CN112084205A

CN112084205A - Database updating method and device, computer readable storage medium and electronic equipment

Info

Publication number: CN112084205A
Application number: CN202010950102.5A
Authority: CN
Inventors: 刘娜
Original assignee: Beike Technology Co Ltd
Current assignee: Beike Technology Co Ltd
Priority date: 2020-09-10
Filing date: 2020-09-10
Publication date: 2020-12-15

Abstract

The embodiment of the disclosure discloses a database updating method and device, a computer readable storage medium and electronic equipment. The method comprises the following steps: determining room source data to be collected aiming at a room source database; determining an object associated with the room source data to be collected; distributing a data acquisition task related to the room source data to be acquired to the object, and acquiring feedback information returned by the object in response to the data acquisition task; wherein any feedback information comprises reference house source data; according to the feedback information, room source data to be collected are obtained; and updating the house source database by using the acquired house source data to be acquired. The embodiment of the disclosure can not only improve the updating efficiency of the house source database, but also improve the data quality of the house source database, and ensure the updating effect of the house source database.

Description

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

Technical Field

The present disclosure relates to the field of database technologies, and in particular, to a database updating method and apparatus, a computer-readable storage medium, and an electronic device.

Background

The room source database can store data with multiple dimensions, but the room source database has problems in data quality such as collection error, data failure, insufficient granularity and the like, and the reliability of the room source database is affected by the problems. In order to improve the data quality of the house source database, data with quality problems in the house source database generally needs to be checked manually and updated manually, so that the efficiency is low and the effect is poor.

Disclosure of Invention

The present disclosure is proposed to solve the above technical problems. The embodiment of the disclosure provides a database updating method and device, a computer-readable storage medium and electronic equipment.

According to an aspect of an embodiment of the present disclosure, there is provided a database update method, including:

determining room source data to be collected aiming at a room source database;

determining an object associated with the room source data to be collected;

distributing a data acquisition task related to the room source data to be acquired to the object, and acquiring feedback information returned by the object in response to the data acquisition task;

acquiring the room source data to be acquired according to the feedback information; wherein any one of the feedback information comprises reference house source data;

and updating the house source database by using the acquired house source data to be acquired.

In one alternative example of this, the user may,

the distributing a data acquisition task associated with the room source data to be acquired to the object and acquiring feedback information returned by the object in response to the data acquisition task includes:

initiating a simulated interactive session to the object;

in the simulation interactive session, sending a simulation user problem associated with the room source data to be collected, and acquiring feedback information of the object aiming at the simulation user problem;

alternatively, the first and second electrodes may be,

distributing data acquisition tasks related to the room source data to be acquired to a plurality of objects, and acquiring feedback information returned by at least part of the objects in the plurality of objects.

In an optional example, the obtaining, according to the feedback information, the room source data to be collected includes:

determining the acquisition demand type of the room source data to be acquired; wherein the acquisition requirement type comprises at least one of: a missing supplement type, a personalized supplement type, and an update replacement type;

according to a screening mode corresponding to the acquisition demand type, screening target reference house source data from the acquired feedback information;

and taking the target reference room source data as the room source data to be acquired.

In one alternative example of this, the user may,

under the condition that the acquisition demand type is a missing supplement type or an individualized supplement type, the method for screening the target reference room source data from the acquired feedback information according to the screening mode corresponding to the acquisition demand type comprises the following steps:

screening target reference room source data from the acquired feedback information according to the weight of each object returning the feedback information;

after the target reference room source data is used as the obtained room source data to be collected, the method further includes:

and updating the weight of the corresponding object according to the matching degree of each reference room source data and the room source data to be collected.

In an optional example, when the acquisition requirement type is a missing supplement type or a personalized supplement type, the screening target reference room source data from the acquired feedback information according to a screening manner corresponding to the acquisition requirement type includes:

determining a first data set; wherein the first data set comprises the reference room source data appearing in at least one piece of the feedback information, and any two reference room source data in the first data set are different from each other;

determining the occurrence number of each reference room source data in the first data set in all the feedback information;

and screening the reference house source data with the maximum occurrence frequency corresponding to the first data set as target reference house source data.

In an optional example, when the acquisition demand type is an update replacement type, the screening target reference room source data from the acquired feedback information according to a screening manner corresponding to the acquisition demand type includes:

determining a second data set; wherein the second data set comprises the reference room source data appearing in at least one piece of the feedback information, and any two reference room source data in the second data set are different from each other;

determining a confidence level for each of the reference room-source data in the second data set; wherein the confidence of any one of the reference room source data is as follows: a ratio of the number of the feedback information including the reference room source data to the total number of the feedback information;

screening the reference room source data with the maximum corresponding confidence coefficient from the second data set;

and under the condition that the confidence coefficient of the screened reference room source data is greater than the preset confidence coefficient, taking the screened reference room source data as target reference room source data.

In an optional example, the distributing, to the object, the data collection task associated with the room source data to be collected includes:

screening at least part of the objects from a plurality of objects associated with the room source data to be collected according to a preset mapping relation; the preset mapping relation is a mapping relation between an object and a weight, or the preset mapping relation is a mapping relation between an object attribute and the weight;

distributing a data acquisition task associated with the room source data to be acquired to at least part of the screened objects;

the method further comprises the following steps:

and updating the preset mapping relation according to the feedback performance of at least part of the screened objects.

In an optional example, the determining the object associated with the room source data to be collected includes at least one of:

determining an object having a preset relationship with a house source corresponding to the house source data to be collected;

and determining an object with a preset behavior on the house source corresponding to the house source data to be collected in a target time period.

In an optional example, the determining room source data to be collected for the room source database includes at least one of:

taking the missing data in the house source database as house source data to be collected;

taking the house source data which are stored in the house source database and correspond to which the recommended unadopted times are more than the preset times as the house source data to be collected;

and aiming at the house source database, taking the house source data meeting the preset supplement conditions as the house source data to be collected.

