WO2021004318A1 - Resource data processing method and apparatus, computer device and storage medium - Google Patents

Abstract

A resource data processing method, relating to the field of artificial intelligence prediction technology. Said method comprises: receiving a query request sent by a terminal, the query request being used to acquire resource data prediction information concerning a region to be predicted, the query request carrying the region identifier of said region (S201); acquiring a resource data prediction index corresponding to the region identifier, querying a database according to the resource data prediction index, to acquire data to be predicted corresponding to the resource data prediction index (S202); acquiring a preset data conversion instruction, and converting, according to the preset data conversion instruction, the acquired data corresponding to the resource data prediction index, to obtain an associated value corresponding to the resource data prediction index (S203); acquiring a preset value conversion instruction, and converting, according to the preset value conversion instruction, the obtained associated value corresponding to the resource data prediction index, to obtain a reference value corresponding to the resource data prediction index (S204); inputting the reference value corresponding to the resource data prediction index into a pre-trained resource data prediction model, to obtain a resource data prediction value corresponding to the region identifier (S205); and generating resource data prediction information according to the resource data prediction value corresponding to the region identifier, and sending the resource data prediction information to the terminal for display (S206).

Description

Resource data processing method, device, computer equipment and storage medium

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on July 9, 2019. The application number is 201910612928.8 and the application name is "Resource data processing methods, devices, computer equipment and storage media." The reference is incorporated in this application.

Technical field

This application relates to the field of artificial intelligence prediction technology, in particular to a method, device, computer equipment and storage medium for processing resource data.

Background technique

Resource data forecast information refers to the resource data in the region, such as the forecast value of housing prices in a period of time in the future. The resource data forecast information in different regions is different; in order to grasp the forecast value of resource data in a period of time in the future, Resource data is very important for forecasting.

At present, the forecast of resource data in a region is usually based on the same type of specific data manually collected by the server, such as geographic information, policy information, urbanization level information, etc., and combined with a single qualitative evaluation model, The resource data is evaluated and forecasted. However, the inventor realizes that resource data is affected by multiple factors, and the main influencing factors of resource data in different regions are different; if the resource data in each region is targeted, it is only based on the same type of specific data collected manually , Combined with a single qualitative evaluation model to predict resource data, resulting in the inability to achieve a comprehensive tracking evaluation of the resource data prediction value, resulting in the resource data prediction information obtained prone to deviations, resulting in low accuracy of the resource data prediction information obtained .

Summary of the invention

According to various embodiments disclosed in the present application, a resource data processing method, device, computer equipment, and storage medium are provided.

A method for generating resource data prediction information includes:

Receiving a query request sent by a terminal; the query request is used to obtain resource data prediction information of the area to be predicted; the query request carries the area identifier of the area to be predicted;

Obtaining a resource data prediction index corresponding to the area identifier, querying a database according to the resource data prediction index, and obtaining data to be predicted corresponding to the resource data prediction index;

Acquiring a preset data conversion instruction, and converting the acquired data to be predicted corresponding to the resource data prediction index according to the preset data conversion instruction to obtain an associated value corresponding to the resource data prediction index;

Obtaining a preset value conversion instruction, and according to the preset value conversion instruction, convert the obtained associated value corresponding to the resource data prediction index to obtain a reference value corresponding to the resource data prediction index;

Input the reference value corresponding to the resource data prediction index into a pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the area identifier; and

Generate resource data prediction information according to the resource data prediction value corresponding to the area identifier, and send the resource data prediction information to the terminal for display.

