Data query method, electronic device, storage medium and program product

Abstract

The invention provides a data query method, electronic equipment, a readable storage medium and a computer program product.The query method comprises the steps that firstly, M attribute value combinations are obtained by combining selectable attribute values of N attributes, and available combinations meeting specified conditions are recognized from the M attribute value combinations; obtaining an operation mode corresponding to the available combination through a specified condition, then responding to a received query request, querying the target object based on a query condition in the query request to obtain a preliminary query result, and then obtaining a query result through an attribute value of the target object in the preliminary query result; and identifying a target available combination adapted to the target object in the preliminary query result from the available combinations, and finally generating a target query result representing the preliminary query result and the target operation mode and feeding back the target query result.

Description

Technical Field

[0001] The present disclosure relates to the technical field of computers, and more particularly to a data query method, an electronic device, a readable storage medium, and a computer program product. Background Art

[0002] Data query is a method of retrieving specific information that meets specific conditions from a data source. Currently, the most common method of data query is to filter out data items that meet the query conditions from multiple data stored in the data source based on the query conditions entered by the user and provide feedback.

[0003] The above data query method can usually only be queried based on given query conditions. The query process will be limited by the query conditions themselves, making the query results given relatively simple. The query results provide fewer query dimensions and cannot reflect more comprehensive information in the query results. Summary of the invention

[0004] The present disclosure provides a data query method, an electronic device, a readable storage medium, and a computer program product.

[0005] The first aspect of the present disclosure proposes a data query method, comprising: obtaining M attribute value combinations by combining optional attribute values ​​of N attributes, N>1, M>1, the N attributes include one or all of user attributes and attributes of target objects, at least some of the N attributes correspond to multiple different optional attribute values, each of the attribute value combinations includes N optional attribute values ​​belonging to the N attributes, and the N optional attribute values ​​included in different attribute value combinations are different; identifying an available combination that meets a specified condition from the M attribute value combinations, and obtaining an operation mode corresponding to the available combination through the specified condition, the specified condition including Allowed attribute value combinations and allowed corresponding operation modes, the operation mode is used to represent the operations that can be performed on the target object; in response to receiving a query request, querying the target object based on the query conditions in the query request to obtain a preliminary query result, the preliminary query result indicating that there is at least one target object; identifying a target available combination adapted to the target object in the preliminary query result from the available combinations through the attribute value of the target object in the preliminary query result; and generating a target query result representing the preliminary query result and a target operation mode and providing feedback, the target operation mode being an operation mode corresponding to the target available combination.

[0006] According to some embodiments of the present disclosure, M attribute value combinations are obtained by combining optional attribute values ​​of N attributes, including: a first combination step; the first combination step includes: combining at least part of the optional attribute values ​​of the N attributes to obtain a plurality of attribute value combinations; determining a combination ratio of each attribute value combination in the plurality of attribute value combinations based on the attribute values ​​contained in the existing actual attribute data; and determining M attribute value combinations from the plurality of attribute value combinations based on the combination ratios.

[0007] According to some embodiments of the present disclosure, the combination ratio of each attribute value combination in the multiple attribute value combinations is determined based on the attribute values ​​contained in the existing actual attribute data, including: determining the attribute value ratio of the attribute value in the existing actual attribute data in the corresponding attribute; and for each attribute value combination in the multiple attribute value combinations, obtaining the combination ratio of the attribute value combination through the attribute value ratio of each attribute value contained in the attribute value combination.

[0008] According to some embodiments of the present disclosure, M attribute value combinations are determined from the multiple attribute value combinations based on the combination proportions, including: accumulating the combination proportions of the multiple attribute value combinations in order from high to low until the accumulated value is greater than or equal to a proportion threshold, and taking the accumulated attribute value combinations as the M attribute value combinations.

[0009] According to some embodiments of the present disclosure, M attribute value combinations are obtained by combining optional attribute values ​​of N attributes, including: a second combination step; the second combination step includes: screening the attribute values ​​contained in the existing actual attribute data to obtain combinable attribute values; and combining the combinable attribute values ​​to obtain M attribute value combinations.

[0010] According to some embodiments of the present disclosure, the attribute values ​​contained in the existing actual attribute data are screened to obtain combinable attribute values, including: determining the proportion of the attribute values ​​in the existing actual attribute data in the attribute values ​​to which they belong; and screening out the attribute values ​​whose proportion of the attribute values ​​is higher than a proportion threshold and using them as combinable attribute values.

[0011] According to some embodiments of the present disclosure, the proportion of attribute values ​​in existing actual attribute data in the attribute to which they belong is determined, including: counting the number of occurrences of multiple optional attribute values ​​through the existing actual attribute data; and determining the proportion of the number of occurrences of the optional attribute values ​​in the number of occurrences of all attribute values ​​contained in the attribute to which they belong, to obtain the attribute value proportion.

[0012] According to some embodiments of the present disclosure, the specified condition includes multiple sub-conditions, and a combination of attribute values ​​that satisfies all sub-conditions included in the same specified condition is identified as the available combination.

[0013] According to some embodiments of the present disclosure, the method further includes: determining the historical identification status of at least some of the multiple sub-conditions contained in the specified condition, and the historical identification status is used to represent the ratio of the number of times the sub-condition is met to the number of times the sub-condition participates in the identification.

[0014] According to some embodiments of the present disclosure, available combinations that meet specified conditions are identified from the M attribute value combinations, including: for each attribute value combination in the M attribute value combinations, in ascending order of the ratios represented by the historical identification situations, the combinations are identified in sequence through at least some of the sub-conditions to obtain available combinations that meet the specified conditions, and the attribute value combinations to be identified of the sub-conditions identified later are the identification results of the sub-conditions identified earlier.

[0015] According to some embodiments of the present disclosure, the specified condition filters the attribute values ​​in the attribute value combination through a value range of at least one attribute value.

[0016] According to some embodiments of the present disclosure, the specified conditions also include specified scenarios, and the specified scenarios are used to represent allowed areas, and multiple specified scenarios correspond to multiple different operating modes.

[0017] According to some embodiments of the present disclosure, the operation method includes one or more of an operation object, an operation amount, and an operation period.

[0018] According to some embodiments of the present disclosure, the operation object is a lender, the operation limit is an upper limit of the user's loan limit, and the operation period is the loan term of the user.

[0019] According to some embodiments of the present disclosure, the target object is a house, and the attributes of the target object represent characteristics of the house.

[0020] According to some embodiments of the present disclosure, after identifying the available combinations and obtaining the operation modes corresponding to the available combinations, the method further includes: storing data of the available combinations and the corresponding operation modes.