According to another aspect of the embodiments of the present disclosure, there is provided a database updating apparatus including:

the first determining module is used for determining room source data to be collected aiming at a room source database;

the second determination module is used for determining an object associated with the room source data to be collected;

the processing module is used for distributing a data acquisition task related to the room source data to be acquired to the object and acquiring feedback information returned by the object in response to the data acquisition task; wherein any one of the feedback information comprises reference house source data;

the acquisition module is used for acquiring the house source data to be acquired according to the feedback information;

and the first updating module is used for updating the house source database by using the acquired house source data to be acquired.

In one alternative example of this, the user may,

the processing module comprises:

the initiating submodule is used for initiating a simulation interactive session to the object;

the processing submodule is used for sending a simulated user problem associated with the room source data to be collected in the simulated interactive session and acquiring feedback information of the object aiming at the simulated user problem;

alternatively, the first and second electrodes may be,

the processing module comprises:

In one optional example, the obtaining module includes:

the first determining submodule is used for determining the acquisition demand type of the room source data to be acquired; wherein the acquisition demand type comprises at least one of: a missing supplement type, a personalized supplement type, and an update replacement type;

the first screening submodule is used for screening target reference house source data from the acquired feedback information according to a screening mode corresponding to the acquisition demand type;

and the second determining submodule is used for taking the target reference room source data as the obtained room source data to be acquired.

In one alternative example of this, the user may,

under the condition that the acquisition demand type is a missing supplement type or an individualized supplement type, the first screening submodule is specifically configured to:

the device further comprises:

and the second updating module is used for updating the weight of the corresponding object according to the matching degree of each reference room source data and the room source data to be acquired after the target reference room source data is used as the acquired room source data to be acquired.

In an optional example, in a case that the acquisition requirement type is a missing supplement type or a personalized supplement type, the first filtering submodule includes:

a first determination unit for determining a first data set; wherein the first data set includes the reference room-source data appearing in at least one piece of the feedback information, and any two reference room-source data in the first data set are different from each other;

a second determining unit, configured to determine the number of occurrences of each of the reference origin data in the first data set in all the feedback information;

and the first screening unit is used for screening the reference house source data with the largest occurrence frequency from the first data set as target reference house source data.

In an optional example, in a case that the collection requirement type is an update replacement type, the first filtering submodule includes:

a third determining unit for determining a second data set; wherein the second data set comprises the reference room-source data appearing in at least one piece of the feedback information, and any two reference room-source 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 room source data in the second data set; wherein the confidence of any one of the reference room source data is as follows: a ratio of the number of the feedback information including the reference room source data to the total number of the feedback information;

the second screening unit is used for screening the reference room source data with the maximum corresponding confidence coefficient from the second data set;

and the fifth determining unit is used for taking the screened reference room source data as target reference room source data under the condition that the confidence coefficient of the screened reference room source data is greater than the preset confidence coefficient.

In one optional example, the processing module includes:

the second screening submodule is used for screening at least part of the objects from the objects related to the room source data to be collected according to a preset mapping relation; the preset mapping relationship is a mapping relationship between an object and a weight, or the preset mapping relationship is a mapping relationship between an object attribute and a weight;

the distribution module is used for distributing a data acquisition task related to the room source data to be acquired to at least part of the screened objects;

the device further comprises:

and the third updating module is used for updating the preset mapping relation according to the feedback performance of at least part of the screened objects.

In an optional example, the second determining module is specifically configured to at least one of: determining an object having a preset relationship with a house source corresponding to the house source data to be collected; and determining an object with a preset behavior on the house source corresponding to the house source data to be collected in the target time period.

In an optional example, the first determining module is specifically configured to at least one of: taking the missing data in the house source database as house source data to be collected; taking the house source data which are stored in the house source database and correspond to which the recommended unadopted times are more than the preset times as the house source data to be collected; and aiming at the house source database, taking the house source data meeting the preset supplement conditions as the house source data to be collected.

According to still another aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium storing a computer program for executing the above database updating method.

According to still another aspect of an embodiment of the present disclosure, there is provided an electronic device including:

a processor;

a memory for storing the processor-executable instructions;

the processor is used for reading the executable instruction from the memory and executing the instruction to realize the database updating method.

In the embodiment of the disclosure, after determining the room source data to be collected for the room source database, the object associated with the room source data to be collected may be determined so as to reliably locate the object that may provide the room source data to be collected. And then, distributing a data acquisition task to the determined object so as to obtain accurate and reliable room source data to be acquired according to feedback information from the object and including the reference room source data, and updating the room source database by using the obtained room source data to be acquired. Therefore, in the embodiment of the disclosure, the room source database is updated without manual checking and manual updating, and the accuracy and reliability of the room source data used in updating the room source database can be ensured by distributing the data acquisition task to the specific user.

The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent by describing in more detail embodiments of the present disclosure with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the principles of the disclosure and not to limit the disclosure. In the drawings, like reference numbers generally represent like parts or steps.

Fig. 1 is a schematic flowchart of a database updating method according to an exemplary embodiment of the present disclosure.

FIG. 2 is a flow chart of a user's interaction with a broker as implemented by a user simulator in another exemplary embodiment of the present disclosure.

Fig. 3 is a schematic diagram of the update of the house source database in yet another exemplary embodiment of the present disclosure.

Fig. 4 is a schematic structural diagram of a database updating apparatus according to an exemplary embodiment of the present disclosure.

Fig. 5 is a schematic structural diagram of a database updating apparatus according to another exemplary embodiment of the present disclosure.

Fig. 6 is a block diagram of an electronic device provided in an exemplary embodiment of the present disclosure.

Detailed Description

Hereinafter, example embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of the present disclosure and not all embodiments of the present disclosure, and that the present disclosure is not limited by the example embodiments described herein.

