Method, device and system for processing data of multiple source systems, and readable storage medium

Abstract

The application provides a plurality of source system data processing method, device, system and readable storage medium. Wherein, the plurality of source system data processing method, including: obtaining a plurality of source system data to be processed, and obtaining a plurality of target fields of the data to be processed;Obtain a plurality of first feature fields in the preset model;Establish a preset matching rule, and according to the preset matching rule, the plurality of target fields are matched with the plurality of first feature fields as the reference, and a plurality of matching results are obtained;According to the plurality of matching results, the plurality of first feature fields are updated;The plurality of first feature fields after updating are saved to the preset model;According to the matching rule, the data to be processed corresponding to the plurality of target fields is saved to the preset model.

Description

Technical Field

[0001] This invention relates to the field of data governance, and more specifically, to a method for processing data from multiple source systems, a device for processing data from multiple source systems, a system for processing data from multiple source systems, and a readable storage medium. Background Technology

[0002] In existing technologies, most companies store, customize, and maintain their own data for each department separately, which isolates them from each other, restricts enterprise management, causes data duplication, and leads to low efficiency in data retrieval. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art or related art.

[0004] Therefore, the first aspect of the present invention is to provide a method for processing data from multiple source systems.

[0005] A second aspect of the present invention is to provide a processing apparatus for multiple source system data.

[0006] A third aspect of the present invention is to propose a system for processing data from multiple source systems.

[0007] A fourth aspect of the present invention is to provide a readable storage medium.

[0008] In view of this, according to one aspect of the present invention, a method for processing data from multiple source systems is proposed, comprising: acquiring data to be processed from multiple source systems, and acquiring multiple target fields of the data to be processed; acquiring multiple first feature fields in a preset model; establishing preset matching rules, and performing matching processing on the multiple target fields based on the multiple first feature fields according to the preset matching rules to obtain multiple matching results; updating the multiple first feature fields according to the multiple matching results; saving the updated multiple first feature fields to the preset model; and saving the data to be processed corresponding to the multiple target fields to the preset model according to the matching rules.

[0009] The present invention provides a method for processing data from multiple source systems. First, the server acquires the data to be processed from multiple source systems for centralized processing. Specifically, the server acquires the data to be processed from multiple source systems using data extraction. Data extraction refers to the process of extracting data from a data source; the embodiments of this application use an interface extraction method to extract the data to be processed. After acquiring the data to be processed, the server acquires multiple target fields from the data. Target fields are fields that can reflect the data information of the data to be processed, such as data parameters, data categories, etc. Different data sets may have the same target fields. Subsequently, the server acquires multiple first feature fields from a preset model. The first feature fields are fields that conform to user-defined standards, and the preset model is used to store the processed data from multiple source systems. After acquiring multiple first feature fields, the server establishes preset matching rules and performs matching processing on the multiple target fields based on the multiple first feature fields according to the preset matching rules, obtaining multiple matching results.

[0010] Specifically, the preset matching rule is a set of limiting conditions for matching multiple target fields with multiple first feature fields. When multiple target fields and multiple first feature fields meet the preset matching rule, the multiple target fields and multiple first feature fields are matched. The matching process is the process of determining multiple target fields that match multiple first feature fields among multiple target fields according to the preset matching rule. The matching result is the conclusion of whether multiple target fields match multiple first feature fields after the matching process.

[0011] After obtaining multiple matching results, the server updates multiple first feature fields based on the multiple matching results and saves the updated multiple first feature fields to the preset model, which means adding the first feature fields based on multiple matching results.

[0012] Specifically, since the data to be processed from multiple source systems has multiple target fields, there are multiple data ranges corresponding to these fields. However, the data range corresponding to the feature fields in the preset model may not be within the data range of the data to be processed. Therefore, the first feature field needs to be updated to increase the data range corresponding to the first feature field, thereby enabling the preset model to further process the data to be processed in a larger range.

[0013] After updating the first feature field and the preset model, the server saves the data to be processed corresponding to multiple target fields into the preset model according to the matching rules.

[0014] The server places multiple target fields into a preset model based on the matching results. In other words, the preset model classifies and merges the multiple target fields, combining multiple similar target fields into a set with a first feature field as the feature. This achieves the technical effect of merging duplicate data scattered across different source systems, enabling unified definition and storage of system data from multiple source systems. At the same time, users can accurately search for data based on the first feature field, solving the technical problem of low data search efficiency caused by directly searching system data from multiple source systems. It also saves the cost of manually processing duplicate data and searching fields, and automates the processing of data from multiple source systems.

[0015] The data processing method for multiple source systems according to the present invention may also have the following technical features:

[0016] In the above technical solution, the matching rules include a first matching condition and a second matching condition. According to the preset matching rules, multiple target fields are matched based on multiple first feature fields. Specifically, this includes: when the matching rule is the first matching condition, comparing the multiple first feature fields with the multiple target fields one by one; identifying multiple target fields that are completely identical to the multiple first feature fields, and taking the multiple target fields as multiple matching results that conform to the matching rules; when the matching rule is the second matching condition, calculating the similarity between the multiple target fields and the multiple first feature fields; identifying multiple target fields whose similarity is within a preset threshold range, and taking the multiple target fields as multiple matching results that conform to the matching rules.

[0017] In this technical solution, the matching rules include a first matching condition and a second matching condition. Specifically, the first matching condition is an exact match; the second matching condition is a fuzzy match.

[0018] Specifically, when the matching rule is the first matching condition, the server compares each of the multiple target fields with each of the multiple first feature fields one by one. When the field information of the target field is completely equal to the field information of the feature field, the server makes a judgment that the target field matches the first feature field.

[0019] When the matching rule is the second matching condition, the server calculates the similarity between multiple target fields and multiple first feature fields. When the similarity between the field information of the target field and the field information of the feature field is within the preset threshold range, the server makes a judgment that the target field matches the first feature field.

[0020] Subsequently, the server retrieves multiple matching results that conform to the matching rules and treats them as multiple matching results.

[0021] The technical solution of this application achieves flexible matching between the target field and the first feature field through two matching conditions set by the server, thereby enabling the server to set different matching rules according to different source systems, improving the matching success rate and processing effect.

