Grouping method and apparatus for data
By retrieving highly similar data from the database and using keyword matching, the problem of inefficient grouping in the case of fragmented data is solved, achieving efficient and accurate data integration and precise push.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SUZHOU LANGDONG NET TEC CO LTD
- Filing Date
- 2022-12-06
- Publication Date
- 2026-05-29
Smart Images
Figure CN115905637B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of data processing technology, and in particular to a data grouping method, apparatus, computer equipment, and storage medium. Background Technology
[0002] A data processing platform is used to filter and integrate data and then push it to users. In some application scenarios, when there are many data sources, there will be a large amount of fragmented data. The data processing platform needs to extract data that matches the user's needs from this data and push it to the user.
[0003] Traditional techniques allow data to be organized directly based on the relationships between the data themselves, resulting in multiple sets of related data. However, for large amounts of scattered data, this method is labor-intensive and inefficient. Summary of the Invention
[0004] Therefore, it is necessary to provide an efficient and accurate data grouping method, apparatus, computer equipment, storage medium, and computer program product to address the aforementioned technical problems.
[0005] In a first aspect, embodiments of this disclosure provide a method for grouping data. The method includes:
[0006] First data is obtained from the database that has a similarity greater than a first preset value to the data to be grouped, wherein the database includes multiple data groups;
[0007] If the preset keyword of the data to be grouped matches the corresponding keyword of the first data, the target data group to which the first data belongs is obtained.
[0008] If the preset keyword and the corresponding keyword of the second data in the target data group meet the second preset condition, the data to be grouped is stored in the target data group, wherein the second data is the data in the target data group other than the first data.
[0009] In one embodiment, obtaining the first data from the database that has a similarity greater than a first preset value to the data to be grouped includes:
[0010] Retrieve multiple first data points from the database that have a similarity greater than a first preset value to the data to be grouped.
[0011] The multiple first data are sorted in descending order according to their similarity to obtain the first data group;
[0012] The first data is obtained sequentially from the first data group.
[0013] In one embodiment, obtaining first data from the database that has a similarity greater than a preset value to the data to be grouped includes:
[0014] Retrieve the preset keywords for the data to be grouped and the corresponding keywords from the database;
[0015] If the similarity between the preset keyword and the corresponding keyword is greater than a second preset value, the data is determined to be the first data.
[0016] In one embodiment, obtaining first data from the database that has a similarity greater than a preset value to the data to be grouped includes:
[0017] Retrieve the preset keywords for the data to be grouped and the corresponding keywords from the database;
[0018] If the corresponding keyword is empty, obtain the similarity between the data and the data to be grouped; if the similarity is greater than a first threshold, determine the data as the first data; and / or,
[0019] If the preset keyword is the same as the corresponding keyword, the similarity between the data and the data to be grouped is obtained. If the similarity is greater than a second threshold, the data is determined to be the first data.
[0020] In one embodiment, the data in the data group is marked with corresponding data tags, and obtaining the target data group to which the first data belongs includes:
[0021] Determine the target data label for the first data;
[0022] The target data group is determined from the database based on the target data label.
[0023] In one embodiment, the step of obtaining the target data group to which the first data belongs when the preset keyword of the data to be grouped matches the corresponding keyword of the first data under a first preset condition includes:
[0024] Obtain the first preset keyword of the data to be grouped and the corresponding second preset keyword of the first data;
[0025] If the first preset keyword and the second preset keyword are the same, obtain the target data group to which the first data belongs; and / or,
[0026] If the first preset keyword is empty and / or the second preset keyword is empty, obtain the target data group to which the first data belongs.
[0027] Secondly, embodiments of this disclosure also provide a data grouping apparatus. The apparatus includes:
[0028] The first acquisition module is used to acquire first data from the database that has a similarity greater than a first preset value to the data to be grouped, wherein the database includes multiple data groups;
[0029] The second acquisition module is used to acquire the target data group to which the first data belongs when the preset keyword of the data to be grouped matches the corresponding keyword of the first data under the first preset condition.
