Database updating method, device, computer-readable storage medium, and electronic device

Abstract

Embodiments of the present disclosure disclose a database update method, apparatus, computer-readable storage medium, and electronic device. The method includes: determining, for a housing database, housing data to be collected; determining objects associated with the housing data to be collected; distributing data collection tasks associated with the housing data to be collected to the objects, and obtaining feedback information returned by the objects in response to the data collection tasks; wherein any feedback information includes reference housing data; obtaining the housing data to be collected based on the feedback information; and updating the housing database using the obtained housing data to be collected. The embodiments of the present disclosure can not only improve the efficiency of housing database updates, but also enhance the data quality of the housing database, ensuring the effectiveness of housing database updates.

Description

Technical Field

[0001] The present disclosure relates to the field of database technology, and in particular to a database updating method, device, computer-readable storage medium, and electronic device. Background Art

[0002] Real estate databases can store data across multiple dimensions, but they can be subject to data quality issues such as collection errors, data expiration, and insufficient granularity, all of which can impact their reliability. To improve data quality, manual verification and updating of problematic data is often required, which is both inefficient and ineffective. Summary of the Invention

[0003] In order to solve the above technical problems, the present disclosure is proposed. Embodiments of the present disclosure provide a database updating method, device, computer-readable storage medium, and electronic device.

[0004] According to one aspect of an embodiment of the present disclosure, a database updating method is provided, comprising:

[0005] For the property database, determine the property data to be collected;

[0006] Determining an object associated with the property data to be collected;

[0007] Distributing a data collection task associated with the to-be-collected property data to the object, and obtaining feedback information returned by the object in response to the data collection task;

[0008] According to the feedback information, the housing data to be collected is obtained; wherein any of the feedback information includes reference housing data;

[0009] The obtained housing data to be collected is used to update the housing database.

[0010] In an alternative example,

[0011] The distributing the data collection task associated with the to-be-collected property data to the object, and obtaining feedback information returned by the object in response to the data collection task, includes:

[0012] Initiating a simulated interactive session with the object;

[0013] In the simulated interactive session, a simulated user question associated with the housing data to be collected is sent, and feedback information of the subject in response to the simulated user question is obtained;

[0014] or,

[0015] The distributing the data collection task associated with the to-be-collected property data to the object, and obtaining feedback information returned by the object in response to the data collection task, includes:

[0016] The data collection tasks associated with the to-be-collected property data are distributed to the plurality of objects, and feedback information returned by at least some of the plurality of objects is obtained.

[0017] In an optional example, obtaining the to-be-collected property data according to the feedback information includes:

[0018] Determine a collection requirement type for the property data to be collected; wherein the collection requirement type includes at least one of the following: a missing supplement type, a personalized supplement type, and an update and replacement type;

[0019] Filter target reference property data from the obtained feedback information according to the filtering method corresponding to the collection requirement type;

[0020] The target reference property data is used as the obtained property data to be collected.

[0021] In an alternative example,

[0022] In the case where the collection requirement type is a missing supplement type or a personalized supplement type, the filtering of target reference housing data from the obtained feedback information according to the filtering method corresponding to the collection requirement type includes:

[0023] Filtering target reference property data from the obtained feedback information according to the weight of each of the objects that returned the feedback information;

[0024] After using the target reference property data as the obtained property data to be collected, the method further includes:

[0025] The weight of the corresponding object is updated according to the matching degree between each reference property data and the property data to be collected.

[0026] In an optional example, when the collection requirement type is a missing supplement type or a personalized supplement type, filtering target reference property data from the obtained feedback information according to a filtering method corresponding to the collection requirement type includes:

[0027] Determine a first data set; wherein the first data set includes the reference property data that appears in at least one of the feedback information, and any two reference property data in the first data set are different from each other;

[0028] Determine the number of occurrences of each reference property data in the first data set in all the feedback information;

[0029] From the first data set, the reference property data with the largest number of occurrences is selected as the target reference property data.

[0030] In an optional example, when the collection requirement type is an update and replacement type, filtering target reference property data from the obtained feedback information according to a filtering method corresponding to the collection requirement type includes:

[0031] Determine a second data set; wherein the second data set includes the reference property data that appears in at least one of the feedback information, and any two reference property data in the second data set are different from each other;

[0032] Determining the confidence level of each reference property data in the second data set; wherein the confidence level of any reference property data is: the ratio of the number of feedback information including the reference property data to the total number of feedback information;

[0033] Selecting the reference property data with the highest confidence from the second data set;

[0034] When the confidence level of the screened reference property data is greater than a preset confidence level, the screened reference property data is used as the target reference property data.

[0035] In an optional example, distributing the data collection task associated with the to-be-collected property data to the object includes:

[0036] Filtering at least some of the objects from the plurality of objects associated with the to-be-collected property data according to a preset mapping relationship; wherein the preset mapping relationship is a mapping relationship between objects and weights, or the preset mapping relationship is a mapping relationship between object attributes and weights;

[0037] Distributing data collection tasks associated with the to-be-collected property data to at least some of the screened objects;

[0038] The method further comprises:

[0039] The preset mapping relationship is updated according to the feedback performance of at least part of the screened objects.

[0040] In an optional example, determining the object associated with the to-be-collected property data includes at least one of the following:

[0041] Determine an object having a preset relationship with the property corresponding to the property data to be collected;

[0042] Determine the objects that have performed preset behaviors on the property corresponding to the property data to be collected within a target time period.

[0043] In an optional example, determining the property data to be collected from the property database includes at least one of the following:

[0044] Treat the missing data in the property database as property data to be collected;

[0045] The property data stored in the property database, whose corresponding recommendations have not been adopted more than a preset number of times, are used as the property data to be collected;

[0046] For the property database, property data that meets the preset supplementary conditions will be used as property data to be collected.

[0047] According to another aspect of an embodiment of the present disclosure, there is provided a database updating device, comprising:

[0048] The first determination module is used to determine the housing data to be collected from the housing database;

[0049] A second determining module is used to determine the object associated with the housing data to be collected;

[0050] a processing module, configured to distribute a data collection task associated with the to-be-collected property data to the subject, and obtain feedback information returned by the subject in response to the data collection task; wherein any feedback information includes reference property data;

[0051] An acquisition module, configured to obtain the housing data to be collected based on the feedback information;

[0052] The first updating module is used to update the housing database using the obtained housing data to be collected.

[0053] In an alternative example,

[0054] The processing module includes:

[0055] An initiating submodule, configured to initiate a simulated interactive session to the object;

[0056] a processing submodule, configured to send, in the simulated interactive session, a simulated user question associated with the to-be-collected property data, and obtain feedback information from the subject regarding the simulated user question;

[0057] or,

[0058] The processing module includes:

[0059] The data collection tasks associated with the to-be-collected property data are distributed to the plurality of objects, and feedback information returned by at least some of the plurality of objects is obtained.