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

It will be understood by those of skill in the art that the terms "first," "second," and the like in the embodiments of the present disclosure are used merely to distinguish one element from another, and are not intended to imply any particular technical meaning, nor is the necessary logical order between them.

It is also understood that in embodiments of the present disclosure, "a plurality" may refer to two or more and "at least one" may refer to one, two or more.

It is also to be understood that any reference to any component, data, or structure in the embodiments of the disclosure, may be generally understood as one or more, unless explicitly defined otherwise or stated otherwise.

In addition, the term "and/or" in the present disclosure is only one kind of association relationship describing an associated object, and means that three kinds of relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in the present disclosure generally indicates that the former and latter associated objects are in an "or" relationship.

It should also be understood that the description of the various embodiments of the present disclosure emphasizes the differences between the various embodiments, and the same or similar parts may be referred to each other, so that the descriptions thereof are omitted for brevity.

Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual scale relationship for the convenience of description.

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

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

The disclosed embodiments may be applied to electronic devices such as terminal devices, computer systems, servers, etc., which are operational 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 that may be suitable for use with electronic devices, such as terminal devices, computer systems, servers, and the like, include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputers, mainframe computer systems, distributed cloud computing environments that include any of the above systems, and the like.

Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.

Exemplary method

Fig. 1 is a schematic flowchart of a database updating method according to an exemplary embodiment of the present disclosure. The method shown in fig. 1 may include step 101, step 102, step 103, step 104 and step 105, which are described separately below.

Step 101, determining room source data to be collected aiming at a room source database.

Here, the house source database may be a building dictionary.

It should be noted that the specific implementation form of step 101 is various, and the following description is made by way of example.

In a specific implementation form, step 101 includes:

and taking the missing data in the house source database as the house source data to be collected.

Here, statistics may be performed on missing data triggered by high frequency in the room source database, and a format of the statistical result may be:

< house source id, { missing field 1: pv1, missing field 2: pv2, … … } >)

< floor id, { missing field 1: pv1, missing field 2: pv2, … … } >)

< cell id, { missing field 1: pv1, missing field 2: pv2, … … } >)

Optionally, all missing data triggered at high frequency in the room source database may be used as data to be acquired; or, under the condition that the data volume of the missing data triggered by high frequency in the room source database is very large, the missing data can be sorted, and part of the missing data is selected as the room source data to be collected according to the sorting sequence.

In the implementation form, from the perspective of data loss, the room source data to be collected can be determined very conveniently.

In another specific implementation form, step 101 includes:

and taking the house source data which are stored in the house source database and correspond to which the recommended unadopted times are more than the preset times as the house source data to be collected.

Here, it is also possible to count the house source data that has been stored in the house source database and is triggered with high frequency. Generally speaking, when a user communicates with a broker (specifically, a property broker) on a service platform of a house enterprise, according to the requirement of the user, an intelligent assistant of the house enterprise may extract corresponding house source data from a house source database and recommend the extracted house source data to the broker, and the broker may select to adopt the house source data recommended by the intelligent assistant and provide the house source data to the user, or may select not to adopt the house source data recommended by the intelligent assistant and provide other house source data to the user. Under the condition that the intelligent assistant recommends any house source data which is stored in the house source database and is triggered by high frequency to the broker, and the broker does not adopt the house source data, 1 can be added on the basis of the existing count value of the recommended unadopted times corresponding to the house source data. If the recommended unadopted times corresponding to any room source data which has been stored in the room source database and is triggered at high frequency are greater than the preset times (for example, 8 times, 10 times, 20 times, etc.), it may be determined that the room source data is likely to be inaccurate (possibly caused by data registration errors, data not being updated in time, etc.), and then the room source data which has been stored in the room source database and is triggered at high frequency may be counted, and the format of the statistical result may be:

< house source id, { modification field 1: pv1, modification field 2: pv2, … … } >)

< floor id, { modification field 1: pv1, modification field 2: pv2, … … } >)

< cell id, { modification field 1: pv1, modification field 2: pv2, … … } >)

Optionally, all the room source data which are stored in the room source database and are triggered at high frequency and whose corresponding recommended unadopted times are greater than the preset times may be used as the data to be collected; or, the house source data may be sorted under the condition that the house source database has been stored and is triggered at a high frequency, and the data volume of the corresponding house source data with the recommended unadopted times larger than the preset times is very large, and part of the house source data is selected as the house source data to be collected according to the sorting result.

In the implementation form, from the perspective of data accuracy, the house source data to be collected can be determined very conveniently.

In another specific implementation form, step 101 includes:

Generally speaking, part of house source data is only stored in the house source database in the form of "yes" or "no", and a more detailed description is missing, for example, for the problem of "whether the house type is square" proposed by the user, only no can be obtained by using the house source database, and how large the degree of the squareness is, what the squareness is, cannot be known specifically, so that the personalized requirements of the user are difficult to meet. In view of this, the house source data describing the house type in detail may be used as the house source data meeting the preset supplement condition, and the house source data meeting the preset supplement condition may be counted, and the format of the statistical result may be:

< house source id, { field description 1: pv1, field description 2: pv2, … … } >)

< building id, { field description 1: pv1, field description 2: pv2, … … } >)

< cell id, { field description 1: pv1, field description 2: pv2, … … } >)

Optionally, all the house source data meeting the preset supplement condition can be used as the house source data to be collected; or, the house source data can be sorted under the condition that the data volume of the house source data meeting the preset supplement condition is very large, and part of the house source data is selected as the house source data to be collected according to the sorting result.

In the implementation form, from the perspective of data individuation, the house source data to be collected can be determined very conveniently.

Step 102, determining an object associated with the room source data to be collected.

Here, the number of objects associated with the room source data to be collected may be one, two, three, or more than three, which are not listed here.