[0022] In any of the above technical solutions, the matching process of multiple target fields based on multiple first feature fields according to a preset matching rule further includes: when the matching rule is the first matching condition, if the multiple target fields are not completely identical to each of the multiple first feature fields, the target fields do not match the first feature fields; when the matching rule is the second matching condition, if the similarity between the multiple target fields and the multiple first feature fields is not within a preset threshold range, the target fields do not match the first feature fields.

[0023] In this technical solution, the matching results between the target field and the first feature field are further limited.

[0024] Specifically, when the matching rule is the first matching condition, if the field information of the target field is not completely equal to the field information of the first feature field, the server makes a judgment that the target field and the first feature field do not match.

[0025] Specifically, when the matching rule is the second matching condition, if the similarity between the field information of the target field and the field information of the first feature field is outside the preset threshold range, the server makes a judgment that the target field and the first feature field do not match.

[0026] The technical solution of this application achieves the technical effect of obtaining target fields that are different from or dissimilar to the first feature field by using the server's judgment process on whether the target field and the first feature field match, thus enriching the matching results.

[0027] In any of the above technical solutions, updating multiple first feature fields based on multiple matching results specifically includes: adding the target field to multiple first feature fields when the target field does not match the first feature fields; and updating the added multiple first feature fields.

[0028] In this technical solution, when a target field does not match the first feature field, the server, after making a judgment, obtains the target field that does not match the first feature field, that is, obtains the target field that is not the same as or similar to the first feature field.

[0029] Since the data range of the target field to be processed may be inconsistent with the data range of the first feature field, the server adds the target field into multiple first feature fields. In other words, the server expands the first feature field, thereby increasing the data range of the data corresponding to the first feature field. This allows the first feature field to perform matching work on a wider range of target fields, increasing the applicability and improving the user experience.

[0030] In any of the above technical solutions, saving the data to be processed corresponding to multiple target fields to a preset model according to the matching rules includes: when the target field matches the corresponding first feature field among multiple first feature fields, establishing multiple first data lists based on the multiple first feature fields; adding the data to be processed corresponding to the target field to the multiple first data lists; updating the multiple first data lists, and saving the data to be processed in the multiple first data lists to the preset model.

[0031] In this technical solution, after updating the first feature field, the server creates a first data list for each of the multiple first feature fields.

[0032] Specifically, each first feature field is the header of the corresponding first data list, so that information about the data in the corresponding first data list can be obtained based on the first feature field.

[0033] Subsequently, the server establishes a mapping relationship between multiple target fields and multiple corresponding matching first feature fields. Then, based on the mapping relationship, the server adds multiple data to be processed corresponding to multiple target fields to the first data list established by the corresponding first feature fields.

[0034] After all the pending data points are added to the corresponding first data lists, the server updates the first data lists. The updated first data lists contain pending data corresponding to the field information of the first data list header, i.e., the field information of the first feature field. The server then saves all the pending data in the first data lists for users to view.

[0035] Furthermore, the server will generate a data report based on the first data list in the preset model and the data to be processed in the first data list. In the data report, users can view the matching relationship and similarity between the target field and the first feature field. When updating the data to be processed, the server will prioritize updating the data to be processed that has the highest similarity to the first feature field and contains data attribute information. Users can also select any data to be processed to view according to their own needs. The data report generated by the server more clearly and intuitively shows the matching relationship between the target field and the first feature field, making it convenient for users to view.

[0036] In any of the above technical solutions, before calculating the similarity between multiple target fields and multiple first feature fields, the processing method for multiple source system data further includes: configuring multiple weights for multiple first feature fields, wherein the multiple weights are used to adjust the similarity between different first feature fields and their corresponding target fields.

[0037] In this technical solution, before the server calculates the similarity between multiple target fields and multiple first feature fields, it also configures multiple weights for the multiple first feature fields. The data to be processed can be broken down into multiple different feature fields, and different feature fields represent different data information. Therefore, the server sets multiple different weights based on the importance of different fields to the experience data information and configures them on different first feature fields. Different weights have different effects on similarity. By configuring different weights for different first feature fields, the accuracy of the server's matching judgment is increased.

[0038] In any of the above technical solutions, after saving the data to be processed corresponding to multiple target fields to a preset model according to the matching rules, the method for processing multiple source system data further includes: setting multiple second feature fields, and filtering the data to be processed in multiple first data lists according to the multiple second feature fields.

[0039] In this technical solution, after the server saves the data to be processed to the preset model, the server also sets a second feature field to further filter multiple target fields that match multiple first feature fields. Through the second feature field set by the server, more accurate filtering of target fields can be achieved.

[0040] Furthermore, after the server filters the target field, the first data list can be updated, that is, the data to be processed corresponding to the filtered target field in the first data list can be filtered for the user to view.

[0041] According to a second aspect of the present invention, a processing apparatus for multiple source system data is provided, comprising: an acquisition unit for acquiring data to be processed from multiple source systems; the acquisition unit is further configured to: acquire multiple target fields of the data to be processed; acquire multiple first feature fields; a processing unit for establishing preset matching rules, and performing matching processing on the multiple target fields based on the multiple first feature fields according to the preset matching rules to obtain multiple matching results; the processing unit is further configured to: update the multiple first feature fields according to the multiple matching results; save the updated multiple first feature fields to a preset model; and save the data to be processed corresponding to the multiple target fields to the preset model according to the matching rules.

[0042] The present invention provides a processing apparatus for multiple source system data, comprising an acquisition unit and a processing unit. The acquisition unit first acquires the data to be processed from multiple source systems, so that the processing unit can centrally process the data.

[0043] In the technical solution of this invention, the acquisition unit acquires data to be processed from multiple source systems using a data extraction method. Data extraction refers to the process of extracting data from a data source. The technical solution of this application uses an interface extraction method to extract data to be processed. After the acquisition unit acquires the data to be processed, it acquires multiple target fields from the data to be processed. Target fields are fields that can reflect the data information of the data to be processed, such as data parameters, data categories, etc. Different data to be processed may have the same target fields. Subsequently, the acquisition unit acquires multiple first feature fields from a preset model. The first feature fields are fields that conform to user-defined standards or definitions. That is, the first feature fields have user-defined features used to distinguish other fields. The preset model is used to store the processed data from multiple source systems.

[0044] After the acquisition unit acquires multiple first feature fields, the processing unit establishes a preset matching rule and performs matching processing on multiple target fields based on the multiple first feature fields according to the preset matching rule, thereby obtaining multiple matching results.

