A data matching method, device and electronic equipment

Abstract

The application discloses a data matching method and device and electronic equipment. The data matching method comprises the following steps: obtaining target data to be standardized and a plurality of standardized data elements; matching the target data and all the standardized data elements according to at least one of a first matching rule corresponding to a field Chinese name, a second matching rule corresponding to a field English name and a third matching rule corresponding to data content, to obtain a matching result containing a matching degree value; determining at least one matching result in which the matching degree value is within a preset range among all the matching results, and determining the standardized data element corresponding to each matching result. Based on the above method, the target data to be standardized and the standardized data elements are matched through the matching rules corresponding to the field Chinese name, the field English name and the data content respectively. The multi-strategy matching mode can screen out the standardized data elements that are more matched with the target data, and improves the reliability of the matching result.

Description

TECHNICAL FIELD

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

[0002] With the rapid development of big data, various industries have begun to build big data platforms and hope to complete digital transformation through big data platforms. However, the following problems may be encountered in the process of building big data platforms: the first aspect is about data agnostic, that is, the data source is unclear, the business meaning of the data is not understood, and the key information of the data cannot be grasped; the second aspect is about uncontrollable, that is, the data standards between various departments and various business systems are not unified, resulting in inconsistent data definitions, and further causing difficulty in understanding business meaning; the third aspect is about data unavailability, that is, the data standards between various departments are not unified, resulting in difficulty in data sharing between various departments. Therefore, establishing unified data standards can help standardize data management, unify the consistency of data definitions and understanding of various departments, and break down data barriers between departments to facilitate data sharing. For this purpose, a series of documents have been issued by the state and various industries, such as: “XXX big data processing data element writing rules” “XXX data element qualifier” and the like. After the data standards promulgated by various industries are obtained, the corresponding standard data elements of various industries can be obtained, so that the target data to be standardized can be matched with the standard data elements, and then the standard data element information corresponding to the target data to be standardized can be obtained, which can realize the standardization of the target data.

[0003] At present, the standard data element corresponding to the target data to be standardized is mainly determined by text similarity matching between the target data to be standardized and the standardized data element. However, due to the non-standard situation of the target data to be standardized, such as: special characters in table name or field name, mixing of Chinese characters and English characters, and Chinese pinyin abbreviations in English name, the text similarity value between the determined standard data element and the target data is low, which further affects the reliability of standardizing the target data. SUMMARY

[0004] The present application provides a data matching method, device, program and electronic equipment, which matches the target data to be standardized with the standardized data element through the matching rules corresponding to the field Chinese name, the field English name and the data content, respectively. This multi-strategy matching method can filter out the standardized data element that matches the target data more, and improve the reliability of the matching result.

[0005] In a first aspect, the present application provides a data matching method, comprising:

[0006] Obtaining target data to be standardized and a plurality of standardized data elements, wherein the target data comprises a field Chinese name, a field English name and data content;

[0007] According to at least one of the first matching rule corresponding to the field Chinese name, the second matching rule corresponding to the field English name and the third matching rule corresponding to the data content, the target data and all the standardized data elements are matched to obtain a matching result containing a matching degree value;

[0008] Determine at least one matching result in all matching results whose matching degree value is in a preset range, and determine the standardized data element corresponding to each matching result in the at least one matching result.

[0009] Through the above method, the target data and the standardized data elements are matched through the matching rules corresponding to the field Chinese name, the field English name and the data content respectively. This multi-strategy matching method can screen out the standardized data elements that are more matched with the target data, and improve the reliability of the matching result.

[0010] In a possible design, according to at least one of the first matching rule corresponding to the field Chinese name, the second matching rule corresponding to the field English name and the third matching rule corresponding to the data content, the target data and all the standardized data elements are matched to obtain a matching result containing a matching degree value, comprising:

[0011] Text similarity matching is performed between the field Chinese name and at least one standardized data element in the plurality of standardized data elements to obtain a matching result containing a text similarity value; and / or

[0012] Semantic similarity matching is performed between the field English name and at least one standardized data element in the plurality of standardized data elements to obtain a matching result containing a semantic similarity value; and / or

[0013] Attribute value matching is performed between the data content and at least one standardized data element in the plurality of standardized data elements to obtain a matching result containing an attribute matching degree value; and / or

[0014] Representation word matching is performed between the first preset keyword in the field Chinese name, the second preset keyword in the field English name and the data feature value in the data content and at least one standardized data element in the plurality of standardized data elements respectively to obtain a matching result containing a representation word matching degree value.

[0015] The field Chinese name, the field English name and the data content in the target data are matched by different matching methods to determine the standardized data element corresponding to the target data, and the representation word corresponding to the target data is further determined, so that the standardized data element matched by the target data is more accurate and comprehensive, and the reliability of the matching result is improved.

[0016] In a possible design, the field Chinese name is matched with at least one standardized data element in the plurality of standardized data elements in text similarity to obtain a matching result containing a text similarity value, including:

[0017] Redundant information in the field Chinese name is removed, where the redundant information includes preset symbols and numbers.

[0018] The sentence in the field Chinese name is segmented.

[0019] Missing information in the field Chinese name is supplemented according to the field English name information.

[0020] The preprocessed field Chinese name is matched with at least one standardized data element in the plurality of standardized data elements in text similarity to obtain a matching result containing a text similarity value.

[0021] By the method, the field Chinese name in the target data is preprocessed, and the preprocessed field Chinese name is matched with the standardized data element in text similarity, so that the influence of interference information on the matching process is eliminated, and the matching efficiency and the reliability of the matching result are improved.

[0022] In a possible design, the field English name is matched with at least one standardized data element in the plurality of standardized data elements in semantic similarity to obtain a matching result containing a semantic similarity value, including:

[0023] The preset character contained in the field English name is deleted.

[0024] The letters in the field English name are sorted.

[0025] The English words and English abbreviations in the field English name are translated into Chinese pinyin or synonyms.

[0026] The preprocessed field English name is matched with at least one standardized data element in the plurality of standardized data elements in semantic similarity to obtain a matching result containing a semantic similarity value.

[0027] The English field name in the target data is preprocessed by the method, and the preprocessed Chinese field name is matched with the standardized data element in terms of semantic similarity, so as to eliminate the influence of interference information on the matching process, improve the matching efficiency and the reliability of the matching result.

[0028] In a possible design, the data content is matched with at least one standardized data element in the plurality of standardized data elements in terms of attribute value, to obtain a matching result containing an attribute matching degree value, including:

[0029] Dirty data in the data content is cleaned, where the dirty data at least includes illegal data and data beyond the business scope.

[0030] The cleaned data content is matched with at least one standardized data element in the plurality of standardized data elements in terms of attribute value, to obtain a matching result containing an attribute matching degree value.

[0031] By the method, the data content in the target data is matched with the standardized data element in terms of attribute value after the dirty data in the target data is cleaned, so as to eliminate the influence of interference information on the matching process, improve the matching efficiency and the reliability of the matching result.