[0030] The grouping module is used to store the data to be grouped into the target data group when the preset keyword and the corresponding keyword of the second data in the target data group meet the second preset condition, wherein the second data is the data in the target data group other than the first data.
[0031] In one embodiment, the first acquisition module includes:
[0032] The first acquisition submodule is used to acquire multiple first data from the database that have a similarity greater than a first preset value to the data to be grouped.
[0033] The sorting module is used to sort the plurality of first data in descending order according to the similarity size to obtain a first data group;
[0034] The second acquisition submodule is used to sequentially acquire the first data from the first data group.
[0035] In one embodiment, the first acquisition module includes:
[0036] The third acquisition submodule is used to acquire the preset keywords of the data to be grouped and the corresponding keywords of the data in the database.
[0037] The first determining module is used to determine the data as first data when the similarity between the preset keyword and the corresponding keyword is greater than a second preset value.
[0038] In one embodiment, the first acquisition module includes:
[0039] The fourth submodule is used to obtain the preset keywords of the data to be grouped and the corresponding keywords of the data in the database;
[0040] The second determining module is configured to, when the corresponding keyword is empty, obtain the similarity between the data and the data to be grouped, and determine the data as the first data if the similarity is greater than a first threshold; and / or,
[0041] The third determining module is used to obtain the similarity between the data and the data to be grouped when the preset keyword is the same as the corresponding keyword, and to determine the data as the first data when the similarity is greater than a second threshold.
[0042] In one embodiment, the data in the data group is marked with corresponding data tags, and the second acquisition module includes:
[0043] The fourth determining module is used to determine the target data label of the first data;
[0044] The fifth determining module is used to determine the target data group from the database based on the target data tag.
[0045] In one embodiment, the second acquisition module includes:
[0046] The fifth acquisition submodule is used to acquire the first preset keyword of the data to be grouped and the corresponding second preset keyword of the first data;
[0047] The sixth acquisition submodule is used to acquire the target data group to which the first data belongs when the first preset keyword and the second preset keyword are the same; and / or,
[0048] The seventh acquisition submodule is used to acquire the target data group to which the first data belongs when the first preset keyword is empty and / or the second preset keyword is empty.
[0049] Thirdly, embodiments of this disclosure also provide a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the embodiments of this disclosure.
[0050] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the method described in any one of the embodiments of this disclosure.
[0051] Fifthly, embodiments of this disclosure also provide a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the embodiments of this disclosure.
[0052] In this embodiment, when grouping data, first data with a similarity greater than a first preset value to the data to be grouped is retrieved from the database. Keywords of the data to be grouped and the first data are analyzed and judged. If the preset keyword of the data to be grouped and the corresponding keyword of the first data meet the first preset condition, the target data group to which the first data belongs is obtained. If the preset keyword of the data to be grouped and the corresponding keyword of other data in the target data group meet the second preset condition, the data to be grouped is stored in the target data group. This realizes the grouping of the data to be grouped. By comparing the field values of the data to be grouped with the data already grouped in the database, the data group corresponding to the data to be grouped can be determined according to the relationship between the data to be grouped and the data in the database. This improves the efficiency and accuracy of data grouping, efficiently and accurately realizes data integration and filtering, and the implementation method is simple. This enables precise data push to users and improves the user experience. Attached Figure Description
[0053] Figure 1 This is a flowchart illustrating a data grouping method in one embodiment;
[0054] Figure 2 This is a flowchart illustrating a data grouping method in one embodiment;
[0055] Figure 3 This is a flowchart illustrating a data grouping method in one embodiment;
[0056] Figure 4 This is a flowchart illustrating a data grouping method in one embodiment;
[0057] Figure 5 This is a flowchart illustrating a data grouping method in one embodiment;
[0058] Figure 6 This is a structural block diagram of a data grouping device in one embodiment;
[0059] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation
[0060] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of this disclosure and are not intended to limit the embodiments of this disclosure.