[0045] Specifically, the preset matching rule is a set of limiting conditions for matching multiple target fields with multiple first feature fields. When multiple target fields and multiple first feature fields meet the preset matching rule, the multiple target fields and multiple first feature fields are matched. The matching process is the process of determining multiple target fields that match multiple first feature fields among multiple target fields according to the preset matching rule. The matching result is the conclusion of whether multiple target fields match multiple first feature fields after the matching process. It should be noted that the matching result does not only refer to multiple target fields that match multiple first feature fields.

[0046] After obtaining multiple matching results, the processing unit updates multiple first feature fields based on the multiple matching results and saves the updated multiple first feature fields to the preset model, that is, adding the first feature fields based on multiple matching results.

[0047] Specifically, since the data to be processed from multiple source systems has multiple target fields, there are multiple data ranges corresponding to these fields. However, the data range corresponding to the feature fields in the preset model may not be within the data range of the data to be processed. Therefore, the first feature field needs to be updated to increase the data range corresponding to the first feature field, thereby enabling the preset model to further process the data to be processed in a larger range.

[0048] After updating the first feature field and the preset model, the processing unit saves the data to be processed corresponding to multiple target fields into the preset model according to the matching rules.

[0049] In this technical solution, the processing unit puts multiple target fields into a preset model based on the matching results. In other words, the preset model classifies and merges the multiple target fields, combining multiple similar target fields into a set with a first feature field as the feature. This achieves the technical effect of merging duplicate data scattered in different source systems, enabling unified definition and unified storage of system data from multiple source systems. At the same time, users can accurately search for data based on the first feature field, solving the technical problem of low data search efficiency caused by directly searching system data from multiple source systems. It also saves the cost of manually processing duplicate data and searching fields, and automates the processing of data from multiple source systems.

[0050] According to a third aspect of the present invention, a processing system for multiple source system data is proposed, comprising a memory storing a program; and a processor executing the program to implement the processing method for multiple source system data as described in any of the above technical solutions.

[0051] The processing system for multiple source system data provided by this invention includes a memory and a processor. The memory stores a program; the processor is connected to the memory and executes the program, implementing the processing method for multiple source system data as described in any of the above technical solutions when executing the program. Therefore, it possesses all the beneficial effects of the processing method for multiple source system data as described in any of the above technical solutions.

[0052] According to a fourth aspect of the present invention, a readable storage medium is provided on which a program is stored, which, when executed by a processor, implements a method for processing multiple source system data as described in any of the above technical solutions.

[0053] The readable storage medium provided by the present invention is used to store a program that, when executed by a processor, implements a method for processing multiple source system data as described in any of the above technical solutions. Therefore, it has all the beneficial effects of the method for processing multiple source system data as described in any of the above technical solutions.

[0054] Additional aspects and advantages of the invention will become apparent in the following description or may be learned by practice of the invention. Attached Figure Description

[0055] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0056] Figure 1 This is a schematic flowchart illustrating one of the methods for processing multiple source system data according to an embodiment of the present invention;

[0057] Figure 2 This is a second schematic flowchart illustrating a method for processing multiple source system data according to an embodiment of the present invention;

[0058] Figure 3 This is the third flowchart illustrating a method for processing multiple source system data according to an embodiment of the present invention;

[0059] Figure 4 This is the fourth flowchart illustrating a method for processing multiple source system data according to an embodiment of the present invention;

[0060] Figure 5 The fifth of the flowcharts illustrates a method for processing multiple source system data according to an embodiment of the present invention;

[0061] Figure 6 This is the sixth flowchart illustrating a method for processing multiple source system data according to an embodiment of the present invention;

[0062] Figure 7 This is the seventh flowchart illustrating a method for processing multiple source system data according to an embodiment of the present invention;

[0063] Figure 8 This diagram illustrates the establishment of preset matching rules in a method for processing multiple source system data according to an embodiment of the present invention.

[0064] Figure 9 This illustration shows a schematic diagram of updating multiple first data lists in a method for processing multiple source system data according to an embodiment of the present invention;

[0065] Figure 10 This diagram illustrates the filtering of data to be processed in a method for processing multiple source system data according to an embodiment of the present invention.

[0066] Figure 11 One of the schematic diagrams of a data report according to an embodiment of the present invention is shown;

[0067] Figure 12 A second schematic diagram of a data report according to an embodiment of the present invention is shown;

[0068] Figure 13 A structural block diagram of a processing apparatus for multiple source system data according to an embodiment of the present invention is shown.

[0069] Figure 14 A schematic diagram of a processing apparatus for multiple source system data according to an embodiment of the present invention is shown. Detailed Implementation

[0070] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0071] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0072] The following reference Figures 1 to 14 This invention describes a method for processing multiple source system data, an apparatus for processing multiple source system data, a system for processing multiple source system data, and a readable storage medium according to some embodiments of the present invention.

[0073] Example 1:

[0074] One embodiment of the present invention proposes a method for processing data from multiple source systems. Figure 1 A flowchart of one of the methods for processing source system data according to embodiments of this application is shown, such as... Figure 1 As shown, the processing methods for data from multiple source systems include:

[0075] Step 102: Obtain the data to be processed from multiple source systems, and obtain multiple target fields of the data to be processed;

[0076] Step 104: Obtain multiple first feature fields from the preset model;

[0077] Step 106: Establish preset matching rules, and according to the preset matching rules, perform matching processing on multiple target fields based on multiple first feature fields to obtain multiple matching results;

[0078] Step 108: Update multiple first feature fields based on multiple matching results;

[0079] Step 110: Save the updated first feature fields to the preset model;

[0080] Step 112: Save the data to be processed corresponding to multiple target fields to the preset model according to the matching rules.

[0081] In this embodiment of the application, the server first acquires the data to be processed from multiple source systems in order to process the data in a centralized manner.

[0082] In this embodiment, data extraction is used to obtain data to be processed from multiple source systems. Data extraction refers to the process of extracting data from a data source. Data extraction methods can include script-based extraction based on the data source, ETL (Extract-Transform-Load) tool extraction, interface extraction, etc. This embodiment uses interface extraction to extract the data to be processed.

[0083] After the data to be processed is acquired, the server retrieves multiple target fields from the data to be processed. Target fields are fields that can reflect the data information of the data to be processed, such as data parameters and data categories of the data to be processed. Different data to be processed may have the same target fields.

