Application program adjustment method and related device

Abstract

An embodiment of the present application provides an application adjustment method and related devices, which include: obtaining first reference text information in an initial medical insurance data processing program; removing pause words from the first reference text information to obtain first target text information; performing word filtering on the first target text information to obtain second target text information; obtaining third target text information corresponding to the second target text information from the medical insurance data of the target area; adjusting the initial medical insurance data processing program according to the third target text information to obtain a target medical insurance data processing program, which can improve the efficiency of adjusting the initial medical insurance data processing program.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an application adjustment method and related devices. Background Art

[0002] Big data risk control models are a key tool for medical insurance bureaus to detect insurance fraud. These models rely on large amounts of high-quality medical insurance data. However, initial medical insurance data is often unsuitable for big data risk control analysis. Therefore, this initial medical insurance data needs to be standardized and processed uniformly for risk control analysis. This can be done using an initial medical insurance data processing program. However, since parameters such as field names and dictionary values ​​may vary among local medical insurance bureaus, manual adjustments to the initial medical insurance data processing program are often required when deploying big data risk control models in different cities and regions. This results in low efficiency when these adjustments are made. Summary of the Invention

[0003] The embodiments of the present application provide an application adjustment method and related devices, which can improve the efficiency of adjusting the initial medical insurance data processing program.

[0004] A first aspect of an embodiment of the present application provides an application adjustment method, the method comprising:

[0005] Obtaining first reference text information in an initial medical insurance data processing program;

[0006] performing stop word removal processing on the first reference text information to obtain first target text information;

[0007] Performing word filtering on the first target text information to obtain second target text information;

[0008] Acquire third target text information corresponding to the second target text information from the medical insurance data of the target area;

[0009] The initial medical insurance data processing program is adjusted according to the third target text information to obtain a target medical insurance data processing program.

[0010] A second aspect of an embodiment of the present application provides an application adjustment device, the device comprising:

[0011] A first acquiring unit, configured to acquire first reference text information in an initial medical insurance data processing program;

[0012] a first processing unit, configured to remove pause words from the first reference text information to obtain first target text information;

[0013] a second processing unit, configured to perform word filtering processing on the first target text information to obtain second target text information;

[0014] a second acquiring unit, configured to acquire third target text information corresponding to the second target text information from the medical insurance data of the target area;

[0015] An adjusting unit is configured to adjust the initial medical insurance data processing program according to the third target text information to obtain a target medical insurance data processing program.

[0016] A third aspect of an embodiment of the present application provides a terminal, comprising a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions and execute the step instructions in the first aspect of the embodiment of the present application.

[0017] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application.

[0018] A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0019] Implementing the embodiments of the present application has at least the following beneficial effects:

[0020] By obtaining the first reference text information in the initial medical insurance data processing program, removing pause words from the first reference text information to obtain the first target text information, performing word filtering processing on the first target text information to obtain the second target text information, obtaining the third target text information corresponding to the second target text information from the medical insurance data of the target area, and adjusting the initial medical insurance data processing program according to the third target text information to obtain the target medical insurance data processing program, the initial medical insurance data processing program can be adjusted by the third target text information obtained from the medical insurance data of the target area to obtain the target medical insurance data processing program, thereby improving the efficiency of obtaining the target medical insurance data processing program. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 A flowchart of an application adjustment method is provided for an embodiment of the present application;

[0023] Figure 2 A flowchart of another application adjustment method is provided for an embodiment of the present application;

[0024] Figure 3 A schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0025] Figure 4 A structural diagram of an application adjustment device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0028] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0029] See also Figure 1 , Figure 1The present invention provides a flowchart of an application adjustment method. Figure 1 As shown, the method includes:

[0030] 101. Obtain first reference text information in the initial medical insurance data processing program.

[0031] The initial medical insurance data processing program can be a program for normalizing medical insurance data. For example, it can be a data cleaning script that normalizes the raw medical insurance data by generating a product table and then a wide table. The medical insurance data includes the field names and dictionary values ​​of the medical insurance bureau in the region where the medical insurance data is located, as well as the project codes for important fields. Medical insurance data from different regions may have different field names, dictionary values, and project codes for important fields.