It should be noted that the specific implementation of step 102 is various, and the following description is given by way of example.

In one specific implementation form, step 102 includes:

and determining an object with a preset relationship with the house source corresponding to the house source data to be collected.

Here, the fact that any one of the house sources has a preset relationship with any one of the objects means that: the object is a maintainer, a surveyor, a person with a watcher, a key holder and the like of the house source; or the object is a maintainer, a surveyor, a person with a watcher and the like of the building where the house source is located; alternatively, the object is a maintainer, a surveyor, a person with a viewer, etc. of the cell in which the house source is located.

In the implementation form, based on the preset relationship, the object which may provide the house source data to be collected can be conveniently and reliably positioned.

In another specific implementation form, step 102 includes:

and determining the object of the preset behavior on the house source corresponding to the house source data to be collected in the target time period.

Here, the target time period may be the last 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 investigation behavior, a maintenance behavior, a watch-with behavior, and the like.

In the implementation mode, the object which possibly provides the house source data to be collected can be conveniently and reliably positioned based on the preset behavior.

It should be noted that, no matter what implementation form the step 102 is implemented, the objects associated with the house source data to be collected include, but are not limited to, brokers, property workers, and the like, and for convenience of understanding, the embodiments of the present disclosure are described by taking the object associated with the house source data to be collected as a broker as an example.

103, distributing a data acquisition task related to the room source data to be acquired to the object, and acquiring feedback information returned by the object in response to the data acquisition task; wherein, any feedback information comprises reference house source data.

Here, the data collection task associated with the room source data to be collected may be: and the data acquisition task is used for acquiring the room source data to be acquired.

Optionally, if the number of the objects associated with the room source data to be collected is multiple (i.e. at least two), the data collection task may be distributed to each object of the multiple objects; alternatively, at least some objects may be screened from the plurality of objects according to a policy, and only the screened at least some objects may be distributed with the data collection task.

Alternatively, in the case where the number of objects associated with the room source data to be collected is plural, the data collection task may be expressed in the form of:

< entity id, [ field 1, field 2, … … ], [ Broker 1, Broker 2, … … ] >)

In one embodiment, distributing a data collection task associated with the room source data to be collected to the object, and acquiring feedback information returned by the object in response to the data collection task may include:

and distributing a data acquisition task related to the room source data to be acquired to the plurality of objects, and acquiring feedback information returned by at least part of the plurality of objects.

It should be noted that the number of the objects that return the feedback information may be one or more than one, and when the number of the objects that return the feedback information is more than one, the number of the feedback information is also more than one, wherein, the reference house source data in the feedback information returned by different objects may be the same or different, and the reference house source data in each feedback information may be data that can assist in obtaining the house source data to be collected, and by distributing the data collection task to a plurality of objects, it is beneficial to obtain more feedback information, thereby facilitating the obtaining of the house source data to be collected.

And step 104, acquiring the room source data to be acquired according to the feedback information.

After the feedback information from the object is obtained, the feedback information may be analyzed to obtain reference room source data in the feedback information, and room source data to be collected is obtained based on the obtained reference room source data.

And 105, updating the house source database by using the acquired house source data to be acquired.

In step 101, in the case that the room source data to be collected is determined from the perspective of data missing or the perspective of data personalization, the room source data to be collected obtained in step 104 may be added to the room source database; in the case that the room source data to be collected is determined from the viewpoint of data accuracy in step 101, the room source data to be collected obtained in step 104 may be used to replace the corresponding room source data existing in the room source database.

In an optional example, distributing a data acquisition task associated with the room source data to be acquired to the object, and acquiring feedback information returned by the object in response to the data acquisition task, includes:

initiating a simulated interactive session to the object;

in the simulation interactive session, a simulation user problem associated with the room source data to be collected is sent, and feedback information of the object aiming at the simulation user problem is obtained.

Here, the user simulator may be one function module in a website provided on the network side or one function module in a client application installed in the computer.

It should be noted that, in actual operation, the user simulator may determine session framework information according to the room source data to be collected, where the session framework information may be information for defining a range of session content, and the session framework information may also be referred to as a user target, where the user target may be represented as Goal G ═ C, R, where C represents a constraint condition, and R represents query content. In one example, the constraints and query contents may be as shown in Table 1 below.

TABLE 1

Next, in accordance with the user goal, the user simulator can generate a reasonable user action (which can characterize the preset semantic tags), and for the property domain, the dialog actions include but are not limited to semantic tags asking for house price, asking for house location, asking if there is a house, informing of house purchase price, informing of house purchase location, and informing of property type. Reasonable user actions generated by the user modeler may then be translated into human-understandable linguistic expressions to generate a simulated user message and to open up a conversation with the broker (which is equivalent to simulating an interactive conversation); after the broker replies or asks questions (which is equivalent to returning feedback information) according to its own understanding and knowledge, the user simulator may again generate a reasonable user action and generate a simulated user message accordingly, and so on until the whole session is ended.

Optionally, in order to generate reasonable user actions, an action model may be established in advance, and in addition, a plurality of variables may be formed based on existing dialog contents and session framework information between the user simulator and the broker, and the plurality of variables are encoded respectively (for example, one-hot encoding or the like) so that each variable forms one vector, and the plurality of vectors are spliced to form corresponding dialog context information. Then, the formed dialog context information may be provided to the action model to obtain a probability of each user action of K user actions (K is the total number of the preset user actions) output by the action model, so that K probabilities may be obtained, and the sum of the K probabilities may be 1; wherein the probability of any conversational action may represent the likelihood that the present conversational action is ultimately determined to be a legitimate conversational action. Then, the probability with the maximum value can be selected from the K probabilities, and the user action corresponding to the selected probability is determined as a reasonable user action.

