Value range table matching method and device, equipment and storage medium

Abstract

The application discloses a value range table matching method and device, equipment and a storage medium, which can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and Internet of Vehicles, and the method comprises the steps of obtaining a value range table set, each value range table in the value range table set comprising at least two value ranges; constructing a preset value range index table according to the respective preset value ranges and preset value range table identifiers of at least two preset value range tables; the preset value range index table representing the corresponding relationship between the preset value ranges and the preset value range table identifiers; determining the similarity results of the source value ranges in a source value range table and the preset value ranges in the preset value range index table; according to the similarity results, screening candidate value ranges from the preset value ranges in the preset value range index table to obtain a candidate value range set; and according to the preset value range index table and the candidate value range set, determining a target value range table matched with the source value range table. The application improves the matching accuracy and efficiency of the value range table.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, and particularly relates to a value range table matching method and device, equipment and a storage medium. BACKGROUND

[0002] The data standard value range table is a key component element in the development of medical data application or medical artificial intelligence application by a medical data manufacturer, which mainly defines the value range table and related information of medical data standards in a specific project, and also provides a reference of the benchmark standard value range. Such a value range table can help to quickly bridge multiple project data tables in a real project, thereby providing a large amount of aligned data for artificial intelligence algorithms or data analysis. In addition, such a value range table also strongly constrains the data in a certain project, thereby further improving the business data quality. The value range table matching is an important algorithm basis for the above functions, and the effectiveness and accuracy of the calculation greatly determine the usability of the functions and products. SUMMARY

[0003] The present application provides a value range table matching method, device, equipment and storage medium, which can improve the accuracy and efficiency of value range table matching.

[0004] In one aspect, the present application provides a value range table matching method, which comprises:

[0005] obtaining a value range table set, each value range table in the value range table set comprising at least two value ranges; the value range table set comprising a source value range table corresponding to a first application and at least two preset value range tables corresponding to a second application;

[0006] constructing a preset value range index table according to preset value ranges and preset value range table identifiers corresponding to the at least two preset value range tables; the preset value range index table representing the corresponding relationship between the preset value ranges and the preset value range table identifiers;

[0007] determining the similarity results of the source value ranges in the source value range table and the preset value ranges in the preset value range index table;

[0008] According to the similarity results, filtering out candidate value ranges from the preset value ranges in the preset value range index table to obtain a candidate value range set;

[0009] determining a target value range table matched with the source value range table according to the preset value range index table and the candidate value range set.

[0010] Another aspect provides a value range table matching device, which comprises:

[0011] The value range table set acquisition module is configured to acquire a value range table set, each value range table in the value range table set including at least two value ranges; the value range table set including a source value range table corresponding to a first application and at least two preset value range tables corresponding to a second application;

[0012] The index table construction module is configured to construct a preset value range index table according to the preset value ranges corresponding to the at least two preset value range tables respectively and preset value range table identifiers; the preset value range index table representing a corresponding relationship between a preset value range and a preset value range table identifier;

[0013] The similarity result determination module is configured to determine a similarity result of a source value range in the source value range table and a preset value range in the preset value range index table;

[0014] The candidate value range set determination module is configured to filter out a candidate value range from the preset value ranges in the preset value range index table according to the similarity result, to obtain a candidate value range set;

[0015] The target value range table determination module is configured to determine a target value range table matched with the source value range table according to the preset value range index table and the candidate value range set.

[0016] In another aspect, a value range table matching device is provided, the device including a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the value range table matching method as described above.

[0017] In another aspect, a computer storage medium is provided, the computer storage medium storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by a processor to implement the value range table matching method as described above.

[0018] In another aspect, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform to implement the value range table matching method as described above.

[0019] The value range table matching method, device, equipment and storage medium provided by the present application have the following technical effects:

[0020] The application obtains a value range table set, each value range table in the value range table set includes at least two value ranges; the value range table set includes a source value range table corresponding to a first application and at least two preset value range tables corresponding to a second application; a preset value range index table is constructed according to preset value ranges corresponding to the at least two preset value range tables and preset value range table identifiers; the preset value range index table represents a corresponding relationship between preset value ranges and preset value range table identifiers; a similarity result of a source value range in the source value range table and a preset value range in the preset value range index table is determined; a candidate value range set is obtained by screening a candidate value range from preset value ranges in the preset value range index table according to the similarity result; and a target value range table matched with the source value range table is determined according to the preset value range index table and the candidate value range set. The application constructs a preset value range index table through at least two preset value range tables, constructs a corresponding relationship between preset value ranges and preset value range table identifiers, thereby realizing integration of multiple value range tables into one value range index table; after the preset value range index table is constructed, for value ranges common to each preset value range table, only one matching with value ranges in the source value range table is needed, thereby greatly reducing the matching times of value ranges in the value range table; the candidate value range set is screened through a similarity result between a source value range in the source value range table and a preset value range in the preset value range index table, and finally the target value range table matched with the source value range table is quickly and accurately determined through the preset value range index table and the candidate value range set, thereby improving the matching accuracy and efficiency of the value range table. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative effort based on these drawings.

[0022] Figure 1 is a schematic diagram of a value range table matching system provided by an embodiment of the present application;

[0023] Figure 2 is a flowchart of a value range table matching method provided by an embodiment of the present application;

[0024] Figure 3 is a flowchart of a method for determining a similarity result of a source value range in the source value range table and a preset value range in the preset value range index table;

[0025] Figure 4 is a flowchart of a method for determining a target value range table matched with the source value range table according to the preset value range index table and the candidate value range set;

[0026] Figure 5 is a flowchart of a method for determining a target value domain table identifier according to the occurrence frequency of each candidate value domain table identifier provided by an embodiment of the present application;

[0027] Figure 6 is a flowchart of a method for determining the target value domain table identifier based on the at least two screening value domain table identifiers provided by an embodiment of the present application;

[0028] Figure 7 is a structure diagram of a value domain table in two applications provided by an embodiment of the present application;

[0029] Figure 8 is a table diagram in a value domain table matching process provided by an embodiment of the present application;

[0030] Figure 9 is a diagram of two value domain tables matched successfully provided by an embodiment of the present application;

[0031] Figure 10 is a value domain table identifier matching result diagram provided by an embodiment of the present application;

[0032] Figure 11 is a structure diagram of a value domain table matching device provided by an embodiment of the present application;

[0033] Figure 12 is a structure diagram of a server provided by an embodiment of the present application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0035] First, some nouns or terms appearing in the description of the embodiments of the present application are explained as follows:

[0036] Data standard value domain table (RangeTable): refers to a data standard table defined and used by a certain medical data manufacturer in a certain project, the data standard of which needs to follow certain medical rationality and meet certain migration.

[0037] Pre-trained Language Model: refers to the training of a model through a large amount of unlabeled language text data, obtaining a model parameter and using the model parameter to initialize the language model, or modeling some short texts to obtain their semantic vectors.

[0038] Edit Distance: refers to the quantitative measurement of the difference between two strings, which measures how many times of processing are needed to change one string into another.

[0039] Smart traffic is to fully utilize the Internet of Things, spatial perception, cloud computing, mobile Internet and other new generation information technologies in the entire transportation field, and comprehensively use traffic science, system method, artificial intelligence, knowledge mining and other theories and tools to achieve the goal of comprehensive perception, deep integration, active service and scientific decision-making. Through the construction of a real-time dynamic information service system, deep mining of transportation related data, formation of problem analysis model, realization of the optimization of industry resource allocation, public decision-making ability, industry management ability and public service ability, and promotion of the safe, efficient, convenient, economic, environmental and comfortable operation and development of transportation, the transformation and upgrading of transportation related industries are driven.