[0084] Subsequently, the server retrieves multiple first feature fields from the preset model. These first feature fields conform to user-defined standards or criteria; that is, they possess user-defined characteristics used to distinguish them from other fields. In the embodiments of this application, the first feature fields conform to the standard definition of SSOT (Single Trusted Source of Data), and the preset model is used to store the processed data from multiple source systems. The preset model can be selected from existing models that conform to the SSOT model standard, or a new model can be created independently.

[0085] After obtaining multiple primary feature fields, the server establishes preset matching rules and performs matching processing on multiple target fields based on the multiple primary feature fields according to the preset matching rules, thereby obtaining multiple matching results.

[0086] In this embodiment, the preset matching rule is a limiting condition set to match multiple target fields with multiple first feature fields. When multiple target fields and multiple first feature fields meet the preset matching rule, the multiple target fields and multiple first feature fields are matched. The matching process is the process of determining multiple target fields that match multiple first feature fields among multiple target fields according to the preset matching rule. The matching result is the conclusion of whether multiple target fields match multiple first feature fields after the matching process. It should be noted that the matching result does not only refer to multiple target fields that match multiple first feature fields.

[0087] After obtaining multiple matching results, the server updates multiple first feature fields based on the multiple matching results and saves the updated multiple first feature fields to the preset model, which means adding the first feature fields based on multiple matching results.

[0088] Specifically, since the data to be processed from multiple source systems has multiple target fields, there are multiple data ranges corresponding to these fields. However, the data range corresponding to the feature fields in the preset model may not be within the data range of the data to be processed. Therefore, the first feature field needs to be updated to increase the data range corresponding to the first feature field, thereby enabling the preset model to further process the data to be processed in a larger range.

[0089] After updating the first feature field and the preset model, the server saves the data to be processed corresponding to multiple target fields into the preset model according to the matching rules.

[0090] In this embodiment, the server places multiple target fields into a preset model based on the matching results. In other words, the preset model classifies and merges the multiple target fields placed therein, combining multiple similar target fields into a set with a first feature field as the feature. This achieves the technical effect of merging duplicate data scattered across different source systems, enabling the system data from multiple source systems to achieve unified definition and unified storage. At the same time, users can accurately search for data based on the first feature field, solving the technical problem of low data search efficiency caused by directly searching system data from multiple source systems. It also saves the cost of manually processing duplicate data and searching for fields, and automates the processing of data from multiple source systems.

[0091] Example 2

[0092] An embodiment of the present invention provides a method for processing data from multiple source systems, such as... Figure 2 As shown, the processing methods for data from multiple source systems include:

[0093] Step 202: Obtain data to be processed from multiple source systems, and obtain multiple target fields of the data to be processed;

[0094] Step 204: Obtain multiple first feature fields from the preset model;

[0095] Step 206: Establish preset matching rules;

[0096] Step 208: When the matching rule is the first matching condition, compare the multiple first feature fields with the multiple target fields one by one;

[0097] Step 210: Identify multiple target fields that are completely identical to multiple first feature fields, and treat the multiple target fields as multiple matching results that conform to the matching rules;

[0098] Step 212: When the matching rule is the second matching condition, calculate the similarity between multiple target fields and multiple first feature fields;

[0099] Step 214: Determine multiple target fields whose similarity falls within a preset threshold range, and use these multiple target fields as multiple matching results that conform to the matching rules;

[0100] Step 216: Update multiple first feature fields based on multiple matching results;

[0101] Step 218: Save the updated first feature fields to the preset model;

[0102] Step 220: Save the data to be processed corresponding to multiple target fields to the preset model according to the matching rules.

[0103] The matching rules include a first matching condition and a second matching condition.

[0104] In this embodiment, the matching rules include a first matching condition and a second matching condition. Specifically, the first matching condition is an exact match; the second matching condition is a fuzzy match.

[0105] Specifically, when the matching rule is the first matching condition, the server compares each of the multiple target fields with each of the multiple first feature fields one by one. When the field information of the target field is completely equal to the field information of the feature field, the server makes a judgment that the target field matches the first feature field.

[0106] When the matching rule is the second matching condition, the server calculates the similarity between multiple target fields and multiple first feature fields. When the similarity between the field information of the target field and the field information of the feature field is within the preset threshold range, the server makes a judgment that the target field matches the first feature field.

[0107] Subsequently, the server retrieves multiple matching results that conform to the matching rules and treats them as multiple matching results.

[0108] The embodiments of this application achieve flexible matching between the target field and the first feature field through two matching conditions set by the server. Specifically, since some source systems in different industries, such as the medical industry or the manufacturing industry, may have polysemous words in their data, in this case, exact matching cannot confirm whether the target field matches the first feature field. However, fuzzy matching can increase the accuracy of matching between the target field and the first feature field, thereby enabling the server to set different matching rules according to different source systems, improving the matching success rate and processing effect.

[0109] For example, such as Figure 8 As shown, when establishing preset matching rules, users can select the matching rules. Specifically, users can choose exact matching or fuzzy matching. It should be noted that when selecting exact matching, users do not need to set a threshold, while when selecting fuzzy matching, users need to set a threshold. Specifically, the threshold value ranges from 0.5 to 1.0. A threshold of 1.0 means that the target field and the corresponding first feature field are completely identical, and a threshold of 0.5 means that the target field and the corresponding first feature field have a similarity of 50%.

[0110] Furthermore, when users select fuzzy matching, they can not only target fields of a single category, but also choose fuzzy matching of simplified and traditional Chinese characters, fuzzy matching of pinyin, and fuzzy matching of synonyms. Specifically, when selecting fuzzy matching of simplified and traditional Chinese characters, the server can perform fuzzy matching on either the traditional or simplified Chinese character field; when selecting fuzzy matching of pinyin, the server can perform fuzzy matching on the pinyin; when selecting fuzzy matching of synonyms, the server can perform fuzzy matching on the target field and the first feature field with similar word meanings.

[0111] For example, when fuzzy matching is selected, the server first obtains multiple target fields and multiple first feature fields. It then combines a single first feature field with multiple target fields to form a set and performs word segmentation fuzzy matching, which means using the cosine similarity algorithm to calculate data similarity. The result is used as a similarity score. Each target field in the set is compared with the first feature field using the cosine similarity algorithm. The obtained similarity scores are sorted in descending order, and the highest similarity score is used as the denominator, while the remaining similarity scores are used as the numerator. Finally, the similarity between the first feature field and any one of the multiple target fields is obtained. The similarity is then compared with a preset threshold to determine whether a match is made.