In a specific implementation, the user simulator in fig. 2 may be used to simulate that a user (i.e., a real user) interacts with an object (e.g., a broker) associated with the house source data to be collected, and as shown in fig. 2, a specific interaction process may be:

(1) determining a user target: the user goal may be expressed as: g ═ C, R, C as a constraint condition specifically is a house/floor id (i.e., an id of a house source or a floor corresponding to the house source data to be collected) to be collected, R as query content specifically corresponds to house source attribute information (which represents the house source data to be collected) to be collected, for example, as shown in table 2 below.

TABLE 2

(2) The user model comprises the following steps:

the method is based on an Agenda (which is a timing task management module) method, realizes the simulation user action of data acquisition, and stores and maintains a user action set by using a stack; the stack top is an action for informing house/building id (house card and building card appear in session), the stack bottom is an action of bye, and the middle is the content of inquiry, which is specifically shown as follows:

C₀＝[house＝123]

(3) and (3) natural language generation:

the broker is initiated a simulated user session and at the start of the session, a house/floor id notification action is performed. Specifically, the corresponding card interface may be called according to the house/floor id, the corresponding house card/floor card is generated, and the generated house card/floor card is sent to the broker.

Subsequent actions each maintain a pool of questions, one question at a time randomly taken as a simulated user question, for example: the question pool corresponding to the request _ floor (inquiry floor) may be:

{

is this a few stories?

Several layers?

How many layers?

Is this house on several floors?

}

The complete example and stack changes in the simulated interactive session can be as follows:

C＝[house＝123]

C₀＝[house＝123]

a broker: what can help you?

Customer: i want to know 123 this set of houses.

C′₁＝[house＝123]

A broker: well, your overall need is what-like.

C₂＝[house＝123]

Customer: is this house on several floors?

C′₃＝[house＝123]

A broker: this is on level 4. Not the higher layer.

C₄＝[house＝123]

Customer: fine, right after all; how this is oriented?

C′₅＝[house＝123]

A broker: the north and south are transparent, and the ventilation is particularly good.

C₆＝[house＝123]

Customer: what is the pupil of the mouth-checking area?

C′₇＝[house＝123]

A broker: this is the best school in Beijing City, and is Qinghua and Xiao. The study area room woollen cloth.

C₈＝[house＝123]

Customer: good, thanks you, i know.

C′₉＝[house＝123]

A broker: you can consult me at any time for any problem. 18810000000000.

In the above examples, "the house is in several stories", "how this orientation", "what school zone is a goal zone" are respectively as simulated user questions; "this is at level 4. Not high, the south and north are transparent, the ventilation is particularly good, and the school is the best school in Beijing city, and is small in size. The study area room woollen materials are respectively used as feedback information. In addition, as the conversation progresses, Agenda dynamically updates and pops up user conversation behavior from the top of Agenda to form a round of user actions.

After the feedback information in the above example is obtained, the room source data to be collected may be obtained based on the feedback information by using Natural Language Understanding (NLU).

In the embodiment of the disclosure, after the room source data to be collected and the object associated with the room source data to be collected are determined, the problem of the simulation user can be thrown out and the feedback information can be acquired in the simulation interaction session, so that the data collection task can be performed in a reasonable scene, and the enthusiasm of the object associated with the room source data to be collected for answering the problem can be stimulated.

In an optional example, obtaining the room source data to be collected according to the feedback information includes:

according to a screening mode corresponding to the type of the acquisition demand, screening target reference house source data from the acquired feedback information;

and taking the target reference room source data as the acquired room source data to be acquired.

Under the condition that the number of the objects related to the room source data to be collected is multiple, the data collection tasks related to the room source data to be collected can be distributed to the multiple objects, feedback information returned in response to the data collection tasks is obtained, each feedback information can comprise reference room source data, and the reference room source data in the feedback information returned by different objects can be the same or different.

Next, the type of acquisition requirement of the room source data to be acquired may be determined. Specifically, in the step 101, when the room source data to be collected is determined from the data missing perspective, the collection requirement type of the room source data to be collected may be a missing supplement type; in the step 101, in the case that the room source data to be collected is determined from the data accuracy perspective, the collection requirement type of the room source data to be collected may be an update replacement type; in the step 101, in the case that the room source data to be collected is determined from the data personalization perspective, the collection requirement type of the room source data to be collected may be a personalized supplement type.

Then, a screening mode corresponding to the acquisition demand type can be determined, and the target reference house source data is screened from the acquired feedback information according to the determined screening mode so as to be used as the acquired house source data to be acquired.

In a specific embodiment, when the acquisition demand type is a missing supplement type or a personalized supplement type, the method for screening target reference room source data from the acquired feedback information according to a screening manner corresponding to the acquisition demand type includes:

screening target reference room source data from the acquired feedback information according to the weight of each object which returns the feedback information;

after the target reference room source data is used as the obtained room source data to be collected, the method further comprises the following steps:

It should be noted that, weights may be set for each broker in advance, and the weight of any broker may be adjusted according to actual conditions.

Assuming that after the distribution of the data acquisition task, five objects return feedback information, which are D1, D2, D3, D4 and D5 respectively, wherein the reference room source data in the feedback information returned by D1 is K1, the reference room source data in the feedback information returned by D2 is K2, the reference room source data in the feedback information returned by D3 is K3, the reference room source data in the feedback information returned by D4 is K4, the reference room source data in the feedback information returned by D5 is K5, and the respective weights of D1 to D5 are different from each other, the object with the largest corresponding weight can be selected from D1 to D5, and if the selected object is D5, K5 in the feedback information returned by D5 can be used as the target reference room source data.

In addition, the matching degrees of K1 to K4 and K5 can be respectively calculated, and D1 to D4 can be updated according to the calculated matching degrees. For example, for K1, the similarity between K1 and K5 may be calculated, the calculated similarity is used to characterize the matching degree between K1 and K5, if the matching degree between K1 and K5 is higher, the weight of K1 may be increased, and if the matching degree between K1 and K5 is lower, the weight of K1 may be decreased.