[0060] In an optional example, the obtaining module includes:

[0061] A first determining submodule is configured to determine a collection requirement type of the housing data to be collected; wherein the collection requirement type includes at least one of the following: a missing supplement type, a personalized supplement type, and an update and replacement type;

[0062] A first screening submodule is configured to screen target reference property data from the acquired feedback information according to a screening method corresponding to the acquisition requirement type;

[0063] The second determining submodule is configured to use the target reference property data as the obtained property data to be collected.

[0064] In an alternative example,

[0065] When the collection requirement type is a missing supplement type or a personalized supplement type, the first screening submodule is specifically configured to:

[0066] Filtering target reference property data from the obtained feedback information according to the weight of each of the objects that returned the feedback information;

[0067] The device further comprises:

[0068] The second updating module is configured to update the weight of the corresponding object according to the matching degree between each reference property data and the property data to be collected after the target reference property data is used as the obtained property data to be collected.

[0069] In an optional example, when the collection requirement type is a missing supplement type or a personalized supplement type, the first screening submodule includes:

[0070] A first determining unit is configured to determine a first data set, wherein the first data set includes the reference property data that appears in at least one piece of feedback information, and any two reference property data in the first data set are different from each other;

[0071] A second determining unit is configured to determine the number of occurrences of each reference property data in the first data set in all the feedback information;

[0072] The first screening unit is configured to screen the reference property data with the largest number of occurrences from the first data set as target reference property data.

[0073] In an optional example, when the acquisition requirement type is an update and replacement type, the first screening submodule includes:

[0074] a third determining unit, configured to determine a second data set; wherein the second data set includes the reference property data appearing in at least one piece of feedback information, and any two reference property data in the second data set are different from each other;

[0075] a fourth determining unit, configured to determine a confidence level of each of the reference property data in the second data set; wherein the confidence level of any of the reference property data is: a ratio of the number of the feedback information including the reference property data to the total number of the feedback information;

[0076] A second screening unit is configured to screen the reference property data corresponding to the maximum confidence level from the second data set;

[0077] The fifth determining unit is configured to use the screened reference property data as target reference property data when the confidence level of the screened reference property data is greater than a preset confidence level.

[0078] In an optional example, the processing module includes:

[0079] A second screening submodule is configured to screen at least some of the objects from the plurality of objects associated with the to-be-collected property data according to a preset mapping relationship; wherein the preset mapping relationship is a mapping relationship between objects and weights, or a mapping relationship between object attributes and weights;

[0080] a distribution module, configured to distribute data collection tasks associated with the to-be-collected housing data to at least some of the screened objects;

[0081] The device further comprises:

[0082] The third updating module is configured to update the preset mapping relationship according to the feedback performance of at least part of the screened objects.

[0083] In an optional example, the second determination module is specifically used for at least one of the following: determining an object having a preset relationship with the property corresponding to the property data to be collected; determining an object that has performed a preset behavior on the property corresponding to the property data to be collected within a target time period.

[0084] In an optional example, the first determination module is specifically used for at least one of the following: using missing data in the housing database as housing data to be collected; using housing data stored in the housing database whose corresponding recommendations have not been adopted more than a preset number of times as housing data to be collected; and for the housing database, using housing data that meets preset supplementary conditions as housing data to be collected.

[0085] According to another aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the above-mentioned database updating method.

[0086] According to another aspect of the embodiments of the present disclosure, there is provided an electronic device, including:

[0087] processor;

[0088] a memory for storing instructions executable by the processor;

[0089] The processor is configured to read the executable instructions from the memory and execute the instructions to implement the above database updating method.

[0090] In an embodiment of the present disclosure, after determining the property data to be collected for a property database, the objects associated with the property data to be collected can be determined to reliably locate the objects that may provide the property data to be collected. Afterwards, a data collection task can be distributed to the determined objects so that accurate and reliable property data to be collected can be obtained based on feedback information from the objects, including reference property data, and the property database can be updated using the obtained property data to be collected. It can be seen that in an embodiment of the present disclosure, the property database does not need to be manually checked and updated, and the accuracy and reliability of the property data used when updating the property database can be guaranteed by distributing data collection tasks to specific users. Therefore, compared with the prior art, the embodiment of the present disclosure can not only improve the updating efficiency of the property database, but also improve the data quality of the property database, thereby ensuring the updating effect of the property database.

[0091] The technical solution of the present disclosure is further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The above and other purposes, features, and advantages of the present disclosure will become more apparent through a more detailed description of the embodiments of the present disclosure in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and are not intended to limit the present disclosure. In the drawings, the same reference numerals generally represent the same components or steps.

[0093] Figure 1 It is a flowchart of a database updating method provided by an exemplary embodiment of the present disclosure.

[0094] Figure 2 It is a flowchart of the interaction between a user and a broker implemented by a user simulator in another exemplary embodiment of the present disclosure.

[0095] Figure 3 This is a schematic diagram of the updating principle of the housing database in another exemplary embodiment of the present disclosure.

[0096] Figure 4 It is a structural diagram of a database updating device provided by an exemplary embodiment of the present disclosure.

[0097] Figure 5 It is a structural diagram of a database updating device provided by another exemplary embodiment of the present disclosure.

[0098] Figure 6 is a structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0099] Below, the exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described herein.

[0100] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[0101] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, and do not represent any specific technical meanings, nor do they indicate a necessary logical order between them.

[0102] It should also be understood that in the embodiments of the present disclosure, “a plurality of” may refer to two or more than two, and “at least one” may refer to one, two, or more than two.

[0103] It should also be understood that any component, data or structure mentioned in the embodiments of the present disclosure can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0104] In addition, the term "and / or" in this disclosure is merely a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this disclosure generally indicates that the related objects are in an "or" relationship.

[0105] It should also be understood that the description of the various embodiments in this disclosure focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced with each other. For the sake of brevity, they will not be described one by one.

[0106] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0107] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0108] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0109] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0110] The embodiments of the present disclosure can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate in conjunction with numerous other general-purpose or special-purpose computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above systems, among others.

[0111] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system-executable instructions (such as program modules) executed by the computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, and the like that perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are performed by remote processing devices linked via a communications network. In distributed cloud computing environments, program modules can be located on local or remote computer system storage media, including storage devices.

[0112] Exemplary Methods

[0113] Figure 1It is a flowchart of a database updating method provided by an exemplary embodiment of the present disclosure. Figure 1 The method shown may include step 101, step 102, step 103, step 104 and step 105, and each step is described below.

[0114] Step 101: Determine the housing data to be collected from the housing database.

[0115] Here, the housing database may be a real estate dictionary.

[0116] It should be noted that the specific implementation of step 101 is varied, which is described below with examples.

[0117] In a specific implementation, step 101 includes:

[0118] The missing data in the property database is used as the property data to be collected.

