Data query method and device, computer equipment and storage medium
By splitting and combining the standard data identifiers, combining character similarity and semantic similarity models, accurate associated data identifiers are generated, which solves the problem of low accuracy of data query in the existing technology and improves the accuracy of data query and diversity.
Patent Information
- Application Number
- CN202411421269.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-12
- Publication Date
- 2025-01-10
AI Technical Summary
In the prior art, the system has a low accuracy rate when automatically retrieving and extracting related data, which is mainly due to the concise query information input by the user and the long data stored in the system, resulting in differences.
By splitting the standard data identifiers, extracting element information, and combining them to form associated data identifiers, the character similarity and semantic similarity models are used to determine the similarity of the target data identifier, so as to select the corresponding form and generate more accurately , relevant and diverse associated data identification.
It improves the diversity, richness and accuracy of associated data identification, enhances the accuracy and flexibility of data query, ensures the correlation between associated data identification and standard data identification, and improves the effectiveness of data query.
Smart Images
Figure CN119293084A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of big data technology, and in particular to a data query method, apparatus, computer equipment, storage medium and computer program product. Background Art
[0002] With the continuous development and evolution of big data technology, the ability to process and analyze data has been significantly improved, allowing users to enter query information more conveniently. The system automatically retrieves and extracts relevant data based on these inputs and provides corresponding results. However, in the existing technical system, a large amount of data stored in the system usually has more detailed identifiers, which are often long. At the same time, the query information entered by the user is relatively concise, and some features of the data identifier (such as time, regional range, etc.) are often omitted. This difference leads to a low accuracy rate when the system automatically retrieves and extracts relevant data. Summary of the invention
[0003] Based on this, it is necessary to provide a data query method, device, computer equipment, computer-readable storage medium and computer program product to address the above technical problems.
[0004] In a first aspect, the present application provides a data query method. The method comprises:
[0005] Acquire a data query statement during a session; wherein the data query statement includes identification information of a target form;
[0006] Determine the similarity between the identification information of the target form and each data identification in a preset data identification set respectively; wherein the data identification set includes a standard data identification and an associated data identification; the associated data identification is obtained by performing element splitting on the standard data identification of the form, obtaining a first number of element information, screening a second number of element information from the first number of element information, and combining the second number of element information; wherein the associated data identification includes at least an entity element; the second number is less than the first number;
[0007] According to the similarity, a target data identifier is selected from the data identifier set, and a corresponding form is obtained.
[0008] In one embodiment, the step of splitting the standard data identifier of the form into elements to obtain a first quantity of element information, screening a second quantity of element information from the first quantity of element information, and combining the second quantity of element information to obtain the first quantity of element information comprises:
[0009] The standard data identifier of the form is split into elements to obtain attribute elements and entity elements;
[0010] A preset number of target attribute elements are selected from the attribute elements and merged with entity elements to obtain the associated data identifier.
[0011] In one embodiment, selecting a target data identifier from the data identifier set according to the similarity and obtaining a corresponding form includes:
[0012] Selecting a target data identifier from the data identifier set according to the similarity;
[0013] In the case where the target data identifier is not a standard data identifier, a target standard data identifier corresponding to the target data identifier is matched and obtained, and a form corresponding to the target standard data identifier is obtained;
[0014] In the case where the target data identifier is a standard data identifier, a form corresponding to the target data identifier is obtained.
[0015] In one embodiment, the respectively determining the similarity between the identification information of the target form and each data identifier in the preset data identifier set includes:
[0016] Using a preset character similarity strategy, respectively determine the initial similarity between the identification information of the target form and each data identification in a preset data identification set;
[0017] Determining a candidate data identifier from the data identifier set according to the initial similarity;
[0018] The candidate data identifier and the identifier information of the target form are input into a preset semantic similarity model to obtain the similarity between the candidate data identifier and the identifier information of the target form.