[0032] In a possible design, a first preset keyword in the Chinese field name, a second preset keyword in the English field name and a data feature value in the data content are respectively matched with at least one standardized data element in the plurality of standardized data elements in terms of representation word, to obtain a matching result containing a representation word matching degree value, including:

[0033] The first preset keyword in the Chinese field name is matched with at least one standardized data element in the plurality of standardized data elements in terms of representation word category, to obtain a first matching result containing a representation word matching degree value.

[0034] The second preset keyword in the English field name is matched with at least one standardized data element in the plurality of standardized data elements in terms of representation word category, to obtain a second matching result containing a representation word matching degree value.

[0035] The data feature in the data content is matched with at least one standardized data element in the plurality of standardized data elements in terms of representation word, to obtain a third matching result containing a representation word matching degree value.

[0036] By the method, the keywords and the data features in the target data are determined to correspond to respective representation word types, where the representation word types belong to the standardized data elements. In the process of matching the target data with the standardized data elements, the representation word matching is added, so as to improve the comprehensiveness of the standardized data elements matched with the target data, and further improve the reliability of the final matching result.

[0037] In a possible design, the at least one matching result whose matching degree value is in the preset range is determined from all matching results, including:

[0038] The matching result whose text similarity value included in the matching result is greater than a first preset threshold is determined.

[0039] The matching result whose semantic similarity value included in the matching result is greater than a second preset threshold is determined.

[0040] The matching result whose attribute matching degree value included in the matching result is greater than a third preset threshold is determined.

[0041] The matching result whose matching degree value of the representation word is merged according to a first preset rule, to obtain a matching result containing at least one representation word.

[0042] According to the method, the matching result whose matching degree value meets the user requirement is screened from all matching results, and the reliability of the matching result is improved.

[0043] In a possible design, the matching result whose matching degree value of the representation word is merged according to a first preset rule, to obtain a matching result containing at least one representation word, including:

[0044] A fourth matching result whose matching degree value of the representation word is greater than a fourth preset threshold is determined from the first matching result.

[0045] A fifth matching result whose matching degree value of the representation word is greater than a fifth preset threshold is determined from the second matching result.

[0046] A sixth matching result whose matching degree value of the representation word is greater than a sixth preset threshold is determined from the third matching result.

[0047] When the representation word corresponding to the first preset keyword in the fourth matching result and the representation word corresponding to the second preset keyword in the fifth matching result are consistent with the representation word corresponding to the data content of the same keyword in the sixth matching result, the representation word corresponding to the same keyword and the representation word corresponding to the data content of the same keyword are merged.

[0048] According to the method, the representation words corresponding to the Chinese name of the field, the English name of the field and the content data in the target data are screened, and then the representation words are further merged, so that the accuracy of the matching result is improved.

[0049] In a possible design, after determining at least one matching result in which the matching degree value in all matching results is within a preset range, and determining the standardized data element corresponding to each of the at least one matching result respectively, the method further includes:

[0050] setting a weight value for the standardized data element corresponding to each of the at least one matching result respectively, and counting N weight values corresponding to the first standardized data element, where N is an integer greater than or equal to 1;

[0051] calculating an overall weight value corresponding to the N weight values according to a preset algorithm;

[0052] calculating a confidence degree of the first standardized data element information according to the overall weight value and a matching frequency corresponding to the first standardized data element, where the matching frequency indicates a ratio between a number of times of being determined and a number of matching rules;

[0053] when the confidence degree value is greater than a seventh preset threshold, regarding the first standardized data element as a target standardized data element.

[0054] By the foregoing method, the confidence degree corresponding to each standardized data element is calculated, and the standardized data element whose confidence degree value meets the user demand is screened out, thereby further improving the matching result accuracy and the reliability of the matching result.

[0055] In a possible design, after regarding the first standardized data element as a target standardized data element, the method further includes:

[0056] extracting a data feature value corresponding to the data content;

[0057] determining a value range of the data feature value according to a second preset rule;

[0058] judging whether the target standardized data element is in the value range;

[0059] when the target standardized data element is in the value range, regarding the target standardized data element as a final standardized data element.

[0060] By the foregoing method, the standardized data element whose confidence degree meets the user demand is further screened, thereby improving the matching result accuracy and the reliability of the matching result.

[0061] In a second aspect, the present application provides a data matching device, which includes:

[0062] an acquisition module configured to acquire target data to be standardized and a plurality of standardized data elements, where the target data includes a Chinese name of a field, an English name of the field, and data content;

[0063] The matching module is configured to match the target data and all standardized data elements according to at least one of a first matching rule corresponding to the Chinese name of the field, a second matching rule corresponding to the English name of the field, and a third matching rule corresponding to the data content, to obtain a matching result containing a matching degree value.

[0064] The first determining module is configured to determine at least one matching result with a matching degree value within a preset range from all matching results, and determine a standardized data element corresponding to each matching result in the at least one matching result.

[0065] In a possible design, the matching module is specifically configured to:

[0066] perform text similarity matching on the Chinese name of the field and at least one standardized data element from the plurality of standardized data elements, to obtain a matching result containing a text similarity value; and / or

[0067] perform semantic similarity matching on the English name of the field and at least one standardized data element from the plurality of standardized data elements, to obtain a matching result containing a semantic similarity value; and / or

[0068] perform attribute value matching on the data content and at least one standardized data element from the plurality of standardized data elements, to obtain a matching result containing an attribute matching degree value; and / or

[0069] perform representation word matching between a first preset keyword in the Chinese name of the field, a second preset keyword in the English name of the field, and a data feature value in the data content, and at least one standardized data element from the plurality of standardized data elements, to obtain a matching result containing a representation word matching degree value.

[0070] In a possible design, the matching module is further configured to:

[0071] remove redundant information in the Chinese name of the field, wherein the redundant information includes preset symbols and numbers;

[0072] segment a sentence in the Chinese name of the field;

[0073] supplement missing information in the Chinese name of the field according to the English name information of the field;

[0074] perform text similarity matching on the preprocessed Chinese name of the field and at least one standardized data element from the plurality of standardized data elements, to obtain a matching result containing a text similarity value.

[0075] In a possible design, the matching module is further configured to:

[0076] delete a preset character contained in the English name of the field;

[0077] organize the letters in the English name of the field;

[0078] translate English words and English abbreviations in the English name of the field into Chinese pinyin or synonyms;

[0079] perform semantic similarity matching on the preprocessed English name of the field and at least one standardized data element in the plurality of standardized data elements, to obtain a matching result containing a semantic similarity value.

[0080] In a possible design, the matching module is further configured to:

[0081] clean dirty data in the data content, wherein the dirty data at least includes illegal data and data beyond the business scope;

[0082] perform attribute value matching on the cleaned data content and at least one standardized data element in the plurality of standardized data elements, to obtain a matching result containing an attribute matching degree value.

[0083] In a possible design, the matching module is further configured to:

[0084] perform representation word category matching on a first preset keyword in the Chinese name of the field and at least one standardized data element in the plurality of standardized data elements, to obtain a first matching result containing a representation word matching degree value;

[0085] perform representation word category matching on a second preset keyword in the English name of the field and at least one standardized data element in the plurality of standardized data elements, to obtain a second matching result containing a representation word matching degree value;

[0086] perform representation word matching on a data feature in the data content and at least one standardized data element in the plurality of standardized data elements, to obtain a third matching result containing a representation word matching degree value.