In the embodiment, the target reference house source data can be conveniently and reliably screened out based on the weight of the object, and the reliability of the screening result can be further ensured through dynamic updating of the weight, so that the reliability of the house source data for updating the house source database is better ensured.

In another specific embodiment, when the acquisition demand type is a missing supplement type or a personalized supplement type, screening target reference room source data from the acquired feedback information according to a screening manner corresponding to the acquisition demand type, including:

determining a first data set; the first data set comprises reference house source data appearing in at least one piece of feedback information, and any two reference house source data in the first data set are different;

determining the occurrence frequency of each reference room source data in the first data set in all feedback information;

and screening the reference house source data with the most corresponding occurrence times from the first data set as target reference house source data.

Continuing with the example in the previous embodiment, assuming that the five objects D1-D5 return feedback information after the distribution of the data collection task, the feedback information of D1-D5 includes K1-K5 in turn, the first data set may be determined according to K1-K5. Assuming that the room source data to be collected is floor data, K1 is a fourth floor, K2 is a third floor, K3 is a fourth floor, K4 is a fourth floor, and K5 is a fifth floor, the first data set may be represented as: { third floor, fourth floor, and fifth floor }, and it can also be determined that the number of occurrences of the third floor in all feedback information is one, the number of occurrences of the fourth floor in all feedback information is three, and the number of occurrences of the fifth floor in all feedback information is one, and it is obvious that the number of occurrences corresponding to the fourth floor is the largest, and therefore, the fourth floor can be used as a target for referring to house source data.

In the implementation mode, the target reference house source data can be conveniently and reliably screened out in a majority voting mode, so that the reliability of the house source data used for updating the house source database is better ensured.

It should be noted that the above two embodiments can be used in combination to realize the screening of the target reference house source data by a weighted majority table.

In another specific embodiment, in a case that the acquisition demand type is an update replacement type, the screening target reference room source data from the acquired feedback information according to a screening manner corresponding to the acquisition demand type includes:

determining a second data set; the second data set comprises reference room-source data appearing in at least one piece of feedback information, and any two reference room-source data in the second data set are different;

determining a confidence level of each reference room source data in the second data set; the confidence of any reference room source data is as follows: the ratio of the number of the feedback information of the reference room source data to the total number of the feedback information is included;

screening the reference house source data with the maximum corresponding confidence coefficient from the second data set;

and under the condition that the confidence coefficient of the screened reference room source data is greater than the preset confidence coefficient, taking the screened reference room source data as the target reference room source data.

Continuing with the example in the previous embodiment, assuming that the five objects D1-D5 return feedback information after the distribution of the data collection task, the feedback information of D1-D5 includes K1-K5 in turn, the second data set may be determined according to K1-K5. Assuming that the room source data to be collected is floor data, K1 is a fourth floor, K2 is a third floor, K3 is a fourth floor, K4 is a fourth floor, and K5 is a fifth floor, the second data set can be represented as: { third, fourth, and fifth buildings }, and it can also be determined that the confidence of the third building is 1/5, the confidence of the fourth building is 3/5, and the confidence of the fifth building is 1/5. Then, the reference house source data with the maximum corresponding confidence degree can be screened from the second data set, namely, the fourth building is screened, and then, whether the confidence degree (namely 3/5) of the fourth building is greater than a preset confidence degree can be judged, and under the condition that the judgment result is yes, the fourth building can be used as the target reference house source data; otherwise, it may be determined that the target reference house source data screening fails.

In this embodiment, the confidence of the reference house source data used as the target reference house source data is greater than the preset confidence, that is, for the house source data that needs to be updated and replaced, only if most brokers provide uniform answers, the house source data will be replaced, so that the updating effect of the house source database can be ensured.

In the embodiment of the disclosure, the target reference house source data is screened in a screening mode corresponding to the type of the acquisition demand of the house source data to be acquired, so that the reliability of the target reference house source data obtained by screening can be ensured, and the updating effect of the house source database is better ensured.

In one optional example, distributing to the subject a data collection task associated with the room source data to be collected includes:

screening at least part of objects from a plurality of objects associated with the room source data to be collected according to a preset mapping relation; the preset mapping relationship is a mapping relationship between an object and a weight, or the preset mapping relationship is a mapping relationship between an object attribute and a weight;

the method further comprises the following steps:

It should be noted that a preset mapping relationship may be set, where the preset mapping relationship may be a mapping relationship between an object attribute and a weight, or the preset mapping relationship may be a mapping relationship between an object and a weight, and for convenience of understanding, the case where the preset mapping relationship is a mapping relationship between an object attribute and a weight is taken as an example for description here. Alternatively, the object attributes include, but are not limited to, job level attributes, store level attributes, gender attributes, age attributes, etc., for example, manager level, general staff level, and trainee level may be respectively one object attribute, and for example, youth attribute and middle-age attribute may be respectively one object attribute.

Assuming that after determining the objects associated with the house source data to be collected, the number of the determined objects is six, the six objects are respectively E1, E2, E3, E4, E5 and E6, wherein the attributes of the objects of E1, E2, E3 and E4 are in the level of common staff, the object attribute of E5 is in the level of manager, and the object level of E6 is in the level of trainee, at least part of the objects can be screened from E1 to E6 according to the preset mapping relationship, and the preset mapping relationship is updated according to the feedback expression of the screened at least part of the objects; feedback performance includes, but is not limited to, feedback aggressiveness, feedback accuracy, etc.