[0021] According to some embodiments of the present disclosure, storing data of the available combinations and the corresponding operating modes includes: generating codes of the available combinations, and establishing a mapping relationship between the codes and the corresponding operating modes.

[0022] According to some embodiments of the present disclosure, target available combinations adapted to the target object in the preliminary query result are identified from the available combinations, specifically: target available combinations adapted to the target object in the preliminary query result are identified from the stored available combinations; a target query result representing the preliminary query result and the target operation mode is generated and fed back, specifically: a target query result representing the preliminary query result and the stored corresponding target operation mode is generated and fed back.

[0023] According to some implementations of the present disclosure, the query request includes one or more information of text information, voice information, at least one selected attribute value, and existing user information.

[0024] According to some embodiments of the present disclosure, querying the target object based on the query conditions in the query request includes: identifying the information contained in the query request, removing specific symbols, and processing at least one of word segmentation; and querying the target object using the processing result as at least a part of the query conditions.

[0025] According to some embodiments of the present disclosure, a target query result representing the preliminary query result and the target operation method is generated and feedback is provided, including: generating a target query result representing the preliminary query result and the target operation method; sorting the target objects of the target query result according to user information, wherein the user information is used to indicate repayment ability; and providing feedback on the sorted target query result.

[0026] A second aspect of the present disclosure proposes an electronic device, comprising: a memory storing execution instructions; and a processor executing the execution instructions stored in the memory, so that the processor executes the method described in any one of the above embodiments.

[0027] A third aspect of the present disclosure provides a readable storage medium, wherein the readable storage medium stores a computer program, and when the computer program is executed by a processor, it is used to implement the method described in any of the above embodiments.

[0028] A fourth aspect of the present disclosure provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to implement the method described in any of the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings illustrate exemplary embodiments of the present disclosure and together with the description serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0030] Figure 1 A schematic diagram of an application scenario of a data query method according to some embodiments of the present disclosure is shown.

[0031] Figure 2-Figure 6 A schematic diagram of the overall process of a data query method M10 according to some embodiments of the present disclosure is shown.

[0032] Figure 7 It is a schematic block diagram of the structure of a data query device according to an embodiment of the present disclosure.

[0033] Figure 8 1 is a schematic block diagram of the structure of an electronic device 1000 according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0034] The present disclosure is further described in detail below in conjunction with the accompanying drawings and implementations. It is understood that the specific implementations described herein are only used to explain the relevant content, rather than to limit the present disclosure. It should also be noted that, for ease of description, only the parts related to the present disclosure are shown in the accompanying drawings.

[0035] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure can be combined with each other. The technical solution of the present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0036] Existing data query methods mainly include information extraction, information filtering, information recommendation and other parts. The data query process mainly performs query and retrieval according to given query conditions (search conditions). Therefore, the results of data query only depend on the query conditions given by the user, which limits the comprehensiveness of the information in the search results. It cannot include some information that users have not thought of but may be interested in, or some information that users need but cannot get from the current query platform. Therefore, it cannot meet the user's further needs.

[0037] Take the transaction in the real estate market as an example. When users need to buy a house or rent a second-hand house, they enter the given geographical location range and house characteristics when searching for houses online. The query platform will query the house source based on these query conditions and feedback the house source information to the user. However, the house source fed back is only retrieved through the search conditions given by the user, so it cannot contain some further information. For example, if the user purchases the retrieved house source, whether they can get a loan for the house source and other corresponding loan information; if the user rents the retrieved house source, whether they can withdraw the amount from the corresponding account to offset part of the rent and other corresponding information.

[0038] To this end, the present disclosure proposes a data query method.

[0039] Figure 1 The following is a schematic diagram of an application scenario of a data query method of some embodiments of the present disclosure. In the application scenario, a client 10 and a server 20 may be included. The client 10 can communicate with the server 20 to send and receive data or instructions. In the present disclosure, the client 10 and the server 20 each include at least one processor and at least one memory.

[0040] Exemplarily, the client 10 may be a terminal device held by a user, and the server 20 may include a server. The server stores loan conditions that can be adapted to different types of housing sources, such as lender information, loan amount limit, etc. The user enters query conditions and initiates a query request on the client 10. After receiving the query request, the server 20 performs a housing source query according to the instructions of the query request and obtains a preliminary housing source query result, and then automatically further queries the loan conditions that are adapted to each housing source in the query result, and feeds back the housing source and the loan conditions to the client 10.

[0041] exist Figure 1 The shapes and structures of the client 10 and the server 20 shown in the figure should not be understood as limiting the scope of protection of the present disclosure. In the present disclosure, "terminal devices" can be different types of electronic devices, for example, terminal devices can be mobile phones, tablet computers, laptop computers or desktop computers. In addition, the server included in the server 20 can be a server with a physical form or a cloud server, and the present disclosure does not limit the type of server.

[0042] Figure 2 The overall flow chart of the data query method M10 in some embodiments of the present disclosure is shown. Figure 2 The method shown includes step S100, step S200, step S300, step S400 and step S500. The method can be executed by the server.

[0043] S100, M attribute value combinations are obtained by combining optional attribute values ​​of N attributes. Wherein, N>1, M>1. The N attributes include one or all of user attributes and attributes of target objects. At least some of the N attributes correspond to multiple different optional attribute values. Each attribute value combination includes N optional attribute values ​​belonging to the N attributes. The N optional attribute values ​​included in different attribute value combinations are different.

[0044] The target object is the object to be queried. The target object can be a place, a commodity, an electronic product, or other types of physical or virtual objects. The target object needs to be able to perform some operations. For example, in the scenario of querying housing sources, the target object is the housing source (house), and the corresponding operation can be purchase or rental; in the scenario of querying scenic spots, the target object is the scenic spot, and the corresponding operation can be buying tickets; in the scenario of querying electronic products, the target object is the electronic product, and the corresponding operation can be purchase or after-sales service query.

[0045] Attributes are used to describe the characteristics of the target object. An attribute can usually describe one characteristic or one aspect of a characteristic. For example, the attributes of a house (target object) may include: house planning purpose, house area, house age, house property type, etc. User attributes may include: credit status, income level, occupation type, etc.

[0046] The N types of attributes may include only the attributes of the target object, only the user attributes, or both the attributes of the target object and the user attributes. The target object, as the object to be queried, may have its attribute types acquired in advance, for example, by counting the attribute data of multiple different target objects stored in the database. The attribute types of user attributes may be extracted from the information pre-entered into the system by the user, for example, some user information provided by the user during offline consultation at an offline store.