[0061] In one embodiment, such as Figure 1 As shown, a data processing method is provided, the method comprising:
[0062] Step S110: Obtain first data from the database that has a similarity greater than a first preset value to the data to be grouped, wherein the database includes multiple data groups;
[0063] In this embodiment of the disclosure, when grouping data, the data to be grouped is first obtained, which typically includes one or more keywords. In one example, multiple keywords can be distinguished by fields. First data with a similarity greater than a first preset value to the data to be grouped is obtained from the database. The database includes multiple data groups, and the data stored in the database includes data that has already been grouped. In one example, the data corresponds to a publication time. After obtaining the publication time of the data to be grouped, database data within a preset time interval of the publication time is obtained. The preset time interval is usually set according to the actual application scenario; for example, the preset time interval can be set to database data for six months before and after the publication time. When determining the similarity between data, it can be determined by comparing preset keywords or by comparing the entire data. In one example, it can be determined by comparing keywords corresponding to preset fields. The first preset value is usually set according to the actual application scenario. When the similarity between the data in the database and the data to be grouped is greater than the first preset value, it can be considered that the data and the data to be grouped have a relatively close relationship, and further judgment operations can be performed. Typically, the data in the database and the data to be grouped are data of the same category, therefore the attributes of the corresponding keywords are also the same. In one example, the data to be grouped includes bidding data, which can be obtained from published bidding documents.
[0064] Step S120: If the preset keyword of the data to be grouped matches the corresponding keyword of the first data under the first preset condition, obtain the target data group to which the first data belongs.
[0065] In this embodiment of the disclosure, preset keywords for data to be grouped are obtained. In one example, the data to be grouped includes multiple keywords, each with a corresponding keyword attribute. The preset keywords corresponding to the data to be grouped are obtained through the preset keyword attributes. Keywords corresponding to the keyword attributes of the first data and the preset keywords are obtained. When the preset keywords of the data to be grouped and the corresponding keywords of the first data meet a first preset condition, the target data group to which the first data belongs is obtained. The first preset condition is usually set according to the actual application scenario. In one example, the first preset condition may be set to include the preset keyword and the corresponding keyword being the same, or the similarity between the preset keyword and the corresponding keyword being greater than a preset threshold. Generally, when the first preset condition is met, it can be considered that the data to be grouped and the first data are likely to belong to the same data group, and further judgment can be made. Therefore, further analysis can be performed on other data in the data group to which the first data belongs. At this time, the target data group to which the first data belongs is obtained. In one example, data in the database has corresponding data tags based on the different data groups they belong to. Data with the same data tags belong to one data group. When obtaining the target data group, the data group consisting of data in the database with the same data tags as the first data can be determined as the target data group based on the data tags of the first data. In one example, when the data to be grouped corresponds to bidding data, keywords may include, but are not limited to, project number, bidding company, agency company, province / city code, project name, etc.
[0066] Step S130: If the preset keyword and the corresponding keyword of the second data in the target data group meet the second preset condition, the data to be grouped is stored in the target data group, wherein the second data is the data in the target data group other than the first data.
[0067] In this embodiment, after obtaining the target data group, the data other than the first data, i.e., the second data, is acquired from the target data group. The preset keywords of the data to be grouped are compared with the corresponding keywords of the second data. If the preset keywords of the data to be grouped and the corresponding keywords of the second data meet a second preset condition, the data to be grouped is stored in the target data group. The second preset condition is typically set based on the actual application scenario; in one example, the second preset condition and the first preset condition can be the same. Since there are relationships between data within the same data group, judging the data to be grouped by combining other data within the same data group can make the final data grouping result more accurate and reliable. Generally, when the second preset condition is met, the data to be grouped can be considered to belong to the target data group, and storing the data to be grouped in the target data group achieves the grouping of the data to be grouped. In one example, data in the database has corresponding data tags based on the data group it belongs to; data with the same data tag belongs to one data group. When storing the data to be grouped in the target data group, the data tag corresponding to the first data can be added to the data to be grouped. In one example, if the second preset condition is not met, a new data group can be created, and the data to be grouped can be stored in the new data group. In one example, the second data can be one or more. When there is only one second data, the data to be grouped is stored in the target data group if the keyword corresponding to the second data meets the second preset condition. When there are multiple second data, the data to be grouped is stored in the target data group if the keywords corresponding to all the second data meet the second preset condition. In one example, the second data can also be empty. When the second data is empty, the data to be grouped can be stored in a new data group or in the target data group, depending on the actual application scenario.