[0019] In one embodiment, the data identification set further includes a semantic vector of each data identification; and using a preset character similarity strategy to respectively determine the initial similarity between the identification information of the target form and each data identification in the preset data identification set includes:
[0020] Determine the semantic vector of the identification information of the target form by using a preset semantic vector strategy;
[0021] By using a preset character similarity strategy, the semantic vector of the identification information of the target form is compared with the semantic vector of each data identifier in a preset data identifier set to obtain the initial similarity between the identification information of the target form and each data identifier in the preset data identifier set.
[0022] In one embodiment, determining the candidate data identifier from the data identifier set according to the initial similarity includes:
[0023] From the data identifier set, candidate data identifiers whose initial similarities satisfy a preset ranking are screened.
[0024] In a second aspect, the present application also provides a data query device. The device comprises:
[0025] A query statement acquisition module, used to acquire a data query statement during a session; wherein the data query statement includes identification information of a target form;
[0026] A similarity acquisition module, used to respectively determine the similarity between the identification information of the target form and each data identification in a preset data identification set; wherein the data identification set includes a standard data identification and an associated data identification; the associated data identification is obtained by performing element splitting on the standard data identification of the form, obtaining a first number of element information, screening a second number of element information from the first number of element information, and combining the second number of element information; wherein the associated data identification includes at least an entity element; the second number is less than the first number;
[0027] The form acquisition module is used to select a target data identifier from the data identifier set according to the similarity and obtain a corresponding form.
[0028] In one embodiment, the similarity acquisition module includes:
[0029] The identification splitting submodule is used to split the standard data identification of the form into elements to obtain attribute elements and entity elements;
[0030] The element merging submodule is used to select a preset number of target attribute elements from the attribute elements and merge them with the entity elements to obtain the associated data identifier.
[0031] In one embodiment, the form acquisition module includes:
[0032] An identifier acquisition submodule, used for selecting a target data identifier from the data identifier set according to the similarity;
[0033] A form matching submodule, for matching the target data identifier to obtain a target standard data identifier corresponding to the target data identifier when the target data identifier is not a standard data identifier, and obtaining a form corresponding to the target standard data identifier;
[0034] The form submodule is further configured to obtain a form corresponding to the target data identifier when the target data identifier is a standard data identifier.
[0035] In one embodiment, the similarity acquisition module includes:
[0036] A character similarity acquisition submodule, used to use a preset character similarity strategy to respectively determine the initial similarity between the identification information of the target form and each data identification in a preset data identification set;
[0037] A candidate identifier determination submodule, used to determine a candidate data identifier from the data identifier set according to the initial similarity;
[0038] The semantic similarity acquisition submodule is used to input the candidate data identifier and the identification information of the target form into a preset semantic similarity model to obtain the similarity between the candidate data identifier and the identification information of the target form.
[0039] In one embodiment, the data identification set further includes a semantic vector of each data identification; the character similarity acquisition submodule includes:
[0040] A semantic vector determination unit, used to determine the semantic vector of the identification information of the target form by using a preset semantic vector strategy;
[0041] The initial similarity acquisition unit is used to compare the semantic vector of the identification information of the target form with the semantic vector of each data identifier in the preset data identifier set by using a preset character similarity strategy, so as to obtain the initial similarity between the identification information of the target form and each data identifier in the preset data identifier set.
[0042] In one embodiment, the candidate identifier determination submodule is further used to:
[0043] From the data identifier set, candidate data identifiers whose initial similarities satisfy a preset ranking are screened.
[0044] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for processing resource application as described in any one of the embodiments of the present disclosure is implemented.
[0045] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for processing resource application as described in any one of the embodiments of the present disclosure is implemented.
[0046] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method for processing resource application as described in any one of the embodiments of the present disclosure is implemented.