[0112] Specifically, the formula for calculating the similarity of a single field is:

[0113]

[0114] Among them, A i B i These are the target field and the first feature field, respectively. For A i B i The value of is set to 1 when the field appears in the corresponding data, and to 0 when the field does not appear in the corresponding data.

[0115] Example 3

[0116] An embodiment of the present invention provides a method for processing data from multiple source systems, such as... Figure 3 As shown, the processing methods for data from multiple source systems include:

[0117] Step 302: Obtain data to be processed from multiple source systems, and obtain multiple target fields of the data to be processed;

[0118] Step 304: Obtain multiple first feature fields from the preset model;

[0119] Step 306: Establish preset matching rules;

[0120] Step 308: When the matching rule is the first matching condition, compare the multiple first feature fields with the multiple target fields one by one;

[0121] Step 310: Identify multiple target fields that are completely identical to multiple first feature fields, and treat the multiple target fields as multiple matching results that conform to the matching rules;

[0122] Step 312: If multiple target fields are not completely identical to each of the multiple first feature fields, then the target fields do not match the first feature fields.

[0123] Step 314: When the matching rule is the second matching condition, calculate the similarity between multiple target fields and multiple first feature fields;

[0124] Step 316: Determine multiple target fields whose similarity falls within a preset threshold range, and use these multiple target fields as multiple matching results that conform to the matching rules;

[0125] Step 318: If the similarity between multiple target fields and multiple first feature fields is not within a preset threshold range, the target fields and the first feature fields do not match.

[0126] Step 320: Update multiple first feature fields based on multiple matching results;

[0127] Step 322: Save the updated first feature fields to the preset model;

[0128] Step 324: Save the data to be processed corresponding to multiple target fields to the preset model according to the matching rules.

[0129] In this embodiment, the matching result between the target field and the first feature field is further defined.

[0130] Specifically, when the matching rule is the first matching condition, if the field information of the target field is not completely equal to the field information of the first feature field, the server makes a judgment that the target field and the first feature field do not match.

[0131] Specifically, when the matching rule is the second matching condition, if the similarity between the field information of the target field and the field information of the first feature field is outside the preset threshold range, the server makes a judgment that the target field and the first feature field do not match.

[0132] The embodiments of this application achieve the technical effect of obtaining target fields that are different from or dissimilar to the first feature field by the server's judgment process of the target field not matching the first feature field, which enriches the matching results and facilitates the server to perform further processing on the first feature field and the preset model.

[0133] Example 4

[0134] An embodiment of the present invention provides a method for processing data from multiple source systems, such as... Figure 4 As shown, the processing methods for data from multiple source systems include:

[0135] Step 402: Obtain the data to be processed from multiple source systems, and obtain multiple target fields of the data to be processed;

[0136] Step 404: Obtain multiple first feature fields from the preset model;

[0137] Step 406: Establish preset matching rules;

[0138] Step 408: When the matching rule is the first matching condition, compare the multiple first feature fields with the multiple target fields one by one.

[0139] Step 410: Identify multiple target fields that are completely identical to multiple first feature fields, and treat the multiple target fields as multiple matching results that conform to the matching rules;

[0140] Step 412: When the matching rule is the second matching condition, calculate the similarity between multiple target fields and multiple first feature fields;

[0141] Step 414: Determine multiple target fields whose similarity falls within a preset threshold range, and use these multiple target fields as multiple matching results that conform to the matching rules;

[0142] Step 416: If the target field does not match the first feature field, add the target field into multiple first feature fields;

[0143] Step 418: Update the multiple first feature fields after addition;

[0144] Step 420: Save the updated first feature fields to the preset model;

[0145] Step 422: Save the data to be processed corresponding to multiple target fields to the preset model according to the matching rules.

[0146] In this embodiment, when a target field does not match the first feature field, the server, after making a judgment, obtains the target field that does not match the first feature field, that is, obtains the target field that is not the same as or similar to the first feature field.

[0147] Since the data range of the target field to be processed may be inconsistent with the data range of the first feature field, the server adds the target field into multiple first feature fields. In other words, the server expands the first feature field, thereby increasing the data range of the data corresponding to the first feature field. This allows the first feature field to perform matching work on a wider range of target fields, increasing the applicability and improving the user experience.

[0148] Example 5

[0149] An embodiment of the present invention provides a method for processing data from multiple source systems, such as... Figure 5 As shown, the processing methods for data from multiple source systems include:

[0150] Step 502: Obtain the data to be processed from multiple source systems, and obtain multiple target fields of the data to be processed;

[0151] Step 504: Obtain multiple first feature fields from the preset model;

[0152] Step 506: Establish preset matching rules;

[0153] Step 508: When the matching rule is the first matching condition, compare the multiple first feature fields with the multiple target fields one by one.

[0154] Step 510: Identify multiple target fields that are completely identical to multiple first feature fields, and treat the multiple target fields as multiple matching results that conform to the matching rules;

[0155] Step 512: When the matching rule is the second matching condition, calculate the similarity between multiple target fields and multiple first feature fields;

[0156] Step 514: Determine multiple target fields whose similarity falls within a preset threshold range, and use these multiple target fields as multiple matching results that conform to the matching rules;

[0157] Step 516: If the target field does not match the first feature field, add the target field into multiple first feature fields;

[0158] Step 518: Update the multiple first feature fields after addition;

[0159] Step 520: Save the updated first feature fields to the preset model;

[0160] Step 522: If the target field matches the corresponding first feature field among multiple first feature fields, multiple first data lists are established based on the multiple first feature fields;

[0161] Step 524: Add the data to be processed corresponding to the target field to multiple first data lists;

[0162] Step 526: Update multiple first data lists and save the data to be processed in the multiple first data lists to the preset model.

[0163] In this embodiment, after updating the first feature field, the server creates a first data list for each of the multiple first feature fields.

[0164] Specifically, each first feature field is the header of the corresponding first data list, so that information about the data in the corresponding first data list can be obtained based on the first feature field.

