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e., a computer program) for performing the data processing method in the above-described embodiments.

[0146] The computer readable storage medium provided by the application may, for example, be a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system or device, or any combination thereof. More specific examples of the computer readable storage medium can include, but are not limited to, an electric connection having one or more conductive wires, a portable computer disk, 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 disk read only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the embodiments, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), etc., or any suitable combination thereof.

[0147] The computer readable storage medium described above can be contained in a data processing device or can exist separately without being assembled into the data processing device.

[0148] The computer readable storage medium described above carries one or more programs, which, when executed by the data processing device, cause the data processing device to: In response to the commodity information reading instruction sent by the data processing end, first commodity information data to be processed in the local database is determined, and a first data type corresponding to the data processing end is determined; Based on the first data type, the first commodity information data is adjusted to obtain second commodity information data; The second commodity information data is sent to the data processing end, so that the data processing end processes the second commodity information data based on a preset data processing strategy to obtain target data; The target data sent by the data processing end is received, and the target data is adjusted based on a second data type corresponding to the local database to obtain adjusted target data; Based on the adjusted target data, the local database is updated.

[0149] Computer program code for carrying out operations of the present application can be written in one or more programming languages or combinations of languages including object or visual programming languages such as Java, Smalltalk, C++ or conventional procedural programming languages such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0150] The flow diagrams and the block diagrams in the drawings are illustrations of possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0151] The modules described in the embodiments of the present application can be implemented in the form of software or in the form of hardware. In some cases, the name of the module does not constitute a limitation on the module itself.

[0152] The readable storage medium provided by the present application is a computer readable storage medium, which stores computer readable program instructions (i.e., a computer program) for executing the above-mentioned data processing method, and can solve the technical problem of data processing. Compared with the related art, the computer readable storage medium provided by the present application has the same beneficial effects as the data processing method provided by the above-mentioned embodiments, which will not be repeated here.

[0153] The present application also provides a computer program product, comprising a computer program, which is executed by a processor to implement the steps of the data processing method as described above.

[0154] The computer program product provided by the present application can solve the technical problem of data processing. Compared with the related art, the computer program product provided by the present application has the same beneficial effects as the data processing method provided by the above-mentioned embodiments, which will not be repeated here.

[0155] The above only describes some embodiments of the present application, and does not limit the protection scope of the present application. Any equivalent structural transformation made by using the content of the present application specification and drawings, or direct / indirect application in other related technical fields is included in the protection scope of the present application.

Claims

1. A data processing method, characterized in that, The data processing method, applied to a data management terminal, includes: In response to a product information read instruction sent by the data processing terminal, the system determines the first product information data to be processed in the local database and determines the first data type corresponding to the data processing terminal. Based on the first data type, the first product information data is adjusted to obtain the second product information data; The second product information data is sent to the data processing terminal so that the data processing terminal can process the second product information data based on a preset data processing strategy to obtain the target data; Receive the target data sent by the data processing terminal, adjust the target data based on the second data type corresponding to the local database, and obtain the adjusted target data; Update the local database based on the adjusted target data.

2. The data processing method as described in claim 1, characterized in that, The step of adjusting the first product information data based on the first data type to obtain the second product information data further includes: Determine the first mapping relationship between the first field name corresponding to the first data type and the first field, and determine the second mapping relationship between the second field name corresponding to the first product information data and the second field; Based on semantic analysis, the similarity between the first field name and the second field name is determined; Based on the similarity, the first mapping relationship, and the second mapping relationship, a third mapping relationship between the first field and the second field is determined; Based on the third mapping relationship, the first product information data is adjusted to obtain the second product information data.

3. The data processing method as described in claim 1, characterized in that, Before the step of responding to the product information processing instruction sent by the data processing terminal, the method further includes: In response to data processing instructions, multiple sets of first products are obtained, and multi-source heterogeneous data corresponding to the first products are obtained; Based on a preset AI intelligent analysis strategy, the preset AI intelligent analysis model is used to analyze the multi-source heterogeneous data to obtain the structured product information corresponding to the first product. A local database is constructed based on the structured product information.

4. The data processing method as described in claim 3, characterized in that, Before the step of using a preset AI intelligent parsing model to parse the multi-source heterogeneous data and obtain the structured product information corresponding to the first product, the method further includes: Acquire sample data, which corresponds to the first data processing result; The sample data is processed using the current AI intelligent analysis model to obtain a second data processing result; Determine whether the first data processing result is consistent with the second data processing result; If the first data processing result is inconsistent with the second data processing result, the parameters of the current AI intelligent analysis model are adjusted. Based on the current AI intelligent analysis model with adjusted parameters, the process of using the current AI intelligent analysis model to process the sample data and obtain the second data processing result is repeated until the first data processing result is consistent with the second data processing result, thus obtaining the preset AI intelligent analysis model.

5. The data processing method as described in claim 3, characterized in that, After the step of constructing a local database based on the structured product information, the method further includes: Real-time acquisition of market data corresponding to the first product, and acquisition of business rules corresponding to the first product; Based on the business rules, the market data, and the preset data verification strategy, the accuracy of the data in the local database is verified to obtain the data verification result. Based on the data verification results, update the local database.

6. A data processing method, characterized in that, Applied to the data processing end, the data processing method includes: In response to a product information processing instruction, the system determines the first product information data to be processed in the data management terminal and the second data type corresponding to the data management terminal. Based on the first product information data, a product information reading instruction is generated and sent to the data management terminal, so that the data management terminal can adjust the structure of the first product information data based on the product information reading instruction to obtain the second product information data, and then send the second product information data to the data processing terminal; The system receives second product information data sent by the data management terminal, processes the second product information data based on a preset data processing strategy, and obtains the target data. The target data is sent to the data management terminal so that the data management terminal can update its local database based on the target data.

7. A data processing apparatus, characterized in that, The data processing device includes: The first determining module is used to respond to the product information reading instruction sent by the data processing terminal, determine the first product information data to be processed in the local database, and determine the first data type corresponding to the data processing terminal; An adjustment module is used to adjust the first product information data based on the first data type to obtain second product information data; The first sending module is used to send the second product information data to the data processing terminal, so that the data processing terminal can process the second product information data based on a preset data processing strategy to obtain target data; The receiving module is used to receive the target data sent by the data processing terminal, and adjust the target data based on the second data type corresponding to the local database to obtain the adjusted target data. An update module is used to update the local database based on the adjusted target data.

8. A data processing device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the data processing method as claimed in any one of claims 1 to 5, or claim 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the data processing method as described in any one of claims 1 to 5 or claim 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the data processing method as described in any one of claims 1 to 5 or 6.

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