[0119] Here, we can collect statistics on the missing data that is frequently triggered in the property database. The format of the statistical results can be:

[0120] <property id, {missing field 1: pv1, missing field 2: pv2, ...}>

[0121] <property id, {missing field 1: pv1, missing field 2: pv2, ...}>

[0122] <cell id, {missing field 1: pv1, missing field 2: pv2, ...}>

[0123] Optionally, all missing data that are frequently triggered in the property database can be used as data to be collected; or, when the amount of missing data that are frequently triggered in the property database is very large, these missing data can be sorted, and some missing data can be selected as property data to be collected based on the sorting order.

[0124] In this implementation form, from the perspective of data missing, it is very convenient to determine the housing data to be collected.

[0125] In another specific implementation, step 101 includes:

[0126] The housing data stored in the housing database, whose corresponding recommendations have not been adopted more than a preset number of times, are used as the housing data to be collected.

[0127] Here, statistics can also be collected for frequently triggered listings stored in the listing database. Generally speaking, when a user communicates with a broker (specifically, a real estate agent) on a real estate company's service platform, the company's intelligent assistant can extract relevant listings from the listing database based on the user's needs and recommend them to the broker. The broker can then choose to adopt the listing recommended by the intelligent assistant and provide it to the user, or choose not to adopt the listing recommended by the intelligent assistant and provide other listings to the user. If the intelligent assistant recommends any frequently triggered listing stored in the listing database to the broker, and the broker does not adopt the listing, the existing count of the number of unaccepted recommendations for the listing can be incremented by 1. If the number of unaccepted recommendations for any frequently triggered property in the property database exceeds a preset number (e.g., 8, 10, 20, etc.), it can be determined that the property data is likely inaccurate (possibly due to data registration errors, data not being updated in a timely manner, etc.). Statistics can then be collected for the frequently triggered property data in the property database that have been unaccepted and have a greater number of unaccepted recommendations than the preset number. The statistical results can be in the following format:

[0128] <property id, {modify field 1: pv1, modify field 2: pv2, ...}>

[0129] <property id, {modify field 1: pv1, modify field 2: pv2, ...}>

[0130] <Cell ID, {modify field 1: pv1, modify field 2: pv2, ...}>

[0131] Optionally, all the property data that has been stored in the property database and is triggered at a high frequency, and the number of corresponding recommendations that have not been adopted is greater than a preset number, can be used as the data to be collected; or, when the amount of property data that has been stored in the property database and is triggered at a high frequency, and the number of corresponding recommendations that have not been adopted is greater than a preset number, is very large, these property data can be sorted, and based on the sorting results, part of the property data can be selected as the property data to be collected.

[0132] In this implementation form, from the perspective of data accuracy, it is possible to very conveniently determine the property data to be collected.

[0133] In another specific implementation, step 101 includes:

[0134] For the property database, property data that meets the preset supplementary conditions will be used as property data to be collected.

[0135] Generally speaking, some property data is stored in the property database only as "yes" or "no", lacking a more detailed description. For example, when a user asks "Is the apartment square?", the property database can only answer "no", without knowing the specific extent of the irregularity or the specific aspects of the irregularity. This makes it difficult to meet the user's personalized needs. In view of this, property data with a detailed description of the apartment type can be regarded as property data that meets the preset supplementary conditions, and statistics are collected on the property data that meets the preset supplementary conditions. The statistical results can be in the format of:

[0136] <property id, {field description 1: pv1, field description 2: pv2, ...}>

[0137] <property id, {field description 1: pv1, field description 2: pv2, ...}>

[0138] <Cell ID, {field description 1: pv1, field description 2: pv2, ...}>

[0139] Optionally, all property data that meet the preset supplementary conditions can be used as the property data to be collected; or, when the amount of property data that meet the preset supplementary conditions is very large, these property data can be sorted, and based on the sorting results, part of the property data can be selected as the property data to be collected.

[0140] In this implementation form, from the perspective of data personalization, it is possible to very conveniently determine the property data to be collected.

[0141] Step 102: Determine the objects associated with the property data to be collected.

[0142] Here, the number of objects associated with the property data to be collected may be one, two, three, or more than three, and will not be listed one by one here.

[0143] It should be noted that the specific implementation of step 102 is varied, which is described below with examples.

[0144] In a specific implementation, step 102 includes:

[0145] Determine an object that has a preset relationship with the property corresponding to the property data to be collected.

[0146] Here, any property and any object have a preset relationship, which means: the object is the maintainer, surveyor, show host, key holder, etc. of the property; or, the object is the maintainer, surveyor, show host, etc. of the building where the property is located; or, the object is the maintainer, surveyor, show host, etc. of the residential complex where the property is located.

[0147] In this implementation, based on the preset relationship, objects that may provide the property data to be collected can be located conveniently and reliably.

[0148] In another specific implementation, step 102 includes:

[0149] Identify the objects that have performed preset behaviors on the listing corresponding to the listing data to be collected within the target time period.

[0150] Here, the target time period can be a recent period of time, such as the last three days, the last week, the last month, the last quarter, etc.; the preset behavior can be an inspection behavior, a maintenance behavior, a viewing behavior, etc.

[0151] In this implementation, based on preset behaviors, objects that may provide the property data to be collected can be located conveniently and reliably.

[0152] It should be pointed out that no matter what implementation form is adopted for step 102, the objects associated with the property data to be collected include but are not limited to brokers, property staff, etc. For ease of understanding, the embodiments of the present disclosure are explained by taking the case where the object associated with the property data to be collected is a broker as an example.

[0153] Step 103: distribute the data collection task associated with the property data to be collected to the object, and obtain feedback information returned by the object in response to the data collection task; wherein any feedback information includes reference property data.

[0154] Here, the data collection task associated with the to-be-collected property data may be: a data collection task for acquiring the to-be-collected property data.

[0155] Optionally, if there are multiple objects associated with the property data to be collected (i.e., at least two), the data collection task can be distributed to each of the multiple objects; or, according to a certain strategy, at least some of the objects can be filtered from the multiple objects, and the data collection task can be distributed only to at least some of the filtered objects.

[0156] Optionally, when there are multiple objects associated with the property data to be collected, the data collection task can be expressed as follows:

[0157] <entity id, [field 1, field 2, ...], [broker 1, broker 2, ...]>

[0158] In a specific embodiment, distributing a data collection task associated with the to-be-collected property data to an object and obtaining feedback information returned by the object in response to the data collection task may include:

[0159] Distribute data collection tasks associated with the property data to be collected to multiple objects, and obtain feedback information returned by at least some of the multiple objects.

[0160] It should be noted that the number of objects that return feedback information can be one or more. When the number of objects that return feedback information is more than one, the number of feedback information is also more than one. The reference property data in the feedback information returned by different objects can be the same or different. The reference property data in each feedback information can be data that can assist in obtaining the property data to be collected. By distributing data collection tasks to multiple objects, it is beneficial to obtain more feedback information, which is beneficial to the acquisition of the property data to be collected.

[0161] Step 104: Obtain the housing data to be collected based on the feedback information.