[0032] The method for obtaining the first reference text information in the initial medical insurance data processing program may be: extracting the first reference text information from the initial medical insurance data processing program according to a preset rule. The first reference text information may be a field name, a dictionary value, a table name, etc. Specifically, it may be:

[0033] Extraction is performed using the rules in the assembly language of the initial medical insurance data processing program. The initial medical insurance data processing program can be written using SQL statements. Extracting the first reference text information can be as follows: (1) Take the string from select to from, split it by commas and use it as the field name. If the split text contains parentheses, take the fields in the parentheses, i.e., exclude max(aka20), min(aka20), avg(aka20), and count(aka20); if the split text contains spaces, take the last part, i.e., exclude distinct aka20. (2) The field after group by is the field name. (3) ! The field name is before ==like. (4) The field name is after . (e) The first word after from is the table name. (5) The first word after join is the table name. Thus, the first reference text information is extracted.

[0034] 102. Perform stop word removal processing on the first reference text information to obtain first target text information.

[0035] Text recognition can be performed on the first reference text information to obtain pause words in the first reference text information, and the pause words in the first reference text information can be removed to obtain the first target text information. Therefore, removing the pause words in the first reference text information can prevent the pause words from affecting subsequent matching, thereby improving matching accuracy.

[0036] 103. Perform word filtering processing on the first target text information to obtain second target text information.

[0037] The second target text information may be obtained by performing filtering processing based on the occurrence frequency of words and the category information of the words in the first target text information.

[0038] Alternatively, semantic analysis may be performed on the words in the first target text information, and filtering may be performed based on the semantic analysis result to obtain the second target text information.

[0039] 104. Obtain third target text information corresponding to the second target text information from the medical insurance data of the target area.

[0040] Second reference text information can be extracted from the medical insurance data of the target area, and the third target text information can be determined from the second reference text information. The second reference text information is of the same type as the first reference text information. Specifically, the second reference text information can be understood as a field name, a dictionary value, a table name, etc.

[0041] 105. Adjust the initial medical insurance data processing program according to the third target text information to obtain a target medical insurance data processing program.

[0042] The corresponding second target text information in the initial medical insurance data processing program can be replaced according to the third target text information to obtain the target medical insurance data processing program.

[0043] In this example, the first reference text information in the initial medical insurance data processing program is obtained, the first reference text information is processed to remove pause words to obtain the first target text information, the first target text information is processed to filter words to obtain the second target text information, and the third target text information corresponding to the second target text information is obtained from the medical insurance data of the target area. The initial medical insurance data processing program is adjusted according to the third target text information to obtain the target medical insurance data processing program. Therefore, the initial medical insurance data processing program can be adjusted by the third target text information obtained from the medical insurance data of the target area to obtain the target medical insurance data processing program, thereby improving the efficiency of obtaining the target medical insurance data processing program.

[0044] In one possible implementation, a possible method of performing word filtering on the first target text information to obtain the second target text information includes:

[0045] A1. performing word segmentation processing on the first target text information to obtain a first word set, wherein the first word set includes at least one word;

[0046] A2. Obtaining the frequency of occurrence of each word in the first word set in the first text information;

[0047] A3. Obtain category information of each word in the first word set;

[0048] A4. Perform word filtering on the first text information according to the frequency of occurrence of each word in the first word set in the first text information and the category information of each word in the first word set to obtain the second target text information.

[0049] Among them, during word segmentation processing, a general maximum word segmentation processing method can be used for word segmentation processing. For example, when a single word is segmented into multiple words, the single word and the following word can form a word, and the single word and the following two consecutive words can form a word, then the word is determined to be a three-character word. By analogy, if there are more four-character words, the four characters are determined as one word, thereby improving the accuracy of word segmentation processing.

[0050] The frequency of each word appearing in the first text information may be the number of times it appears, etc. The categories indicated by the category information of each word include a first category and a second category, where the first category is important words and the second category is unimportant words. Specifically, the word may be compared with words in a preset unimportant word library. If the word matches any word in the unimportant word library, the word is determined to be in the second category, otherwise it is determined to be in the first category. The words in the unimportant word library may include, for example, "information", etc., which is only used as an example here.

[0051] During the filtering process, if a word in the first word set is determined to be in the first category, but its frequency of occurrence is low, the word can be deleted; if a word in the first word set is determined to be in the second category, the word is deleted; if a word in the first word set is determined to be in the first category, but its frequency of occurrence is high, the word is retained.