[0165] Subsequently, the server establishes a mapping relationship between multiple target fields and multiple corresponding matching first feature fields. Then, based on the mapping relationship, the server adds multiple data to be processed corresponding to multiple target fields to the first data list established by the corresponding first feature fields.

[0166] After all the pending data points are added to the corresponding first data lists, the server updates the first data lists. The updated first data lists contain pending data corresponding to the field information of the first data list header, i.e., the field information of the first feature field. The server then saves all the pending data in the first data lists for users to view.

[0167] For example, such as Figure 9As shown, the data model to be processed contains multiple data to be processed and multiple target fields corresponding to the data to be processed, while the preset model contains multiple first feature fields. When a target field matches a corresponding first feature field among the multiple first feature fields, for example, the "approval number" in the data model to be processed matches the "approval number" in the preset model, a mapping relationship is established between the two. After the mapping relationship is established, the data to be processed in the data model can be added to the first data list established by the corresponding first feature field and the preset model according to the mapping relationship between the target field and the corresponding first feature field, thereby realizing the update of the first data list and the preset model.

[0168] Furthermore, the server will also generate a data report based on the first data list in the preset model and the data to be processed in the first data list, such as... Figure 11 As shown, users can view the matching relationship between the target field and the first feature field in the report, including information such as similarity, product name, and place of origin. Furthermore, in the matching data box, such as... Figure 12 As shown, users can view the matching details between the target field and the first feature field. Users can also select any piece of data to be processed to view and modify the data's order, for example, in... Figure 12 The report lists data for laptops of brand A and model B. When matching is performed using brand A and model B as the first feature field, the report displays information such as the product name, product specifications, matching rules between the target field and the first feature field, product model, and product abbreviation. When updating data to be processed, the server prioritizes updating the data with the highest similarity to the first feature field and containing data attribute information. The data report generated by the server clearly and intuitively displays the matching relationship between the target field and the first feature field, making it convenient for users to view.

[0169] Example 6

[0170] An embodiment of the present invention provides a method for processing data from multiple source systems, such as... Figure 6 As shown, the processing methods for data from multiple source systems include:

[0171] Step 602: Obtain data to be processed from multiple source systems, and obtain multiple target fields of the data to be processed;

[0172] Step 604: Obtain multiple first feature fields from the preset model;

[0173] Step 606: Establish preset matching rules;

[0174] Step 608: When the matching rule is the first matching condition, compare the multiple first feature fields with the multiple target fields one by one.

[0175] Step 610: Identify multiple target fields that are completely identical to multiple first feature fields, and treat the multiple target fields as multiple matching results that conform to the matching rules;

[0176] Step 612: When the matching rule is the second matching condition, configure multiple weights for multiple first feature fields;

[0177] Step 614: Calculate the similarity between multiple target fields and multiple first feature fields;

[0178] Step 616: Determine multiple target fields whose similarity falls within a preset threshold range, and use these multiple target fields as multiple matching results that conform to the matching rules;

[0179] Step 618: If the target field does not match the first feature field, add the target field into multiple first feature fields;

[0180] Step 620: Update the multiple first feature fields after addition;

[0181] Step 622: Save the updated first feature fields to the preset model;

[0182] Step 624: If the target field matches the corresponding first feature field among multiple first feature fields, multiple first data lists are established based on the multiple first feature fields;

[0183] Step 626: Add the data to be processed corresponding to the target field to multiple first data lists;

[0184] Step 628: Update multiple first data lists and save the data to be processed in the multiple first data lists to the preset model.

[0185] Multiple weights are used to adjust the similarity between different first feature fields and their corresponding target fields.

[0186] In this embodiment, before the server calculates the similarity between multiple target fields and multiple first feature fields, the server also configures multiple weights for the multiple first feature fields.

[0187] A piece of data can be broken down into multiple different feature fields, and different feature fields represent different information about the data. Therefore, the server sets multiple different weights for the importance of the experience data information based on different fields, and configures them on different first feature fields. Different weights have different effects on similarity.

[0188] For example, when an important target field in the data to be processed—that is, a target field that largely reflects the information of the data—matches its corresponding first feature field, the server will assign a higher weight to the first feature field to increase the similarity between the target field and the corresponding first feature field. Conversely, when a less important target field in the data to be processed—that is, a target field that little reflects the information of the data—matches its corresponding first feature field, the server will assign a lower weight to the first feature field to decrease the similarity between the target field and the corresponding first feature field. By assigning different weights to different first feature fields, the accuracy of the server's matching judgment is increased.

[0189] For example, such as Figure 8 As shown, users can configure weights based on the importance of different fields. Furthermore, in multi-field similarity calculations, if weights are considered, the formula for calculating multi-field similarity is: Among them, X i λ represents the similarity of the fields. i Represents weight.

[0190] Example 7

[0191] An embodiment of the present invention provides a method for processing data from multiple source systems, such as... Figure 7 As shown, the processing methods for data from multiple source systems include:

[0192] Step 702: Obtain data to be processed from multiple source systems, and obtain multiple target fields of the data to be processed;

[0193] Step 704: Obtain multiple first feature fields from the preset model;

[0194] Step 706: Establish preset matching rules;

[0195] Step 708: When the matching rule is the first matching condition, compare the multiple first feature fields with the multiple target fields one by one.

[0196] Step 710: Identify multiple target fields that are completely identical to multiple first feature fields, and treat the multiple target fields as multiple matching results that conform to the matching rules;

[0197] Step 712: When the matching rule is the second matching condition, calculate the similarity between multiple target fields and multiple first feature fields;

[0198] Step 714: Determine multiple target fields whose similarity falls within a preset threshold range, and treat these multiple target fields as multiple matching results that conform to the matching rules;

[0199] Step 716: If the target field does not match the first feature field, add the target field into multiple first feature fields;

[0200] Step 718: Update the multiple first feature fields after addition;

[0201] Step 720: Save the updated first feature fields to the preset model;

[0202] Step 722: If the target field matches a corresponding first feature field among multiple first feature fields, establish multiple first data lists based on the multiple first feature fields.

[0203] Step 724: Add the data to be processed corresponding to the target field to multiple first data lists;

[0204] Step 726: Update multiple first data lists and save the data to be processed in multiple first data lists to the preset model;

[0205] Step 728: Set multiple second feature fields, and filter the data to be processed in multiple first data lists based on the multiple second feature fields.