[0087] In a possible design, the first determining module is specifically configured to:

[0088] determine a matching result in which the text similarity value in the matching result containing the text similarity value is greater than a first preset threshold value;

[0089] determine a matching result in which the semantic similarity value in the matching result containing the semantic similarity value is greater than a second preset threshold value;

[0090] determine a matching result in which the attribute matching degree value in the matching result containing the attribute matching degree value is greater than a third preset threshold value;

[0091] merge the matching results containing the word matching degree values according to a first preset rule to obtain a matching result containing at least one word.

[0092] In a possible design, the matching module is further configured to:

[0093] determine a fourth matching result in the first matching result, in which a word matching degree value is greater than a fourth preset threshold value;

[0094] determine a fifth matching result in the second matching result, in which a word matching degree value is greater than a fifth preset threshold value;

[0095] determine a sixth matching result in the third matching result, in which a word matching degree value is greater than a sixth preset threshold value;

[0096] merge the same keyword corresponding word and the same keyword corresponding word of data content when the first preset keyword corresponding word in the fourth matching result and the second preset keyword corresponding word in the fifth matching result are consistent with the same keyword corresponding word of data content in the sixth matching result.

[0097] In a possible design, the apparatus further includes:

[0098] a statistical module configured to set a weight value for each standard data element corresponding to each matching result, and to count N weight values corresponding to a first standard data element, where N is an integer greater than or equal to 1;

[0099] a first calculation module configured to calculate an overall weight value corresponding to the N weight values according to a preset algorithm, and to calculate a confidence degree of first standard data element information according to the overall weight value and a matching frequency of the first standard data element, where the matching frequency indicates a ratio between a number of times of being determined and a number of matching rules;

[0100] a second determination module configured to determine the first standard data element as a target standard data element when the confidence degree value is greater than a seventh preset threshold value.

[0101] In a possible design, the apparatus further includes:

[0102] an extraction module configured to extract a data feature value corresponding to the data content;

[0103] a second calculation module configured to determine a value range of the data feature value according to a second preset rule;

[0104] a judgment module configured to determine whether the target standard data element is in the value range.

[0105] A third determining module is configured to determine the target standard data element as a final standard data element when the value range is reached.

[0106] In a third aspect, the present application provides an electronic device, comprising:

[0107] A memory is configured to store a computer program.

[0108] A processor is configured to execute the computer program stored in the memory to implement the data matching method.

[0109] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the data matching method.

[0110] According to the above data matching method, the target data to be standardized and the standardized data element are matched through the matching rules corresponding to the field Chinese name, the field English name and the data content, respectively. The multi-strategy matching method can filter out the standardized data element that matches the target data more, and improve the reliability of the matching result.

[0111] The above-mentioned aspects and the technical effects that can be achieved by each aspect of the second aspect to the fourth aspect are described above with reference to the technical effects that can be achieved by the first aspect or the various possible schemes of the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0112] Figure 1 A flowchart of a data matching method provided by the present application is shown in the figure.

[0113] Figure 2 A structural schematic diagram of a data matching device provided by the present application is shown in the figure.

[0114] Figure 3 An example diagram of a data matching method provided by the present application is shown in the figure.

[0115] Figure 4 A structural schematic diagram of an electronic device provided by the present application is shown in the figure. DETAILED DESCRIPTION

[0116] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The specific operational methods in the method embodiments can also be applied to the device embodiments or system embodiments. It should be noted that in the description of this application, "multiple" is understood as "at least two". "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. A connected to B can represent: A and B directly connected, and A and B connected through C. Furthermore, in the description of this application, terms such as "first" and "second" are used only for distinguishing the purpose of description and should not be construed as indicating or implying relative importance or order.

[0117] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0118] Currently, the standard data elements corresponding to the target data to be standardized are mainly determined by matching the text similarity between the target data to be standardized and the standard data elements. However, since the target data to be standardized is often non-standard, such as special characters in table names or field names, a mixture of Chinese and English characters, or Chinese pinyin abbreviations in English names, the text similarity value between the determined standard data elements and the target data is low, which affects the reliability of standardizing the target data.

[0119] To address the aforementioned issues, this application provides a data matching method. This method matches target data with standardized data elements using matching rules corresponding to the Chinese name, English name, and data content of each field. This multi-strategy matching approach can filter out standardized data elements that better match the target data, improving the reliability of the matching results. The methods and apparatus described in the embodiments of this application are based on the same technical concept. Since the principles by which the methods and apparatus solve the problems are similar, embodiments of the apparatus and methods can be referred to interchangeably, and repeated details will not be elaborated further.

[0120] like Figure 1 The diagram shown is a flowchart of a data matching method provided in this application, which specifically includes the following steps:

[0121] S11, Obtain the target data to be standardized and multiple standardized data elements;

[0122] S12, based on at least one of the first matching rule corresponding to the Chinese name of the field in the target data, the second matching rule corresponding to the English name of the field, and the third matching rule corresponding to the data content, match the target data with all standardized data elements to obtain a matching result containing the matching degree value;

[0123] S13, determining at least one matching result in which the matching degree value is in the preset range from all the matching results, and determining the standardized data element corresponding to each matching result respectively.

[0124] In the embodiment of the present application, the target data includes data table English name, data table Chinese name, field Chinese name, field English name, data content, and field type.

[0125] A data element is a data unit defined, identified, represented, and allowed by a set of attributes. The standard data element mainly includes: data element internal identifier, identifier, Chinese name, representation word, object class word, characteristic word, synonym, value range, data type, representation format, and the like. For example, the standard data element of the citizen ID number includes: internal identifier DE00001, Chinese name citizen ID number, identifier GMSFHM, representation word number, object class word citizen, characteristic word identity, value range in accordance with XX ID number, and the like.

[0126] According to the industry and data type in which the target data is located, a plurality of standardized data elements corresponding to the field of the target data are obtained.

[0127] After obtaining the target data and the plurality of standardized data elements, the next purpose is to determine the standardized data element matched with the target data from the plurality of standard data elements, and the specific method is as follows:

[0128] The obtained target data is preprocessed, and then the influence of redundant information and interference term information is eliminated, and the accuracy of the matching result is improved. The specific preprocessing measures mainly include the following three aspects:

[0129] The first aspect is to preprocess the field Chinese name in the target data. The field Chinese name is mainly an explanation of the field English name. The information is often not standardized, such as: long sentence, containing non-standard characters, mixed English and Chinese, information missing, and the like. Therefore, in the embodiment of the present application, the method for preprocessing the field Chinese name includes:

[0130] The redundant information in the field Chinese name is removed, wherein the redundant information includes preset symbols and numbers;

[0131] The long sentence in the field Chinese name is divided into short sentences. The long sentence in the field Chinese name is usually an explanation of the field Chinese name, which can be divided by a preset symbol;

[0132] According to the field English name information, the missing information in the field Chinese name is supplemented. For example, the field English name is Fq_sfzh, and the field Chinese name is father. Then, the field Chinese name can be supplemented to father ID number according to the field English name.