[0162] After obtaining feedback information from the object, the feedback information can be parsed to obtain the reference property data in the feedback information, and based on the obtained reference property data, the property data to be collected can be obtained. Since there are various specific implementation methods for obtaining the property data to be collected based on the obtained reference property data, for the sake of clarity, examples will be given below.

[0163] Step 105: Update the housing database using the acquired housing data to be collected.

[0164] In the case where the property data to be collected is determined from the perspective of data missing or data personalization in step 101, the property data to be collected obtained in step 104 can be added to the property database; in the case where the property data to be collected is determined from the perspective of data accuracy in step 101, the property data to be collected obtained in step 104 can be used to replace the corresponding property data already in the property database.

[0165] In an embodiment of the present disclosure, after determining the property data to be collected for a property database, the objects associated with the property data to be collected can be determined to reliably locate the objects that may provide the property data to be collected. Afterwards, a data collection task can be distributed to the determined objects so that accurate and reliable property data to be collected can be obtained based on feedback information from the objects, including reference property data, and the property database can be updated using the obtained property data to be collected. It can be seen that in an embodiment of the present disclosure, the property database does not need to be manually checked and updated, and the accuracy and reliability of the property data used when updating the property database can be guaranteed by distributing data collection tasks to specific users. Therefore, compared with the prior art, the embodiment of the present disclosure can not only improve the updating efficiency of the property database, but also improve the data quality of the property database, thereby ensuring the updating effect of the property database.

[0166] In an optional example, distributing a data collection task associated with the to-be-collected property data to an object, and obtaining feedback information returned by the object in response to the data collection task, includes:

[0167] Initiate a simulated interactive session with the object;

[0168] In the simulated interactive session, simulated user questions associated with the property data to be collected are sent, and feedback information regarding the simulated user questions is obtained from the subject.

[0169] Here, the user simulator may be a functional module in a website provided by the network side or a functional module in a client application installed in a computer.

[0170] It should be noted that in actual operation, the user simulator can first determine the conversation framework information based on the property data to be collected. The conversation framework information can be used to define the scope of the conversation content. The conversation framework information can also be called the user goal. The user goal can be expressed as Goal G = (C, R), where C represents the constraints and R represents the query content. In one example, the constraints and query content can be shown in Table 1 below.

[0171]

[0172] Table 1

[0173] Next, based on the user goal, the user simulator generates a reasonable user action (which can be represented by pre-set semantic tags). In the real estate sector, conversational actions include, but are not limited to, asking about the price of the house, asking about the location of the house, asking about availability, telling about the purchase price, telling about the purchase location, and telling about the property type. The reasonable user actions generated by the user modeler are then translated into human-readable language to generate a simulated user message, which then initiates a conversation with the real estate agent (this simulates an interactive conversation). After the real estate agent responds or asks questions based on their understanding and knowledge (this is equivalent to providing feedback), the user simulator generates another reasonable user action, which in turn generates a simulated user message, and this cycle continues until the conversation concludes.

[0174] Optionally, to generate reasonable user actions, an action model can be pre-established. Furthermore, multiple variables can be generated based on the existing conversation content and conversation framework information between the user simulator and the agent. These variables are then encoded (e.g., one-hot encoding) so that each variable forms a vector. The multiple vectors are then concatenated to form the corresponding conversation context information. This conversation context information can then be fed into the action model to obtain the probability of each of the K user actions output by the action model (K is a preset total number of user actions). This yields K probabilities, the sum of which can be 1. The probability of any conversation action represents the likelihood that the action will ultimately be determined to be a reasonable conversation action. The maximum probability can then be selected from the K probabilities, and the user action corresponding to the selected probability is determined to be a reasonable user action.

[0175] In specific implementation, it can be Figure 2 The user simulator in ,simulates the interaction between the user (i.e., real user) and the object (e.g., broker) associated with the property data to be collected, such as Figure 2 As shown, the specific interaction process can be:

[0176] (1) Determine the user goal: The user goal can be expressed as: G = (C, R), where C, as a constraint, is the ID of the house / property for which data needs to be collected (i.e., the ID of the house or property corresponding to the property data to be collected), and R, as the query content, corresponds to the property attribute information to be collected (which represents the property data to be collected), as shown in Table 2 below.

[0177]

[0178] Table 2

[0179] (2) User model:

[0180] The rule-based Agenda method (a scheduled task management module) simulates user actions for data collection and uses a stack to store and maintain user action sets. The top of the stack is the action of informing the house / property ID (which appears as a house card or property card in the conversation), the bottom of the stack is the bye action, and the middle is the query content, as shown below:

[0181]

[0182] (3) Natural Language Generation:

[0183] Initiate a simulated user session with the broker. At the start of the session, perform the action of notifying the house / property ID. Specifically, based on the house / property ID, call the corresponding card interface to generate the corresponding house / property card, and send the generated house / property card to the broker.

[0184] Each subsequent action maintains a question pool and randomly selects a question as the simulated user question each time. For example, the question pool corresponding to request_floor (asking about the floor) can be:

[0185] {

[0186] Which floor is this?

[0187] How many floors?

[0188] How many floors are there?

[0189] Which floor is this house on?

[0190] }

[0191] The complete example and stack changes in a simulated interactive session can be shown below:

[0192]

[0193]

[0194] Agent: How can I help you?

[0195] Customer: I would like to know more about the house No. 123.

[0196]

[0197] Agent: Okay, what are your overall needs?

[0198]

[0199] Client: Which floor is this house on?

[0200]

[0201] Agent: This is on the 4th floor. Not a high floor.

[0202]

[0203] Customer: Oh, not bad; how about this orientation?

[0204]

[0205] Agent: It’s open from north to south, and the ventilation is very good.

[0206]

[0207] Customer: Which primary school is the corresponding school district?

[0208]

[0209] Agent: This is the school in AA city, BB, CCCC. DD room.

[0210]

[0211] Customer: OK, thank you, I understand.

[0212]

[0213] Agent: You're welcome. If you have any questions, you can always consult me. 18810000000000.

[0214] In the above example, "Which floor is this house on?", "What's its orientation?", and "Which elementary school is in the corresponding school district?" are simulated user questions; "This is on the 4th floor. Not a high-rise building?", "It's open from north to south, so the ventilation is excellent.", and "This is the school in AA city, BB, CCCC. What about the room in DD?" are responses. Furthermore, as the conversation progresses, the Agenda will dynamically update, and user actions will pop up at the top of the Agenda to form a rotating user action.

[0215] After obtaining the feedback information in the above example, you can use Natural Language Understanding (NLU) to obtain the property data to be collected based on the feedback information.

[0216] In an embodiment of the present disclosure, after determining the property data to be collected and the objects associated with the property data to be collected, simulated user questions can be raised in a simulated interactive session and feedback information can be obtained. This allows the data collection task to be performed in a reasonable scenario and stimulates the enthusiasm of the objects associated with the property data to be collected to answer questions.