[0068] In this embodiment, when grouping data, first data with a similarity greater than a first preset value to the data to be grouped is retrieved from the database. Keywords of the data to be grouped and the first data are analyzed and judged. If the preset keywords of the data to be grouped and the corresponding keywords of the first data meet a first preset condition, the target data group to which the first data belongs is obtained. If the preset keywords of the data to be grouped and the corresponding keywords of other data in the target data group meet a second preset condition, the data to be grouped is stored in the target data group. This achieves the grouping of the data to be grouped. By comparing the field values of the data to be grouped with the data already grouped in the database, the data group corresponding to the data to be grouped can be determined based on the relationship between the data to be grouped and the data in the database. This improves the efficiency and accuracy of data grouping, and the implementation method is simple, efficiently and accurately achieving data integration and filtering, thereby providing users with precise data push and improving the user experience. It can integrate and filter data from different sources and determine data groups based on the relationship between the data. Through this embodiment, it is possible to associate different types of files published at different time periods of the same project into groups.
[0069] In one embodiment, such as Figure 2 As shown, obtaining the first data from the database that has a similarity greater than a first preset value to the data to be grouped includes:
[0070] Step S111: Obtain multiple first data points from the database that have a similarity greater than a first preset value to the data to be grouped;
[0071] Step S112: Sort the plurality of first data in descending order according to the similarity to obtain the first data group;
[0072] Step S113: Sequentially obtain the first data from the first data group.
[0073] In this embodiment of the disclosure, when obtaining the first data, multiple pieces of first data with a similarity greater than a first preset value to the data to be grouped are retrieved from the database. The similarity between the multiple pieces of first data and the data to be grouped may vary. In this embodiment, the multiple pieces of first data are sorted in descending order according to their similarity, resulting in a first data group. When comparing the data to be grouped and the first data, the first data are retrieved sequentially according to the order of the first data group for comparison and judgment. In one example, for first data with the same similarity, other priority order can be set, such as the publication time of the first data. Generally, the higher the similarity between data, the stronger the correlation between the two pieces of data is considered. In one example, when comparing and analyzing the data to be grouped and the first data, the first data can be retrieved from the first data group through a traversal until the traversal is complete.
[0074] In this embodiment of the disclosure, the first data is sorted according to the similarity to obtain a first data group. When comparing the data, the first data is obtained in order, so that when grouping the data to be grouped, the comparison can be made from the first data with close correlation, thereby determining the data group to which the data to be grouped belongs as quickly as possible. While ensuring the accuracy of data grouping, the efficiency of data grouping is improved and the workload of data grouping is reduced.
[0075] In one embodiment, such as Figure 3 As shown, obtaining the first data from the database that has a similarity greater than a preset value to the data to be grouped includes:
[0076] Step S114: Obtain the preset keywords of the data to be grouped and the corresponding keywords of the data in the database;
[0077] Step S115: If the similarity between the preset keyword and the corresponding keyword is greater than a second preset value, the data is determined to be the first data.
[0078] In this embodiment of the disclosure, when comparing the similarity between the data to be grouped and the first data, keywords can be compared. Preset keywords for the data to be grouped are obtained, and keywords corresponding to the data in the database are obtained. If the similarity between the preset keyword and the corresponding keyword is greater than a second preset value, the data in the database is determined to be the first data. The second preset value is typically set based on the actual application scenario. When the similarity is greater than the second preset value, it can be considered that the two data sets have a relatively close relationship and may belong to the same data group. In one example, when obtaining keywords, the preset keywords for the data to be grouped corresponding to the preset keyword attribute and the corresponding keywords for the data in the database can be determined based on the keyword attributes. In one example, the data may contain multiple fields, each with a corresponding keyword. When obtaining keywords, the keyword for the corresponding field is directly obtained. In one example, the preset keyword can be the data item name of the data to be grouped, and the corresponding keyword in the database can be the data item name of the data in the database. When the similarity between the data item name in the database and the data item name of the data to be grouped is greater than the second preset value, the data is determined to be the first data.