[0133] The second aspect is to preprocess the field English name in the target data. The field English name to be standardized has the following expression forms: Chinese pinyin initial letter abbreviation, Chinese pinyin full spelling, English word, English abbreviation, Chinese pinyin and English mixed, special character, number, etc. Therefore, in the embodiment of the present application, the method for preprocessing the field English name includes:

[0134] The preset character contained in the field English name is deleted or replaced, for example, the special character and space are replaced by an empty string.

[0135] The letters in the field English name are sorted, and the sorting method includes word segmentation classification and conversion. The word segmentation classification refers to content division and type division of different types of English letters in the field English name, for example, the type of the field English name "xbcode" is divided into pinyin initial letter and English word, and the content is divided into xb and code. The conversion refers to uniformity of the letter case in the field English name, for example, the capital letter is converted into a small letter.

[0136] The English word and English abbreviation in the field English name are translated into Chinese pinyin or synonyms.

[0137] The third aspect is to preprocess the data content in the target data. The preprocessing is mainly to clean the dirty data in the data content and exclude the influence of the dirty data. For example, the non-uniform null values in the data content are uniformly converted into an empty character, and the spaces in the data content are removed.

[0138] Through the above method, the field Chinese name, the field English name and the data content in the target data are respectively preprocessed, the influence of the interference information on the matching process is eliminated, and the matching efficiency and the reliability of the final matching result are improved.

[0139] After the target data is preprocessed, the preprocessed target data is matched with a plurality of industry standards according to a plurality of matching rules. Specifically, at least one of the following four matching rules is used:

[0140] The first matching rule is to perform text similarity matching between the field Chinese name and at least one standardized data element in a plurality of standardized data elements, to obtain a matching result containing a text similarity value. It is judged whether there is a matching result with a text similarity value greater than a first preset threshold value in the matching result containing the text similarity value. If there is, the standard data element corresponding to the matching result with the text similarity value greater than the first preset threshold value is determined.

[0141] In the above process, the algorithm for calculating the text similarity includes but is not limited to the edit distance algorithm, the BM25 algorithm, and the semantic recall algorithm, wherein the commonly used models for the semantic recall algorithm are word2vec, SimCSE, Roformer, etc. Therefore, in the embodiments of the present application, based on the text similarity, the matching model for matching the field Chinese name with at least one standardized data source can be field Chinese name recall-distance, field Chinese name recall-SimCSE, and field Chinese name recall-Roformer. After inputting the field Chinese name and the standardized data element into any of the above matching models, the text similarity value between the field Chinese name and the standardized data element can be calculated. If the text similarity value is greater than a first preset threshold value in the matching model, the standardized data element corresponding to the text similarity value is determined.

[0142] The second matching rule is to perform semantic similarity matching between the field English name and at least one standardized data element in the plurality of standardized data elements to obtain a matching result containing a semantic similarity value. It is determined whether there is a matching result with a semantic similarity value greater than a second preset threshold value in the matching result containing the semantic similarity value. If there is, the standardized data element corresponding to the matching result with the semantic similarity value greater than the second preset threshold value is determined.

[0143] In the above process, the matching model for performing semantic similarity matching between the field English name and the standardized data element can be field English name recall-distance, field English name recall-synonym, and field English name recall-semantic. After inputting the field English name and the standardized data element into any of the above matching models, the semantic similarity value between the field English name and the standardized data element can be calculated. If the semantic similarity value is greater than a second preset threshold value in the matching model, the standardized data element corresponding to the semantic similarity value is determined.

[0144] The third matching rule is to perform attribute value matching between the data content and at least one standardized data element in the plurality of standardized data elements to obtain a matching result containing an attribute matching degree value. It is determined whether there is a matching result with an attribute matching degree value greater than a third preset threshold value in the matching result containing the attribute matching degree value. If there is, the standardized data element corresponding to the matching result with the attribute matching degree value greater than the third preset threshold value is determined.

[0145] In the above process, the value range of the attribute value corresponding to the data content in the target data is mainly determined based on the national or industry standard, which is the standard data element. The attribute matching degree value is taken as 1 if the attribute value is within the value range, otherwise the attribute similarity value is taken as 0. At this time, the third preset threshold value is 0. Therefore, in the case of attribute matching degree value being 1, the standard data element corresponding to the attribute value can be determined.

[0146] The fourth matching rule is to perform representation word matching between the first preset keyword in the field Chinese name, the second preset keyword in the field English name, and the data feature value in the data content and at least one standardized data element in the plurality of standardized data elements, to obtain a matching result containing a representation word matching degree value.

[0147] In the embodiments of the present application, the representation word is mainly used to describe the representation form of the data element value domain, which is helpful for data element standardized naming, analysis, analogy, and query. The internationally recognized representation words include: amount, date, date and time, code, description, name, number, quantity, indicator, percentage, and ratio. Determining the representation word corresponding to the target data is mainly based on the data content, the field Chinese name, and the field English name. Specifically, the method for determining the representation word corresponding to the target data includes:

[0148] Performing representation word category matching between the first preset keyword in the field Chinese name and at least one standardized data element in the plurality of standardized data elements to obtain a first matching result containing a representation word matching degree value;

[0149] Performing representation word category matching between the second preset keyword in the field English name and at least one standardized data element in the plurality of standardized data elements to obtain a second matching result containing a representation word matching degree value;

[0150] Performing representation word matching between the data feature in the data content and at least one standardized data element in the plurality of standardized data elements to obtain a third matching result containing a representation word matching degree value.

[0151] Through the above-mentioned manner, the representation word matching between the target data and the standardized data element is completed, and the first matching result, the second matching result, and the third matching result containing the representation word matching degree value are obtained.

[0152] Next, the first matching result, the second matching result, and the third matching result are merged according to the first preset rule to obtain a matching result containing at least one representation word. The specific matching result merging method includes:

[0153] In the first matching result, a fourth matching result indicating that the word matching degree value is greater than a fourth preset threshold value is determined; in the second matching result, a fifth matching result indicating that the word matching degree value is greater than a fifth preset threshold value is determined; in the third matching result, a sixth matching result indicating that the word matching degree value is greater than a sixth preset threshold value is determined; when the representation word corresponding to the first preset keyword in the fourth matching result and the representation word corresponding to the second preset keyword in the fifth matching result are consistent with the representation word corresponding to the same keyword corresponding to the data content in the sixth matching result, the representation word corresponding to the same keyword and the representation word corresponding to the same keyword corresponding to the data content are merged, and the standardized data element corresponding to the merged representation word is determined.

[0154] Through the above method, first, the first matching result, the second matching result and the third matching result are screened by threshold comparison method respectively, and then the screened matching results are merged. For example, if the field Chinese name and the standardized data element are matched by the representation word, and after screening, the fourth matching result obtained is "date representation word", and the data content "20211112" corresponding to the field Chinese name is matched with the standardized data element and screened, and the sixth matching result obtained is "amount representation word". At this time, the "amount representation word" is inconsistent with the "date representation word", so both representation word matching results are retained and do not need to be merged. If the representation words obtained by the fourth matching result and the sixth matching result are both "date representation word" or other consistent results, only one representation word result needs to be retained.