[0052] In this example, by obtaining the frequency of occurrence of each word in the first word set in the first text information, obtaining the category information of each word in the first word set, and performing word filtering processing on the first text information based on the frequency of occurrence of each word in the first word set in the first text information and the category information of each word in the first word set, the second target text information is obtained, thereby improving the accuracy of obtaining the second target text information.

[0053] In one possible implementation, another possible method of performing word filtering on the first target text information to obtain the second target text information includes:

[0054] B1. performing word segmentation processing on the first target text information to obtain a first word set, wherein the first word set includes at least one word;

[0055] B2. performing semantic analysis on the words in the first word set to obtain a semantic analysis result;

[0056] B3. Perform word filtering processing on the first target text information according to the semantic analysis result to obtain second target text information.

[0057] The method for performing word segmentation processing on the first target text information may refer to the method for performing word segmentation processing in step A1 of the aforementioned embodiment, and will not be described in detail here.

[0058] The method for semantically analyzing a word can be a common semantic analysis method, and the semantic analysis result includes the meaning of the word. Based on the semantic analysis result, whether the word is an important word can be determined. Specifically, the semantic analysis result can be compared with a preset semantic analysis result. If the semantic analysis result of the word matches the preset semantic analysis result, the word is determined to be a non-important word; otherwise, the word is determined to be an important word. When performing word filtering on the first target text information, the non-important words are deleted to obtain the second target text information.

[0059] In this example, semantic analysis is performed on the words in the first word set to obtain a semantic analysis result, and word filtering processing is performed on the first target text information based on the semantic analysis result to obtain the second target text information. Filtering processing can be performed from a semantic perspective, thereby improving the accuracy of filtering processing.

[0060] In one possible implementation, a possible method for obtaining third target text information corresponding to the second target text information from the medical insurance data of the target area includes:

[0061] C1. Obtain second reference text information from the medical insurance data, where the second reference text information is of the same type as the first reference text information;

[0062] C2. Determine the similarity between each word in the second target text information and each word in the second reference text information;

[0063] C3. Determine the third target text information from the second reference text information based on the similarity.

[0064] The method for obtaining the second reference text information in the medical insurance data may be to perform a text extraction method, specifically, to obtain the type of text in the medical insurance data, thereby extracting the second reference text information based on the type. The type of the text may be the type of the first reference text.

[0065] Multiple types of similarities may be determined between each word in the second target text information and each word in the second reference text information, and based on the multiple types of similarities, the similarity between each word in the second target text information and each word in the second reference text information may be determined.

[0066] Words in the second reference text information whose similarity to words in the second target text information is higher than a preset threshold may be determined as the third target text information.

[0067] In this example, by determining the similarity between each word in the second target text information and each word in the second reference text information, the third target text information is determined from the second reference text information based on the similarity, thereby improving the accuracy of obtaining the third target text information.

[0068] In one possible implementation, a possible method for determining the similarity between each word in the second target text information and each word in the second reference text information includes:

[0069] D1. Obtaining a first-category similarity between each word in the second target text information and each word in the second reference text information;

[0070] D2. Obtain a second type of similarity between each word in the second target text information and each word in the second reference text information;

[0071] D3. Perform a weighted operation based on the first-category similarity between each word in the second target text information and each word in the second reference text information and the second-category similarity between each word in the second target text information and each word in the second reference text information to obtain the similarity between each word in the second target text information and each word in the second reference text information.

[0072] The first type of similarity can be BERT-based similarity, while the second type can be rule-based similarity. Rule-based similarity can be determined based on edit distance similarity, common word percentage similarity, and character overlap. BERT-based similarity requires fine-tuning on a short text dataset to better suit field description matching for similarity calculation.

[0073] The weights corresponding to the first and second similarities can be obtained, and weighted operations can be performed based on the weights to obtain the word similarity. For example, the weight of the first similarity is 0.2, and the weight of the second similarity is 0.8.

[0074] In this example, a weighted operation is performed on the first-type similarity and the second-type similarity to obtain the similarity between words, thereby improving the accuracy of similarity determination.