A resource data processing device includes:

The request receiving module is configured to receive a query request sent by the terminal; the query request is used to obtain resource data prediction information of the area to be predicted; the query request carries the area identifier of the area to be predicted;

A data acquisition module, configured to acquire a resource data prediction index corresponding to the region identifier, query a database according to the resource data prediction index, and obtain data to be predicted corresponding to the resource data prediction index;

The data conversion module is configured to obtain a preset data conversion instruction, and according to the preset data conversion instruction, convert the obtained data to be predicted corresponding to the resource data predictive index to obtain the corresponding resource data predictive index The associated value;

The numerical value conversion module is configured to obtain a preset value conversion instruction, and according to the preset value conversion instruction, convert the obtained associated value corresponding to the resource data prediction index to obtain a reference corresponding to the resource data prediction index value;

A prediction value acquisition module, configured to input the reference value corresponding to the resource data prediction index into a pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the area identifier; and

The information generating module is configured to generate resource data prediction information according to the resource data prediction value corresponding to the area identifier, and send the resource data prediction information to the terminal for display.

A computer device, including a memory and one or more processors, the memory stores computer readable instructions, when the computer readable instructions are executed by the processor, the one or more processors execute The following steps:

Receiving a query request sent by a terminal; the query request is used to obtain resource data prediction information of the area to be predicted; the query request carries the area identifier of the area to be predicted;

Obtaining a resource data prediction index corresponding to the area identifier, querying a database according to the resource data prediction index, and obtaining data to be predicted corresponding to the resource data prediction index;

Acquiring a preset data conversion instruction, and converting the acquired data to be predicted corresponding to the resource data prediction index according to the preset data conversion instruction to obtain an associated value corresponding to the resource data prediction index;

Obtaining a preset value conversion instruction, and according to the preset value conversion instruction, convert the obtained associated value corresponding to the resource data prediction index to obtain a reference value corresponding to the resource data prediction index;

Input the reference value corresponding to the resource data prediction index into a pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the area identifier; and

Generate resource data prediction information according to the resource data prediction value corresponding to the area identifier, and send the resource data prediction information to the terminal for display.

One or more computer-readable storage media storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the following steps:

Receiving a query request sent by a terminal; the query request is used to obtain resource data prediction information of the area to be predicted; the query request carries the area identifier of the area to be predicted;

Obtaining a resource data prediction index corresponding to the area identifier, querying a database according to the resource data prediction index, and obtaining data to be predicted corresponding to the resource data prediction index;

Acquiring a preset data conversion instruction, and converting the acquired data to be predicted corresponding to the resource data prediction index according to the preset data conversion instruction to obtain an associated value corresponding to the resource data prediction index;

Obtaining a preset value conversion instruction, and according to the preset value conversion instruction, convert the obtained associated value corresponding to the resource data prediction index to obtain a reference value corresponding to the resource data prediction index;

Input the reference value corresponding to the resource data prediction index into a pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the area identifier; and

Generate resource data prediction information according to the resource data prediction value corresponding to the area identifier, and send the resource data prediction information to the terminal for display.

The above-mentioned resource data processing method, device, computer equipment and storage medium use a pre-trained resource data prediction model to evaluate and analyze the to-be-predicted data corresponding to the resource data prediction index related to the resource data prediction value of the area to be predicted. For the resource data prediction value of each region, the resource data is predicted based on the same type of specific data collected manually, combined with a single qualitative evaluation model, so as to realize the comprehensive prediction value of the resource data in the region to be predicted The purpose of tracking evaluation can avoid the defect that the resource data prediction information obtained by the traditional resource data prediction method is prone to deviation, thereby improving the accuracy of the obtained resource data prediction information. At the same time, combining the pre-trained resource data prediction model to analyze the data to be predicted can further improve the accuracy of the obtained resource data prediction information.

The details of one or more embodiments of the application are set forth in the following drawings and description. Other features and advantages of this application will become apparent from the description, drawings and claims.

Description of the drawings

In order to more clearly describe the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

Fig. 1 is an application scenario diagram of a resource data processing method according to one or more embodiments;

2 is a schematic flowchart of a method for processing resource data according to one or more embodiments;

FIG. 3 is a schematic flowchart of the steps of obtaining resource data predicted values according to one or more embodiments;

Fig. 4 is a block diagram of an apparatus for processing resource data according to one or more embodiments;

Figure 5 is a block diagram of a computer device according to one or more embodiments.