[0079] In this embodiment of the disclosure, the first data in the database is determined by comparing data keywords, which ensures the correlation between the obtained first data and the data to be grouped, reduces the workload of data processing, improves the efficiency of first data acquisition, and thus improves the efficiency of data grouping, thereby enabling efficient and accurate data grouping.
[0080] In one embodiment, such as Figure 4As shown, obtaining the first data from the database that has a similarity greater than a preset value to the data to be grouped includes:
[0081] Step S116: Obtain the preset keywords for the data to be grouped and the keywords corresponding to the data in the database;
[0082] Step S117: If the corresponding keyword is empty, obtain the similarity between the data and the data to be grouped; if the similarity is greater than a first threshold, determine the data as the first data; and / or,
[0083] Step S118: If the preset keyword is the same as the corresponding keyword, obtain the similarity between the data and the data to be grouped; if the similarity is greater than a second threshold, determine the data as the first data.
[0084] In this embodiment of the disclosure, when acquiring the first data, preset keywords for the data to be grouped and keywords corresponding to data in the database are acquired, and the preset keywords and corresponding keywords are analyzed and judged. When the corresponding preset keyword is empty, the similarity between the data and the data to be grouped is acquired. If the similarity is greater than a first threshold, the data is determined to be the first data, wherein the first threshold is set according to the actual application scenario. In one example, the preset keywords and corresponding keywords may include keywords corresponding to the project number. When the keyword corresponding to the project number is empty, the similarity between the data and the data to be grouped needs to reach a certain threshold before the two data are considered to be closely related and determined to be the first data. In one example, when comparing similarity, the entire data can be compared and judged, or certain specific keywords in the data can be judged. For example, the similarity of project names can be judged. When the corresponding preset keyword is not empty, the similarity between the data and the data to be grouped is acquired. If the similarity is greater than a second threshold, the data is determined to be the first data, wherein the second threshold is set according to the actual application scenario. In one example, the preset keywords and corresponding keywords can include keywords corresponding to the item number. If the keywords are the same (i.e., the data to be grouped has the same item number), they can be considered similar data. Therefore, the second threshold can be set to a small value, for example, zero. This means that if the keywords are the same, the data can be directly identified as the first data. In another example, when retrieving keywords, the preset keywords for the data to be grouped corresponding to the preset keyword attribute, as well as the corresponding keywords for the data in the database, can be determined based on the keyword attributes. In another example, the data can contain multiple fields, each with a corresponding keyword. When retrieving keywords, the keyword for the corresponding field is directly retrieved. In yet another example, the preset keywords can include data numbers. Similarity is judged when the data in the database has the same data number as the data to be grouped, or when the data number in the database is empty. Generally, when both the preset keywords and corresponding keywords are not empty and are different, the data in the database and the data to be grouped are considered not to belong to the same data group.
[0085] In this embodiment, the preset fields of the data to be grouped and the data in the database are first judged. Different first data determination strategies are adopted according to different keyword comparison results, which improves the accuracy of first data acquisition and can directly exclude some data that does not meet the conditions based on preset keywords, reducing the workload of subsequent similarity comparison, improving the efficiency of first data acquisition, and thus improving the efficiency of data grouping, thereby achieving efficient and accurate data grouping.
[0086] In one embodiment, the data in the data group is marked with corresponding data tags. Obtaining the target data group to which the first data belongs includes:
[0087] Determine the target data label for the first data;
[0088] The target data group is determined from the database based on the target data label.
[0089] In this embodiment of the disclosure, all data in a data group is labeled with a corresponding data tag, and data in the same data group have the same data tag. When obtaining the target data group to which the first data belongs, it can be determined based on the data tag. The target data tag corresponding to the first data is determined, and data with the same data tag as the target data tag is retrieved from the database to obtain the target data group. In one example, the data tag can be represented in the form of a data group number, with one data group number corresponding to the same data group. Typically, the data group number is obtained by labeling the data when it is stored in the database. One data group number can correspond to one or more data items.