[0075] In one possible implementation, a possible method for obtaining a second type of similarity between each word in the second target text information and each word in the second reference text information includes:

[0076] E1. Obtaining edit distance similarity between a word in the second target text information and each word in the second reference text information;

[0077] E2. Obtaining a common word ratio similarity between a word in the second target text information and each word in the second reference text information;

[0078] E3. Obtaining a degree of character overlap between a word in the second target text information and each word in the second reference text information;

[0079] E4. Determine a second type of similarity between each word in the second target text information and each word in the second reference text information based on the edit distance similarity between the words in the second target text information and each word in the second reference text information, the common word ratio similarity between the words in the second target text information and each word in the second reference text information, and the character overlap between the words in the second target text information and each word in the second reference text information.

[0080] Among them, the edit distance similarity can be determined by the method shown in the following formula:

[0081] Edit distance similarity = (1-edit distance / the shortest length of two texts)*100%.

[0082] The common word ratio similarity can be determined by the following formula:

[0083] Common word ratio similarity = number of common words after word segmentation / minimum number of common words in two texts after word segmentation * 100%.

[0084] The number of common words after word segmentation can be understood as the number of common words between the words in the second text information and the words in the second reference text information.

[0085] The degree of character overlap can be determined by the following formula:

[0086] Character overlap = number of identical characters in two texts / minimum length of two texts * 100%;

[0087] The number of identical characters between the two texts can be understood as the number of identical characters between the words in the second text information and the second reference text information, and the two texts can be understood as the words in the second text information and the second reference text information.

[0088] The maximum value among the edit distance similarity, the common word ratio similarity and the character overlap similarity can be determined as the second type of similarity.

[0089] In this example, the edit distance similarity, common word ratio similarity, and character overlap are calculated, and the maximum value among the edit distance similarity, common word ratio similarity, and character overlap is determined as the second type of similarity, thereby improving the accuracy of obtaining the second type of similarity.

[0090] In a possible implementation, the target medical insurance data processing program may also be calibrated as follows:

[0091] F1. Run the target medical insurance data processing program to obtain an operation result;

[0092] F2. Obtain the accuracy of the operation result;

[0093] F3. If the accuracy rate is lower than a preset accuracy rate, the target medical insurance data processing program is corrected to obtain a corrected target medical insurance data processing program.

[0094] The medical insurance data of the target area can be input into the target medical insurance data processing program for operation to obtain the operation results.

[0095] The running result can be compared with the preset running result to obtain a similarity, which is then determined as the accuracy rate. The preset accuracy rate is set based on experience or historical data.

[0096] The method for correcting the target medical insurance data processing program can be to obtain information in the running result that is different from the preset running result, and perform correction processing based on the different information. Specifically, the subroutine or code related to the different information in the target medical insurance data processing program can be corrected so that when the corrected target medical insurance data processing program processes the data, the similarity between the result obtained and the preset running result is higher than the similarity corresponding to the preset accuracy rate.

[0097] In this example, when the accuracy rate is lower than the preset accuracy rate, the target medical insurance data processing program is corrected to obtain a corrected target medical insurance data processing program, thereby improving the accuracy of subsequent processing of the medical insurance data.

[0098] See also Figure 2 , Figure 2The present application embodiment provides a flowchart of another application adjustment method. Figure 2 As shown, the method includes:

[0099] 201. Obtain first reference text information in the initial medical insurance data processing program;

[0100] 202. Perform stop word removal processing on the first reference text information to obtain first target text information;

[0101] 203. Perform word segmentation processing on the first target text information to obtain a first word set, where the first word set includes at least one word;

[0102] 204. Obtain the frequency of occurrence of each word in the first word set in the first text information;

[0103] 205. Obtain category information of each word in the first word set;

[0104] 206. Perform word filtering on the first text information based on the frequency of occurrence of each word in the first word set in the first text information and the category information of each word in the first word set to obtain the second target text information;

[0105] 207. Acquire third target text information corresponding to the second target text information from the medical insurance data of the target area;

[0106] 208. Adjust the initial medical insurance data processing program according to the third target text information to obtain a target medical insurance data processing program.

[0107] In this example, by obtaining the frequency of occurrence of each word in the first word set in the first text information, obtaining the category information of each word in the first word set, and performing word filtering processing on the first text information based on the frequency of occurrence of each word in the first word set in the first text information and the category information of each word in the first word set, the second target text information is obtained, thereby improving the accuracy of obtaining the second target text information.