[0040] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and in the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0041] In some embodiments, a value domain table matching method is provided:

[0042] First, all value domain tables from value domain A and value domain B are matched and calculated one by one, so m(A) x m(B) times need to be calculated, where m represents the number of value domain tables in the value domain, and n(Ai) x n(Bj) times of value domain matching results need to be calculated in each value domain table calculation, that is, the overall calculation complexity is Such calculation can obtain relatively accurate results, but it takes a lot of time and the matching efficiency of the value domain table is low.

[0043] The application provides a value range table matching method, device, equipment and storage medium, which can improve the accuracy and efficiency of value range table matching.

[0044] Please refer to Figure 1 , Figure 1 is a schematic diagram of a value range table matching system provided by an embodiment of the application, as Figure 1 indicated, the value range table matching system can at least include a server 01 and a client 02.

[0045] Specifically, in the embodiment of the application, the server 01 can include a standalone server, or a distributed server, or a server cluster composed of multiple servers, and can also be a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms. The server 01 can include a network communication unit, a processor, a memory and the like. Specifically, the server 01 can be used to determine a target value range table matched with a source value range table from a plurality of preset value range tables.

[0046] Specifically, in the embodiment of the application, the client 02 can include an entity device such as a smart phone, a desktop computer, a tablet computer, a notebook computer, a digital assistant, a smart wearable device, a smart speaker, a vehicle terminal, a smart television, and the like. It can also include software running in the entity device, such as a web page provided by a service provider to a user, and an application provided by the service provider to the user. Specifically, the client 02 can be used to query the value range table matching result online.

[0047] A value range table matching method provided by the application is introduced below, Figure 2 is a flowchart of a value range table matching method provided by an embodiment of the application. The present specification provides method operation steps as described in the embodiments or flowcharts, but more or fewer operation steps can be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one of the many execution orders, and does not represent the only execution order. In actual system or server product execution, the method order shown in the embodiments or the drawings can be executed in sequence or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0048] Specifically as Figure 2 indicated, the method can include:

[0049] S201: Obtain a set of value range tables, wherein each value range table in the set includes at least two value ranges; the set of value range tables includes a source value range table corresponding to the first application and at least two preset value range tables corresponding to the second application.

[0050] In the embodiments of this application, the first application and the second application can be applications in the same or similar fields; the first application and the second application can be applications in the medical field, the education field, the construction field or other fields; for example, the first application can be an application of the CDC in City A, and the second application can be an application of the CDC in City B; or the first application can be an application of XX People's Hospital, and the second application can be an application of YY Affiliated Hospital.

[0051] In this embodiment of the application, the value range table may include, but is not limited to, a data standard value range table; the source value range table may be the value range table of a certain item in the first application, and the preset value range table may be all or part of the value range table corresponding to the second application.

[0052] In this embodiment of the application, each value range table in the value range table set includes at least two value ranges, such as... Figure 7 As shown, Figure 7 This is a structural diagram of the value range tables in two applications, including value range table a and value range table b. Both value range tables represent the value range tables for different ID document categories. Figure 7 Value range table a includes multiple value ranges, namely 01-Resident ID card, 02-Resident household registration book, 03-Passport, 04-Military officer's certificate, etc.; value range table b includes 1-Resident ID card, 2-Resident household registration book, 3-Passport, 4-Military officer's certificate, 5-Driver's license, etc.

[0053] In this embodiment of the application, the acquisition of the value range table set includes:

[0054] Based on the value domain data corresponding to the first application, determine the source value domain table corresponding to the first application;

[0055] In this embodiment of the application, when the second application needs to obtain the value domain data in the first application to update the local value domain data, or needs to obtain the same type of value domain data in the second application based on the value domain data in the first application and merge the value domain data in the two applications, the source value domain table corresponding to the first application can be determined based on the value domain data corresponding to the first application.

[0056] Obtain the at least two preset value range tables corresponding to the second application;

[0057] Specifically, in this embodiment of the application, obtaining the at least two preset value range tables corresponding to the second application includes:

[0058] Determine the category information of the source value range table;

[0059] In the embodiments of the present application, the category information is used to represent the category corresponding to the source value domain table. For example, the category information of the source value domain table can be the category of an identity certificate.

[0060] Obtain the original value domain table set of the second application;

[0061] Specifically, in the embodiments of the present application, the original value domain table set is the collection of all value domain tables in the second application. The value domain tables in the original value domain table set can all be determined as the preset value domain table, or the preset value domain table can be selected from the original value domain table set, thereby improving the matching efficiency of the value domain table.

[0062] Based on the category information, the at least two preset value domain tables are selected from the original value domain table set of the second application.

[0063] Specifically, in the embodiments of the present application, the at least two preset value domain tables matching the category information can be selected from the original value domain table set of the second application according to the category information; that is, the category of the preset value domain table is the same as the category of the source value domain table.

[0064] Based on the source value domain table and the at least two preset value domain tables, the value domain table set is constructed.

[0065] In the embodiments of the present application, the source value domain table and the at least two preset value domain tables can form a set to obtain the value domain table set.

[0066] In the embodiments of the present application, the source value domain table and the preset value domain table can be determined according to the value domain data corresponding to the first application, the value domain tables in the second application are preliminarily screened, the categories of the source value domain table and the preset value domain table are ensured to be the same, and thus the matching efficiency of the value domain table is improved.

[0067] S203: Construct a preset value domain index table according to the preset value domain corresponding to each of the at least two preset value domain tables and the preset value domain table identifier; the preset value domain index table represents the corresponding relationship between the preset value domain and the preset value domain table identifier.

[0068] In the embodiments of the present application, for the same value domain in the at least two preset value domain tables, a de-duplication process is performed when the preset value domain index table is constructed, and only one value domain is retained, thereby reducing the number of subsequent value domain matching.

[0069] In the embodiments of the present application, the construction of the preset value domain index table according to the preset value domain corresponding to each of the at least two preset value domain tables and the preset value domain table identifier includes:

[0070] Obtain the preset value domain corresponding to each of the at least two preset value domain tables and the preset value domain table identifier;

[0071] In the embodiments of the present application, each preset value domain table can include a plurality of preset value domains, and each preset value domain table corresponds to a unique preset value domain table identifier.

[0072] According to the respective preset value domains corresponding to the at least two preset value domain tables and the preset value domain table identifier, a corresponding relationship between the preset value domains and the preset value domain table identifier is determined.

[0073] In the embodiments of the present application, different preset value domain tables correspond to different preset value domain table identifiers, and the corresponding relationship between the preset value domains and the preset value domain table identifier can be a many-to-one relationship.

[0074] According to the corresponding relationship between the preset value domains and the preset value domain table identifier, a preset value domain index table is constructed.

[0075] Specifically, in the embodiments of the present application, the purpose of establishing the index table is to index and map all value domains in the value domain tables in the target value domain; as shown in FIG. 2, Figure 8 Figure 8 a table diagram in a value domain table matching process, Figure 8 In Table 1, the preset value domain index table, all value domains in the m(B) preset value domain tables in the second application are taken out and de-duplicated, and then mapped from value domains to value domain tables. The same value domain can appear in multiple value domain tables, for example, “05-Internal Medicine of Digestive System” can appear in the “department code” or “department dictionary table” value domain table. In addition, a value domain such as “99-Other” can appear in a large number of different value domain tables. Therefore, by establishing the index table, the calculation amount of the recall step can be reduced. It is worth noting that this step of establishing the index table is usually calculated only once, because the target value domain is not modified during the matching process, so the calculation of this part can be performed in advance.