[0217] In an optional example, obtaining the property data to be collected based on the feedback information includes:

[0218] Determine the collection requirement type of the property data to be collected; wherein the collection requirement type includes at least one of the following: missing supplement type, personalized supplement type, and update and replacement type;

[0219] Filter target reference property data from the feedback information obtained based on the screening method corresponding to the collection requirement type;

[0220] The target reference property data is used as the obtained property data to be collected.

[0221] When there are multiple objects associated with the property data to be collected, the data collection tasks associated with the property data to be collected can be distributed to multiple objects, and feedback information returned in response to the data collection tasks can be obtained. Each feedback information can include reference property data, and the reference property data in the feedback information returned by different objects can be the same or different.

[0222] Next, the collection requirement type of the property data to be collected can be determined. Specifically, if the property data to be collected is determined from the perspective of data missing in step 101 above, the collection requirement type of the property data to be collected can be a missing supplement type; if the property data to be collected is determined from the perspective of data accuracy in step 101 above, the collection requirement type of the property data to be collected can be an update and replacement type; if the property data to be collected is determined from the perspective of data personalization in step 101 above, the collection requirement type of the property data to be collected can be a personalized supplement type.

[0223] Afterwards, a screening method corresponding to the collection requirement type may be determined, and target reference property data may be screened from the obtained feedback information according to the determined screening method, so as to be used as the obtained property data to be collected.

[0224] In a specific embodiment, when the collection requirement type is a missing supplement type or a personalized supplement type, the target reference property data is filtered from the obtained feedback information according to the filtering method corresponding to the collection requirement type, including:

[0225] Filter target reference property data from the obtained feedback information based on the weight of each object that has returned feedback information;

[0226] After using the target reference property data as the acquired property data to be collected, the method further includes:

[0227] Update the weight of the corresponding object based on the matching degree between each reference property data and the property data to be collected.

[0228] It should be noted that a weight can be set in advance for each broker, and the weight of any broker can be adjusted according to actual conditions.

[0229] Assume that after the data collection task is distributed, five objects return feedback information. These five objects are D1, D2, D3, D4, and D5. Among them, the reference property data in the feedback information returned by D1 is K1, the reference property data in the feedback information returned by D2 is K2, the reference property data in the feedback information returned by D3 is K3, the reference property data in the feedback information returned by D4 is K4, and the reference property data in the feedback information returned by D5 is K5. The weights of D1 to D5 are different. Then, the object with the largest corresponding weight can be selected from D1 to D5. If the selected object is D5, K5 in the feedback information returned by D5 can be used as the target reference property data.

[0230] In addition, the matching degree of K1 to K4 and K5 can be calculated separately, and D1 to D4 can be updated based on the calculated matching degree. For example, for K1, the similarity between K1 and K5 can be calculated, and the calculated similarity can be used to represent the matching degree of K1 and K5. If the matching degree between K1 and K5 is high, the weight of K1 can be increased; if the matching degree between K1 and K5 is low, the weight of K1 can be decreased.

[0231] In this implementation, based on the weight of the object, the target reference housing data can be conveniently and reliably screened out, and through the dynamic update of the weight, the reliability of the screening results can be further guaranteed, thereby better ensuring the reliability of the housing data used to update the housing database.

[0232] In another specific embodiment, when the collection requirement type is a missing supplement type or a personalized supplement type, the target reference property data is filtered from the obtained feedback information according to the filtering method corresponding to the collection requirement type, including:

[0233] Determine a first data set; wherein the first data set includes reference property data that appears in at least one feedback information, and any two reference property data in the first data set are different from each other;

[0234] Determine the number of occurrences of each reference property listing in the first data set in all feedback information;

[0235] From the first data set, the reference property data with the largest number of occurrences is selected as the target reference property data.

[0236] Continuing with the example from the previous implementation, assume that after the data collection task is distributed, the five objects D1 to D5 return feedback information. The feedback information from D1 to D5 includes K1 to K5, respectively. Then, the first data set can be determined based on K1 to K5. Assuming that the property data to be collected is floor data, and K1 is the fourth floor, K2 is the third floor, K3 is the fourth floor, K4 is the fourth floor, and K5 is the fifth floor, then the first data set can be represented as: {third floor, fourth floor, fifth floor}. Furthermore, it can be determined that the third floor appears once in all feedback information, the fourth floor appears three times in all feedback information, and the fifth floor appears once in all feedback information. Clearly, the fourth floor has the most corresponding occurrences, so the fourth floor can be used as the target reference property data.

[0237] In this implementation, the target reference property data can be conveniently and reliably screened out through majority voting, thereby better ensuring the reliability of the property data used to update the property database.

[0238] It should be noted that the above two specific implementation methods can be used in combination to achieve the screening of target reference housing data through weighted majority voting.

[0239] In another specific embodiment, when the acquisition requirement type is an update / replacement type, the target reference property data is filtered from the acquired feedback information according to a filtering method corresponding to the acquisition requirement type, including:

[0240] Determine a second data set; wherein the second data set includes reference property data that appears in at least one feedback information, and any two reference property data in the second data set are different from each other;

[0241] Determining the confidence level of each reference property data in the second data set; wherein the confidence level of any reference property data is: the ratio of the number of feedback information including the reference property data to the total number of feedback information;

[0242] From the second data set, select the reference property data with the highest confidence level;

[0243] When the confidence level of the screened reference property data is greater than a preset confidence level, the screened reference property data is used as the target reference property data.

[0244] Continuing with the example from the previous embodiment, assume that after the data collection task is assigned, five objects (D1 through D5) return feedback information. The feedback from D1 through D5 includes K1 through K5, in that order. A second dataset can then be determined based on K1 through K5. Assuming the property data to be collected is floor data, and K1 represents the fourth floor, K2 the third floor, K3 the fourth floor, K4 the fourth floor, and K5 the fifth floor, the second dataset can be represented as: {third floor, fourth floor, fifth floor}. Furthermore, it can be determined that the confidence level for the third floor is 1 / 5, the confidence level for the fourth floor is 3 / 5, and the confidence level for the fifth floor is 1 / 5. Subsequently, the second dataset can be filtered for the reference property with the highest confidence level, specifically the fourth floor. Next, it can be determined whether the confidence level for the fourth floor (i.e., 3 / 5) is greater than a preset confidence level. If so, the fourth floor can be selected as the target reference property data. Otherwise, it can be determined that the selection of the target reference property data failed.

[0245] In this embodiment, the confidence level of the reference property data used as the target reference property data is greater than the preset confidence level. That is, for property data that needs to be updated or replaced, it will only be replaced if the majority of brokers provide a unified answer, thereby ensuring the update effect of the property database.

[0246] In the embodiments of the present disclosure, by screening the target reference property data in a screening method corresponding to the collection requirement type of the property data to be collected, the reliability of the screened target reference property data can be ensured, thereby better ensuring the update effect of the property database.