[0206] In this embodiment, after the server saves the data to be processed to the preset model, the server also sets a second feature field to further filter multiple target fields that match multiple first feature fields. Through the second feature field set by the server, more accurate filtering of target fields can be achieved.

[0207] Furthermore, after the server filters the target field, the first data list can be updated, that is, the data to be processed corresponding to the filtered target field in the first data list can be filtered for the user to view.

[0208] For example, in Figure 10 In the system, you can search for multiple feature fields, such as product code and product category, through the query window on the left. Then, you can add feature fields, their corresponding operators, and feature values ​​in the add column on the right to filter the target fields. Furthermore, you can determine whether to take the intersection or union of two features by setting the AND / OR relationship.

[0209] Example 8

[0210] A second aspect of the present invention provides a processing apparatus 1300 for data from multiple source systems. Figure 13 A structural block diagram of a processing apparatus 1300 for multiple source system data according to an embodiment of this application is shown, such as... Figure 13As shown, the processing device 1300 for multiple source system data includes: an acquisition unit 1302, used to acquire data to be processed from multiple source systems; the acquisition unit 1302 is further used to: acquire multiple target fields of the data to be processed; acquire multiple first feature fields; a processing unit 1304, used to establish preset matching rules, and according to the preset matching rules, perform matching processing on multiple target fields based on multiple first feature fields to obtain multiple matching results; the processing unit 1304 is further used to: update multiple first feature fields according to multiple matching results; save the updated multiple first feature fields to a preset model; and save the data to be processed corresponding to multiple target fields to the preset model according to the matching rules.

[0211] The processing apparatus 1300 for multiple source system data provided in this application embodiment includes an acquisition unit 1302 and a processing unit 1304. The acquisition unit 1302 first acquires the data to be processed from multiple source systems so that the processing unit 1304 can centrally process the data to be processed.

[0212] In this embodiment, the acquisition unit 1302 acquires data to be processed from multiple source systems using a data extraction method. Data extraction refers to the process of extracting data from a data source. Data extraction methods can include script-based extraction, ETL (Extract-Transform-Load) tool extraction, interface extraction, etc. This embodiment uses interface extraction to extract the data to be processed.

[0213] After the acquisition unit 1302 acquires the data to be processed, it acquires multiple target fields in the data to be processed. The target fields are fields that can reflect the data information of the data to be processed, such as the data parameters of the data to be processed, the data category of the data to be processed, etc. The target fields of different data to be processed may be the same.

[0214] Subsequently, the acquisition unit 1302 acquires multiple first feature fields from the preset model. These first feature fields conform to user-defined standards or definitions; that is, they possess user-defined characteristics used to distinguish them from other fields. In the embodiments of this application, the first feature fields conform to the standard definition of SSOT (Single Trusted Source of Data), and the preset model is used to store the processed data from multiple source systems. The preset model can be selected from existing models that conform to the SSOT model standard, or a new model can be created independently.

[0215] After the acquisition unit 1302 acquires multiple first feature fields, the processing unit 1304 establishes a preset matching rule and performs matching processing on multiple target fields based on the multiple first feature fields according to the preset matching rule, thereby obtaining multiple matching results.

[0216] In this embodiment, the preset matching rule is a limiting condition set to match multiple target fields with multiple first feature fields. When multiple target fields and multiple first feature fields meet the preset matching rule, the multiple target fields and multiple first feature fields are matched. The matching process is the process of determining multiple target fields that match multiple first feature fields among multiple target fields according to the preset matching rule. The matching result is the conclusion of whether multiple target fields match multiple first feature fields after the matching process. It should be noted that the matching result does not only refer to multiple target fields that match multiple first feature fields.

[0217] After obtaining multiple matching results, the processing unit 1304 updates multiple first feature fields according to the multiple matching results and saves the updated multiple first feature fields to the preset model, that is, adding the first feature fields according to the multiple matching results.

[0218] Specifically, since the data to be processed from multiple source systems has multiple target fields, there are multiple data ranges corresponding to these fields. However, the data range corresponding to the feature fields in the preset model may not be within the data range of the data to be processed. Therefore, the first feature field needs to be updated to increase the data range corresponding to the first feature field, thereby enabling the preset model to further process the data to be processed in a larger range.

[0219] After updating the first feature field and the preset model, the processing unit 1304 saves the data to be processed corresponding to multiple target fields to the preset model according to the matching rules.

[0220] In this embodiment, the processing unit 1304 places multiple target fields into a preset model based on the matching results. In other words, the preset model classifies and merges the multiple target fields placed therein, merging multiple similar target fields into a set with a first feature field as the feature. This achieves the technical effect of merging duplicate data scattered in different source systems, enabling the system data in multiple source systems to achieve the technical effect of unified definition and unified storage. At the same time, users can accurately search for data based on the first feature field, solving the technical problem of low data search efficiency caused by directly searching system data from multiple source systems. It also saves the cost of manually processing duplicate data and searching fields, and realizes the automation of data processing from multiple source systems.

[0221] Example 9

[0222] A second aspect of the present invention provides a processing apparatus 1400 for multiple source system data, wherein the processing apparatus 1400 for multiple source system data includes: a source system unit 1410, a data partitioning unit 1420, a matching unit 1430, and a report generation unit 1440.

[0223] Specifically, the source system unit 1410 is used to place the data to be processed into multiple source systems, and the data partitioning unit 1420 is used to place the data to be processed extracted by the data extraction module into multiple data models to be processed, and to set a preset model. The preset model is used to store the processed data from the multiple source systems. The preset model has multiple first feature fields that conform to the standard definition of SSOT.

[0224] The matching unit 1430 is used to perform matching processing on multiple target fields based on multiple first feature fields according to preset matching rules. Specifically, the preset matching rules include a first matching condition and a second matching condition. The first matching condition is exact matching; the second matching condition is fuzzy matching. When the matching rule is the first matching condition, the matching unit 1430 compares each target field with each feature field of the multiple first feature fields one by one. When the field information of a target field is completely equal to the field information of a feature field, the matching unit 1430 determines that the target field matches the first feature field. When the matching rule is the second matching condition, the matching unit 1430 calculates the similarity between the multiple target fields and the multiple first feature fields. When the similarity between the field information of a target field and the field information of a feature field is within a preset threshold range, the matching unit 1430 determines that the target field matches the first feature field. The matching unit 1430 can not only target fields of a single category, but also select matching combinations such as simplified / traditional Chinese fuzzy matching, pinyin fuzzy matching, and synonym fuzzy matching.