[0076] In the embodiments of the present application, the at least two preset value domain tables can be combined into one preset value domain index table, and the corresponding relationship between the preset value domains and the preset value domain table identifiers is saved. Since one preset value domain can correspond to multiple preset value domain tables, if the value domains in each preset value domain table are matched with the value domains in the source value domain respectively, multiple matching needs to be performed. However, after the preset value domain index table is constructed, for the value domains common to each preset value domain table, only one matching needs to be performed with the value domains in the source value domain table, which greatly reduces the number of value domain matching in the value domain table, thereby improving the matching efficiency of the value domain table.

[0077] S205: Determine the similarity results of the source value domains in the source value domain table and the preset value domains in the preset value domain index table.

[0078] ​In the embodiment of the present application, the similarity between each source value domain in the source value domain table and each preset value domain in the preset value domain index table can be calculated to obtain a similarity result. The source value domain and the preset value domain can be converted into semantic vectors, and the similarity between the value domains in different applications can be determined by calculating the similarity between the semantic vectors.

[0079] In the embodiment of the present application, as shown in Figure 3 The determination of the similarity between each source value domain in the source value domain table and each preset value domain in the preset value domain index table includes:

[0080] S2051: Extracting semantic features of each source value domain in the source value domain table to obtain first value domain features corresponding to each source value domain in the source value domain table.

[0081] In the embodiment of the present application, the first value domain features corresponding to each source value domain can be semantic vectors corresponding to each source value domain. The semantic vectors of each source value domain in the source value domain table can be extracted by a pre-trained language model, BERT or RoBERTa. BERT stands for Bidirectional Encoder Representation from Transformers, which is a pre-trained language representation model. It emphasizes that it is no longer pre-trained by using a traditional one-way language model or by shallowly splicing two one-way language models, but by using a new masked language model (MLM) to generate deep bidirectional language representation. RoBERTa is an improved version of BERT. Compared with BERT, the model size, computing power and data are improved in the following aspects: larger model parameter quantity, larger batch size and more training data.

[0082] S2053: Extracting semantic features of each preset value domain in the preset value domain index table to obtain second value domain features corresponding to each preset value domain in the preset value domain index table.

[0083] In the embodiment of the present application, the second value domain features corresponding to each preset value domain can be semantic vectors corresponding to each preset value domain. The semantic vectors of each preset value domain in the preset value domain index table can be extracted by a pre-trained language model. The pre-trained language model used for semantic feature extraction of the source value domain and the preset value domain can be the same. In the medical scenario, the pre-trained language model can be the MedBERT language model developed by the Tianyan Laboratory. BERT stands for Bidirectional Encoder Representation from Transformers, which is a pre-trained language representation model.

[0084] Specifically, in the embodiments of the present application, the formula for determining the semantic vector through the pre-trained language model is as follows, PLM is a function corresponding to the pre-trained language model, h i is a semantic vector, n i is a value domain in the source value domain table or the preset value domain index table.

[0085] h i = PLM(n i )

[0086] S2055: According to the first value domain feature and the second value domain feature, determine the similarity result of the source value domain in the source value domain table and the preset value domain in the preset value domain index table.

[0087] In the embodiments of the present application, the similarity between the source value domain and the preset value domain can be determined through the similarity of the first value domain feature and the second value domain feature. The feature similarity of any first value domain feature and any second value domain feature is taken as the similarity result of the source value domain corresponding to the any first value domain feature and the preset value domain corresponding to the any second value domain feature.

[0088] In the embodiments of the present application, the similarity between the source value domain and the preset value domain can be determined through the similarity of the first value domain feature and the second value domain feature. The feature similarity of any first value domain feature and any second value domain feature is taken as the similarity result of the source value domain corresponding to the any first value domain feature and the preset value domain corresponding to the any second value domain feature.

[0089] In the embodiments of the present application, the similarity result of the source value domain in the source value domain table and the preset value domain in the preset value domain index table is determined according to the first value domain feature and the second value domain feature, including:

[0090] According to the value domains corresponding to the source value domain table and the preset value domain index table respectively, determine the structure of the similarity table; the similarity table includes a table header, and the table header represents the value domains in the source value domain table and the preset value domain index table; the table header includes a row table header and a column table header, and the row table header and the column table header represent the value domains in different tables;

[0091] In the embodiments of the present application, the similarity table can be a rectangular table, and the row table header and the column table header in the similarity table represent the value domains in different tables; for example, the row table header can represent the source value domain in the source value domain table, and the column table header can represent the preset value domain in the preset value domain index table; at this time, the number of rows of the similarity table is the number of source value domains in the source value domain table, and the number of columns of the similarity table is the number of preset value domains in the preset value domain index table; or the column table header can represent the source value domain in the source value domain table, and the row table header can represent the preset value domain in the preset value domain index table; at this time, the number of columns of the similarity table is the number of source value domains in the source value domain table, and the number of rows of the similarity table is the number of preset value domains in the preset value domain index table.

[0092] determine the similarity table according to the similarity between the first value domain feature corresponding to the source value domain table and the second value domain feature corresponding to the preset value domain index table; the text data table represents the similarity between the value domains represented by the row header and the list header of the text data respectively.

[0093] In the embodiments of the present application, the similarity between the first value domain feature and the second value domain feature can be calculated by using the Faiss tool; Faiss is a library for efficient similarity search and dense vector clustering.

[0094] In the embodiments of the present application, after the first value domain feature and the second value domain feature are determined, a similarity table corresponding to the value domain can be constructed; in a specific embodiment, as shown in Figure 8 , the similarity table of the source value domain table A and a preset value domain table B is included in Figure 8 , the similarity table of the source value domain table A and a preset value domain table B is included in Figure 8 , Table 2 is a rectangular similarity table; the number of rows is the number of all value domains in RT(A)1, and the number of columns is the number of all value domains in Range B.

[0095] S207: According to the similarity result, a candidate value domain is selected from the preset value domain of the preset value domain index table to obtain a candidate value domain set.

[0096] In the embodiments of the present application, the candidate value domain is selected from the preset value domain of the preset value domain index table according to the similarity result to obtain a candidate value domain set, including:

[0097] According to the similarity result, the preset value domain in the preset value domain index table that has a similarity greater than a preset threshold with any source value domain is determined as a candidate value domain;

[0098] In the embodiments of the present application, the preset threshold can be set according to actual conditions, for example, it can be set to 0.9; the preset threshold can be enlarged or reduced as needed, and a larger value indicates that the constraint in the recall stage is more stringent, and the candidate value domain obtained will also be correspondingly reduced.

[0099] Based on the candidate value domain in the preset value domain index table, the candidate value domain set is constructed.

[0100] In the embodiments of the present application, a plurality of candidate value domains can be selected according to the preset threshold to obtain the candidate value domain set.

[0101] S209: According to the preset value domain index table and the candidate value domain set, a target value domain table matched with the source value domain table is determined.

[0102] In the embodiment of the present application, if each candidate value domain in the candidate value domain set corresponds to the same value domain table identifier, the value domain table corresponding to the same value domain table identifier is determined as the target value domain table matched with the source value domain table.