[0155] Based on the above four matching rules, the target data and the standardized data element are matched through the matching rules corresponding to the field Chinese name, the field English name and the data content. This multi-strategy matching method can screen out standardized data elements that are more matched with the target data, and improve the reliability of the matching result.

[0156] In some possible application scenarios, a plurality of standardized data elements determined by the above four matching rules may have different matching frequencies corresponding to the same standard data element. For example, the field English name recall-distance matching result is ('DE00709', 0.38), and the field English name recall-synonym recall matching result is ('DE00709', 0.78). Among them, "0.38" represents the probability of the standard data element "DE00709" being determined based on the field English name recall-distance, and "0.39" represents the probability of the standard data element "DE00709" being determined based on the field English name recall-synonym. The same standard data element "DE00709" corresponds to different probabilities, and at this time, the two probabilities need to be merged into one.

[0157] In order to fuse the same standard data element corresponding to multiple matching rules into one result, the confidence of each data element determined by the data matching method is calculated, and the matching result is further filtered based on the confidence, and the specific process includes: Figure 1 The data matching method determines the confidence of each data element, and further filters the matching result based on the confidence, and the specific process includes:

[0158] A weight value is set for each matching result corresponding to the standard data element, and N weight values corresponding to the first standard data element are counted, where N is an integer greater than or equal to 1; according to a preset algorithm, the overall weight value corresponding to the N weight values is calculated; according to the overall weight value and the matching frequency corresponding to the first standard data element, the confidence value of the first standard data element information is calculated, wherein the matching frequency indicates the ratio between the number of times the standard data element is determined and the number of matching rules; when the confidence value is greater than a seventh preset threshold, the first standard data element is taken as the target standard data element.

[0159] For example, for Figure 1 The matching result obtained by the matching method is shown in Table 1:

[0160]

[0161] Table 1

[0162] The weight value corresponding to the matching result " 'DE10300', 0.33 " in Table 1 is W1, and the weight value corresponding to the matching result " 'DE10300', 0.9763 " is W2, then the initial confidence value corresponding to the standard data element " DE10300 " is calculated W ( DE10300 ) = ( W1 0.33 + W2 0.9763 ) / ( W1 + W2 ); Next, the matching frequency corresponding to the standard data element " DE10300 " is calculated, that is, F ( DE10300 ) = 2 / 3; according to the initial confidence value and the matching frequency, the confidence value of the standard data element can be calculated as: P = W ( DE10300 ) F ( DE10300 ), finally, the confidence value of the standard data element is compared with the preset threshold, and the target standard data element can be obtained by further filtering the matching result.

[0163] Through the above method, the confidence of each standard data element is calculated, and the target standard data element whose confidence value meets the user's demand is filtered out, further improving the matching result accuracy, and further improving the reliability of the matching result.

[0164] In a possible design, in order to further improve the reliability of the matching result, the target standard data element needs to be verified, and the specific verification method includes:

[0165] Extract the data feature values ​​corresponding to the data content; determine the range of data feature values ​​according to the second preset rule, wherein the second preset rule indicates the rule for determining the range of feature values ​​based on national or industry standards; determine whether the target standard data element is within the range; if it is within the range, use the target standard data element as the final standard data element.

[0166] In the above process, data feature extraction mainly targets the feature information contained in the data content, including: feature information based on public statistics, feature information categorized by field type, and feature information extracted by data type. Among them, the feature information of public statistics includes maximum value, minimum value, mean, mode, median, variance, standard deviation, and range; the feature information categorized by field type mainly includes: the proportion of Chinese characters, the proportion of numbers, the proportion of English characters, the proportion of Chinese and numbers, the proportion of Chinese and English characters, the proportion of English characters and numbers, the proportion of Chinese and numbers combined with English characters, and the proportion of data containing special characters; the feature information specific to numerical data includes: the length of decimal places and integer places.

[0167] After extracting the feature values ​​corresponding to the data content, the range of values ​​for each feature value is determined according to the second preset rule. For example, the standard length of an ID card number is 18 digits, where digits 7-14 represent the birth date, digits 15-17 represent the sequence code, and digit 18 represents the check digit format. Next, based on this value range, the target standard data elements are validated, and those that do not conform to the value range are filtered out to obtain the final standard data elements. This method can further filter the target standard data elements, improve the accuracy of the matching results, and thus improve the reliability of the matching results.

[0168] Furthermore, to elaborate on the above data matching method in more detail, a specific application scenario will be provided below. Specifically, for example... Figure 2 The image shown is an example diagram of data matching provided in this application. Figure 2 First, data metadata is obtained. Data metadata includes the original table information to be standardized, the original table data to be standardized, and standard data metadata. The original table information to be standardized includes the English name of the table, the Chinese name of the table, the Chinese name of the fields, the English name of the fields, and the field type. The original table data to be standardized refers to the data content of the table, which can be sampled data or full data.

[0169] Then, the acquired data metadata is preprocessed, including: preprocessing the English names of fields, preprocessing the Chinese names of fields, and preprocessing the data content. For specific preprocessing methods and data characteristics in this example, please refer to [reference needed]. Figure 1 The explanation of the data matching method shown is not repeated here.

[0170] Then, data features corresponding to the data element information are extracted, and after the data features are extracted, the data element information is input into a multi-path recall model to preliminarily obtain standard data elements corresponding to the data element information, wherein the multi-path recall model includes: a field Chinese name recall model, a field English name recall model, a representative word recall model, and a data content recall model, and the methods of processing the data element information by the four models are referred to Figure 1 The four matching rules in the data matching method shown in the figure match the target data and the standardized data elements through the matching rules corresponding to the field Chinese name, the field English name, and the data content, respectively. The multi-strategy matching manner can filter out the standardized data elements that match the target data more, and improve the reliability of the matching result.

[0171] Further, the preliminarily obtained standard data elements are fused and sorted, that is, the data element weights of the standard data elements obtained through the multi-path recall model are calculated, and the recall frequency is further calculated, and then the normalization result is calculated according to the weight value and the recall frequency, wherein the recall frequency is Figure 1 the matching frequency in the data matching method shown in the figure. For example: the field English name recall-distance recall result is ('DE00709', 0.38), and the field English name recall-synonym recall result is ('DE00709', 0.78). The result confidence of the same data element recall is different. In order to fuse the multi-path recall result into one result and output the result confidence from a unified dimension, therefore, the weight and the recall frequency of the data element are weighted and averaged to perform result normalization, and then the result is normalized to obtain the comprehensive confidence corresponding to each data element. Finally, the data standard information of the data table to be standardized is recommended according to the confidence.

[0172] After obtaining the comprehensive confidence, the standard data elements with a comprehensive execution degree greater than a threshold value are selected, and the table standard data elements are verified. The specific verification method is completed based on the data features, and the verification method of the target standard data element in the data matching method shown in Figure 1

[0173] Finally, according to the verification result, the final standard data element corresponding to the data element information is obtained.