Detailed ways

In order to make the technical solutions and advantages of the present application clearer, the following further describes the present application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the application, and not used to limit the application.

The resource data processing method provided in this application can be applied to the application environment shown in FIG. 1. Wherein, the terminal 110 and the server 120 communicate through the network. The terminal 110 is installed with an application program through which the user can query the resource data prediction information corresponding to the area to be predicted, such as the housing price prediction information corresponding to the area to be predicted. The terminal 110 responds to the user's input operation on the query interface displayed by the application program, generates a query request for obtaining resource data prediction information of the region to be predicted, and sends the query request to the server 120. The server 120 parses the query request to obtain the region identification of the region to be predicted entered by the user; obtains the resource data prediction index corresponding to the region identification, queries the database according to the resource data prediction index, and obtains the to-be-predicted data corresponding to the resource data prediction index; The obtained data to be predicted corresponding to the resource data predictive index is converted to obtain the associated value corresponding to the resource data predictive index, and the associated value corresponding to the resource data predictive index is converted to obtain the reference value corresponding to the resource data predictive index ; Input the reference value corresponding to the resource data prediction index into the pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the region identifier; generate resource data prediction information according to the resource data prediction value corresponding to the region identifier, and convert the resource data The prediction information is sent to the terminal 110. According to the received resource data prediction information, the terminal 110 displays the resource data prediction value corresponding to the region identifier of the region to be predicted input by the user. The terminal 110 may be, but is not limited to, various personal computers, notebook computers, smart phones, and tablet computers, and the server 120 may be implemented by an independent server or a server cluster composed of multiple servers.

In one of the embodiments, as shown in FIG. 2, a method for processing resource data is provided. Taking the method applied to the server in FIG. 1 as an example for description, the method includes the following steps:

Step S201: Receive a query request sent by a terminal; the query request is used to obtain resource data prediction information of the area to be predicted; the query request carries the area identifier of the area to be predicted.

In this step, the area to be predicted can refer to an urban area, a province area, or an administrative area; resource data refers to the market value of the buildings in the area and the land occupied by them in a specific time period, such as housing prices; resource data prediction Information refers to the predicted value of the resource data in the region in the future, such as the predicted value of house prices; regional identification refers to the information used to identify the region to be predicted, such as the name of the region, the abbreviation of the region, or the region number, so that the server can distinguish .

The terminal installs an application program for querying resource data prediction information corresponding to the area identifier of the area to be predicted, and the user can query the resource data prediction information corresponding to the area identifier by logging in to the application program. The terminal responds to the user's input operation on the query interface displayed by the application program, generates a query request for obtaining resource data prediction information of the region to be predicted, and sends the query request to the server. The server parses the received query request to obtain the area identifier of the area to be predicted. In addition, the user can also log in to the browser running on the terminal to enter the query interface for resource data prediction information. Based on the query interface, the user enters the region identification of the region to be predicted, and the terminal sends a query request carrying the region identification of the region to be predicted to the server .

Step S202: Obtain the resource data prediction index corresponding to the area identifier, query the database according to the resource data prediction index, and obtain the data to be predicted corresponding to the resource data prediction index.