[0103] In the embodiment of the present application, the matching task of the value domain table needs to give two matching results: 1) the matching result of the value domain table, i.e. the matching result of the value domain table in the first application and the value domain table in the second application; 2) the matching result of the value domain, i.e. the matching result of the value domain in one value domain table and the value domain in another value domain table. And the matching result of 2) will affect the matching result of 1), i.e. only the two value domain tables with successful value domain matching can be matched.

[0104] In the embodiment of the present application, as shown in Figure 4 determining the target value domain table matched with the source value domain table according to the preset value domain index table and the candidate value domain set comprises:

[0105] S2091: determining the candidate value domain table identifier corresponding to each candidate value domain in the candidate value domain set based on the preset value domain index table;

[0106] In the embodiment of the present application, the candidate value domain table identifier corresponding to each candidate value domain can be determined by inverting the index of the preset value domain index table, and a value domain identifier table is constructed; as shown in Figure 8 Figure 8 Table 3 is a value domain identifier table constructed, and Table 3 shows the value domain table identifier corresponding to each candidate value domain.

[0107] S2093: determining the frequency of occurrence of each candidate value domain table identifier in the preset value domain index table according to the number of candidate value domains corresponding to each candidate value domain table identifier;

[0108] In the embodiment of the present application, as shown in Figure 8 Figure 8 Table 3 shows the value domain table identifier corresponding to each candidate value domain; as shown in Table 3, RT(B)1, RT(B)2, RT(B)3, RT(B)4, RT(B)5, RT(B)6, RT(B)7. Then the frequency of occurrence of each candidate value domain table identifier in the index table is counted, and the frequency of occurrence corresponding to each candidate value domain table identifier can be obtained as follows:

[0109] {RT(B)1: 3, RT(B)2: 2, RT(B)3: 2, RT(B)4: 1; RT(B)5: 1, RT(B)6: 3, RT(B)7: 2}

[0110] S2095: determining the target value domain table identifier according to the frequency of occurrence corresponding to each candidate value domain table identifier; ​​

[0111] In the embodiment of the present application, as shown in Figure 5 The target value domain table identifier is determined according to the occurrence frequency corresponding to each candidate value domain table identifier.

[0112] S20951: At least two screening value domain table identifiers are determined according to the occurrence frequency corresponding to each candidate value domain table identifier.

[0113] In the embodiment of the present application, the at least two screening value domain table identifiers are determined according to the occurrence frequency corresponding to each candidate value domain table identifier.

[0114] The frequency threshold is determined according to the number of source value domains in the source value domain table.

[0115] Specifically, in the embodiment of the present application, the frequency threshold can be determined according to the number of source value domains in the source value domain table. For example, for the source value domain table RT(A), the frequency threshold can be determined by the following formula:

[0116] Len(RT(A)) / / 2

[0117] Wherein, Len(RT(A)) is the length of the source value domain table, that is, the number of source value domains in the source value domain table; " / / " is the integer division symbol. When Len(RT(A)) is 5, the result of Len(RT(A)) / / 2 is 2, that is, the frequency threshold is 2. That is, when there are 5 value domains in RT(A)1, there must be at least 2 value domains in the corresponding candidate value domain table RT(B)n with a similarity greater than the preset threshold.

[0118] The candidate value domain table identifier with an occurrence frequency greater than or equal to the frequency threshold is determined as a screening value domain table identifier, and the at least two screening value domain table identifiers are obtained.

[0119] Specifically, in the embodiment of the present application, as shown in Figure 8 The screening value domain table identifiers screened by the frequency threshold are RT(B)1 and RT(B)6, and the corresponding occurrence frequency 3 is greater than the frequency threshold 2.

[0120] S20953: The target value domain table identifier is determined based on the at least two screening value domain table identifiers.

[0121] In the embodiment of the present application, when there are multiple screening value domain table identifiers, the target value domain table identifier can be further screened out. The similarity between the screening value domain table and the source value domain table can be determined by the edit distance, and the at least two screening value domain table identifiers are sorted according to the similarity, and the screening value domain table with the largest similarity with the source value domain table is determined as the target value domain table.

[0122] In the embodiment of the present application, as shown inFigure 6 The determining the target value domain table identifier based on the at least two screening value domain table identifiers comprises:

[0123] S209531: Obtain the screening value domain set corresponding to each of the at least two screening value domain table identifiers respectively;

[0124] S209533: Obtain each source value domain in the source value domain table to obtain a source value domain set;

[0125] S209535: Determine the table similarity between the source value domain table and each screening value domain table corresponding to each screening value domain table identifier according to the screening value domain set and the source value domain set;

[0126] In the embodiment of the present application, the determining the table similarity between the source value domain table and each screening value domain table corresponding to each screening value domain table identifier according to the screening value domain set and the source value domain set comprises:

[0127] determining the string similarity between the screening value domain in the screening value domain set and the source value domain in the source value domain set;

[0128] In the embodiment of the present application, the string similarity can be determined by at least one of the edit distance of the string, the edit distance of the string corresponding to the pinyin, the longest common subsequence of the string, and the number of the same radicals in the string. In the matching process, the value domain table with shorter length can be taken in sequence to calculate the similarity.

[0129] Specifically, in the embodiment of the present application, the determining the string similarity between the screening value domain in the screening value domain set and the source value domain in the source value domain set comprises:

[0130] determining at least two string distances between the screening value domain in the screening value domain set and the source value domain in the source value domain set; the at least two string distances comprise at least two of the edit distance of the string, the edit distance of the string corresponding to the pinyin, the longest common subsequence of the string, and the number of the same radicals in the string;

[0131] determining the string similarity based on each string distance and the weight corresponding to each string distance.

[0132] Specifically, the product of each string distance and the weight corresponding to each string distance can be calculated to obtain the product corresponding to each string distance; and the sum of the products corresponding to various string distances is calculated to obtain the string similarity between the screening value domain in the screening value domain set and the source value domain in the source value domain set.

[0133] determining the first number of the source value domains in the source value domain table;

[0134] determining a second number of screening value domains in the corresponding screening value domain set for each screening value domain table identifier;

[0135] In the embodiments of the present application, the first number and the second number can be compared, and a ratio of the smaller value to the larger value is calculated to obtain the penalty term parameter.

[0136] Based on the first number and the second number corresponding to each screening value domain table identifier, a penalty term parameter corresponding to each screening value domain table identifier is determined.

[0137] In the embodiments of the present application, the determination of the penalty term parameter corresponding to each screening value domain table identifier based on the first number and the second number corresponding to each screening value domain table identifier comprises:

[0138] comparing the first number and the second number corresponding to each screening value domain table identifier to determine a dividend and a divisor corresponding to each screening value domain table identifier, wherein the divisor is smaller than the dividend;

[0139] calculating a quotient of the dividend and the divisor corresponding to each screening value domain table identifier to obtain the penalty term parameter corresponding to each screening value domain table identifier.

[0140] In the embodiments of the present application, the larger value of the first number and the second number can be taken as the divisor, and the smaller value can be taken as the dividend to determine the penalty term parameter.

[0141] Based on the penalty term parameter corresponding to each screening value domain table identifier and the string similarity, a table similarity between the source value domain table and the screening value domain table corresponding to each screening value domain table identifier is determined.