[0174] Based on the data matching method described in the example, the multi-path recall model is used to filter out the standard data elements corresponding to the data element information, then the fusion sorting model is used for the second screening, and finally the standard data elements are verified to realize the third screening of the standard data elements, thereby improving the reliability of the standard data elements corresponding to the data element information.

[0175] Based on the same inventive concept, the data matching device is also provided in the embodiment of the application, as shown in​Figure 3 Fig. 1 is a structural schematic diagram of a data matching device according to an embodiment of the present application. The device comprises:

[0176] The obtaining module 31 is configured to obtain target data to be standardized and a plurality of standardized data elements, wherein the target data comprises a Chinese name of a field, an English name of the field, and data content.

[0177] The matching module 32 is configured to match the target data and all the standardized data elements according to at least one of a first matching rule corresponding to the Chinese name of the field, a second matching rule corresponding to the English name of the field, and a third matching rule corresponding to the data content, to obtain a matching result comprising a matching degree value.

[0178] The first determining module 33 is configured to determine at least one matching result with a matching degree value within a preset range among all the matching results, and determine a standardized data element corresponding to each of the at least one matching result.

[0179] In a possible design, the matching module 32 is specifically configured to:

[0180] perform text similarity matching on the Chinese name of the field and at least one standardized data element among the plurality of standardized data elements, to obtain a matching result comprising a text similarity value; and / or

[0181] perform semantic similarity matching on the English name of the field and at least one standardized data element among the plurality of standardized data elements, to obtain a matching result comprising a semantic similarity value; and / or

[0182] perform attribute value matching on the data content and at least one standardized data element among the plurality of standardized data elements, to obtain a matching result comprising an attribute matching degree value; and / or

[0183] perform representation word matching on a first preset keyword in the Chinese name of the field, a second preset keyword in the English name of the field, and a data feature value in the data content, and at least one standardized data element among the plurality of standardized data elements, to obtain a matching result comprising a representation word matching degree value.

[0184] In a possible design, the matching module 32 is further configured to:

[0185] remove redundant information in the Chinese name of the field, wherein the redundant information comprises preset symbols and numbers.

[0186] segment a sentence in the Chinese name of the field;

[0187] supplement missing information in the Chinese name of the field according to the English name of the field.

[0188] The preprocessed Chinese name of the field is matched with at least one of the plurality of standardized data elements in text similarity, to obtain a matching result containing a text similarity value.

[0189] In a possible design, the matching module 32 is further configured to:

[0190] delete a preset character contained in the English name of the field;

[0191] organize the letters in the English name of the field;

[0192] translate English words and English abbreviations in the English name of the field into Chinese pinyin or synonyms;

[0193] The preprocessed English name of the field is matched with at least one of the plurality of standardized data elements in semantic similarity, to obtain a matching result containing a semantic similarity value.

[0194] In a possible design, the matching module 32 is further configured to:

[0195] clean dirty data in the data content, wherein the dirty data at least includes illegal data and data beyond the business scope;

[0196] The cleaned data content is matched with at least one of the plurality of standardized data elements in attribute value, to obtain a matching result containing an attribute matching degree value.

[0197] In a possible design, the matching module 32 is further configured to:

[0198] The first preset keyword in the Chinese name of the field is matched with at least one of the plurality of standardized data elements in representation word category, to obtain a first matching result containing a representation word matching degree value;

[0199] The second preset keyword in the English name of the field is matched with at least one of the plurality of standardized data elements in representation word category, to obtain a second matching result containing a representation word matching degree value;

[0200] The data feature in the data content is matched with at least one of the plurality of standardized data elements in representation word, to obtain a third matching result containing a representation word matching degree value.

[0201] In a possible design, the first determining module 33 is specifically configured to:

[0202] determine the matching result whose text similarity value in the matching result containing the text similarity value is greater than a first preset threshold value;

[0203] determining a matching result in the matching results with the semantic similarity value greater than a second preset threshold value;

[0204] determining a matching result in the matching results with the attribute matching degree value greater than a third preset threshold value;

[0205] combining the matching results with the representation word matching degree values according to a first preset rule to obtain a matching result containing at least one representation word.

[0206] In a possible design, the matching module 32 is further configured to:

[0207] determining a fourth matching result in the first matching result with the representation word matching degree value greater than a fourth preset threshold value;

[0208] determining a fifth matching result in the second matching result with the representation word matching degree value greater than a fifth preset threshold value;

[0209] determining a sixth matching result in the third matching result with the representation word matching degree value greater than a sixth preset threshold value;

[0210] when the representation word corresponding to the first preset keyword in the fourth matching result and the representation word corresponding to the second preset keyword in the fifth matching result are consistent with the representation word corresponding to the data content of the same keyword in the sixth matching result, combining the representation word corresponding to the same keyword and the representation word corresponding to the data content of the same keyword.

[0211] In a possible design, the apparatus further includes:

[0212] a statistical module configured to set a weight value for each standard data element corresponding to each matching result, and to count N weight values corresponding to a first standard data element, where N is an integer greater than or equal to 1;

[0213] a first calculation module configured to calculate an overall weight value corresponding to the N weight values according to a preset algorithm, and to calculate a confidence degree of the first standard data element information according to the overall weight value and a matching frequency of the first standard data element, where the matching frequency indicates a ratio between a number of times of being determined and a number of matching rules;

[0214] a second determination module configured to determine the first standard data element as a target standard data element when the confidence degree value is greater than a seventh preset threshold value.

[0215] In a possible design, the apparatus further includes:

[0216] extracting a data feature value corresponding to the data content;

[0217] a second calculating module configured to determine a value range of the data feature value according to a second preset rule;

[0218] a judging module configured to judge whether the target standard data element is in the value range;

[0219] a third determining module configured to take the target standard data element as a final standard data element when the target standard data element is in the value range.

[0220] According to the above data matching device, the target data to be standardized and the standardized data element are matched through the matching rules corresponding to the field Chinese name, the field English name and the data content respectively. The multi-strategy matching manner can filter out the standardized data element that is more matched with the target data, and improves the reliability of the matching result.

[0221] Based on the same inventive concept, the embodiment of the present application further provides an electronic device which can realize the functions of the above data matching device, and the functions of the above data matching device are not repeated here. Figure 4 The electronic device comprises:

[0222] at least one processor 41 and a memory 42 connected with the at least one processor 41, and the specific connection medium between the processor 41 and the memory 42 is not limited in the embodiment of the present application, Figure 4 and the connection between the processor 41 and the memory 42 through the bus 40 is taken as an example in the embodiment of the present application. The bus 40 is represented by a thick line in Figure 4 the embodiment of the present application, and the connection mode between other components is only schematically illustrated and is not limited. The bus 40 can be divided into an address bus, a data bus, a control bus and the like, and for the convenience of representation, Figure 4 only one thick line is used in the embodiment of the present application, but it does not mean that there is only one bus or only one type of bus. Alternatively, the processor 41 can also be called a controller, and the name is not limited.