[0047] Among the N attributes mentioned above, some or all of the attributes each correspond to a variety of optional attribute values. For example, the optional attribute values ​​of the house planning use attribute may include options such as residential use, commercial use, and office use; the optional attribute values ​​of the house area attribute may include different area values, such as each integer area value is an optional attribute value or an area value range is an optional attribute value; the optional attribute values ​​of the house property type attribute may include full property and limited property; the credit status attribute may be each integer score value is an optional attribute value, or different rating ranges are represented by different grades and one grade represents an optional attribute value. The attribute values ​​of other types of attributes can be obtained in the same way.

[0048] An attribute value combination is a combination of N optional attribute values, and the N optional attribute values ​​come from N attributes respectively. For the same attribute, only one optional attribute value of the attribute is included in the same attribute value combination. For example, the N attributes include the above-mentioned three attributes of house planning purpose, house property type and house age, that is, N=3. Among them, there are 3 optional attribute values ​​for house planning purpose; 2 optional attribute values ​​for house property type; 3 optional attribute values ​​for house age, which are 0 to 10 years, 10 to 20 years, and more than 20 years. Accordingly, 3×2×3=18 attribute value combinations can be formed, that is, M=18. It can be understood that the attribute types, attribute numbers, optional attribute value types, and optional attribute value numbers are only examples, and other values ​​that can be used or implemented fall within the protection scope of the present invention.

[0049] S200, identifying available combinations that meet specified conditions from M attribute value combinations, and obtaining operation modes corresponding to the available combinations through the specified conditions. The specified conditions include allowed attribute value combinations and allowed corresponding operation modes, and the operation modes are used to represent operations that can be performed on the target object.

[0050] The specified condition includes a representation of an allowed attribute value combination, and the M attribute value combinations are judged and screened by the representation of the allowed attribute value combination, so as to identify the allowed attribute value combination from the M attribute value combinations, that is, the available combination. The available combination corresponds to an operation mode, and the correspondence between the available combination and the operation mode can be one-to-many, many-to-one, or one-to-one, and the specific correspondence is determined according to the actual situation.

[0051] When the target object is a house, the specified condition can be expressed as a loan condition for purchasing a house. The specified condition is used to limit the property requirements that the house must meet to allow the loan. The specified condition can also include the property requirements that the user must meet to allow the loan. If the specified condition includes both the property requirements for the house and the user, the above N attributes need to include both the user attributes and the attributes of the target object (house), so that in step S200, it can be achieved that the attribute value combination that includes both the user attributes and the house attributes is judged and identified with the specified condition that includes both the user restriction condition and the house restriction condition.

[0052] The operation mode may specifically include one or more of the operation object, the operation amount, and the operation period. When the target object is a house and the query purpose is to purchase, the operation object may be the lender of the loan, the operation amount may be the upper limit of the loan amount, and the operation period may be the loan term; or, when the query purpose is to rent, the operation object may be the fund provider that provides funds only for rent deduction, the operation amount may be the fund amount provided regularly, and the operation period may be the period for providing funds. It is understandable that the operation amount and the operation period may be a numerical range or a specific numerical value.

[0053] It is understandable that step S100 and step S200 belong to the data preprocessing part, which will be performed before the query system on the server side is allowed to start implementing the query of the target object (i.e., officially put into use), so as to pre-establish the mapping relationship between different available attribute value combinations and operation modes before actually querying the target object, so as to facilitate the use in the subsequent actual query of the target object. In addition, after the query system is allowed to query the target object, the available attribute value combinations, the corresponding operation modes, and the mapping relationship between the two can also be updated by regularly executing the data preprocessing part.

[0054] S300, in response to receiving a query request, query the target object based on the query condition in the query request to obtain a preliminary query result, wherein the preliminary query result indicates that there is at least one target object.

[0055] Step S300, step S400 and step S500 belong to the target object query part. Starting from step S300, the query system can implement the query of the target object. The user can establish communication with the server by operating a mobile phone, computer or other electronic device as a client, log in to the query system running on the server and initiate a query request for the house by entering the query conditions. The query conditions are used to represent the characteristics of the target object of the demand, such as the location of the house, the number of rooms, the price of the house, etc. It can be understood that the query conditions contained in the query request may include one or more target object attributes of the above-mentioned N attributes. For example, the query request may include the attribute values ​​of the planned purpose of the house and the area of ​​the house to make requirements for the house based on the planned purpose and area.

[0056] After receiving the query request, the query system first identifies the housing sources that meet the query conditions from the multiple housing sources stored in the database based on the query conditions contained in the request, and obtains the preliminary query results. The preliminary query results contain information on multiple different housing sources, all of which meet the above requirements on the housing location, number of rooms and housing prices.

[0057] S400, identifying, from available combinations, target available combinations to which the target object in the preliminary query result is adapted, through attribute values ​​of the target object in the preliminary query result.

[0058] After obtaining the preliminary query results, the query system on the server side can obtain the attribute values ​​corresponding to the target objects contained in the preliminary query results from the database, that is, obtain the characteristic information corresponding to the houses in the preliminary query results, such as planned use, area, age, etc., and then determine the available combinations corresponding to each of these target objects. Each target object usually determines one available combination, and the determined available combination is called a target available combination.

[0059] S500, generating a target query result including a preliminary query result and a target operation mode and providing feedback, wherein the target operation mode is an operation mode corresponding to the target available combination.

[0060] Each available combination and its corresponding operation mode are obtained through step S200, thereby determining the operation mode corresponding to the target available combination, which can be called the target operation mode. The preliminary query result and the target operation mode are fed back to the mobile phone, computer or other electronic device as the client as the final query result (i.e., the target query result). After the client receives the final query result fed back by the server, the final query result can be displayed to inform the user of the basic information of the target object that meets the query conditions, as well as the operations that can be performed on each target object.

[0061] It can be understood that step S300, step S400 and step S500 belong to the object query part, and after the data preprocessing part is executed, the object query part can be executed.

[0062] According to the data query method proposed in the embodiment of the present invention, in addition to being able to query based on given query conditions and obtain target objects that meet user needs, a new query dimension is introduced in the query process. A predetermined attribute value-operation mode mapping relationship is used to obtain target operation modes that can be commonly used by target objects that meet user needs, so that the query process will not be limited by the query conditions themselves, thereby providing more extensive and comprehensive query results; in addition, in the process of determining the target operation mode, since the above-mentioned mapping relationship is pre-established and stored, it is only necessary to provide the attribute value to quickly determine the target operation mode, which can meet the real-time requirements of the overall data query.

[0063] The following is an example of applying the data query method M10 to the real estate field to perform a property search.