[0108] For the same example as above, please refer to Figure 3 , Figure 3 A schematic structural diagram of a terminal provided in an embodiment of the present application, as shown in the figure, includes a processor, an input device, an output device, and a memory, the processor, the input device, the output device, and the memory being interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, the processor being configured to call the program instructions, and the program including instructions for executing the following steps;

[0109] Obtaining first reference text information in an initial medical insurance data processing program;

[0110] performing stop word removal processing on the first reference text information to obtain first target text information;

[0111] Performing word filtering on the first target text information to obtain second target text information;

[0112] Acquire third target text information corresponding to the second target text information from the medical insurance data of the target area;

[0113] The initial medical insurance data processing program is adjusted according to the third target text information to obtain a target medical insurance data processing program.

[0114] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to implement the above functions, the terminal includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of the various examples described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0115] The embodiment of the present application can divide the terminal into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0116] In line with the above, please see Figure 4 , Figure 4 The present invention provides a schematic diagram of the structure of an application adjustment device. Figure 4 As shown, the device includes:

[0117] A first acquiring unit 401 is configured to acquire first reference text information in an initial medical insurance data processing program;

[0118] A first processing unit 402 is configured to remove pause words from the first reference text information to obtain first target text information;

[0119] The second processing unit 403 is configured to perform word filtering on the first target text information to obtain second target text information;

[0120] A second acquiring unit 404 is configured to acquire third target text information corresponding to the second target text information from the medical insurance data of the target area;

[0121] The adjusting unit 405 is configured to adjust the initial medical insurance data processing program according to the third target text information to obtain a target medical insurance data processing program.

[0122] In one possible implementation, the second processing unit 403 is configured to:

[0123] Performing word segmentation processing on the first target text information to obtain a first word set, wherein the first word set includes at least one word;

[0124] Obtaining the frequency of occurrence of each word in the first word set in the first text information;

[0125] Obtaining category information of each word in the first word set;

[0126] The first text information is subjected to word filtering processing according to the occurrence frequency of each word in the first word set in the first text information and the category information of each word in the first word set to obtain the second target text information.

[0127] In one possible implementation, the second processing unit 403 is configured to:

[0128] Performing word segmentation processing on the first target text information to obtain a first word set, wherein the first word set includes at least one word;

[0129] Performing semantic analysis on the words in the first word set to obtain a semantic analysis result;

[0130] The first target text information is subjected to word filtering processing according to the semantic analysis result to obtain second target text information.

[0131] In a possible implementation, the second acquiring unit 404 is configured to:

[0132] Acquire second reference text information in the medical insurance data, where the second reference text information is of the same type as the first reference text information;

[0133] determining a similarity between each word in the second target text information and each word in the second reference text information;

[0134] The third target text information is determined from the second reference text information according to the similarity.

[0135] In one possible implementation, in determining the similarity between each word in the second target text information and each word in the second reference text information, the second acquiring unit 404 is configured to:

[0136] Obtaining a first-category similarity between each word in the second target text information and each word in the second reference text information;

[0137] Obtaining a second type of similarity between each word in the second target text information and each word in the second reference text information;

[0138] A weighted operation is performed based on the first-category similarity between each word in the second target text information and each word in the second reference text information and the second-category similarity between each word in the second target text information and each word in the second reference text information to obtain the similarity between each word in the second target text information and each word in the second reference text information.

[0139] In a possible implementation, in acquiring the second type of similarity between each word in the second target text information and each word in the second reference text information, the second acquiring unit 404 is configured to:

[0140] Obtaining edit distance similarity between a word in the second target text information and each word in the second reference text information;

[0141] Obtaining a common word ratio similarity between a word in the second target text information and each word in the second reference text information;

[0142] Obtaining a degree of character overlap between a word in the second target text information and each word in the second reference text information;

[0143] Determine a second type of similarity between each word in the second target text information and each word in the second reference text information based on the edit distance similarity between the words in the second target text information and each word in the second reference text information, the common word ratio similarity between the words in the second target text information and each word in the second reference text information, and the character overlap between the words in the second target text information and each word in the second reference text information.