[0047] The above-mentioned data query method, device, computer equipment, storage medium and computer program product, by splitting the standard data identifier, extracting element information, selecting the second number of element information, and combining them to form an associated data identifier, thereby respectively determining the similarity between the identification information of the target form in the data query statement during the session and the standard data identifier and the associated data identifier. According to this similarity, the target data identifier and the corresponding form are selected from the data identifier set. The splitting of the standard data identifier can obtain more accurate, relevant and diverse element information, thereby improving the diversity, richness and accuracy of the associated data identifier generation. Setting the second number provides more flexibility and pertinence for the generation of the associated data identifier, further improving the diversity of the associated data identifier generation and the accuracy of data query. In addition, the associated data identifier contains entity elements, which can always maintain the association relationship between the associated data identifier and the standard data identifier, so that the generated associated data identifier is accurate, and further makes the data query more accurate. BRIEF DESCRIPTION OF THE DRAWINGS
[0048] FIG1 is a schematic diagram of a flow chart of a data query method in one embodiment;
[0049] FIG2 is a schematic diagram of a process for generating a related data identifier in one embodiment;
[0050] FIG3 is a schematic diagram of a process of identifying a target data match form in one embodiment;
[0051] FIG4 is a schematic diagram of a process for determining similarity in one embodiment;
[0052] FIG5 is a schematic diagram of a process of obtaining initial similarity in one embodiment;
[0053] FIG6 is a structural block diagram of a data query device in one embodiment;
[0054] FIG. 7 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION
[0055] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0056] In one embodiment, as shown in FIG1 , a data query method is provided. This embodiment is illustrated by applying the method to a server. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:
[0057] Step S100, obtaining a data query statement during a session; wherein the data query statement includes identification information of a target form.
[0058] In an exemplary embodiment, the data query method can be applied to a dialogue model with an account, that is, the account inputs the identifier of the data it wants to obtain, the server queries through the identifier of the data input by the account, queries the data the account wants to obtain, and returns it to the account, etc.
[0059] In an exemplary embodiment, the data query statement may include a form, list, etc. that needs to be queried; for example, the data query statement may include the top 100 students in the final exam scores of XX grade in XX school, etc.
[0060] In an exemplary embodiment, the data query statement may include identification information and non-identification information of the target form; for example, the data query statement is "please query the top 100 students in the final exam scores of grade XX in XX school", in which case "please query" is the non-identification information; and "the top 100 students in the final exam scores of grade XX in XX school" is the identification information of the target form, etc.
[0061] Step S200, respectively determining the similarity between the identification information of the target form and each data identification in a preset data identification set; wherein the data identification set includes a standard data identification and an associated data identification; the associated data identification is obtained by splitting the standard data identification of the form into elements, obtaining a first number of element information, screening a second number of element information from the first number of element information, and combining the second number of element information; wherein the associated data identification includes at least an entity element; the second number is less than the first number.
[0062] In an exemplary embodiment, the data identifier may include the table name, list name, etc. of the target form; for example, the top 100 students in the final exam of XX grade in XX school in XX year, etc.; the data identifier set includes multiple table names, list names, etc.
[0063] In an exemplary embodiment, the standard data identifier may include the original table name, list name, etc. stored in the database; for example, the top 100 students in the final exam scores of XX grade in XX school in XX year, the list can be split to obtain year = XX year, school = XX school, grade = XX grade, TOP value = 100; and students with final exam scores; year, school, grade, TOP value, and final exam scores are the first amount of element information; the second amount of element information is selected from these element information and combined to obtain the associated data identifier; the associated data identifier includes at least entity elements, specifically, students with final exam scores are entity elements, the data obtained are the final exam scores and students, so the scores and students are entity data, which are indispensable, so the associated data identifier includes at least entity elements, etc.
[0064] In an exemplary embodiment, element splitting of the standard data identifier of the form may include defining some elements, and using these elements to identify from the standard data identifier, for example, the elements may be defined as time, region, TOP value, etc. In this way, element information, etc. may be obtained.