[0064] The N attributes may include three attributes, namely, the property use attribute gh, the property type cq, and the age of the house fl. That is, in this example, the N attributes only include the properties of the house, not the user attributes. The optional attribute values ​​of gh may include residential gh1, apartment gh2, and office gh3; the optional attribute values ​​of cq may include full property cq1 and limited property cq2; the optional attribute values ​​of fl may include fl1 (representing 0 to 10 years), fl2 (representing 10 to 20 years), and fl3 (representing more than 20 years). Z represents the attribute value combination of the house, and the M attribute value combinations of the house that can be obtained through step S100 include Z1 to Z18. Among them, Z1={gh1,cq1,fl1}, Z2={gh1,cq1,fl2}, Z3={gh1,cq1,fl3}, Z4={gh1,cq2,fl1}, Z5={gh1,cq2,fl2}, Z6={gh1,cq2,fl3}, Z7={gh2,cq1,fl1}, Z8={gh2,cq1,fl2}, Z9={gh2,cq1,fl3}, Z10 ={gh2,cq2,fl1}, Z11={gh2,cq2,fl2}, Z12={gh2,cq2,fl3}, Z13={gh3,cq1,fl1}, Z14={gh3,c q1,fl2}, Z15={gh3,cq1,fl3}, Z16={gh3,cq2,fl1}, Z17={gh3,cq2,fl2}, Z18={gh3,cq2,fl3}.

[0065] Before executing step S100, the service end may obtain the loan rule information of the lender by interacting with the lender's business system in advance, such as obtaining and identifying the information published by the lender on the web page, thereby extracting the loan rules. There may be multiple lenders, and the loan rules of different lenders may be different. After the service end obtains at least one loan condition of at least one lender, the loan rule information may be processed, such as textualization, semantic analysis, etc., to obtain the specified conditions. The information of the specified conditions is stored in the database and indexed for subsequent quick access. After starting to provide query services, the service end may regularly obtain information from the business systems of each lender to update the loan rule information.

[0066] Assume that there are three lenders. Lender F1's loan rule is: only provide loans for residential properties, with an upper limit of X1 yuan and a loan term of Y1 years. Lender F2's loan rule is: only provide loans for residential properties and apartments within 10 years, with an upper limit of X2 yuan and a loan term of Y2 years. Lender F3's loan rule is: only provide loans for office properties over 10 years old, with an upper limit of X3 yuan and a loan term of Y3 years.

[0067] The server recognizes through step S200 that: the attribute value combinations that meet the specified condition T1 corresponding to the loan rule of lender F1 include Z1 to Z6; the attribute value combinations that meet the specified condition T2 corresponding to the loan rule of lender F2 include Z1, Z4, Z7 and Z10; the attribute value combinations that meet the specified condition T3 corresponding to the loan rule of lender F3 are Z14, Z15, Z17 and Z18. These recognized combinations are available combinations, totaling 12.

[0068] It is determined that the available housing sources of combination Z1 to Z6 correspond to operation mode 1, that is, you can apply for a loan of X1 amount for a period of Y1 years from lender F1; the available housing sources of combination Z1, Z4, Z7 and Z10 correspond to operation mode 2, that is, you can apply for a loan of X2 amount for a period of Y2 years from lender F2; the available housing sources of combination Z14, Z15, Z17 and Z18 correspond to operation mode 3, that is, you can apply for a loan of X3 amount for a period of Y3 years from lender F3. Among them, the available housing sources of combination Z1 and Z4 can be selected from two operation modes (operation mode 1 and operation mode 2).

[0069] After determining the above 12 available combinations and their operation modes, a mapping relationship between the available combinations and the operation modes is established and stored in a designated location. After that, the server begins to operate.

[0070] User U1 initiates a query request to the server through the client. The query conditions in the query request include: located near the business district sq1, three bedrooms and one living room, new house. The server first searches for houses based on the query conditions and determines N1 houses that meet the query conditions, i.e., the preliminary query results.

[0071] The server then obtains the attribute value of each of the N1 houses from the database, thereby determining the houses that meet any of the 12 available combinations, and obtaining N2 houses, N2 ≤ N1. Among the N2 houses, each house corresponds to an available combination. The target operation mode of each house is determined through the above mapping relationship. The information of the N1 houses and the information of the target operation mode of the N2 houses are used as the target query results and fed back to the client.

[0072] After receiving the target query result, the client displays the information of N1 houses, and for N2 houses, the corresponding operation method information can be displayed at the same time, so that the user can understand the loan method and loan information of the house.

[0073] In summary, in addition to being able to provide users with housing information that meets their needs, the data query method M10 can also provide users with information about the lenders of some or all of the housing sources and the specific loan amount and maximum loan term. Users do not need to go to other places to inquire about housing loan information, nor do they need to manually compare the consultation information with the housing information. Instead, the lender information and the specific loan amount and maximum loan term information corresponding to the housing source can be directly displayed.

[0074] Therefore, the lender's loan rules are integrated into the house search algorithm, making the originally vague and opaque loan rules concrete, precise and online. Then, based on the loan rules, customer information (corresponding to the attribute values ​​of the user attributes) and house information (corresponding to the attribute values ​​of the target object attributes), the mortgage products that the customer can apply for are matched. This can provide users with corresponding information from the house loan dimension to meet the customer's refined needs.

[0075] Figure 3 FIG. 1 is a schematic diagram showing the overall flow of a data query method M10 in some other embodiments of the present disclosure. Figure 3 The method of obtaining M attribute value combinations by combining optional attribute values ​​of N attributes (step S100) may include a first combination step.

[0076] The first combination step is to first combine all possible attribute value combinations, and then screen the attribute value combinations according to the proportion of the attribute values, so as to screen out M attribute value combinations. The first combination step may specifically include the following steps S111, S112 and S113.

[0077] S111, combining at least some of the optional attribute values ​​of N attributes to obtain a plurality of attribute value combinations.

[0078] Taking the example of performing a house search described above as an example, all optional attribute values ​​of all three attributes among the N attributes can be combined to obtain 18 attribute value combinations. It is understandable that only all optional attribute values ​​of some attributes can be combined, or some optional attribute values ​​of all attributes can be combined, or some optional attribute values ​​of some attributes can be combined to obtain less than 18 attribute value combinations.

[0079] S112: Determine the combination ratio of each attribute value combination in the above-mentioned multiple attribute value combinations based on the attribute values ​​included in the existing actual attribute data.