In this step, the resource data forecast index refers to the index that affects the forecast value of the resource data in the region. For example, the index that affects the forecast value of the housing price in the region is the Shanghai housing supply index, the Shanghai housing demand index, and the Shanghai policy index. , Shanghai land supply indicator, Shanghai population growth indicator, Guangzhou Commercial Bank's housing loan policy indicator, Guangzhou urbanization level indicator, Guangzhou rental policy indicator, Guangzhou overseas real estate development level indicator, etc.; the resource data forecast indicators corresponding to different regional indicators are different, For example, Shanghai’s housing supply indicators are not the same as Guangzhou’s housing supply indicators; and the main resource data prediction indicators corresponding to different regional indicators are different. For example, Guangzhou’s main resource data prediction indicators are the Guangzhou Commercial Bank’s housing loan policy indicators, Guangzhou urbanization level indicators, Guangzhou rental policy indicators, Guangzhou overseas real estate development level indicators, etc. Shanghai’s main resource data forecast indicators are Shanghai housing supply indicators, Shanghai housing demand indicators, Shanghai policy indicators, Shanghai land supply indicators, and Shanghai population growth indicators.

The server pre-determines the resource data predictive index corresponding to the area identifier; and based on big data, uses each resource data predictive index as a search keyword, and crawls the data corresponding to each resource data predictive index from the Internet in advance, and compares the crawled and Perform preprocessing operations on the data corresponding to each resource data prediction index, such as removing noise data, filtering interference information, etc., to obtain key data content corresponding to each resource data prediction index; take the obtained key data content as the data to be predicted, and treat it The prediction data adds the corresponding resource data prediction index, so as to obtain the data to be predicted corresponding to each resource data prediction index in the same area identifier; package the data to be predicted and the corresponding resource data prediction index belonging to the same area identifier to generate and The data packet corresponding to the area identifier is stored in a pre-established database; it is convenient for the subsequent server to query the database to obtain the data to be predicted corresponding to the resource data prediction index of the area identifier.

In addition, after a certain period of time, such as 3 months, the server re-obtains the to-be-predicted data corresponding to each resource data prediction index based on the Internet; for the same resource data prediction index, the newly acquired data to be predicted is used to overwrite the resource data in the database The original data corresponding to the predictive index and the timely update of the data in the database help to improve the accuracy and timeliness of the obtained data to be predicted corresponding to each resource data predictive index, and further improve the subsequent resource data prediction information. accuracy.

In specific implementation, the server filters out the known area identifiers that match the acquired area identifiers from the preset known area identifiers; obtains the resource data prediction index corresponding to the known area identifiers, and compares them with the known area identifiers. The resource data prediction index corresponding to the identification information is used as the resource data prediction index corresponding to the acquired area identification; according to the resource data prediction index corresponding to the acquired area identification, the pre-established database is queried, and the prediction of each resource data is obtained from the database The to-be-predicted data corresponding to the indicator is convenient for the subsequent server to determine the resource data predicted value corresponding to the area identifier according to the to-be-predicted data corresponding to each resource data prediction indicator, which realizes the comprehensive tracking and evaluation of the resource data predicted value of the area identifier, thereby improving The accuracy of the obtained resource data prediction information.

Step S203: Obtain a preset data conversion instruction, and according to the preset data conversion instruction, convert the acquired data to be predicted corresponding to the resource data prediction index to obtain an associated value corresponding to the resource data prediction index.

In this step, the data conversion instruction is an instruction that can convert the to-be-predicted data corresponding to the resource data prediction index into a corresponding associated value; different resource data prediction indexes have different corresponding data conversion instructions. The associated value refers to the value converted from the data to be predicted corresponding to the resource data prediction index based on the data conversion instruction. For example, if the data to be predicted corresponding to the Shanghai population growth index is converted into the corresponding population growth level, then the population growth level is the associated value corresponding to the Shanghai population growth index.

In specific implementation, the server obtains the preset data conversion instruction, and according to the preset data conversion instruction, converts the obtained data to be predicted corresponding to the resource data prediction index to obtain the corresponding level to be predicted, and the obtained level to be predicted Recognized as the associated value corresponding to the resource data prediction index; it is convenient for the subsequent server to predict the resource data prediction value based on the obtained associated value, avoiding redundant data interference, thereby improving the accuracy of the obtained resource data prediction information.