[0225] Subsequently, when a target field does not match a first feature field, the matching unit 1430, after making a judgment, obtains the target field that does not match the first feature field, that is, establishes an unmatched mapping relationship, and adds the target field to multiple first feature fields according to the unmatched mapping relationship. When a target field matches a first feature field, the matching unit 1430 establishes a mapping relationship between multiple target fields and the corresponding matched first feature fields. Subsequently, according to the mapping relationship, the server adds multiple unprocessed data corresponding to multiple target fields to the first data list established by the corresponding first feature fields according to the mapping relationship.

[0226] Subsequently, the report generation unit 1440 generates a data report based on the data to be processed in the preset model. Users can view the matching relationship between the target field and the first feature field, as well as information such as similarity, product name, and place of origin in the report. When updating the data to be processed, the server will prioritize updating the data with the highest similarity to the first feature field and containing data attribute information. Furthermore, users can also view the matching details between the target field and the first feature field. Users can also select any data to be processed to view according to their own needs and modify the data arrangement order. The data report generated by the server more clearly and intuitively displays the matching relationship between the target field and the first feature field, making it convenient for users to view.

[0227] Example 10

[0228] Another embodiment of the present invention provides a processing system for multiple source system data. The processing system for multiple source system data includes a memory storing a program; and a processor executing the program to implement the processing method for multiple source system data as described in any of the above embodiments.

[0229] The processing system for multiple source system data provided in this application includes a memory and a processor. The memory stores a program; the processor is connected to the memory and executes the program, implementing the processing method for multiple source system data as described in any of the above embodiments. Therefore, it possesses all the beneficial effects of the processing method for multiple source system data as described in any of the above technical solutions, which will not be elaborated further here.

[0230] Example 11

[0231] Another embodiment of the present invention provides a readable storage medium on which a program is stored, which, when executed by a processor, implements a method for processing multiple source system data as described in any of the above technical solutions.

[0232] The readable storage medium provided in this application embodiment is used to store a program that, when executed by a processor, implements the processing method for multiple source system data as described in any of the above technical solutions. Therefore, it has all the beneficial effects of the processing method for multiple source system data as described in any of the above technical solutions, which will not be repeated here.

[0233] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance, unless otherwise expressly specified and limited. The terms "connection," "installation," and "fixing," etc., should be interpreted broadly. For example, "connection" can mean a fixed connection, a detachable connection, or an integral connection; it can mean a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0234] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0235] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for processing data from multiple source systems, characterized in that, include: Obtain the data to be processed from the multiple source systems, and obtain multiple target fields of the data to be processed; Retrieve multiple first feature fields from a preset model; Establish a preset matching rule, and according to the preset matching rule, perform matching processing on the multiple target fields based on the multiple first feature fields to obtain multiple matching results; The multiple first feature fields are updated based on the multiple matching results; Save the updated plurality of first feature fields to a preset model; If the target field matches the corresponding first feature field among the plurality of first feature fields, a plurality of first data lists are established based on the plurality of first feature fields; Add the data to be processed corresponding to the target field to the plurality of first data lists; The plurality of first data lists are updated, and the data to be processed in the plurality of first data lists is saved to the preset model; The matching rules include a first matching condition and a second matching condition. The step of matching the multiple target fields based on the multiple first feature fields according to the preset matching rules specifically includes: When the matching rule is the first matching condition, the plurality of first feature fields are compared with the plurality of target fields one by one; Identify the multiple target fields that are completely identical to the multiple first feature fields, and use the multiple target fields as multiple matching results that conform to the matching rules; When the matching rule is the second matching condition, the similarity between the plurality of target fields and the plurality of first feature fields is calculated; Identify multiple target fields whose similarity falls within a preset threshold range, and use these multiple target fields as multiple matching results that conform to the matching rules; The step of updating the plurality of first feature fields based on the plurality of matching results specifically includes: If the target field does not match the first feature field, the target field is added to the plurality of first feature fields; Update the added first feature fields; The first feature field contains user-defined features; When updating the data to be processed, priority is given to updating the data that has the highest similarity to the first feature field and contains data attribute information.

2. The method for processing data from multiple source systems according to claim 1, characterized in that, The step of matching the multiple target fields based on the multiple first feature fields according to the preset matching rules further includes: When the matching rule is the first matching condition, and the target field does not match the first feature field if each of the plurality of target fields is not completely identical to each of the plurality of first feature fields; When the matching rule is the second matching condition, if the similarity between the plurality of target fields and the plurality of first feature fields is not within a preset threshold range, the target field does not match the first feature field.

3. The method for processing multiple source system data according to claim 1, characterized in that, Before calculating the similarity between the plurality of target fields and the plurality of first feature fields, the processing method for the plurality of source system data further includes: Multiple weights are configured for the plurality of first feature fields, and the plurality of weights are used to adjust the similarity between different first feature fields and the corresponding target fields.

4. The method for processing multiple source system data according to claim 1 or 3, characterized in that, After saving the data to be processed corresponding to the multiple target fields to the preset model according to the matching rules, the processing method for the multiple source system data further includes: Multiple second feature fields are set, and the data to be processed in the multiple first data lists is filtered according to the multiple second feature fields.

5. A processing apparatus for data from multiple source systems, characterized in that, The step of implementing the method for processing data from multiple source systems as described in any one of claims 1 to 4, wherein the processing apparatus for the multiple source system data comprises: The acquisition unit is used to acquire the data to be processed from the multiple source systems; The acquisition unit is also used for: Obtain multiple target fields from the data to be processed; Obtain multiple primary feature fields; The processing unit is used to establish preset matching rules and, based on the preset matching rules, perform matching processing on the multiple target fields using the multiple first feature fields as a benchmark to obtain multiple matching results; The processing unit is also used for: The multiple first feature fields are updated based on the multiple matching results; Save the updated plurality of first feature fields to a preset model; According to the matching rules, the data to be processed corresponding to the multiple target fields is saved to the preset model.

6. A system for processing data from multiple source systems, characterized in that, include: A memory, wherein the memory stores a program; A processor, which, when executing the program, implements a method for processing multiple source system data as described in any one of claims 1 to 4.

7. A readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for processing multiple source system data as described in any one of claims 1 to 4.

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