[0080] The existing actual attribute data is the attribute data of real housing sources pre-stored in the database, and the actual attribute data contains the actual number of at least some of the attribute values ​​of at least some of the above N attributes. For example, the database stores data of 10,000 real housing sources, including 5,000 residential gh1 units, 4,000 apartment gh2 units, and 1,000 office gh3 units; 6,000 full-ownership houses cq1 units, and 4,000 limited-ownership houses cq2 units; 5,000 houses fl1 units with an age of less than 10 years, 4,000 houses fl2 units with an age of 10 to 20 years, and 1,000 houses fl3 units with an age of more than 20 years. By statistically analyzing the attribute data of real housing sources, the proportion of each attribute value combination in the 18 attribute value combinations in the real data is determined.

[0081] Exemplarily, in step S112, the method of determining the combined proportion of each attribute value combination in multiple attribute value combinations based on the attribute values ​​contained in the existing actual attribute data may include the following steps: determining the attribute value proportion of the attribute value in the existing actual attribute data in the corresponding attribute; and for each attribute value combination in the multiple attribute value combinations, obtaining the combined proportion of the attribute value combination by the attribute value proportion of each attribute value contained in the attribute value combination.

[0082] For example, if the number of houses is 5,000, the proportion of the property value of the house gh1 is 0.5. Similarly, the proportions of the property values ​​of the apartment gh2 and the office gh3 are 0.4 and 0.1 respectively; the proportions of the property values ​​of the full-ownership house cq1 and the limited-ownership house cq2 are 0.6 and 0.4 respectively; the proportions of the property values ​​of fl1, fl2, and fl3 are 0.5, 0.4, and 0.1 respectively. The sum of the proportions of the property values ​​of the same property is equal to 1.

[0083] The combination ratio of the same attribute value combination can be obtained by multiplying the attribute values ​​contained in the attribute value combination. For example, for the attribute value combination Z1{gh1,cq1,fl1}, its combination ratio z1 is 0.5×0.6×0.5=0.15; other attribute value combinations can be deduced in the same way, and the combination ratios of 18 attribute value combinations are obtained, and the sum of the 18 combination ratios is equal to 1.

[0084] S113, determining M attribute value combinations from the plurality of attribute value combinations based on the combination proportions.

[0085] Attribute value combinations with higher combination proportions among the 18 attribute value combinations are screened out to obtain M attribute value combinations.

[0086] Exemplarily, in step S113, the method of determining M attribute value combinations from multiple attribute value combinations based on the combination proportions may include the following steps: accumulating the combination proportions of the multiple attribute value combinations in order from high to low until the accumulated value is greater than or equal to the proportion threshold, and taking the accumulated attribute value combinations as the M attribute value combinations.

[0087] The ratio threshold is used to filter out attribute value combinations with a low proportion. Assuming the ratio threshold is 0.99, the accumulation process is: 0.15 (corresponding to z1) + 0.12 (corresponding to z2) + 0.12 (corresponding to z7) + 0.1 (corresponding to z4) + 0.096 (corresponding to z8) + 0.08 (corresponding to z5) + 0.08 (corresponding to z10) + 0.064 (corresponding to z11) + 0.03 (corresponding to z3) + 0.03 (corresponding to z13) + 0.024 (corresponding to z9) + 0.024 (corresponding to z14) + 0.02 (corresponding to z6) + 0.02 (corresponding to z16) + 0.016 (corresponding to z12) + 0.15 (corresponding to z17) = 0.99. Then the M attribute value combinations include the 16 attribute value combinations accumulated above. The remaining 0.01 is the sum of 0.006 (corresponding to z15) and 0.004 (corresponding to z18).

[0088] Through steps S111 to S113, combinations covering 99% of the scenarios can be screened out, and subsequent available combination identification and operation mode determination are not performed for combinations with low probability, thereby reducing the amount of calculation and improving calculation efficiency.

[0089] Continue reading Figure 3 The method of obtaining M attribute value combinations by combining the optional attribute values ​​of N attributes (step S100) may also include a second combination step.

[0090] The second combination step is to first screen out the attribute values ​​with a higher proportion, and then combine the attribute values ​​to obtain M attribute value combinations. The first combination step and the second combination step are both used to obtain M attribute value combinations, and the two combination steps can be performed one by one, or they can be performed together and then the execution results of the two combination steps are taken to obtain the M attribute value combinations.

[0091] The second combining step may specifically include the following steps S121 and S122.

[0092] S121, filter the attribute values ​​contained in the existing actual attribute data to obtain combinable attribute values. The method of filtering the attribute values ​​contained in the existing actual attribute data to obtain combinable attribute values ​​may include the following steps: determining the proportion of attribute values ​​in the existing actual attribute data in the attribute values ​​to which they belong; and filtering out attribute values ​​whose proportion of attribute values ​​is higher than a proportion threshold and using them as combinable attribute values.

[0093] S122, combining the combinable attribute values ​​to obtain M attribute value combinations.

[0094] The screening criteria may be to screen by the proportion of attribute values, take the attribute values ​​with higher proportions as combinable attribute values, and then combine all the combinable attribute values ​​to obtain M attribute value combinations. For example, if there are 6 combinable attribute values ​​screened out, namely gh1, gh2, cq1, cq2, fl1 and fl2, then there are 2×2×2=8 attribute value combinations in total.

[0095] In step S121 and step S112, the specific method of determining the proportion of attribute values ​​in the existing actual attribute data in the attribute values ​​in the belonging attribute can be: counting the number of occurrences of multiple optional attribute values ​​through the existing actual attribute data; and determining the proportion of the number of occurrences of the optional attribute values ​​in the number of occurrences of all attribute values ​​contained in the belonging attribute to obtain the attribute value proportion.

[0096] For example, among the 10,000 real houses stored in the database, all houses have their planned uses recorded, and the number of residential gh1s is 5,000, so the proportion of gh1 is 5,000 / 1,000 = 0.5. The same can be said for other attribute values.

[0097] In step S200, the specified condition may include multiple sub-conditions, and the attribute value combination that satisfies all the sub-conditions included in the same specified condition may be identified as an available combination. Taking the loan rule of lender F2 as an example, the loan rule includes two sub-conditions, sub-condition 1 is that the age of the house is within 10 years, and sub-condition 2 is that the planned use of the house is a residence or an apartment. Only when these two sub-conditions are met at the same time can it be regarded as satisfying the loan rule.

[0098] When using the specified conditions to identify available combinations, the attribute value combination and the sub-conditions of the specified conditions can be input into the rule engine for identification. If a sub-condition returns false, it means that the attribute value combination does not match the sub-condition. At this time, the identification of the attribute value combination is interrupted and the attribute value combination is screened out. If all sub-conditions return true, it means that the attribute value combination matches the specified condition and can be used as an available combination.