[0090] In this embodiment of the disclosure, the target data group corresponding to the first data is determined by data tags, which simplifies the process of obtaining the target data group, reduces the workload of data processing, improves the efficiency of obtaining the target data group, and thus improves the efficiency of data grouping, thereby enabling efficient and accurate data grouping.
[0091] In one embodiment, obtaining the target data group to which the first data belongs when the preset keyword of the data to be grouped matches the corresponding keyword of the first data under a first preset condition includes:
[0092] Obtain the first preset keyword of the data to be grouped and the corresponding second preset keyword of the first data;
[0093] If the first preset keyword and the second preset keyword are the same, obtain the target data group to which the first data belongs; and / or,
[0094] If the first preset keyword is empty and / or the second preset keyword is empty, obtain the target data group to which the first data belongs.
[0095] In this embodiment, when comparing the preset keywords of the data to be grouped with the keywords corresponding to the first data, the first preset keyword of the data to be grouped and the second preset keyword corresponding to the first data are obtained, and the first preset keyword and the second preset keyword are compared. In this embodiment, the first preset condition may include the first preset keyword and the second preset keyword being the same. When they are the same, it can be considered that the data to be grouped and the first data have a close relationship and may belong to the same data group. The first preset condition may also include at least one of the first preset keyword and the second preset keyword being empty. Since in some application scenarios, not all data keywords correspond to information, when at least one of them is empty, it can be considered that they may belong to the same data group, and further judgment processing is required. In one example, the first preset condition can be obtained by combining or selecting the above contents. In some implementations, multiple different preset keywords can also be judged, and multiple preset conditions can be set according to multiple different preset keywords. The multiple preset conditions can be the same or different. When all multiple preset conditions are met, it can be considered that the data to be grouped and the first data may belong to the same data group, and further judgment processing is required. When the first preset condition is met, the target data group to which the first data belongs is obtained.
[0096] In this embodiment of the disclosure, the first preset condition is that the keywords are the same or at least one is empty. This can accurately determine whether the first data and the data to be grouped may be the same data group, ensuring the accuracy of the target data group acquisition and improving the accuracy of data grouping. It also reduces the workload of subsequent data processing of the target data group, improves the efficiency of target data group acquisition, and thus improves the efficiency of data grouping, thereby achieving efficient and accurate data grouping.
[0097] Figure 5 This is a schematic diagram illustrating a data grouping method according to an exemplary embodiment, with reference to... Figure 5As shown, data keywords can be categorized in the form of fields. For example, a data set may include a uniqueness field, publication time, commonality field, and similarity field. The keywords corresponding to the uniqueness field can include the data's ID; the keywords corresponding to the publication time are the data's publication date; the keywords corresponding to the commonality field can include the data's specific content, which may include, but is not limited to, information about resource interaction parties and locations; and the keywords corresponding to the similarity field can include the data's project name. In one example, when the data to be grouped corresponds to bidding data, the uniqueness field can include the project ID; the commonality field can include the project ID, bidding company, agency company, and province / city code; and the similarity field can include the project name. Based on the publication time of the data to be grouped, data within a preset time interval is obtained. When the data has a uniqueness field, data with consistent uniqueness fields are directly obtained and sorted in descending order of similarity field similarity to obtain data group A. When the data does not have a uniqueness field, data without uniqueness fields but whose similarity field similarity reaches a preset threshold are obtained and sorted in descending order of similarity field similarity to obtain data group B. The obtained data groups A and B are then combined to obtain similar data group C. The algorithm iterates through similar data groups, checking whether the common fields of the current data and the data in the similar data group meet preset conditions. In this embodiment, the preset conditions include that the common fields do not conflict and that the value fields meet preset values. Specifically, when the common fields both have values and the values are equal, or both have no values, or one has a value and the other has no value, the two data can be considered not to conflict. The preset condition of the value fields meeting preset values can be set to include the comparison of two data, where at least n fields meet specific conditions, such as that the common fields both have values and do not conflict. If the above conditions are met, the group number of the data is obtained, and data with the same group number is retrieved from the database to obtain data group F. It is then determined whether all data under this group number meets the condition that the common fields do not conflict with the data to be grouped. If it does, the judgment ends, and the data to be grouped is assigned to this group number and stored in the database. If it does not meet the condition, the judgment continues for the next data in data group C until all data in data group C is traversed. If all judgments are completed and no group number corresponding to the data to be grouped is obtained, a new group number is assigned to the data to be grouped and stored in the database.