[0247] In an optional example, distributing a data collection task associated with the property data to be collected to an object includes:

[0248] Filtering at least some of the objects from the plurality of objects associated with the property data to be collected according to a preset mapping relationship; wherein the preset mapping relationship is a mapping relationship between the objects and weights, or the preset mapping relationship is a mapping relationship between the object attributes and weights;

[0249] Distributing data collection tasks associated with the to-be-collected property data to at least some of the screened subjects;

[0250] The method further includes:

[0251] The preset mapping relationship is updated according to the feedback performance of at least part of the screened objects.

[0252] It should be noted that a preset mapping relationship can be set. The preset mapping relationship can be a mapping relationship between object attributes and weights, or the preset mapping relationship can be a mapping relationship between objects and weights. For ease of understanding, the case where the preset mapping relationship is a mapping relationship between object attributes and weights is used as an example for explanation. Optionally, object attributes include but are not limited to job level attributes, store attributes, gender attributes, age attributes, etc. For example, manager level, ordinary employee level, and intern level can each be an object attribute. For another example, youth attribute and middle-aged attribute can each be an object attribute.

[0253] Assume that after determining the objects associated with the property data to be collected, the number of objects determined is six, and these six objects are E1, E2, E3, E4, E5, and E6, among which the object attributes of E1, E2, E3, and E4 are ordinary employee levels, the object attribute of E5 is manager level, and the object level of E6 is intern level. Then, according to the preset mapping relationship, at least some of the objects can be screened from E1 to E6, and the preset mapping relationship can be updated according to the feedback performance of at least some of the screened objects; wherein, the feedback performance includes but is not limited to feedback enthusiasm, feedback accuracy, etc.

[0254] Specifically, if the preset mapping relationship indicates that the weight corresponding to the general employee level is 0.85, the weight corresponding to the manager level is 0.6, and the weight corresponding to the intern level is 0.7, then based on the preset mapping relationship, the object attribute with the largest corresponding weight (i.e., the general employee level) can be determined. Next, objects belonging to the general employee level can be filtered out from E1 to E6, namely, E1 to E4, and data collection tasks can be assigned to E1 to E4. If E1 to E4 are able to quickly provide feedback on the assigned data collection tasks, it can be assumed that E1 to E4 are highly motivated to provide feedback. In this case, the weight mapped to the general employee level in the preset mapping relationship can be increased to update the preset mapping relationship. If only E1 provides rapid feedback, while E2 to E4 do not or provide very slow feedback, the weight mapped to the general employee level in the preset mapping relationship can be decreased to update the preset mapping relationship.

[0255] Alternatively, if the preset mapping relationship specifies that the weight corresponding to the general employee level is 0.85, the weight corresponding to the manager level is 0.6, and the weight corresponding to the intern level is 0.7, then based on the preset mapping relationship, each object attribute (i.e., the general employee level and the intern level) whose corresponding weight is greater than the preset weight (e.g., 0.65) can be determined. Subsequently, objects belonging to the general employee level and objects belonging to the intern level can be filtered out from E1 to E6, i.e., E1 to E4 and E6 can be filtered out, and data collection tasks can be distributed to E1 to E4 and E6. If E1 to E4 can quickly provide feedback on the distributed data collection task, but E6 does not provide feedback or provides very slow feedback, the weight mapped to the general employee level in the preset mapping relationship can be increased, and the weight mapped to the intern level in the preset mapping relationship can be reduced to update the preset mapping relationship.

[0256] In an embodiment of the present disclosure, when there are multiple objects associated with the housing data to be collected, a suitable object can be selected from the multiple objects based on a preset mapping relationship to distribute the data collection task. In addition, the preset mapping relationship can be dynamically updated based on the feedback performance of the object for the data distribution task, thereby further optimizing the selection effect when selecting the object.

[0257] In an alternative example, Figure 3 As shown, the property data to be collected can be determined for the property dictionary. The required types for the collected property data can include at least one of the following: missing data supplementation, personalized data supplementation, and update / replacement. Next, appropriate brokers can be selected based on the property data to be collected, and data collection tasks can be assigned to the selected brokers. The selected brokers' responses (equivalent to the feedback information described above) can then be obtained and used for data analysis. Specifically, NLU can be used to process the brokers' responses into unified structured data, which can then be parsed. The resulting data format can be:

[0258] <entity id, {field 1: {broker 1: v1, broker 2: v2, ...}, field 2: {broker 1: v1, broker 2: v2, ...}, ...}>

[0259] If target reference property data is selected from the parsed results, it can be fed back into the property dictionary as pending property data to update the property dictionary. This allows the disclosed embodiments to leverage the personal knowledge of numerous real estate agents to improve and update the property dictionary, facilitating its use by the intelligent assistant.

[0260] In summary, the embodiments of the present disclosure can find data with high trigger volume and missing or to be updated, accurately locate the group of brokers who are most likely to give the correct answer, make the data collection scenario a standard training ground, integrate it into the daily tasks of brokers, and conveniently and reliably implement the update of the housing database.

[0261] Any database update method provided in the embodiments of the present disclosure can be executed by any appropriate device with data processing capabilities, including but not limited to a terminal device and a server. Alternatively, any database update method provided in the embodiments of the present disclosure can be executed by a processor, such as a processor that executes any database update method mentioned in the embodiments of the present disclosure by invoking corresponding instructions stored in a memory. This will not be further described below.

[0262] Exemplary devices

[0263] Figure 4 is a structural diagram of a database updating device provided by an exemplary embodiment of the present disclosure. Figure 4 The device shown includes a first determining module 401 , a second determining module 402 , a processing module 403 , an obtaining module 404 and an updating module 405 .

[0264] The first determining module 401 is used to determine the housing data to be collected from the housing database;

[0265] The second determining module 402 is used to determine the object associated with the property data to be collected;

[0266] Processing module 403 is configured to distribute data collection tasks associated with the property data to be collected to the subject, and obtain feedback information returned by the subject in response to the data collection task; wherein any feedback information includes reference property data;

[0267] The acquisition module 404 is used to obtain the housing data to be collected based on the feedback information;

[0268] The first updating module 405 is used to update the housing database using the obtained housing data to be collected.

[0269] In an alternative example, Figure 5 As shown, the processing module 403 includes:

[0270] The initiating submodule 4031 is used to initiate a simulated interactive session to the object;

[0271] The processing submodule 4032 is used to send simulated user questions associated with the property data to be collected in the simulated interactive session, and obtain feedback information from the subject regarding the simulated user questions.

[0272] In an optional example, the processing module 403 is specifically configured to:

[0273] Distribute data collection tasks associated with the property data to be collected to multiple objects, and obtain feedback information returned by at least some of the multiple objects.

[0274] In an optional example, the obtaining module 404 includes:

[0275] The first determination submodule is configured to determine a collection requirement type for the property data to be collected; wherein the collection requirement type includes at least one of the following: a missing supplement type, a personalized supplement type, and an update and replacement type;

[0276] The first screening submodule is used to screen target reference property data from the acquired feedback information according to the screening method corresponding to the collection requirement type;

[0277] The second determination submodule is configured to use the target reference property data as the obtained property data to be collected.