[0099] Figure 4 FIG. 1 is a schematic diagram showing the overall flow of a data query method M10 in some other embodiments of the present disclosure. Figure 4 The data query method M10 may further include step S010, and the execution order between step S010 and step S100 may be arbitrary.

[0100] S010, determining the historical recognition status of at least some of the multiple sub-conditions included in the specified condition, wherein the historical recognition status is used to represent the ratio of the number of times the sub-condition is satisfied to the number of times the sub-condition participates in the recognition.

[0101] After the server has completed at least one data query, the server can count and record the historical identification of each sub-condition of different specified conditions in the completed data query. The historical identification is equivalent to the pass rate of the attribute value combination facing the sub-condition. For example, the loan rule of lender F3 contains two sub-conditions, sub-condition 1 is that the age of the house is more than 10 years, and sub-condition 2 is that the planned use of the house is office space. In the historical data query, there are more attribute value combinations that meet sub-condition 1, so that the ratio of the number of times sub-condition 1 is met to the total number of times sub-condition 1 participates in the identification is higher; there are fewer attribute value combinations that meet sub-condition 2, so that the ratio of the number of times sub-condition 2 is met to the total number of times sub-condition 2 participates in the identification is lower. A low ratio means a low pass rate, so the pass rate of sub-condition 2 is lower than the pass rate of sub-condition 1. It can be understood that this ratio is also equivalent to: the ratio between the number of attribute value combinations that meet the sub-conditions and the number of attribute value combinations that participate in the identification of whether the sub-conditions are met.

[0102] Accordingly, the method of identifying the available combination that meets the specified condition from the M attribute value combinations (step S200) may specifically be: for each attribute value combination in the M attribute value combinations, identify it in order from small to large according to the ratio represented by the historical identification situation, through at least some of the sub-conditions, to obtain the available combination that meets the specified condition. The attribute value combination to be identified of the sub-condition identified later is the identification result of the sub-condition identified earlier.

[0103] When identifying the attribute value combination, for multiple sub-conditions contained in the same specified condition, the attribute value combination can be identified and screened by using each sub-condition in turn. At this time, the sub-condition with the smallest ratio can be used for screening first, and then the attribute value combination identified by the screening can be screened by using the sub-condition with the second smallest ratio, and the attribute value combination that is screened out will not participate in the subsequent screening of other sub-conditions, and so on until the screening of all sub-conditions is completed or until the number of attribute value combinations screened out is 0.

[0104] For an attribute value combination that will only be eliminated by sub-condition 2, if sub-condition 1 is used for identification first, the attribute value combination will be identified twice, and if sub-condition 2 is used for identification first, the attribute value combination will be eliminated in the first screening. Therefore, using sub-conditions with low pass rates for identification first can reduce the amount of calculation and increase the recognition speed, without consuming performance to perform identification of sub-conditions that are easy to pass.

[0105] After obtaining the available combinations, the operation mode corresponding to the available combinations can be determined by specifying a mapping relationship between the conditions and the operation modes.

[0106] The specified condition can filter the attribute values ​​in the attribute value combination through the value range of at least one attribute value. For example, sub-condition 1 of lender F3 is that the house age is more than 10 years, which means that the house age attribute value in the attribute value combination is required through the value range.

[0107] The specified conditions may also include specified scenarios, which may include representations of allowed regions, and multiple specified scenarios may correspond to multiple different operating modes. For example, in the query request initiated by the client, the region where the client is located or selected may be included, and the region may be a city, a business district, or other types of regional ranges. The server may store the specified condition data of the lenders in each of the multiple regional ranges, and the specified conditions of different lenders in different regional ranges may be different. The specified conditions of lenders of the same brand in two different regional ranges may be the same, or they may be different due to different regions. After the server receives the query request, when executing step S200, it will first determine the regional range indicated in the query request, then obtain the lenders in the regional range, and then determine the specified conditions of these lenders and execute step S200 as the specified conditions to be identified.

[0108] After completing step S100, the M attribute value combinations can be stored in the database, and before starting to execute step S200, the data of the M attribute value combinations can be read into the memory in a specified data format. On the server side, all data reading can be based on the memory, rather than directly reading the database and directly operating the data in the database. Therefore, before execution, the data can be pre-read into the memory in the required format and the data can be operated in the memory.

[0109] Figure 5 FIG. 1 is a schematic diagram showing the overall flow of a data query method M10 in some other embodiments of the present disclosure. Figure 5 After identifying the available combinations and obtaining the operation modes corresponding to the available combinations (step S200 ), the data query method M10 may further perform step S210 .

[0110] S210, storing data of available combinations and corresponding operation modes. The storage location may be a specified location in a database. Specifically, the specific method of storing data of available combinations and corresponding operation modes in step S210 may be: generating a code of the available combinations, and establishing a mapping relationship between the code and the corresponding operation mode.

[0111] Step S210 is used to store the results of the data preprocessing part. The code is a unique identifier of the available combination, and the available combination can be uniquely determined by the code. A mapping relationship can be established between the code and the storage address of the operation mode, and the data of the available combination and the corresponding operation mode data can be obtained through the code and the mapping relationship.

[0112] Specifically, we can create an index, encode the attribute values ​​of the available combinations separately, and concatenate the encoding results of the attribute values ​​to obtain the encoding of the available combinations, which is the unique key (unique identifier). In addition, the operation method is stored as an additional tag associated with the data of the available combinations, so that the complete mortgage product is stored as a whole. In this way, the operation method can be quickly obtained according to the customer and house qualifications.

[0113] Accordingly, in step S400, the method of identifying the target available combination adapted to the target object in the preliminary query result from the available combinations may specifically be: identifying the target available combination adapted to the target object in the preliminary query result from the stored available combinations. In addition, in step S500, the method of generating a target query result representing the preliminary query result and the target operation mode and providing feedback may specifically be: generating a target query result representing the preliminary query result and the stored corresponding target operation mode and providing feedback.

[0114] In step S400, the data of the available combinations can be read from the storage location through the coding, and then the target available combinations can be identified. In step S500, the data of the target operation mode can be read from the storage location through the coding and mapping relationship of the target available combinations, The query request may include text information, voice information, at least one selected attribute value, or one or more of the existing user information. For example, text information or voice information input by the user on the client, selected attribute value information obtained by the user by selecting a preset attribute value option on the client, and personal information about the user pre-stored on the server. The voice information needs to be converted into text information first.

[0115] Accordingly, in step S300, the method of querying the target object based on the query conditions in the query request may specifically include the following steps: identifying the information contained in the query request, removing specific symbols, and processing at least one of word segmentation; and querying the target object using the processing result as at least part of the query conditions.