[0065] In an exemplary embodiment, the second quantity may include the number of element information preset by the server. It is understandable that the second quantity here may include entity elements or may not include entity elements, etc. For example, the top 100 students in the final exam scores of XX grade in XX school in XX year, and the number is set to 2, then two element information are selected from the year, school, grade, and TOP value to form an associated data identifier with the entity element, such as the students in the final exam scores of XX grade in XX school; the top 100 students in the final exam scores of XX grade, etc. It can be immediately that multiple preset quantities can be set here, for example, the set quantity can be 1 or 2; then associated data identifiers can be generated for 1 and 2 element information respectively, etc. In an exemplary embodiment, after obtaining the number of element information preset by the server, the combination of all element information can be obtained, and the corresponding associated data identifiers can be obtained in sequence, so as to obtain all associated data identifiers corresponding to the number of element information, or the combination of some element information can be selected to obtain the corresponding associated data identifier, and combined with the standard data identifier to obtain a data identifier set, etc.
[0066] In an exemplary embodiment, after establishing an associated data identifier, an association relationship between the associated data identifier and the standard data identifier can be established, that is, the corresponding standard data identifier can be obtained through the associated data identifier. In this way, the data corresponding to the associated data identifier can be obtained without creating a new form.
[0067] In an exemplary embodiment, it is understandable that in actual use, users generally do not enter longer form names, so if only standard data identifiers exist, it will result in a weaker data query match. When generating associated data identifiers, this solution generates a shorter form name through the element information of the longer form name, which not only ensures the association between the associated data identifier and the standard data identifier, but also improves the match between the form name entered by the user and the associated data identifier.
[0068] Step S300: selecting a target data identifier from the data identifier set according to the similarity, and obtaining a corresponding form.
[0069] In an exemplary embodiment, after determining the similarity between the identification information of the target form and each data identification, the one with the highest similarity ranking may be selected as the target identification information, and the corresponding form may be obtained as the target data, etc.
[0070] In the above data query method, by splitting the standard data identifier, extracting element information, selecting the second number of element information, and combining them to form an associated data identifier, the similarity between the identification information of the target form in the data query statement during the session and the standard data identifier and the associated data identifier is determined respectively. According to this similarity, the target data identifier and the corresponding form are selected from the data identifier set. The splitting of the standard data identifier can obtain more accurate, relevant and diverse element information, thereby improving the diversity, richness and accuracy of the associated data identifier generation. Setting the second number provides more flexibility and pertinence for the generation of the associated data identifier, further improving the diversity of the associated data identifier generation and the accuracy of data query. In addition, the associated data identifier contains entity elements, which can always maintain the association relationship between the associated data identifier and the standard data identifier, so that the generated associated data identifier is accurate, and further makes the data query more accurate.
[0071] In one embodiment, as shown in FIG. 2, the element splitting of the standard data identifier of the form to obtain a first number of element information, screening a second number of element information from the first number of element information, and combining the second number of element information to obtain, includes:
[0072] Step S201, splitting the standard data identifier of the form into elements to obtain attribute elements and entity elements.
[0073] In an exemplary embodiment, the entity elements may include elements for determining target data corresponding to a data identifier; for example, grades, students, etc.; the attribute elements may include elements for limiting entity elements, for example, XX grade, XX school, etc.
[0074] Step S202: Select a preset number of target attribute elements from the attribute elements, and merge them with entity elements to obtain the associated data identifier.
[0075] In an exemplary embodiment, multiple preset quantities can be set and corresponding associated data identifiers can be generated to make the generated associated data identifiers more comprehensive; at the same time, it can be understood that the entity element represents the stored target data and is a necessary element. For example, the entity element stores grades, and the associated element cannot generate students, etc.
[0076] In this embodiment, the standard data identifier is split to obtain the attribute elements and entity elements, and a preset number of attribute elements are selected and merged with the entity elements to obtain the associated data identifier. By splitting the annotated data identifier, the accuracy of the obtained attribute elements and entity elements is improved, and by selecting a preset number of attribute elements, the comprehensiveness of the associated data identifier generation is improved, making the data query more targeted and improving the accuracy of the data query.
[0077] In one embodiment, as shown in FIG3 , the target data identifier is selected from the data identifier set according to the similarity, and a corresponding form is obtained, including:
[0078] Step S301: selecting a target data identifier from the data identifier set according to the similarity.