[0278] In an alternative example,

[0279] When the collection requirement type is a missing supplement type or a personalized supplement type, the first screening submodule is specifically used to:

[0280] Filter target reference property data from the obtained feedback information based on the weight of each object that has returned feedback information;

[0281] The device also includes:

[0282] The second updating module is used to update the weight of the corresponding object according to the matching degree between each reference property data and the property data to be collected after taking the target reference property data as the obtained property data to be collected.

[0283] In an optional example, when the collection requirement type is a missing supplement type or a personalized supplement type, the first screening submodule includes:

[0284] A first determining unit is configured to determine a first data set, wherein the first data set includes reference property data that appears in at least one piece of feedback information, and any two reference property data in the first data set are different from each other;

[0285] A second determining unit is configured to determine the number of occurrences of each reference property data in the first data set in all feedback information;

[0286] The first screening unit is used to screen the reference property data with the largest number of occurrences from the first data set as target reference property data.

[0287] In an optional example, when the acquisition requirement type is an update and replacement type, the first screening submodule includes:

[0288] a third determining unit, configured to determine a second data set; wherein the second data set includes reference property data that appears in at least one piece of feedback information, and any two reference property data in the second data set are different from each other;

[0289] a fourth determining unit, configured to determine a confidence level of each reference property data item in the second data set; wherein the confidence level of any reference property data item is: a ratio of the number of feedback information items including the reference property data item to the total number of feedback information items;

[0290] A second screening unit is used to screen the reference property data with the highest confidence from the second data set;

[0291] The fifth determining unit is configured to use the screened reference property data as target reference property data when the confidence level of the screened reference property data is greater than a preset confidence level.

[0292] In an optional example, the processing module 403 includes:

[0293] A second screening submodule is configured to screen at least some of the objects from the plurality of objects associated with the to-be-collected property data based on a preset mapping relationship; wherein the preset mapping relationship is a mapping relationship between the object and the weight, or a mapping relationship between the object attribute and the weight;

[0294] a distribution module, configured to distribute data collection tasks associated with the to-be-collected property data to at least some of the screened objects;

[0295] The device also includes:

[0296] The third updating module is configured to update the preset mapping relationship according to feedback performance of at least part of the screened objects.

[0297] In an optional example, the second determination module 402 is specifically used for at least one of the following: determining an object having a preset relationship with the property corresponding to the property data to be collected; determining an object that has performed a preset behavior on the property corresponding to the property data to be collected within a target time period.

[0298] In an optional example, the first determination module 401 is specifically used for at least one of the following: taking missing data in the housing database as housing data to be collected; taking housing data stored in the housing database whose corresponding recommendations have not been adopted more than a preset number of times as housing data to be collected; for the housing database, taking housing data that meets preset supplementary conditions as housing data to be collected.

[0299] Exemplary electronic devices

[0300] Below, reference Figure 6 The electronic device according to the embodiment of the present disclosure is described. The electronic device may be either or both of the first device and the second device, or a standalone device independent of them, and the standalone device may communicate with the first device and the second device to receive collected input signals from them.

[0301] Figure 6 A block diagram of an electronic device 600 according to an embodiment of the present disclosure is illustrated.

[0302] like Figure 6 As shown, electronic device 600 includes one or more processors 601 and memory 602 .

[0303] The processor 601 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 600 to perform desired functions.

[0304] The memory 602 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 601 may execute the program instructions to implement the database update method of the various embodiments of the present disclosure described above and / or other desired functions. The computer-readable storage medium may also store various contents such as input signals, signal components, and noise components.

[0305] In one example, the electronic device 600 may further include an input device 603 and an output device 604 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0306] For example, when the electronic device 600 is the first device or the second device, the input device 603 may be a microphone or a microphone array. When the electronic device 600 is a standalone device, the input device 603 may be a communication network connector for receiving collected input signals from the first device and the second device.

[0307] In addition, the input device 603 may also include, for example, a keyboard, a mouse, etc.

[0308] The output device 604 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0309] Of course, to simplify, Figure 6 Only some of the components related to the present disclosure in the electronic device 600 are shown, and components such as buses, input / output interfaces, etc. are omitted. In addition, the electronic device 600 may further include any other appropriate components according to specific application scenarios.

[0310] Exemplary computer program products and computer-readable storage media

[0311] In addition to the above-mentioned methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the database update method according to various embodiments of the present disclosure described in the above "Exemplary Method" section of this specification.

[0312] The computer program product may be written in any combination of one or more programming languages to implement the operations of the disclosed embodiments, including object-oriented programming languages such as Java, C++, and conventional procedural programming languages such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0313] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the database update method according to various embodiments of the present disclosure described in the above “Exemplary Method” section of this specification.

[0314] The computer-readable storage medium may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0315] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this disclosure are merely illustrative and not restrictive, and should not be construed as necessarily possessed by each embodiment of the present disclosure. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, rather than as limitations. These details do not limit the present disclosure to necessarily being implemented using these specific details.

[0316] Each embodiment in this specification is described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For system embodiments, since they are essentially identical to the method embodiments, their description is relatively simple. For relevant parts, refer to the descriptions of the method embodiments.

[0317] The block diagrams of the devices, devices, equipment, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0318] The methods and apparatus of the present disclosure may be implemented in many ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers recording media that store programs for executing the methods according to the present disclosure.

[0319] It should also be noted that in the apparatus, device, and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0320] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0321] The above description has been provided for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A database updating method, characterized in that: include: For the property database, determine the property data to be collected; Determining an object associated with the property data to be collected; Distributing a data collection task associated with the to-be-collected property data to the subject, and obtaining feedback information returned by the subject in response to the data collection task; wherein any of the feedback information includes reference property data, which is data that can assist in obtaining the to-be-collected property data; Obtaining the housing data to be collected according to the feedback information; Using the obtained housing data to be collected, updating the housing database; The distributing the data collection task associated with the to-be-collected property data to the object includes: Filtering at least some of the objects from the plurality of objects associated with the to-be-collected property data according to a preset mapping relationship; the preset mapping relationship being a mapping relationship between objects and weights, or the preset mapping relationship being a mapping relationship between object attributes and weights; Distributing data collection tasks associated with the to-be-collected property data to at least some of the screened objects; The method further comprises: The preset mapping relationship is updated according to the feedback performance of at least part of the screened objects; the feedback performance includes at least one of the following: feedback positivity and feedback accuracy.

2. The method according to claim 1, characterized in that The distributing the data collection task associated with the to-be-collected property data to the object, and obtaining feedback information returned by the object in response to the data collection task, includes: Initiating a simulated interactive session with the object; In the simulated interactive session, a simulated user question associated with the housing data to be collected is sent, and feedback information of the subject in response to the simulated user question is obtained; or, The distributing the data collection task associated with the to-be-collected property data to the object, and obtaining feedback information returned by the object in response to the data collection task, includes: The data collection tasks associated with the to-be-collected property data are distributed to the plurality of objects, and feedback information returned by at least some of the plurality of objects is obtained.