[0116] The information input by the user is processed through text recognition, such as converting uppercase and lowercase, and removing specific symbols (specific symbols represent meaningless symbols). Then, word segmentation is performed to divide the text into multiple words. After that, the words can be corrected to obtain the correct words. These words are part or all of the query conditions. The query conditions can also include basic information of the user as another part of the content.

[0117] Figure 6 FIG. 1 is a schematic diagram showing the overall flow of a data query method M10 in some other embodiments of the present disclosure. Figure 6 , generating a target query result indicating a preliminary query result and a target operation mode and providing feedback (step S500) may specifically include the following steps S510, S520 and S530.

[0118] S510, generating a target query result including a preliminary query result and a target operation mode.

[0119] S520, sorting the target objects of the target query results according to the user information, wherein the user information is used to indicate the repayment ability.

[0120] S530: Feedback the sorted target query results.

[0121] The user information may be information pre-stored on the server, for example, the user information may be obtained from the information previously entered by the user in the query system, or the user information may be obtained from the information provided by the user during offline consultation, recorded and uploaded to the server. The user information may include data such as salary, loan principal, monthly repayment amount, etc.

[0122] Sorting can be performed by the minimum operation amount, the maximum operation period or other indicators under the target operation mode. For example, for each house included in the target query result, the user's minimum monthly repayment amount is calculated based on the maximum loanable period of the house, user information, and house price in the house information, and then the monthly minimum repayment amount is sorted from small to large to obtain the order of each house after sorting, and then the target query result under the order is fed back.

[0123] In addition, the server can also calculate the total purchase price under the loan and other information during the above calculation process and add it to the target query result for feedback. Helping users evaluate the total purchase price can make the house query algorithm more accurate, maximize the value of the down payment, and search for better and affordable houses.

[0124] Based on any of the above implementations, the present disclosure also provides a data query device. Figure 7FIG. 1 is a schematic block diagram of a data query device according to an embodiment of the present disclosure. Figure 7 As shown, the data query device includes: an attribute value combination module 100, a combination identification module 200, a preliminary query module 300, a target combination identification module 400 and a query result generation module 500.

[0125] The attribute value combination module 100 is used to obtain M attribute value combinations by combining optional attribute values ​​of N attributes. Wherein, N>1, M>1. The N attributes include one or all of the attributes of the user and the attributes of the target object. At least some of the N attributes correspond to a plurality of different optional attribute values. Each attribute value combination includes N optional attribute values ​​belonging to the N attributes. The N optional attribute values ​​included in different attribute value combinations are different.

[0126] The available combination identification module 200 is used to identify available combinations that meet specified conditions from M attribute value combinations, and obtain the operation mode corresponding to the available combinations through the specified conditions. The specified conditions include the allowed attribute value combinations and the allowed corresponding operation modes, and the operation modes are used to represent the operations that can be performed on the target object.

[0127] The preliminary query module 300 is used to query the target object based on the query condition in the query request in response to receiving the query request, and obtain a preliminary query result, wherein the preliminary query result indicates that there is at least one target object.

[0128] The target combination identification module 400 is used to identify, from the available combinations, target available combinations adapted to the target object in the preliminary query result through the attribute values ​​of the target object in the preliminary query result.

[0129] The query result generation module 500 is used to generate a target query result including a preliminary query result and a target operation mode and provide feedback, wherein the target operation mode is an operation mode corresponding to the target available combination.

[0130] The data query device can be in the form of computer software, and each module of the data query device can be implemented by a computer software module. The implementation process of the functions and effects of each module in the device is specifically described in the implementation process of the corresponding steps in the method, which will not be repeated here.

[0131] The execution subject of the data query method in the specific implementation manner of the present disclosure may be a mobile phone, a computer or other electronic device.

[0132] Therefore, based on any one of the above embodiments, the present disclosure further provides an electronic device, which can execute the data query method of any one of the embodiments described above in the present disclosure. Figure 81 is a block diagram of the structure of an electronic device 1000 of an embodiment of the present disclosure. The hardware structure of the electronic device 1000 can be implemented using a bus architecture. The bus architecture can include any number of interconnecting buses and bridges, depending on the specific application and overall design constraints of the hardware. The bus 1100 connects various circuits including one or more processors 1200, memory 1300 and / or hardware modules together. The bus 1100 can also connect various other circuits 1400 such as peripherals, voltage regulators, power management circuits, external antennas, etc.

[0133] The bus 1100 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the figure only uses one connecting line, but does not mean that there is only one bus or one type of bus.

[0134] The processor 1200 may be a central processing unit (CPU). The processor 1200 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0135] The memory 1300 can be used as a non-transitory computer-readable storage medium, and can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions of the computer program in the embodiment of the present disclosure. The processor 1200 implements the data query method by running the non-transitory software programs, instructions and modules stored in the memory 1300.

[0136] The memory 1300 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created by the processor 1200, such as structural data, model data, sample data, stiffness evaluation results, etc. of the bridge. In addition, the memory 1300 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1300 may optionally include a memory remotely arranged relative to the processor 1200, and these remote memories may be connected to the processor 1200 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0137] The present disclosure also provides a readable storage medium, in which a computer program is stored, and the computer program is used to implement the above method when executed by a processor. "Readable storage medium" can be any device that can contain, store, communicate, propagate or transmit a program for use in an instruction execution system, device or equipment or in combination with these instruction execution systems, devices or equipment. More specific examples of readable storage media include the following: an electrical connection portion (electronic device) with one or more wirings, a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM), etc.

[0138] The present disclosure also provides a computer program product, and the method of the present disclosure can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed, the process or function of the present disclosure is executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, a core network device, an OAM or other programmable device.

[0139] The computer program or instructions may be stored in a readable storage medium or transmitted from one readable storage medium to another readable storage medium, for example, the computer program or instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless means. The readable storage medium may be any available medium that can be accessed or a data storage device such as a server, data center, etc. that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it may also be an optical medium, such as a digital video disk; it may also be a semiconductor medium, such as a solid state drive. The computer readable storage medium may be a volatile or non-volatile storage medium, or may include both volatile and non-volatile types of storage media.

[0140] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0141] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0142] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0144] In the description of this specification, the description with reference to the terms "one embodiment / method", "some embodiments / methods", "example", "specific example", or "some examples" etc. means that the specific features, structures, or characteristics described in conjunction with the embodiment / method or example are included in at least one embodiment / method or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment / method or example. Moreover, the specific features, structures, or characteristics described may be combined in any one or more embodiments / methods or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments / methods or examples described in this specification and features of different embodiments / methods or examples, unless they are contradictory.