[0142] In the embodiments of the present application, the calculation formula of the table similarity between two value domain tables is as follows:

[0143]

[0144]

[0145] wherein, score(A, B) range is the table similarity result between two value domain tables RangA and RangB, γ is the penalty term parameter, sim(rangA a , rangB b ) is the string similarity between the value domain in RangA and the value domain in RangB; N A is the length of the value domain table RangA, i.e., the number of value domains in the value domain table RangA; N Bθ represents the length of the range table RangB, i.e., the number of ranges in RangB; θ is the range name matching threshold; these can be set according to actual conditions.

[0146] In the embodiments of this application, γ can avoid erroneous matching of a long value range table to a short value range table. For example, RT(A)1 and RT(B)1 contain 10 and 100 value ranges respectively, and all value ranges in RT(A)1 appear in RT(B)1. In this case, the similarity between the two value range tables is high. However, after introducing the γ parameter, the maximum value of γ can be 10 / 100 = 0.1, which will greatly reduce the possibility of RT(A)1 matching RT(B)1.

[0147] S209537: Determine the target value range table identifier based on the table similarity corresponding to each of the at least two filter value range table identifiers.

[0148] In this embodiment of the application, the at least two filter value range table identifiers can be sorted according to the table similarity value corresponding to each filter value range table identifier; then, the target value range table identifier can be determined according to the sorting result. For example, the at least two filter value range table identifiers can be sorted from largest to smallest according to the table similarity value, and the filter value range table identifier that ranks first in the sort can be used as the target value range table identifier.

[0149] S2097: The preset value range table corresponding to the target value range table is determined as the target value range table that matches the source value range table.

[0150] Specifically, in the embodiments of this application, such as Figure 9 As shown, Figure 9 This is a schematic diagram of two successfully matched range tables; where RangA is the source range table and RangB is the preset range table; the two range tables have similar ranges and the same length (the number of ranges is the same).

[0151] In this embodiment, the matching relationship between the source value range table and the preset value range table can be 1-n or n-n; there can also be at least two source value range tables. In this case, at least two source value range tables can be processed according to the processing method of multiple preset value range tables; a corresponding source value range index table can be constructed based on at least two source value range tables, and then the similarity result between the source value range in the source value range index table and the preset value range in the preset value range index table can be determined, thereby determining the candidate value range set corresponding to each source value range table, and finally obtaining the target value range table matching each source value range.

[0152] In the embodiments of the present application, the "index table" and "inverted index" of the recall stage can be improved in engine efficiency by using C++ hybrid programming. The calculation of RT(A)1 and each RT(B)n is relatively independent, and the calculation can be accelerated by C++ hybrid programming and multi-process to improve the engine efficiency.

[0153] In the embodiments of the present application, the method of recalling first and then sorting is adopted. The index table of the recall stage is established, the word vector modeling is completed, and the number of candidate value domain tables is reduced by calculating the semantic similarity of the word vector, thereby reducing the calculation amount in the sorting step, greatly reducing the matching time of the value domain table, and improving the matching efficiency of the value domain table. The enhanced edit distance calculation method is adopted to calculate the similarity of the value domain table and multiple value domains in multiple dimensions such as string edit distance (including name edit distance and pinyin edit distance), thereby maximizing the matching accuracy of the value domain table.

[0154] In one specific embodiment, as shown in Figure 10 , it is a value domain table identification matching result diagram, Figure 10 , which includes table 01 and table 02. Table 01 is the matching result of the value domain table identification; and table 02 is the matching result of each value domain in the two matched value domain tables. Figure 10 Specifically, in the embodiments of the present application, the source value domain table is matched to 55 value domain tables and 82 value domain tables, respectively, and the value domain table matching is performed by using the brute force solution method, the recall sorting method, and the recall sorting combined with parallel acceleration method of the embodiments; and the matching time of the value domain table is shown in the following table:

[0155]

[0156] In the embodiments of the present application, the time consumption of the brute force method function is the longest, reaching 766s, which is almost unusable in a real application scenario. In contrast, the recall and sorting process can greatly reduce the time consumption, and can shorten the calculation time consumption to 101s in the case of starting parallel acceleration, which is 6 times higher than the original efficiency.

[0157] In the embodiments of the present application, after determining the target value domain table matched with the source value domain table, the data in the two value domain tables can be merged, or the data in one value domain table can be stored in another value domain table, and other applications can also be performed.

[0158] In the embodiments of the present application, the method further includes:

[0159]

[0160] ​acquire value domain data in the source value domain table based on a data acquisition request of the second application;

[0161] store the value domain data in the target value domain table.

[0162] In the embodiments of the present application, after determining the target value domain table matched with the source value domain table, the data in the source value domain table can be stored in the target value domain table, realizing data sharing from the first application to the second application.

[0163] In the embodiments of the present application, the method further comprises:

[0164] acquire first value domain data of the source value domain table;

[0165] acquire second value domain data in the target value domain table;

[0166] merge the first value domain data and the second value domain data.

[0167] In the embodiments of the present application, after determining the target value domain table matched with the source value domain table, the data of the same type in the first application and the second application can be merged.

[0168] As can be seen from the technical solutions provided by the embodiments of the present application, the embodiments of the present application acquire a value domain table set, each value domain table in the value domain table set comprising at least two value domains; the value domain table set comprising a source value domain table corresponding to a first application and at least two preset value domain tables corresponding to a second application; a preset value domain index table is constructed according to preset value domains corresponding to the at least two preset value domain tables and preset value domain table identifiers; the preset value domain index table represents the corresponding relationship between the preset value domains and the preset value domain table identifiers; a similarity result of a source value domain in the source value domain table and a preset value domain in the preset value domain index table is determined; a candidate value domain set is obtained by screening candidate value domains from the preset value domains in the preset value domain index table according to the similarity result; and a target value domain table matched with the source value domain table is determined according to the preset value domain index table and the candidate value domain set. The embodiments of the present application construct a preset value domain index table through the at least two preset value domain tables, and construct the corresponding relationship between the preset value domains and the preset value domain table identifiers, so as to realize the integration of multiple value domain tables into one value domain index table; after constructing the preset value domain index table, the value domains common to the preset value domain tables only need to be matched with the value domains in the source value domain table once, greatly reducing the matching times of the value domains in the value domain table; the candidate value domain set is screened through the similarity result between the source value domain in the source value domain table and the preset value domain in the preset value domain index table, and finally the target value domain table matched with the source value domain table is quickly and accurately determined through the preset value domain index table and the candidate value domain set, improving the matching accuracy and efficiency of the value domain table.

[0169] The embodiment of the present application further provides a value range table matching device, as shown in the figure, the device comprises: Figure 11

[0170] a value range table set obtaining module 1110, configured to obtain a value range table set, each value range table in the value range table set comprising at least two value ranges; the value range table set comprising a source value range table corresponding to a first application and at least two preset value range tables corresponding to a second application;

[0171] an index table constructing module 1120, configured to construct a preset value range index table according to preset value ranges and preset value range table identifiers corresponding to the at least two preset value range tables; the preset value range index table representing a corresponding relationship between preset value ranges and preset value range table identifiers;

[0172] a similarity result determining module 1130, configured to determine similarity results of source value ranges in the source value range table and preset value ranges in the preset value range index table;

[0173] a candidate value range set determining module 1140, configured to filter out candidate value ranges from preset value ranges in the preset value range index table according to the similarity results, to obtain a candidate value range set;

[0174] a target value range table determining module 1150, configured to determine a target value range table matched with the source value range table according to the preset value range index table and the candidate value range set.