Specifically, if the weight corresponding to the general staff 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 in the preset mapping relationship, the object attribute (i.e., the general staff level) with the largest corresponding weight may be determined according to the preset mapping relationship. Next, from E1 to E6, objects belonging to the general staff member level may be screened, i.e., screened out of E1 to E4, and data collection tasks may be distributed to E1 to E4. If the feedback can be rapidly carried out on the E1-E4 aiming at the distributed data acquisition task, the feedback enthusiasm of the E1-E4 can be considered to be very high, and the weight mapped by the common staff level in the preset mapping relation can be increased so as to realize the updating of the preset mapping relation; if only E1 feeds back quickly, E2 to E4 do not feed back or the feedback is very slow, the weights mapped by the levels of the common staff members in the preset mapping relationship can be turned down to realize the updating of the preset mapping relationship.

Alternatively, if the weight corresponding to the general staff level is 0.85, the weight corresponding to the manager level is 0.6, and the weight corresponding to the trainee level is 0.7 in the preset mapping relationship, the object attributes (i.e., the general staff level and the trainee level) whose corresponding weights are greater than the preset weight (e.g., 0.65) may be determined according to the preset mapping relationship. Thereafter, it is possible to screen out the objects belonging to the general staff member level and the objects belonging to the intern level, i.e., screen out E1 to E4 and E6, from E1 to E6, and distribute the data collecting task to E1 to E4 and E6. If feedback can be rapidly carried out on E1-E4 aiming at distributed data collection tasks, and feedback is not carried out on E6 or is very slow, the weight mapped by the level of common staff members in the preset mapping relation can be increased, and the weight mapped by the level of interns in the preset mapping relation can be decreased, so that updating of the preset mapping relation is realized.

In the embodiment of the disclosure, when there are a plurality of objects associated with the room source data to be collected, a suitable object may be selected from the plurality of objects to perform distribution of the data collection task based on the preset mapping relationship, and the preset mapping relationship may be dynamically updated according to the feedback performance of the object for the data distribution task, so as to further optimize the selection effect when the object is selected.

In an alternative example, as shown in fig. 3, the house source data to be collected may be determined for a building dictionary; the acquisition demand type of the room source data to be acquired may include at least one of the following: a missing supplement type, a personalized supplement type, and an update replacement type. Next, an appropriate broker may be selected according to the house source data to be collected, and a data collection task may be distributed to the selected broker. The answers returned by the selected broker (equivalent to the feedback information above) may then be obtained and the data analysis performed accordingly. Specifically, the NLU may be used to process the broker answer into unified structured data, and analyze the structured data obtained by processing, where a data format of an analysis result may be:

< entity id, { field 1: { Broker 1: v1, Broker 2: v2, … … }, field 2: { menstrual person 1: v1, Broker 2: v2, … …, … … } >, and

if the target reference house source data are screened out from the analysis result, the target reference house source data can be used as house source data to be collected and flow back to the building dictionary, so that the updating of the building dictionary is realized. It can be seen that embodiments of the present disclosure can leverage the personal knowledge of a large number of brokers to refine and update the floor dictionary for the use of the floor dictionary by an intelligent assistant.

In summary, the embodiments of the present disclosure can find out data that is high in trigger quantity and is missing or to be updated, accurately locate the broker group that is most likely to give the correct answer, change a data collection scenario into a standard configuration of a training field, integrate into a broker daily task, and conveniently and reliably implement updating of a house source database.

Any of the database update methods provided by embodiments of the present disclosure may be performed by any suitable device having data processing capabilities, including but not limited to: terminal equipment, a server and the like. Alternatively, any of the database update methods provided by the embodiments of the present disclosure may be executed by a processor, for example, the processor may execute any of the database update methods mentioned by the embodiments of the present disclosure by calling a corresponding instruction stored in a memory. And will not be described in detail below.

Exemplary devices

Fig. 4 is a schematic structural diagram of a database updating apparatus according to an exemplary embodiment of the present disclosure, and the apparatus shown in fig. 4 includes a first determining module 401, a second determining module 402, a processing module 403, an obtaining module 404, and an updating module 405.

A first determining module 401, configured to determine room source data to be acquired for a room source database;

a second determining module 402, configured to determine an object associated with the room source data to be collected;

the processing module 403 is configured to distribute a data acquisition task associated with the room source data to be acquired to the object, and acquire feedback information returned by the object in response to the data acquisition task; wherein any feedback information comprises reference house source data;

an obtaining module 404, configured to obtain room source data to be collected according to the feedback information;

and a first updating module 405, configured to update the house source database by using the obtained house source data to be collected.

In an alternative example, as shown in fig. 5, the processing module 403 includes:

an initiating submodule 4031 for initiating a simulated interactive session to an object;

the processing sub-module 4032 is configured to send, in the simulation interaction session, a simulation user question associated with the room source data to be collected, and acquire feedback information of the object for the simulation user question.

In an alternative example, the processing module 403 is specifically configured to:

In an alternative example, the obtaining module 404 includes:

and the second determining submodule is used for taking the target reference room source data as the acquired room source data to be acquired.

In one alternative example of this, the user may,

under the condition that the acquisition demand type is a missing supplement type or an individualized supplement type, the first screening submodule is specifically used for:

the device also includes:

and the second updating module is used for updating the weight of the corresponding object according to the matching degree of each reference room source data and the room source data to be collected after the target reference room source data is used as the obtained room source data to be collected.

In an optional example, in a case that the collection requirement type is a missing supplement type or a personalized supplement type, the first filtering submodule includes:

a first determination unit for determining a first data set; the first data set comprises reference house source data appearing in at least one piece of feedback information, and any two reference house source data in the first data set are different;

the second determining unit is used for determining the occurrence frequency of each reference room source data in the first data set in all feedback information;

and the first screening unit is used for screening the reference house source data with the largest occurrence frequency from the first data set as the target reference house source data.