[0145] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0146] Those skilled in the art should understand that the above embodiments are only for the purpose of clearly illustrating the present disclosure, and are not intended to limit the scope of the present disclosure. For those skilled in the art, other changes or modifications may be made based on the above disclosure, and these changes or modifications are still within the scope of the present disclosure.

[0147] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0148] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0149] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0150] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0151] At the same time, it is understandable that the data involved in the technical solution of the present disclosure (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

Claims

1. A data query method, characterized in that: include: M attribute value combinations are obtained by combining optional attribute values ​​of N attributes, where N>1 and M>1, the N attributes include one or all of user attributes and attributes of target objects, at least some of the N attributes correspond to multiple different optional attribute values, each of the attribute value combinations includes N optional attribute values ​​belonging to the N attributes, and different attribute value combinations include different N optional attribute values; Identifying an available combination that satisfies a specified condition from the M attribute value combinations, and obtaining an operation mode corresponding to the available combination according to the specified condition, wherein the specified condition includes an allowed attribute value combination and an allowed corresponding operation mode, and the operation mode is used to represent an operation that can be performed on the target object; In response to receiving the query request, querying the target object based on the query condition in the query request to obtain a preliminary query result, wherein the preliminary query result indicates that there is at least one target object; Identifying, from the available combinations, a target available combination to which the target object in the preliminary query result is adapted, by using the attribute value of the target object in the preliminary query result; as well as A target query result indicating the preliminary query result and a target operation mode is generated and fed back, wherein the target operation mode is an operation mode corresponding to the target available combination.

2. The data query method according to claim 1, characterized in that: The method comprises: combining optional attribute values ​​of N attributes to obtain M attribute value combinations, including: a first combination step; The first combining step comprises: Combining at least some of the optional attribute values ​​of the N attributes to obtain multiple attribute value combinations; Determining a combination ratio of each attribute value combination in the plurality of attribute value combinations based on the attribute values ​​included in the existing actual attribute data; and M attribute value combinations are determined from the multiple attribute value combinations based on the combination proportions.

3. The data query method according to claim 2, characterized in that: Determining the combination ratio of each attribute value combination in the plurality of attribute value combinations based on the attribute values ​​included in the existing actual attribute data includes: Determine the proportion of the attribute value in the existing actual attribute data to the attribute value in the attribute to which it belongs; and For each attribute value combination in the multiple attribute value combinations, the combination ratio of the attribute value combination is obtained through the attribute value ratio of each attribute value included in the attribute value combination.

4. The data query method according to claim 2 or 3, characterized in that: Determining M attribute value combinations from the multiple attribute value combinations based on the combination proportions includes: The combination proportions of the multiple attribute value combinations are accumulated in order from high to low until the accumulated value is greater than or equal to the proportion threshold, and the accumulated attribute value combinations are taken as M attribute value combinations.

5. The data query method according to claim 1 or 2, characterized in that: The method comprises: combining optional attribute values ​​of N attributes to obtain M attribute value combinations, including: a second combination step; The second combining step comprises: Filtering the attribute values ​​contained in the existing actual attribute data to obtain combinable attribute values; and The combinable attribute values ​​are combined to obtain M attribute value combinations.

6. The data query method according to claim 5, characterized in that: The attribute values ​​contained in the existing actual attribute data are screened to obtain combinable attribute values, including: Determine the proportion of the attribute value in the existing actual attribute data to the attribute value in the attribute to which it belongs; and Attribute values ​​whose attribute value ratio is higher than the ratio threshold are screened out and used as combinable attribute values.

7. The data query method according to any one of claims 1 to 6, characterized in that: Determine the proportion of the attribute value in the existing actual attribute data to the attribute value in the attribute to which it belongs, including: Counting the number of occurrences of multiple optional attribute values ​​using existing actual attribute data; and Determine the proportion of the number of occurrences of the optional attribute value in the number of occurrences of all attribute values ​​included in the attribute to which it belongs, and obtain the attribute value proportion; Optionally, the specified condition includes multiple sub-conditions, and a combination of attribute values ​​that satisfies all sub-conditions included in the same specified condition is identified as the available combination; Optionally, the method further comprises: Determine historical recognition status of at least some of the multiple sub-conditions included in the specified condition, where the historical recognition status is used to represent the ratio of the number of times the sub-condition is satisfied to the number of times the sub-condition participates in recognition; Optionally, identifying an available combination that meets a specified condition from the M attribute value combinations includes: For each of the M attribute value combinations, the at least some of the sub-conditions are sequentially identified in ascending order of the ratios represented by the historical identification situations to obtain an available combination that meets the specified condition, and the attribute value combination to be identified of the sub-condition identified later is the identification result of the sub-condition identified earlier; Optionally, the specified condition filters the attribute values ​​in the attribute value combination by a value range of at least one attribute value; Optionally, the specified condition further includes a specified scenario, where the specified scenario is used to indicate a permitted region, and a plurality of the specified scenarios correspond to a plurality of different operation modes; Optionally, the operation mode includes one or more of an operation object, an operation amount, and an operation period; The operation object is the lender, the operation limit is the upper limit of the user's loan limit, and the operation period is the loan term of the user; Optionally, the target object is a house, and the attribute of the target object represents a feature of the house; Optionally, after identifying the available combinations and obtaining the operation modes corresponding to the available combinations, the method further includes: storing data of the available combinations and the corresponding operation modes; Optionally, storing the available combinations and the data of the corresponding operation modes includes: Generate a code of the available combination, and establish a mapping relationship between the code and the corresponding operation mode; Optionally, identifying the target available combination adapted to the target object in the preliminary query result from the available combinations is specifically: identifying the target available combination adapted to the target object in the preliminary query result from the stored available combinations; Generating a target query result indicating the preliminary query result and the target operation mode and providing feedback, specifically: generating a target query result indicating the preliminary query result and the stored corresponding target operation mode and providing feedback; Optionally, the query request includes one or more information of text information, voice information, at least one selected attribute value, and existing user information; Optionally, querying the target object based on the query condition in the query request includes: Processing the information contained in the query request by at least one of identification, specific symbol removal, and word segmentation; and Performing a query on the target object using the processing result as at least part of the query condition; Optionally, generating a target query result indicating the preliminary query result and the target operation mode and providing feedback includes: Generate a target query result including the preliminary query result and the target operation mode; Sort the target objects of the target query results according to user information, wherein the user information is used to indicate repayment ability; and Feedback the sorted target query results.

8. An electronic device, characterized in that: include: A memory storing execution instructions; as well as A processor, wherein the processor executes the execution instructions stored in the memory, so that the processor executes the method according to any one of claims 1 to 7.

9. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, it is used to implement the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program is used to implement the method according to any one of claims 1 to 7.

Images