[0098] Through this embodiment, after obtaining the data to be grouped, similar data is found in the database (where all data is grouped), resulting in data group C. Each data group is then checked for conflicts with the data to be grouped. If there are no conflicts, the corresponding data group F is determined based on the group number of the non-conflicting data. If all data in data group F does not conflict with the data to be grouped, the data to be grouped belongs to this group, and the data to be grouped is assigned this group number and stored in the database. This achieves efficient and accurate data grouping, enabling the integration and filtering of data from different sources. Data groups are determined based on the relationships between data, reducing workload and improving user experience. This embodiment also enables the grouping of bidding data and the association of different types of documents published at different times within the same project into groups.
[0099] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the accompanying drawings may include multiple steps or stages, which are not necessarily completed at the same time, but may be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0100] Based on the same inventive concept, this disclosure also provides a data grouping apparatus for implementing the data grouping method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, specific limitations in one or more data grouping apparatus embodiments provided below can be found in the limitations of the data grouping method described above, and will not be repeated here.
[0101] In one embodiment, such as Figure 6 As shown, a data grouping device 600 is provided, comprising:
[0102] The first acquisition module 610 is used to acquire first data from the database that has a similarity greater than a first preset value to the data to be grouped, wherein the database includes multiple data groups;
[0103] The second acquisition module 620 is used to acquire the target data group to which the first data belongs when the preset keyword of the data to be grouped matches the corresponding keyword of the first data under the first preset condition.
[0104] The grouping module 630 is used to store the data to be grouped into the target data group when the preset keyword and the corresponding keyword of the second data in the target data group meet the second preset condition, wherein the second data is the data in the target data group other than the first data.
[0105] In one embodiment, the first acquisition module includes:
[0106] The first acquisition submodule is used to acquire multiple first data from the database that have a similarity greater than a first preset value to the data to be grouped.
[0107] The sorting module is used to sort the plurality of first data in descending order according to the similarity size to obtain a first data group;
[0108] The second acquisition submodule is used to sequentially acquire the first data from the first data group.
[0109] In one embodiment, the first acquisition module includes:
[0110] The third acquisition submodule is used to acquire the preset keywords of the data to be grouped and the corresponding keywords of the data in the database.
[0111] The first determining module is used to determine the data as first data when the similarity between the preset keyword and the corresponding keyword is greater than a second preset value.
[0112] In one embodiment, the first acquisition module includes:
[0113] The fourth submodule is used to obtain the preset keywords of the data to be grouped and the corresponding keywords of the data in the database;
[0114] The second determining module is configured to, when the corresponding keyword is empty, obtain the similarity between the data and the data to be grouped, and determine the data as the first data if the similarity is greater than a first threshold; and / or,
[0115] The third determining module is used to obtain the similarity between the data and the data to be grouped when the preset keyword is the same as the corresponding keyword, and to determine the data as the first data when the similarity is greater than a second threshold.
[0116] In one embodiment, the data in the data group is marked with corresponding data tags, and the second acquisition module includes:
[0117] The fourth determining module is used to determine the target data label of the first data;
[0118] The fifth determining module is used to determine the target data group from the database based on the target data tag.
[0119] In one embodiment, the second acquisition module includes:
[0120] The fifth acquisition submodule is used to acquire the first preset keyword of the data to be grouped and the corresponding second preset keyword of the first data;
[0121] The sixth acquisition submodule is used to acquire the target data group to which the first data belongs when the first preset keyword and the second preset keyword are the same; and / or,
[0122] The seventh acquisition submodule is used to acquire the target data group to which the first data belongs when the first preset keyword is empty and / or the second preset keyword is empty.
[0123] Each module in the aforementioned data grouping device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.
[0124] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores database data and other data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a data grouping method.