[0223] In the embodiment of the present application, the memory 42 stores instructions executable by the at least one processor 41, and the at least one processor 41 can execute the data matching method discussed above by executing the instructions stored in the memory 42. The processor 41 can realize the functions of each module in the device shown in Figure 3 .

[0224] The processor 41 is the control center of the device, can connect each part of the whole control device through various interfaces and lines, and can realize the functions and process data of the device by running or executing the instructions stored in the memory 42 and calling the data stored in the memory 42, thereby monitoring the whole device.

[0225] In a possible design, the processor 41 can include one or more processing units, and the processor 41 can integrate an application processor and a modem processor, where the application processor mainly processes operating systems, user interfaces, and application programs, and the modem processor mainly processes wireless communication. It can be understood that the foregoing modem processor can also not be integrated into the processor 41. In some embodiments, the processor 41 and the memory 42 can be implemented on the same chip, and in some embodiments, they can also be respectively implemented on independent chips.

[0226] The processor 41 can be a general-purpose processor, for example, a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the data matching method disclosed in combination with the embodiments of the present application can be directly embodied as execution completed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0227] The memory 42 is a non-volatile computer-readable storage medium, and can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The memory 42 can include at least one type of storage medium, for example, can include a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (RAM), a static random access memory (SRAM), a programmable read-only memory (PROM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic storage, a magnetic disk, an optical disk, and the like. The memory 42 is any other medium capable of carrying or storing desired program codes in the form of instructions or data structures and capable of being accessed by a computer, but is not limited thereto. The memory 42 in the embodiments of the present application can also be a circuit or any other device capable of implementing a storage function, for storing program instructions and / or data.

[0228] By designing and programming the processor 41, the codes corresponding to the data matching method introduced in the foregoing embodiments can be fixed into the chip, so that the chip can execute the codes when running Figure 1The steps of the data matching method of the illustrated embodiment. How to design and program the processor 41 is known to those skilled in the art, and will not be described here.

[0229] Based on the same inventive concept, the embodiments of the present application also provide a storage medium, which stores computer instructions, and when the computer instructions run on a computer, the computer instructions make the computer execute the data matching method discussed above.

[0230] In some possible implementation manners, various aspects of the data matching method provided by the present application can also be implemented in the form of a program product, which includes program codes for causing the control device to execute the steps in the data matching method according to various exemplary embodiments of the present application described above in the specification when the program product runs on the device.

[0231] Those skilled in the art should understand that the embodiments of the present application can be provided in the form of a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code.

[0232] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device implemented in accordance with the flowcharts and / or block diagrams. Figure 1 The function specified in one or more flows and / or blocks Figure 1 The device that implements the function specified in one or more flows and / or blocks.

[0233] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices that implement the flowcharts and / or block diagrams. Figure 1 The function specified in one or more flows and / or blocks Figure 1 The device that implements the function specified in one or more flows and / or blocks.

[0234] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide processes for implementing the functions specified in the flowchart Figure 1 one or more flows and / or blocks Figure 1 one or more blocks or steps of the functions specified in the flowchart

[0235] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A data matching method, characterized by, The method comprises: acquiring target data to be standardized and a plurality of standardized data elements, wherein the target data comprises a field Chinese name, a field English name and data content; matching the target data and all standardized data elements according to at least one of a first matching rule corresponding to the field Chinese name, a second matching rule corresponding to the field English name and a third matching rule corresponding to the data content, to obtain a matching result comprising a matching degree value; determining at least one matching result in which the matching degree value is within a preset range among all matching results, and determining a standardized data element corresponding to each matching result in the at least one matching result respectively; after determining at least one matching result in which the matching degree value is within a preset range among all matching results, and determining a standardized data element corresponding to each matching result in the at least one matching result respectively, the method further comprises: setting a weight value for each matching result corresponding to the standardized data element, and counting N weight values corresponding to a first standardized data element, wherein N is an integer greater than or equal to 1; calculating an overall weight value corresponding to the N weight values according to a preset algorithm; calculating a confidence degree of the first standardized data element information according to the overall weight value and a matching frequency corresponding to the first standardized data element, and outputting data standard information of the final recommended data table to be standardized in order according to the confidence degree, wherein the matching frequency indicates the ratio between the number of times of being determined and the number of matching rules; when the confidence degree value is greater than a seventh preset threshold value, the first standardized data element is taken as a target standardized data element.

2. The method of claim 1, wherein, The matching of the target data and all standardized data elements according to at least one of the first matching rule corresponding to the field Chinese name, the second matching rule corresponding to the field English name and the third matching rule corresponding to the data content to obtain a matching result comprising a matching degree value comprises: text similarity matching the field Chinese name with at least one standardized data element in the plurality of standardized data elements to obtain a matching result comprising a text similarity value; and / or semantic similarity matching the field English name with at least one standardized data element in the plurality of standardized data elements to obtain a matching result comprising a semantic similarity value; and / or attribute value matching the data content with at least one standardized data element in the plurality of standardized data elements to obtain a matching result comprising an attribute matching degree value; and / or representation word matching a first preset keyword in the field Chinese name, a second preset keyword in the field English name and a data feature value in the data content with at least one standardized data element in the plurality of standardized data elements to obtain a matching result comprising a representation word matching degree value.

3. The method of claim 2, wherein, The text similarity matching the field Chinese name with at least one standardized data element in the plurality of standardized data elements to obtain a matching result comprising a text similarity value comprises: removing redundant information in the field Chinese name, wherein the redundant information comprises preset symbols and numbers; segmenting sentences in the field Chinese name; According to the field English name information, supplement the missing information in the field Chinese name; Text similarity matching is performed between the preprocessed field Chinese name and at least one of the plurality of standardized data elements, to obtain a matching result containing a text similarity value.

4. The method of claim 2, wherein, Semantic similarity matching is performed between the field English name and at least one of the plurality of standardized data elements, to obtain a matching result containing a semantic similarity value, including: The preset characters contained in the field English name are deleted; The letters in the field English name are arranged; English words and English abbreviations in the field English name are translated into Chinese pinyin or synonyms; Semantic similarity matching is performed between the preprocessed field English name and at least one of the plurality of standardized data elements, to obtain a matching result containing a semantic similarity value.

5. The method of claim 2, wherein, Attribute value matching is performed between the data content and at least one of the plurality of standardized data elements, to obtain a matching result containing an attribute matching degree value, including: Dirty data in the data content is cleaned, wherein the dirty data at least includes illegal data and data outside the business scope; Attribute value matching is performed between the cleaned data content and at least one of the plurality of standardized data elements, to obtain a matching result containing an attribute matching degree value.

6. The method of claim 2, wherein, Representation word matching is performed between the first preset keyword in the field Chinese name, the second preset keyword in the field English name, and the data feature value in the data content and at least one of the plurality of standardized data elements, to obtain a matching result containing a representation word matching degree value, including: The first preset keyword in the field Chinese name is matched with at least one of the plurality of standardized data elements in the representation word category, to obtain a first matching result containing a representation word matching degree value; The second preset keyword in the field English name is matched with at least one of the plurality of standardized data elements in the representation word category, to obtain a second matching result containing a representation word matching degree value; The data feature in the data content is matched with at least one of the plurality of standardized data elements in the representation word, to obtain a third matching result containing a representation word matching degree value.