[0079] Step S302: when the target data identifier is not a standard data identifier, a target standard data identifier corresponding to the target data identifier is matched and a form corresponding to the target standard data identifier is obtained.
[0080] Step S303: when the target data identifier is a standard data identifier, obtain a form corresponding to the target data identifier.
[0081] In an exemplary embodiment, when establishing an associated data identifier, an association relationship between the standard data identifier and the associated data identifier may be established, and the target data corresponding to the associated data identifier may not be established. Therefore, the associated data identifier may not have corresponding data, and may only have a standard data identifier corresponding to it, etc. It can be understood that the associated data identifier may include multiple standard data identifiers associated with it. In this case, it is necessary to correspond to all standard data identifiers and return the data corresponding to all standard data identifiers; for example, the standard data identifiers include the top 100 students in the fifth grade final exam of XX school, and the top 100 students in the sixth grade final exam of XX school. Both standard data identifiers have an associated data identifier of the top 100 students in the final exam of XX school. Therefore, in this case, it is considered that both data are target data, and the target data and the corresponding standard data identifiers can be returned at the same time.
[0082] In this embodiment, the target data identifier is identified to determine whether it is a standard data identifier. If it is not a standard data identifier, the corresponding standard data identifier needs to be obtained to obtain the corresponding form. This method avoids the establishment of a list of related data identifiers, saves data storage resources, and improves the correlation between related data identifiers and standard data identifiers, thereby improving the transparency of data query.
[0083] In one embodiment, as shown in FIG. 4 , respectively determining the similarity between the identification information of the target form and each data identification in the preset data identification set includes:
[0084] Step S211 , using a preset character similarity strategy, respectively determining the initial similarity between the identification information of the target form and each data identifier in a preset data identifier set.
[0085] In an exemplary embodiment, the character similarity strategy may include cosine distance similarity comparison, and using cosine distance similarity comparison, the first similarity between the identification information of the target form and each data identification in the preset data identification set is determined respectively.
[0086] Step S212: determining a candidate data identifier from the data identifier set according to the initial similarity.
[0087] In an exemplary embodiment, a data identifier whose initial similarity is greater than a preset threshold may be selected as a candidate data identifier, or a data identifier with a preset position in the initial similarity ranking may be selected as a candidate data identifier, etc.
[0088] Step S213: input the candidate data identifier and the identifier information of the target form into a preset semantic similarity model to obtain the similarity between the candidate data identifier and the identifier information of the target form.
[0089] In an exemplary embodiment, the semantic similarity model may include a UTC model, and the UTC model is used to sequentially compare the semantic similarity of the identification information of the target form and the candidate data identification. Specifically, the candidate data identification may be further compared according to the release time and regional scope of the form; for example, the list release time corresponding to the candidate data identification is June, but the identification information of the target form is July. Obviously, there is no data in July in June, so the candidate data identification is excluded.
[0090] In this embodiment, the character similarity strategy is used to obtain the initial similarity between the identification information of the target form and each data identification in the preset data identification set, and the candidate data identification is determined, and the semantic similarity model is used to obtain the similarity between the candidate data identification and the identification information of the target form. The character similarity strategy and the semantic similarity model can be used respectively to improve the accuracy of data identification matching and further improve the accuracy of data query.
[0091] In one embodiment, as shown in FIG5 , the data identification set further includes a semantic vector of each data identification; the using a preset character similarity strategy to respectively determine the initial similarity between the identification information of the target form and each data identification in the preset data identification set includes:
[0092] Step S221: Determine the semantic vector of the identification information of the target form by using a preset semantic vector strategy.
[0093] Step S222, using a preset character similarity strategy, compares the semantic vector of the identification information of the target form with the semantic vector of each data identifier in a preset data identifier set to obtain an initial similarity between the identification information of the target form and each data identifier in the preset data identifier set.