[0175] In some embodiments, the target value range table determining module can comprise:

[0176] a candidate value range table identifier determining unit, configured to determine, based on the preset value range index table, a candidate value range table identifier corresponding to each candidate value range in the candidate value range set;

[0177] a frequency of occurrence determining unit, configured to determine, according to a number of candidate value ranges corresponding to each candidate value range table identifier, a frequency of occurrence of the each candidate value range table identifier in the preset value range index table;

[0178] a target value range table identifier determining unit, configured to determine, according to the frequency of occurrence corresponding to the each candidate value range table identifier, a target value range table identifier;

[0179] a target value range table determining unit, configured to determine, as the target value range table matched with the source value range table, a preset value range table corresponding to the target value range table identifier.

[0180] In some embodiments, the similarity result determining module can comprise:

[0181] ​The first value domain feature extraction unit is configured to extract semantic features of each source value domain in the source value domain table to obtain first value domain features corresponding to each source value domain in the source value domain table.

[0182] The second value domain feature extraction unit is configured to extract semantic features of each preset value domain in the preset value domain index table to obtain second value domain features corresponding to each preset value domain in the preset value domain index table.

[0183] The similarity result determination unit is configured to determine similarity results of source value domains in the source value domain table and preset value domains in the preset value domain index table according to the first value domain features and the second value domain features.

[0184] In some embodiments, the similarity result determination unit can include:

[0185] The table structure determination subunit is configured to determine a structure of a similarity table according to value domains corresponding to the source value domain table and the preset value domain index table respectively; the similarity table includes a table header, and the table header represents value domains in the source value domain table and the preset value domain index table; the table header includes a row table header and a column table header, and the row table header and the column table header represent value domains in different tables.

[0186] The table data determination subunit is configured to determine body data of the similarity table according to similarities of the first value domain features corresponding to the source value domain table and the second value domain features corresponding to the preset value domain index table; the body data represents similarity results between value domains represented by the row table header and the column table header corresponding to the body data respectively.

[0187] In some embodiments, the target value domain table identification determination unit can include:

[0188] The screening value domain table identification determination subunit is configured to determine at least two screening value domain table identifications according to the occurrence frequencies corresponding to each candidate value domain table identification.

[0189] The target value domain table identification determination subunit is configured to determine the target value domain table identification based on the at least two screening value domain table identifications.

[0190] In some embodiments, the screening value domain table identification determination subunit can include:

[0191] The frequency threshold determination subunit is configured to determine a frequency threshold according to a number of source value domains in the source value domain table.

[0192] The screening subunit is configured to determine a candidate value domain table identification with an occurrence frequency greater than or equal to the frequency threshold as a screening value domain table identification to obtain the at least two screening value domain table identifications.

[0193] In some embodiments, the target value domain table identifier determination subunit can include:

[0194] The screening value domain acquisition subunit is configured to acquire a screening value domain set corresponding to each of the at least two screening value domain table identifiers;

[0195] The source value domain set acquisition subunit is configured to acquire each source value domain in the source value domain table to obtain a source value domain set;

[0196] The table similarity determination subunit is configured to determine, according to the screening value domain set and the source value domain set, a table similarity between the source value domain table and each screening value domain table corresponding to each screening value domain table identifier;

[0197] The target value domain table identifier determination subunit is configured to determine the target value domain table identifier according to the table similarity corresponding to each of the at least two screening value domain table identifiers.

[0198] In some embodiments, the table similarity determination subunit can include:

[0199] The string similarity determination subunit is configured to determine a string similarity between a screening value domain in the screening value domain set and a source value domain in the source value domain set;

[0200] The first quantity determination subunit is configured to determine a first quantity of source value domains in the source value domain table;

[0201] The second quantity determination subunit is configured to determine a second quantity of screening value domains in the screening value domain set corresponding to each screening value domain table identifier;

[0202] The penalty term parameter determination subunit is configured to determine, based on the first quantity and the second quantity corresponding to each screening value domain table identifier, a penalty term parameter corresponding to each screening value domain table identifier;

[0203] The similarity determination subunit is configured to determine, based on the penalty term parameter corresponding to each screening value domain table identifier and the string similarity, the table similarity between the source value domain table and each screening value domain table corresponding to each screening value domain table identifier.

[0204] In some embodiments, the candidate value domain set determination module can include:

[0205] The candidate value domain determination unit is configured to determine, according to the similarity result, a preset value domain in the preset value domain index table as a candidate value domain if the similarity between the preset value domain and any source value domain is greater than a preset threshold value;

[0206] The candidate value domain set construction unit is configured to construct the candidate value domain set based on the candidate value domain in the preset value domain index table.

[0207] In some embodiments, the value domain table set obtaining module can include:

[0208] a source value domain table determining unit, configured to determine a source value domain table corresponding to the first application based on value domain data corresponding to the first application;

[0209] a preset value domain table obtaining unit, configured to obtain the at least two preset value domain tables corresponding to the second application;

[0210] a value domain table set constructing unit, configured to construct the value domain table set based on the source value domain table and the at least two preset value domain tables.

[0211] In some embodiments, the apparatus can further include:

[0212] a value domain data obtaining module, configured to obtain value domain data in the source value domain table based on a data obtaining request of the second application;

[0213] a data storage module, configured to store the value domain data in the target value domain table.

[0214] In some embodiments, the apparatus can further include:

[0215] a first value domain data obtaining module, configured to obtain first value domain data of the source value domain table;

[0216] a second value domain data obtaining module, configured to obtain second value domain data in the target value domain table;

[0217] a data merging module, configured to merge the first value domain data and the second value domain data.

[0218] The apparatus in the apparatus embodiment and the method embodiment are based on the same inventive concept.

[0219] Embodiments of the present application provide a value domain table matching device, which includes a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by the processor to implement the value domain table matching method provided in the above method embodiment.

[0220] Embodiments of the present application also provide a computer storage medium, which can be arranged in a terminal to save at least one instruction or at least one program related to a value domain table matching method in the method embodiment, the at least one instruction or at least one program being loaded and executed by the processor to implement the value domain table matching method provided in the above method embodiment.

[0221] The embodiment of the present application further provides a computer program product or computer program, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device performs a value range table matching method provided by the above method embodiment.

[0222] Optionally, in the embodiment of the present application, the storage medium can be located in at least one of the plurality of network servers of the computer network. Optionally, in the embodiment, the storage medium can include, but is not limited to, a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0223] The memory in the embodiment of the present application can be used to store software programs and modules, and the processor performs various function applications and data processing by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required by functions, etc.; and the data storage area can store data created according to the use of the device, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory can also include a memory controller to provide access of the processor to the memory.

[0224] The value range table matching method provided by the embodiment of the present application can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Taking the case of running on a server as an example, Figure 12 is a hardware structure block diagram of a server providing a value range table matching method according to the embodiment of the present application. As shown in Figure 12As shown, the server 1200 can vary greatly in configuration and performance, and can include one or more Central Processing Units (CPU) 1210 (which can include, but is not limited to, a microprocessor, a microcontroller, a programmable logic device, etc.), a memory 1230 for storing data, one or more storage media 1220 (such as one or more mass storage devices) for storing applications 1223 or data 1222. The memory 1230 and the storage media 1220 can be of any type of volatile or non-volatile storage, including, but not limited to, magnetic, optical, or semiconductor-based storage. The applications 1223 stored in the storage media 1220 can include one or more modules, each of which can include a series of instructions for operating a server. Further, the CPU 1210 can be configured to communicate with the storage media 1220 to execute the series of instructions in the storage media 1220 to operate the server 1200. The server 1200 can also include one or more power supplies 1260, one or more wired or wireless network interfaces 1250, one or more input / output interfaces 1240, and / or one or more operating systems 1221, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0225] The input / output interface 1240 can be used to receive or send data via a network. Examples of the network can include a wireless network provided by a communication provider of the server 1200. In one example, the input / output interface 1240 includes a network interface controller (NIC) that can connect to other network devices through a base station to communicate with the Internet. In one example, the input / output interface 1240 can be a radio frequency (RF) module that can communicate with the Internet through a wireless manner.