[0125] Those skilled in the art will understand that Figure 7 The structures shown are merely block diagrams of some structures related to the embodiments of this disclosure and do not constitute a limitation on the computer devices on which the embodiments of this disclosure are applied. Specific computer devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.
[0126] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.
[0127] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.
[0128] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.
[0129] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this disclosure are all information and data authorized by the user or fully authorized by all parties.
[0130] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this disclosure may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this disclosure may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0131] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0132] The above-described embodiments are merely illustrative of several implementation methods of the present disclosure, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the patent for the embodiments of the present disclosure. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of the present disclosure, and these all fall within the protection scope of the embodiments of the present disclosure. Therefore, the protection scope of the embodiments of the present disclosure should be determined by the appended claims.
Claims
1. A data grouping method, characterized in that, The method includes: First data is obtained from the database that has a similarity greater than a first preset value to the data to be grouped, wherein the database includes multiple data groups; If the preset keyword of the data to be grouped meets the first preset condition with the corresponding keyword of the first data, the target data group to which the first data belongs is obtained. The first preset condition includes at least one of the following: the preset keyword and the corresponding keyword are the same, the preset keyword and the corresponding keyword have a similarity greater than a preset threshold, or the preset keyword or the corresponding keyword is empty. If the preset keyword and the corresponding keyword of the second data in the target data group meet the second preset condition, the data to be grouped is stored in the target data group, wherein the second data is the data in the target data group other than the first data, and the second preset condition is the same as the first preset condition.
2. The method according to claim 1, characterized in that, The step of retrieving first data from the database that has a similarity greater than a first preset value to the data to be grouped includes: Retrieve multiple first data points from the database that have a similarity greater than a first preset value to the data to be grouped. The multiple first data are sorted in descending order according to their similarity to obtain the first data group; The first data is obtained sequentially from the first data group.
3. The method according to claim 1, characterized in that, The step of retrieving first data from the database that has a similarity greater than a preset value to the data to be grouped includes: Retrieve the preset keywords for the data to be grouped and the corresponding keywords from the database; If the similarity between the preset keyword and the corresponding keyword is greater than a second preset value, the data is determined to be the first data.
4. The method according to claim 1, characterized in that, The step of retrieving first data from the database that has a similarity greater than a preset value to the data to be grouped includes: Retrieve the preset keywords for the data to be grouped and the corresponding keywords from the database; If the corresponding keyword is empty, obtain the similarity between the data and the data to be grouped; if the similarity is greater than a first threshold, determine the data as the first data; and / or, If the preset keyword is the same as the corresponding keyword, the similarity between the data and the data to be grouped is obtained. If the similarity is greater than a second threshold, the data is determined to be the first data.
5. The method according to claim 1, characterized in that, The data in the data group is marked with corresponding data tags. Obtaining the target data group to which the first data belongs includes: Determine the target data label for the first data; The target data group is determined from the database based on the target data label.
6. The method according to claim 1, characterized in that, The step of obtaining the target data group to which the first data belongs when the preset keyword of the data to be grouped matches the corresponding keyword of the first data under a first preset condition includes: Obtain the first preset keyword of the data to be grouped and the corresponding second preset keyword of the first data; If the first preset keyword and the second preset keyword are the same, obtain the target data group to which the first data belongs; and / or, If the first preset keyword is empty and / or the second preset keyword is empty, obtain the target data group to which the first data belongs.
7. A data grouping device, characterized in that, The device includes: The first acquisition module is used to acquire first data from the database that has a similarity greater than a first preset value to the data to be grouped, wherein the database includes multiple data groups; The second acquisition module is used to acquire the target data group to which the first data belongs when the preset keyword of the data to be grouped meets the first preset condition. The first preset condition includes at least one of the following: the preset keyword and the corresponding keyword are the same, the similarity between the preset keyword and the corresponding keyword is greater than a preset threshold, and the preset keyword or the corresponding keyword is empty. The grouping module is used to store the data to be grouped into the target data group when the corresponding keyword of the preset keyword and the second data in the target data group meet the second preset condition. The second data is the data in the target data group other than the first data, and the second preset condition is the same as the first preset condition.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the data grouping method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the data grouping method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the data grouping method according to any one of claims 1 to 6.