3. The method according to claim 1 or 2, characterized in that The step of obtaining the housing data to be collected based on the feedback information includes: Determine a collection requirement type for the property data to be collected; wherein the collection requirement type includes at least one of the following: a missing supplement type, a personalized supplement type, and an update and replacement type; Filter target reference property data from the obtained feedback information according to the filtering method corresponding to the collection requirement type; The target reference property data is used as the obtained property data to be collected.

4. The method according to claim 3, characterized in that In the case where the collection requirement type is a missing supplement type or a personalized supplement type, the filtering of target reference housing data from the obtained feedback information according to the filtering method corresponding to the collection requirement type includes: Filtering target reference property data from the obtained feedback information according to the weight of each of the objects that returned the feedback information; After using the target reference property data as the obtained property data to be collected, the method further includes: The weight of the corresponding object is updated according to the matching degree between each reference property data and the property data to be collected.

5. The method according to claim 3, characterized in that In the case where the collection requirement type is a missing supplement type or a personalized supplement type, the filtering of target reference housing data from the obtained feedback information according to the filtering method corresponding to the collection requirement type includes: Determine a first data set; wherein the first data set includes the reference property data that appears in at least one of the feedback information, and any two reference property data in the first data set are different from each other; Determine the number of occurrences of each reference property data in the first data set in all the feedback information; From the first data set, the reference property data with the largest number of occurrences is selected as the target reference property data.

6. The method according to claim 3, characterized in that In the case where the collection requirement type is an update / replacement type, filtering target reference property data from the acquired feedback information according to a filtering method corresponding to the collection requirement type includes: Determine a second data set; wherein the second data set includes the reference property data that appears in at least one of the feedback information, and any two reference property data in the second data set are different from each other; Determining the confidence level of each reference property data in the second data set; wherein the confidence level of any reference property data is: the ratio of the number of feedback information including the reference property data to the total number of feedback information; Selecting the reference property data with the highest confidence from the second data set; When the confidence level of the screened reference property data is greater than a preset confidence level, the screened reference property data is used as the target reference property data.

7. The method according to claim 1, characterized in that Determining the object associated with the to-be-collected property data includes at least one of the following: Determine an object having a preset relationship with the property corresponding to the property data to be collected; Determine the objects that have performed preset behaviors on the property corresponding to the property data to be collected within a target time period.

8. The method according to claim 1, characterized in that Determining the property data to be collected from the property database includes at least one of the following: Treat the missing data in the property database as property data to be collected; The property data stored in the property database, whose corresponding recommendations have not been adopted more than a preset number of times, are used as the property data to be collected; For the property database, property data that meets the preset supplementary conditions will be used as property data to be collected.

9. A database updating device, characterized in that: include: The first determination module is used to determine the housing data to be collected from the housing database; A second determining module is used to determine the object associated with the housing data to be collected; a processing module, configured to distribute a data collection task associated with the to-be-collected property data to the object, and obtain feedback information returned by the object in response to the data collection task; Any of the feedback information includes reference property data, which is data that can assist in obtaining the property data to be collected; An acquisition module, configured to obtain the housing data to be collected based on the feedback information; A first updating module is configured to update the housing database using the obtained housing data to be collected; The processing module includes: a second screening submodule, configured to screen at least some of the objects from the plurality of objects associated with the to-be-collected housing data according to a preset mapping relationship, wherein the preset mapping relationship is a mapping relationship between objects and weights, or a mapping relationship between object attributes and weights; a distribution module, configured to distribute data collection tasks associated with the to-be-collected housing data to at least some of the screened objects; The device further comprises: The third updating module is configured to update the preset mapping relationship according to the feedback performance of at least part of the screened objects; the feedback performance includes at least one of the following: feedback positivity and feedback accuracy.

10. The device according to claim 9, characterized in that The processing module includes: An initiating submodule, configured to initiate a simulated interactive session to the object; a processing submodule, configured to send, in the simulated interactive session, a simulated user question associated with the to-be-collected property data, and obtain feedback information from the subject regarding the simulated user question; or, The processing module is specifically used to: The data collection tasks associated with the to-be-collected property data are distributed to the plurality of objects, and feedback information returned by at least some of the plurality of objects is obtained.

11. The device according to claim 9 or 10, characterized in that The acquisition module includes: A first determining submodule is configured to determine a collection requirement type of the housing data to be collected; wherein the collection requirement type includes at least one of the following: a missing supplement type, a personalized supplement type, and an update and replacement type; A first screening submodule is configured to screen target reference property data from the acquired feedback information according to a screening method corresponding to the acquisition requirement type; The second determining submodule is configured to use the target reference property data as the obtained property data to be collected.

12. The device according to claim 11, characterized in that When the collection requirement type is a missing supplement type or a personalized supplement type, the first screening submodule is specifically configured to: Filtering target reference property data from the obtained feedback information according to the weight of each of the objects that returned the feedback information; The device further comprises: The second updating module is configured to update the weight of the corresponding object according to the matching degree between each reference property data and the property data to be collected after the target reference property data is used as the obtained property data to be collected.

13. The device according to claim 11, characterized in that In the case where the collection requirement type is a missing supplement type or a personalized supplement type, the first screening submodule includes: A first determining unit is configured to determine a first data set, wherein the first data set includes the reference property data that appears in at least one piece of feedback information, and any two reference property data in the first data set are different from each other; A second determining unit is configured to determine the number of occurrences of each reference property data in the first data set in all the feedback information; The first screening unit is configured to screen the reference property data with the largest number of occurrences from the first data set as target reference property data.

14. The device according to claim 11, characterized in that When the acquisition requirement type is an update and replacement type, the first screening submodule includes: a third determining unit, configured to determine a second data set; wherein the second data set includes the reference property data appearing in at least one piece of feedback information, and any two reference property data in the second data set are different from each other; a fourth determining unit, configured to determine a confidence level of each of the reference property data in the second data set; wherein the confidence level of any of the reference property data is: a ratio of the number of the feedback information including the reference property data to the total number of the feedback information; A second screening unit is configured to screen the reference property data corresponding to the maximum confidence level from the second data set; The fifth determining unit is configured to use the screened reference property data as target reference property data when the confidence level of the screened reference property data is greater than a preset confidence level.

15. The device according to claim 9, characterized in that The second determination module is specifically used for at least one of the following: determining an object having a preset relationship with the property corresponding to the property data to be collected; and determining an object that has performed a preset behavior on the property corresponding to the property data to be collected within a target time period.

16. The device according to claim 9, characterized in that The first determining module is specifically configured to perform at least one of the following: using missing data in the housing database as housing data to be collected; using housing data stored in the housing database whose corresponding recommendations have not been adopted more than a preset number of times as housing data to be collected; For the property database, property data that meets the preset supplementary conditions will be used as property data to be collected.

17. A computer-readable storage medium storing a computer program, characterized in that: The computer program is used to execute the database updating method according to any one of claims 1 to 8.

18. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the database updating method according to any one of claims 1 to 8.

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