[0226] Those of ordinary skill in the art can understand, Figure 12 The structure shown is merely illustrative and does not limit the structure of the electronic device described above. For example, the server 1200 can include more or fewer components than those shown, or have a different configuration of components than those shown. Figure 12 The structure shown is merely illustrative and does not limit the structure of the electronic device described above. For example, the server 1200 can include more or fewer components than those shown, or have a different configuration of components than those shown. Figure 12

[0227] ​It can be seen from the embodiments of the value range table matching method, device, equipment or storage medium provided in the application that the application obtains a value range table set, each value range table in the value range table set including at least two value ranges; the value range table set includes a source value range table corresponding to a first application and at least two preset value range tables corresponding to a second application; a preset value range index table is constructed according to preset value ranges and preset value range table identifiers corresponding to the at least two preset value range tables; the preset value range index table represents a corresponding relationship between a preset value range and a preset value range table identifier; a similarity result of a source value range in the source value range table and a preset value range in the preset value range index table is determined; a candidate value range set is obtained by screening a candidate value range from the preset value range in the preset value range index table according to the similarity result; and a target value range table matched with the source value range table is determined according to the preset value range index table and the candidate value range set. The application constructs a preset value range index table through at least two preset value range tables, and constructs a corresponding relationship between a preset value range and a preset value range table identifier, so that multiple value range tables are integrated into one value range index table; after the preset value range index table is constructed, the value ranges common to each preset value range table only need to be matched with the value ranges in the source value range table once, so that the number of times of matching the value ranges in the value range table is greatly reduced; the candidate value range set is screened through the similarity result between the source value range in the source value range table and the preset value range in the preset value range index table, and finally the target value range table matched with the source value range table is quickly and accurately determined through the preset value range index table and the candidate value range set, so that the matching accuracy and efficiency of the value range table are improved.

[0228] It should be noted that the above-mentioned embodiments of the application are only for description, and do not represent the advantages and disadvantages of the embodiments. The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be executed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0229] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment and storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0230] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiments can be completed by hardware, or can be instructed by a program to complete the related hardware, and the program can be stored in a computer storage medium. The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0231] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A value range table matching method, characterized by, The method comprises: acquiring a value domain table set, each value domain table in the value domain table set comprising at least two value domains; the value domain table set comprising a source value domain table corresponding to a first application and at least two preset value domain tables corresponding to a second application; constructing a preset value domain index table according to preset value domains corresponding to each of the at least two preset value domain tables and preset value domain table identifiers; the preset value domain index table representing a corresponding relationship between preset value domains and preset value domain table identifiers; extracting semantic features of each source value domain in the source value domain table to obtain first value domain features corresponding to each source value domain in the source value domain table; extracting semantic features of each preset value domain in the preset value domain index table to obtain second value domain features corresponding to each preset value domain in the preset value domain index table; determining a structure of a similarity table according to value domains corresponding to the source value domain table and the preset value domain index table respectively; the similarity table comprising a table header, the table header representing value domains in the source value domain table and the preset value domain index table; the table header comprising a row table header and a column table header, the row table header and the column table header representing value domains in different tables; determining text data of the similarity table according to similarities between the first value domain features corresponding to the source value domain table and the second value domain features corresponding to the preset value domain index table; the text data representing similarity results between value domains represented by the row table header and the column table header corresponding to the text data respectively; screening candidate value domains from preset value domains of the preset value domain index table according to the similarity results to obtain a candidate value domain set; determining a target value domain table matched with the source value domain table according to the preset value domain index table and the candidate value domain set.

2. The method of claim 1, wherein, The determining of the target value domain table matched with the source value domain table according to the preset value domain index table and the candidate value domain set comprises: determining candidate value domain table identifiers corresponding to each candidate value domain in the candidate value domain set based on the preset value domain index table; determining occurrence frequencies of each candidate value domain table identifier in the preset value domain index table according to a number of candidate value domains corresponding to each candidate value domain table identifier; determining a target value domain table identifier according to the occurrence frequencies corresponding to each candidate value domain table identifier; determining a preset value domain table corresponding to the target value domain table identifier as the target value domain table matched with the source value domain table.

3. The method of claim 2, wherein, The determining of the target value domain table identifier according to the occurrence frequencies corresponding to each candidate value domain table identifier comprises: determining at least two screening value domain table identifiers according to the occurrence frequencies corresponding to each candidate value domain table identifier; determining the target value domain table identifier based on the at least two screening value domain table identifiers.

4. The method of claim 3, wherein, The determining of the at least two screening value domain table identifiers according to the occurrence frequencies corresponding to each candidate value domain table identifier comprises: determining a frequency threshold value according to a number of source value domains in the source value domain table; determining candidate value domain table identifiers with occurrence frequencies greater than or equal to the frequency threshold value as screening value domain table identifiers to obtain the at least two screening value domain table identifiers.

5. The method of claim 3, wherein, The determining of the target value domain table identifier based on the at least two screening value domain table identifiers comprises: obtaining each source value domain in the source value domain table to obtain a source value domain set; obtaining each source value domain in the source value domain table to obtain a source value domain set; determining table similarity between the source value domain table and each filtering value domain table corresponding to each filtering value domain table identifier according to the filtering value domain set and the source value domain set; determining the target value domain table identifier according to the table similarity corresponding to each filtering value domain table identifier of the at least two filtering value domain table identifiers.

6. The method of claim 5, wherein, The method further comprises: determining the table similarity between the source value domain table and each filtering value domain table corresponding to each filtering value domain table identifier according to the filtering value domain set and the source value domain set, comprising: determining string similarity between filtering value domains in the filtering value domain set and source value domains in the source value domain set; determining a first number of source value domains in the source value domain table; determining a second number of filtering value domains in the filtering value domain set corresponding to each filtering value domain table identifier; determining a penalty term parameter corresponding to each filtering value domain table identifier based on the first number and the second number corresponding to each filtering value domain table identifier; 7. The method of claim 1, wherein, determining the table similarity between the source value domain table and each filtering value domain table corresponding to each filtering value domain table identifier based on the penalty term parameter corresponding to each filtering value domain table identifier and the string similarity. The method further comprises: determining the table similarity between the source value domain table and each filtering value domain table corresponding to each filtering value domain table identifier according to the filtering value domain set and the source value domain set, comprising:

8. The method of claim 1, wherein, determining string similarity between filtering value domains in the filtering value domain set and source value domains in the source value domain set; determining a first number of source value domains in the source value domain table; determining a second number of filtering value domains in the filtering value domain set corresponding to each filtering value domain table identifier; determining a penalty term parameter corresponding to each filtering value domain table identifier based on the first number and the second number corresponding to each filtering value domain table identifier; 9. The method of claim 8, wherein, determining the table similarity between the source value domain table and each filtering value domain table corresponding to each filtering value domain table identifier based on the penalty term parameter corresponding to each filtering value domain table identifier and the string similarity. The method further comprises: determining the table similarity between the source value domain table and each filtering value domain table corresponding to each filtering value domain table identifier according to the filtering value domain set and the source value domain set, comprising:

10. The method of claim 8, wherein, determining string similarity between filtering value domains in the filtering value domain set and source value domains in the source value domain set; determining a first number of source value domains in the source value domain table; determining a second number of filtering value domains in the filtering value domain set corresponding to each filtering value domain table identifier; determining a penalty term parameter corresponding to each filtering value domain table identifier based on the first number and the second number corresponding to each filtering value domain table identifier; 11. A value range table matching apparatus characterized by comprising: determining the table similarity between the source value domain table and each filtering value domain table corresponding to each filtering value domain table identifier based on the penalty term parameter corresponding to each filtering value domain table identifier and the string similarity. The method further comprises: obtaining value domain data in the source value domain table based on a data acquisition request of the second application; storing the value domain data in the target value domain table. The method further comprises: obtaining first value domain data of the source value domain table; obtaining second value domain data in the target value domain table; merging the first value domain data and the second value domain data. The apparatus comprises: a value domain table set obtaining module, configured to obtain a value domain table set, each value domain table in the value domain table set comprising at least two value domains; the value domain table set comprising a source value domain table corresponding to a first application and at least two preset value domain tables corresponding to a second application; an index table constructing module, configured to construct a preset value domain index table according to preset value domains and preset value domain table identifiers corresponding to the at least two preset value domain tables; the preset value domain index table representing a corresponding relationship between a preset value domain and a preset value domain table identifier; a similarity result determining module, configured to determine a similarity result of a source value domain in the source value domain table and a preset value domain in the preset value domain index table. The candidate value domain set determination module is configured to filter candidate value domains from the preset value domains in the preset value domain index table according to the similarity result, to obtain a candidate value domain set; The target value domain table determination module is configured to determine a target value domain table matched with the source value domain table according to the preset value domain index table and the candidate value domain set; The similarity result determination module comprises: The first value domain feature extraction unit is configured to extract semantic features of each source value domain in the source value domain table, to obtain a first value domain feature corresponding to each source value domain in the source value domain table; The second value domain feature extraction unit is configured to extract semantic features of each preset value domain in the preset value domain index table, to obtain a second value domain feature corresponding to each preset value domain in the preset value domain index table; The similarity result determination unit is configured to determine a structure of a similarity table according to the value domains corresponding to the source value domain table and the preset value domain index table respectively; the similarity table comprises a table header, which represents the value domains in the source value domain table and the preset value domain index table; the table header comprises a row table header and a column table header, which represent the value domains in different tables; the similarity result determination unit is configured to determine text data of the similarity table according to the similarity between the first value domain features corresponding to the source value domain table and the second value domain features corresponding to the preset value domain index table; the text data represents the similarity between the value domains represented by the row table header and the column table header corresponding to the text data respectively.

12. The apparatus of claim 11, wherein, The target value domain table determination module comprises: The candidate value domain table identification determination unit is configured to determine a candidate value domain table identification corresponding to each candidate value domain in the candidate value domain set based on the preset value domain index table; The occurrence frequency determination unit is configured to determine an occurrence frequency of each candidate value domain table identification in the preset value domain index table according to the number of candidate value domains corresponding to the candidate value domain table identification; The target value domain table identification determination unit is configured to determine a target value domain table identification according to the occurrence frequency corresponding to each candidate value domain table identification; The target value domain table determination unit is configured to determine a preset value domain table corresponding to the target value domain table identification as the target value domain table matched with the source value domain table.

13. The apparatus of claim 11, wherein, The target value domain table identification determination unit comprises: The screening value domain table identification determination subunit is configured to determine at least two screening value domain table identifications according to the occurrence frequency corresponding to each candidate value domain table identification; The target value domain table identification determination subunit is configured to determine the target value domain table identification based on the at least two screening value domain table identifications.

14. The apparatus of claim 11, wherein, The screening value domain table identification determination subunit comprises: The frequency threshold determination subunit is configured to determine a frequency threshold according to the number of source value domains in the source value domain table; The screening subunit is configured to determine a candidate value domain table identification with an occurrence frequency greater than or equal to the frequency threshold as a screening value domain table identification, to obtain the at least two screening value domain table identifications.

15. The apparatus of claim 11, wherein, The target value domain table identification determination subunit comprises: The screening value domain acquisition subunit is configured to acquire a screening value domain set corresponding to each of the at least two screening value domain table identifications; The source value domain set acquisition subunit is configured to acquire each source value domain in the source value domain table, to obtain a source value domain set; The table similarity determining subunit is configured to determine a table similarity between the source value domain table and each of the screening value domain tables corresponding to the screening value domain table identifiers according to the screening value domain set and the source value domain set; The target value domain table identifier determining subunit is configured to determine the target value domain table identifier according to the table similarities corresponding to the at least two screening value domain table identifiers.

16. The apparatus of claim 11, wherein, The table similarity determining subunit includes: The string similarity determining subunit is configured to determine a string similarity between a screening value domain in the screening value domain set and a source value domain in the source value domain set; The first quantity determining subunit is configured to determine a first quantity of source value domains in the source value domain table; The second quantity determining subunit is configured to determine a second quantity of screening value domains in the screening value domain set corresponding to each of the screening value domain table identifiers; The penalty term parameter determining subunit is configured to determine a penalty term parameter corresponding to each of the screening value domain table identifiers based on the first quantity and the second quantity corresponding to each of the screening value domain table identifiers; The similarity determining subunit is configured to determine the table similarity between the source value domain table and each of the screening value domain tables corresponding to the screening value domain table identifiers based on the penalty term parameter corresponding to each of the screening value domain table identifiers and the string similarity.

17. The apparatus of claim 11, wherein, The candidate value domain set determining module includes: The candidate value domain determining unit is configured to determine a candidate value domain in the preset value domain index table as a candidate value domain if a similarity between the candidate value domain and any source value domain is greater than a preset threshold according to the similarity result; The candidate value domain set constructing unit is configured to construct the candidate value domain set based on the candidate value domains in the preset value domain index table.

18. The apparatus of claim 11, wherein, The value domain table set obtaining module includes: The source value domain table determining unit is configured to determine a source value domain table corresponding to the first application based on value domain data corresponding to the first application; The preset value domain table obtaining unit is configured to obtain the at least two preset value domain tables corresponding to the second application; The value domain table set constructing unit is configured to construct the value domain table set based on the source value domain table and the at least two preset value domain tables.

19. The apparatus of claim 11, wherein, The device further includes: The value domain data obtaining module is configured to obtain value domain data in the source value domain table based on a data obtaining request of the second application; The data storage module is configured to store the value domain data in the target value domain table.

20. The apparatus of claim 11, wherein, The device further includes: The first value domain data obtaining module is configured to obtain first value domain data of the source value domain table; The second value domain data obtaining module is configured to obtain second value domain data in the target value domain table; The data merging module is configured to merge the first value domain data and the second value domain data.

21. A value range table matching device, characterized by, The device includes a processor and a memory, and the memory stores at least one instruction or at least one program, which is loaded and executed by the processor to implement the value domain table matching method according to any one of claims 1-10.

22. A computer storage medium, comprising, The computer storage medium stores at least one instruction or at least one program, which is loaded and executed by the processor to implement the value domain table matching method according to any one of claims 1-10.

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