[0144] In one possible implementation, the device is further configured to:

[0145] Running the target medical insurance data processing program to obtain an operating result;

[0146] Obtaining the accuracy of the running result;

[0147] If the accuracy rate is lower than the preset accuracy rate, the target medical insurance data processing program is corrected to obtain a corrected target medical insurance data processing program.

[0148] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any application adjustment method described in the above method embodiments.

[0149] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute part or all of the steps of any application adjustment method recorded in the above method embodiments.

[0150] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0151] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0152] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0153] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0154] In addition, the functional units in the various embodiments of the application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software program modules.

[0155] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0156] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0157] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for adjusting an application, characterized in that: The method comprises: Acquire first reference text information in an initial medical insurance data processing program, where the first reference text information is a field name, a dictionary value, or a table name; performing stop word removal processing on the first reference text information to obtain first target text information; Performing word filtering on the first target text information to obtain second target text information; Obtaining a second reference text from the medical insurance data of the target area, where the second reference text information is of the same type as the first reference text information; determining a similarity between each word in the second target text information and each word in the second reference text information; and determining, based on the similarity, from the second reference text information, a third target text information corresponding to the second target text information; The corresponding second target text information in the initial medical insurance data processing program is replaced according to the third target text information to obtain a target medical insurance data processing program.

2. The method according to claim 1, characterized in that The performing word filtering on the first target text information to obtain second target text information includes: Performing word segmentation processing on the first target text information to obtain a first word set, wherein the first word set includes at least one word; Obtaining the frequency of occurrence of each word in the first word set in the first target text information; Obtaining category information of each word in the first word set; According to the occurrence frequency of each word in the first word set in the first target text information and the category information of each word in the first word set, the first target text information is subjected to word filtering processing to obtain the second target text information.

3. The method according to claim 1, characterized in that The performing word filtering on the first target text information to obtain second target text information includes: Performing word segmentation processing on the first target text information to obtain a first word set, wherein the first word set includes at least one word; Performing semantic analysis on the words in the first word set to obtain a semantic analysis result; The first target text information is subjected to word filtering processing according to the semantic analysis result to obtain second target text information.

4. The method according to claim 1, wherein Determining the similarity between each word in the second target text information and each word in the second reference text information includes: Obtaining a first-category similarity between each word in the second target text information and each word in the second reference text information; Obtaining a second type of similarity between each word in the second target text information and each word in the second reference text information; A weighted operation is performed based on the first-category similarity between each word in the second target text information and each word in the second reference text information and the second-category similarity between each word in the second target text information and each word in the second reference text information to obtain the similarity between each word in the second target text information and each word in the second reference text information.

5. The method according to claim 4, characterized in that The obtaining of a second type of similarity between each word in the second target text information and each word in the second reference text information includes: Obtaining edit distance similarity between a word in the second target text information and each word in the second reference text information; Obtaining a common word ratio similarity between a word in the second target text information and each word in the second reference text information; Obtaining a degree of character overlap between a word in the second target text information and each word in the second reference text information; Determine a second type of similarity between each word in the second target text information and each word in the second reference text information based on the edit distance similarity between the words in the second target text information and each word in the second reference text information, the common word ratio similarity between the words in the second target text information and each word in the second reference text information, and the character overlap between the words in the second target text information and each word in the second reference text information.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Running the target medical insurance data processing program to obtain an operation result; Obtaining the accuracy of the running result; If the accuracy rate is lower than the preset accuracy rate, the target medical insurance data processing program is corrected to obtain a corrected target medical insurance data processing program.

7. An application adjustment device, characterized in that: The device comprises: A first acquiring unit is configured to acquire first reference text information in an initial medical insurance data processing program, wherein the first reference text information is a field name, a dictionary value, or a table name; a first processing unit, configured to remove pause words from the first reference text information to obtain first target text information; a second processing unit, configured to perform word filtering processing on the first target text information to obtain second target text information; a second acquisition unit configured to acquire a second reference text from the medical insurance data of the target area, the second reference text information being of the same type as the first reference text information; determine a similarity between each word in the second target text information and each word in the second reference text information; and determine, based on the similarity, from the second reference text information, third target text information corresponding to the second target text information; An adjusting unit is configured to replace the corresponding second target text information in the initial medical insurance data processing program according to the third target text information to obtain a target medical insurance data processing program.

8. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 6.

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