[0094] In an exemplary embodiment, after generating the associated data identifier, a semantic vector strategy can be used to establish a semantic vector for each associated data identifier. Specifically, the text2vec-base-chinese model can be used. In an exemplary embodiment, the semantic vector strategy for determining the semantic vector of the identification information of the target form needs to be consistent with the semantic vector strategy used to generate the semantic vector of each data identifier in the data identifier set.
[0095] In an exemplary embodiment, the character similarity strategy may include a cosine distance similarity comparison, which compares the semantic vector of the identification information of the target form with the semantic vector of each data identifier in a preset data identifier set to obtain the initial similarity between the identification information of the target form and each data identifier in the preset data identifier set.
[0096] In this embodiment, by including the semantic vector of each data identifier in the data identifier set, when obtaining the initial similarity, it is only necessary to obtain the semantic vector of the identification information of the target form, and compare the semantic vector of the identification information of the target form with the semantic vector of each data identifier in the preset data identifier set. This can avoid the need to confirm the semantic vector of each data identifier in the data identifier set for each character similarity comparison, thereby improving the efficiency of the query.
[0097] In one embodiment, determining the candidate data identifier from the data identifier set according to the initial similarity includes:
[0098] From the data identifier set, candidate data identifiers whose initial similarities satisfy a preset ranking are screened.
[0099] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.
[0100] Based on the same inventive concept, the embodiment of the present application also provides a data query device for implementing the data query method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more data query device embodiments provided below can refer to the limitations on the data query method above, and will not be repeated here.
[0101] In one embodiment, as shown in FIG. 6 , a data query device 100 is provided, comprising: a query statement acquisition module 101, a similarity acquisition module 102 and a form acquisition module 103, wherein:
[0102] A query statement acquisition module, used to acquire a data query statement during a session; wherein the data query statement includes identification information of a target form;
[0103] A similarity acquisition module, used to respectively determine the similarity between the identification information of the target form and each data identification in a preset data identification set; wherein the data identification set includes a standard data identification and an associated data identification; the associated data identification is obtained by performing element splitting on the standard data identification of the form, obtaining a first number of element information, screening a second number of element information from the first number of element information, and combining the second number of element information; wherein the associated data identification includes at least an entity element; the second number is less than the first number;
[0104] The form acquisition module is used to select a target data identifier from the data identifier set according to the similarity and obtain a corresponding form.
[0105] In one embodiment, the similarity acquisition module includes:
[0106] The identification splitting submodule is used to split the standard data identification of the form into elements to obtain attribute elements and entity elements;
[0107] The element merging submodule is used to select a preset number of target attribute elements from the attribute elements and merge them with the entity elements to obtain the associated data identifier.
[0108] In one embodiment, the form acquisition module includes:
[0109] An identifier acquisition submodule, used for selecting a target data identifier from the data identifier set according to the similarity;
[0110] A form matching submodule, for matching the target data identifier to obtain a target standard data identifier corresponding to the target data identifier when the target data identifier is not a standard data identifier, and obtaining a form corresponding to the target standard data identifier;
[0111] The form submodule is further configured to obtain a form corresponding to the target data identifier when the target data identifier is a standard data identifier.
[0112] In one embodiment, the similarity acquisition module includes:
[0113] A character similarity acquisition submodule, used to use a preset character similarity strategy to respectively determine the initial similarity between the identification information of the target form and each data identification in a preset data identification set;
[0114] A candidate identifier determination submodule, used to determine a candidate data identifier from the data identifier set according to the initial similarity;
[0115] The semantic similarity acquisition submodule is used to input the candidate data identifier and the identification information of the target form into a preset semantic similarity model to obtain the similarity between the candidate data identifier and the identification information of the target form.
[0116] In one embodiment, the data identification set further includes a semantic vector of each data identification; the character similarity acquisition submodule includes:
[0117] A semantic vector determination unit, used to determine the semantic vector of the identification information of the target form by using a preset semantic vector strategy;
[0118] The initial similarity acquisition unit is used to compare the semantic vector of the identification information of the target form with the semantic vector of each data identifier in the preset data identifier set by using a preset character similarity strategy, so as to obtain the initial similarity between the identification information of the target form and each data identifier in the preset data identifier set.