In an optional example, in a case that the collection requirement type is an update replacement type, the first filtering sub-module includes:

a third determining unit for determining a second data set; the second data set comprises reference room-source data appearing in at least one piece of feedback information, and any two reference room-source data in the second data set are different;

a fourth determining unit, configured to determine a confidence of each reference room source data in the second data set; the confidence of any reference room source data is as follows: the ratio of the number of the feedback information of the reference room source data to the total number of the feedback information is included;

the second screening unit is used for screening the reference house source data with the maximum corresponding confidence coefficient from the second data set;

and the fifth determining unit is used for taking the screened reference room source data as the target reference room source data under the condition that the confidence coefficient of the screened reference room source data is greater than the preset confidence coefficient.

In an alternative example, the processing module 403 includes:

the second screening submodule is used for screening at least part of objects from a plurality of objects related to the house source data to be collected according to a preset mapping relation; the preset mapping relationship is a mapping relationship between an object and a weight, or the preset mapping relationship is a mapping relationship between an object attribute and a weight;

the distribution module is used for distributing data acquisition tasks related to the house source data to be acquired to at least part of the screened objects;

the device also includes:

In an optional example, the second determining module 402 is specifically configured to at least one of: determining an object having a preset relationship with a house source corresponding to house source data to be collected; and determining an object with a preset behavior on the house source corresponding to the house source data to be collected in the target time period.

In an optional example, the first determining module 401 is specifically configured to at least one of: taking the missing data in the house source database as house source data to be collected; taking the house source data which are stored in the house source database and correspond to which the recommended unadopted times are more than the preset times as the house source data to be collected; and aiming at the house source database, taking the house source data meeting the preset supplement conditions as the house source data to be collected.

Exemplary electronic device

Next, an electronic apparatus according to an embodiment of the present disclosure is described with reference to fig. 6. The electronic device may be either or both of the first device and the second device, or a stand-alone device separate from them that may communicate with the first device and the second device to receive the acquired input signals therefrom.

Fig. 6 illustrates a block diagram of an electronic device 600 in accordance with an embodiment of the disclosure.

As shown in fig. 6, the electronic device 600 includes one or more processors 601 and memory 602.

The processor 601 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 600 to perform desired functions.

Memory 602 may include one or more computer program products that 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), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), a hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer-readable storage medium and executed by the processor 601 to implement the database update methods of the various embodiments of the present disclosure described above and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.

In one example, the electronic device 600 may further include: an input device 603 and an output device 604, which are interconnected by a bus system and/or other form of connection mechanism (not shown).

For example, when the electronic device 600 is a first device or a second device, the input means 603 may be a microphone or a microphone array. When the electronic device 600 is a stand-alone device, the input means 603 may be a communication network connector for receiving the acquired input signals from the first device and the second device.

The input device 603 may also include, for example, a keyboard, a mouse, and the like.

The output device 604 can output various kinds of information to the outside. The output devices 604 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, among others.

Of course, for simplicity, only some of the components of the electronic device 600 relevant to the present disclosure are shown in fig. 6, omitting components such as buses, input/output interfaces, and the like. In addition, electronic device 600 may include any other suitable components depending on the particular application.

Exemplary computer program product and computer-readable storage Medium

In addition to the above-described methods and apparatus, embodiments of the present disclosure may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the database updating method according to various embodiments of the present disclosure described in the "exemplary methods" section above of this specification.

The computer program product may write program code for carrying out operations for embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.

Furthermore, embodiments of the present disclosure may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform steps in a database update method according to various embodiments of the present disclosure described in the "exemplary methods" section above in this specification.

The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.

In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The block diagrams of devices, apparatuses, systems referred to in this disclosure are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" as used herein refers to the phrase "such as, but not limited to," and is used interchangeably therewith.

The methods and apparatus of the present disclosure may be implemented in a number of 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-described order for the steps of 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 specifically stated otherwise. Further, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

It is also noted that in the devices, apparatuses, and methods of the present disclosure, each component or step can be decomposed and/or recombined. These decompositions and/or recombinations are to be considered equivalents of the present disclosure.

The previous 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 generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the disclosure to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.

Claims

1. A database update method, comprising:

determining room source data to be collected aiming at a room source database;

determining an object associated with the room source data to be collected;

distributing a data acquisition task related to the room source data to be acquired to the object, and acquiring feedback information returned by the object in response to the data acquisition task; wherein any one of the feedback information comprises reference house source data;

acquiring the room source data to be acquired according to the feedback information;

2. The method of claim 1,

initiating a simulated interactive session to the object;

alternatively, the first and second electrodes may be,

3. The method according to claim 1 or 2, wherein the obtaining the room source data to be collected according to the feedback information comprises:

4. The method of claim 3,

under the condition that the acquisition demand type is a missing supplement type or an individualized supplement type, the step of screening target reference room source data from the acquired feedback information according to a screening mode corresponding to the acquisition demand type comprises the following steps:

after the target reference room source data is used as the room source data to be acquired, the method further comprises:

5. The method according to claim 3, wherein in a case that the acquisition demand type is a missing supplement type or a personalized supplement type, the screening of the target reference room source data from the acquired feedback information according to a screening manner corresponding to the acquisition demand type comprises:

determining a first data set; wherein the first data set includes the reference room-source data appearing in at least one piece of the feedback information, and any two reference room-source data in the first data set are different from each other;

6. The method according to claim 3, wherein in a case that the acquisition demand type is an update replacement type, the screening target reference room source data from the acquired feedback information according to a screening manner corresponding to the acquisition demand type includes:

determining a second data set; wherein the second data set comprises the reference room-source data appearing in at least one piece of the feedback information, and any two reference room-source data in the second data set are different from each other;

7. The method of claim 1, wherein distributing the data collection task associated with the room source data to be collected to the subject comprises:

the method further comprises the following steps:

8. A database update apparatus, comprising:

9. A computer-readable storage medium, in which a computer program is stored, the computer program being configured to perform the database updating method according to any one of claims 1 to 7.

10. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

the processor is configured to read the executable instructions from the memory and execute the instructions to implement the database updating method of any one of claims 1 to 7.