7. The method of claim 2, wherein, At least one matching result in all matching results with a matching degree value within a preset range is determined, including: A matching result with a text similarity value greater than a first preset threshold value in the matching result containing a text similarity value is determined; A matching result with a semantic similarity value greater than a second preset threshold value in the matching result containing a semantic similarity value is determined; A matching result with an attribute matching degree value greater than a third preset threshold value in the matching result containing an attribute value matching degree is determined; The matching results containing representation word matching degree values are merged according to a first preset rule, to obtain a matching result containing at least one representation word.

8. The method of claim 6, wherein, The matching results containing representation word matching degree values are merged according to a first preset rule, to obtain a matching result containing at least one representation word, including: determining a fourth matching result indicating that the word matching degree value is greater than a fourth preset threshold value in the first matching result; determining a fifth matching result indicating that the word matching degree value is greater than a fifth preset threshold value in the second matching result; determining a sixth matching result indicating that the word matching degree value is greater than a sixth preset threshold value in the third matching result; when the representation word corresponding to the first preset keyword in the fourth matching result and the representation word corresponding to the second preset keyword in the fifth matching result are consistent with the representation word corresponding to the same keyword and the data content corresponding to the same keyword in the sixth matching result, merging the representation word corresponding to the same keyword and the representation word corresponding to the data content corresponding to the same keyword.

9. The method of claim 1, wherein, After taking the first standard data element as the target standard data element, the method further includes: extracting a data feature value corresponding to the data content; determining a value range of the data feature value according to a second preset rule; judging whether the target standard data element is in the value range; when the target standard data element is in the value range, taking the target standard data element as a final standard data element.

10. A data matching apparatus, characterized by comprising: The device includes: an acquisition module configured to acquire target data to be standardized and a plurality of standardized data elements, wherein the target data includes a field Chinese name, a field English name, and data content; a matching module configured to match the target data and all standardized data elements according to at least one of a first matching rule corresponding to the field Chinese name, a second matching rule corresponding to the field English name, and a third matching rule corresponding to the data content, to obtain matching results containing matching degree values; a first determination module configured to determine at least one matching result in which the matching degree value is in a preset range among all matching results, and determine a standardized data element corresponding to each matching result in the at least one matching result; The device further includes: a statistical module configured to set a weight value for each matching result corresponding to the standardized data element, and count N weight values corresponding to a first standard data element, wherein N is an integer greater than or equal to 1; a first calculation module configured to calculate an overall weight value corresponding to the N weight values according to a preset algorithm, calculate a confidence degree of the first standard data element information according to the overall weight value and a matching frequency corresponding to the first standard data element, and sort and output data standard information of a final recommended data table to be standardized according to the confidence degree, wherein the matching frequency indicates a ratio between the number of times that the standardized data element is determined and the number of matching rules; a second determination module configured to take the first standard data element as a target standard data element when the confidence degree value is greater than a seventh preset threshold value.

11. The apparatus of claim 10, wherein, The matching module is specifically configured to: perform text similarity matching on the field Chinese name and at least one standardized data element in the plurality of standardized data elements to obtain matching results containing text similarity values; and / or perform semantic similarity matching on the field English name and at least one standardized data element in the plurality of standardized data elements to obtain matching results containing semantic similarity values; and / or attribute value matching the data content with at least one of the plurality of standardized data elements to obtain a matching result containing an attribute matching degree value; and / or performing representation word matching between the first preset keyword in the field Chinese name, the second preset keyword in the field English name and the data feature value in the data content and at least one of the plurality of standardized data elements to obtain a matching result containing a representation word matching degree value.

12. The apparatus of claim 11, wherein, The matching module is further configured to: remove redundant information in the field Chinese name, wherein the redundant information includes preset symbols and numbers; segment the sentence in the field Chinese name; supplement missing information in the field Chinese name according to the field English name information; perform text similarity matching between the preprocessed field Chinese name and at least one of the plurality of standardized data elements to obtain a matching result containing a text similarity value.

13. The apparatus of claim 11, wherein, The matching module is further configured to: delete preset characters contained in the field English name; organize the letters in the field English name; translate English words and English abbreviations in the field English name into Chinese pinyin or synonyms; perform semantic similarity matching between the preprocessed field English name and at least one of the plurality of standardized data elements to obtain a matching result containing a semantic similarity value.

14. The apparatus of claim 11, wherein, The matching module is further configured to: clean dirty data in the data content, wherein the dirty data at least includes illegal data and data outside the business scope; perform attribute value matching between the cleaned data content and at least one of the plurality of standardized data elements to obtain a matching result containing an attribute matching degree value.

15. The apparatus of claim 11, wherein, The matching module is further configured to: perform representation word category matching between the first preset keyword in the field Chinese name and at least one of the plurality of standardized data elements to obtain a first matching result containing a representation word matching degree value; perform representation word category matching between the second preset keyword in the field English name and at least one of the plurality of standardized data elements to obtain a second matching result containing a representation word matching degree value; perform representation word matching between the data feature in the data content and at least one of the plurality of standardized data elements to obtain a third matching result containing a representation word matching degree value.

16. The apparatus of claim 11, wherein, The first determining module is specifically configured to: determine the matching result whose text similarity value in the matching result containing a text similarity value is greater than a first preset threshold value; determine the matching result whose semantic similarity value in the matching result containing a semantic similarity value is greater than a second preset threshold value; determine the matching result whose attribute matching degree value in the matching result containing an attribute matching degree value is greater than a third preset threshold value; merge the matching result containing a representation word matching degree value according to a first preset rule to obtain a matching result containing at least one representation word.

17. The apparatus of claim 15, wherein, The matching module is further configured to: determine a fourth matching result whose representation word matching degree value in the first matching result is greater than a fourth preset threshold value; In the second matching result, a fifth matching result indicating that the word matching degree value is greater than a fifth preset threshold is determined; In the third matching result, a sixth matching result indicating that the word matching degree value is greater than a sixth preset threshold is determined; When the first preset keyword corresponding representation word in the fourth matching result and the second preset keyword corresponding representation word in the fifth matching result are consistent with the same keyword corresponding representation word and the same keyword corresponding data content corresponding representation word in the sixth matching result, the same keyword corresponding representation word and the same keyword corresponding data content corresponding representation word are merged.

18. The apparatus of claim 10, wherein, The device further comprises: an extraction module configured to extract a data feature value corresponding to the data content; a second calculation module configured to determine a value range of the data feature value according to a second preset rule; a judgment module configured to judge whether the target standard data element is in the value range; a third determination module configured to, when the value range is in the value range, take the target standard data element as a final standard data element.

19. An electronic device, comprising: comprise: a memory configured to store a computer program; a processor configured to execute the computer program stored in the memory to implement the method steps in any one of claims 1-9.

20. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method steps in any one of claims 1-9.

Images