[0119] In one embodiment, the candidate identifier determination submodule is further used to:
[0120] From the data identifier set, candidate data identifiers whose initial similarities satisfy a preset ranking are screened.
[0121] Each module in the above data query device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.
[0122] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG7. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a data query method is implemented. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.
[0123] Those skilled in the art will understand that the structure shown in FIG. 7 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.
[0124] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.
[0125] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.
[0126] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0127] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.
Claims
1. A data query method, characterized in that: The method comprises: Acquire a data query statement during a session; wherein the data query statement includes identification information of a target form; Determine the similarity between the identification information of the target form and each data identification in a preset data identification set respectively; wherein the data identification set includes a standard data identification and an associated data identification; the associated data identification is obtained by performing element splitting on the standard data identification of the form, obtaining a first number of element information, screening a second number of element information from the first number of element information, and combining the second number of element information; wherein the associated data identification includes at least an entity element; the second number is less than the first number; According to the similarity, a target data identifier is selected from the data identifier set, and a corresponding form is obtained.
2. The method according to claim 1, characterized in that The step of performing element splitting on the standard data identifier of the form to obtain a first quantity of element information, screening a second quantity of element information from the first quantity of element information, and combining the second quantity of element information to obtain the first quantity of element information comprises: The standard data identifier of the form is split into elements to obtain attribute elements and entity elements; A preset number of target attribute elements are selected from the attribute elements and merged with entity elements to obtain the associated data identifier.
3. The method according to claim 1, characterized in that The step of selecting a target data identifier from the data identifier set according to the similarity and obtaining a corresponding form includes: Selecting a target data identifier from the data identifier set according to the similarity; In the case where the target data identifier is not a standard data identifier, a target standard data identifier corresponding to the target data identifier is matched and obtained, and a form corresponding to the target standard data identifier is obtained; In the case where the target data identifier is a standard data identifier, a form corresponding to the target data identifier is obtained.
4. The method according to claim 1, characterized in that: The respectively determining the similarity between the identification information of the target form and each data identification in the preset data identification set includes: Using a preset character similarity strategy, respectively determine the initial similarity between the identification information of the target form and each data identification in a preset data identification set; Determining a candidate data identifier from the data identifier set according to the initial similarity; The candidate data identifier and the identifier information of the target form are input into a preset semantic similarity model to obtain the similarity between the candidate data identifier and the identifier information of the target form.
5. The method according to claim 4, characterized in that The data identification set also includes a semantic vector of each data identification; the using a preset character similarity strategy to respectively determine the initial similarity between the identification information of the target form and each data identification in the preset data identification set includes: Determine the semantic vector of the identification information of the target form by using a preset semantic vector strategy; By using a preset character similarity strategy, the semantic vector of the identification information of the target form is compared with the semantic vector of each data identifier in a preset data identifier set to obtain the initial similarity between the identification information of the target form and each data identifier in the preset data identifier set.
6. The method according to claim 4, characterized in that Determining a candidate data identifier from the data identifier set according to the initial similarity includes: From the data identifier set, candidate data identifiers whose initial similarities satisfy a preset ranking are screened.
7. A data query device, characterized in that: The device comprises: A query statement acquisition module, used to acquire a data query statement during a session; wherein the data query statement includes identification information of a target form; A similarity acquisition module, used to respectively determine the similarity between the identification information of the target form and each data identification in a preset data identification set; wherein the data identification set includes a standard data identification and an associated data identification; the associated data identification is obtained by performing element splitting on the standard data identification of the form, obtaining a first number of element information, screening a second number of element information from the first number of element information, and combining the second number of element information; wherein the associated data identification includes at least an entity element; the second number is less than the first number; The form acquisition module is used to select a target data identifier from the data identifier set according to the similarity and obtain a corresponding form.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.