For digital data to be predicted, for example, the data to be assessed corresponding to the Shanghai population growth index is converted into the corresponding population growth level, and the obtained population growth level is used as the associated value corresponding to the Shanghai population growth index; suppose the population growth number is a , The corresponding population growth level is level 1, that is, the correlation value corresponding to the Shanghai population growth index is 1; assuming that the population growth number is b, the corresponding population growth level is level 2, that is, the correlation corresponding to the Shanghai population growth index The value is 2. For the theoretical data to be predicted, for example, according to the degree of influence of the data to be predicted corresponding to the Guangzhou Commercial Bank's housing loan policy indicators on the increase in the predicted value of resource data, the data to be predicted is converted into the corresponding policy level, and the obtained policy level is taken as The correlation value corresponding to the Guangzhou Commercial Bank's housing loan policy indicator; for example, the to-be-predicted data corresponding to the Guangzhou Commercial Bank's housing loan policy indicator has a higher impact on the increase in the predicted value of resource data, and the corresponding policy level is level 1, that is, with Guangzhou The correlation value corresponding to the commercial bank's housing loan policy indicator is 1.

Step S204: Obtain a preset value conversion instruction, and according to the preset value conversion instruction, convert the obtained associated value corresponding to the resource data prediction index to obtain a reference value corresponding to the resource data prediction index.

In this step, the numerical conversion instruction is an instruction capable of converting the associated value corresponding to the resource data predictive index into a corresponding reference value; different resource data predictive indexes have different corresponding numerical conversion instructions. The reference value refers to the value converted from the associated value corresponding to the resource data prediction index based on the value conversion instruction; it is used to convert the associated value into a value that is convenient for the calculation of the resource data prediction model. There is a one-to-one correspondence between the associated value and the reference value. Different associated values have different corresponding reference values. For example, if the correlation value corresponding to the Shanghai population growth index is 10, and the parameter value corresponding to the correlation value 10 is 1, then the parameter value corresponding to the Shanghai population growth index is 1.

In specific implementation, the server obtains the preset value conversion instruction, and according to the preset value conversion instruction, converts the obtained associated value corresponding to the resource data prediction index to obtain the reference value corresponding to the associated value, and use the reference value as The reference value corresponding to the resource data prediction index; it is convenient for the subsequent server to predict the resource data prediction value according to the obtained reference value, avoiding redundant data interference, thereby improving the accuracy of the obtained resource data prediction information.

Step S205: Input the reference value corresponding to the resource data prediction index into the pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the area identifier.

In this step, the resource data prediction model is a model that can determine the predicted value of the resource data corresponding to the area identifier according to the input reference value corresponding to the resource data prediction index. The predicted value of resource data corresponding to the area identifier refers to the market value of the real estate in the future time period of the buildings in the area and the land occupied by them.

The server inputs the obtained reference value corresponding to each resource data prediction index into the pre-trained resource data prediction model, and calculates and analyzes the reference value corresponding to each resource data prediction index through the resource data prediction model to obtain the prediction of each resource data The resource data prediction result corresponding to the index; the resource data prediction result corresponding to each resource data prediction index is integrated to obtain the resource data prediction value corresponding to the area identifier, which is conducive to the comprehensive tracking and evaluation of the resource data prediction value, thereby improving the obtained The accuracy of resource data prediction information.

Step S206: Generate resource data prediction information according to the resource data prediction value corresponding to the area identifier, and send the resource data prediction information to the terminal for display.

In this step, the server obtains the preset information template, imports the resource data prediction value corresponding to the area identifier into the preset information template, generates corresponding resource data prediction information, and sends the generated resource data prediction information to the corresponding The terminal displays the predicted value of the resource data corresponding to the area identifier through the display interface of the terminal, which is convenient for users to intuitively and comprehensively understand the predicted value of the resource data corresponding to the area identifier in a period of time in the future. The predicted value of resource data is fully tracked and evaluated.

In the above method for generating resource data prediction information, the pre-trained resource data prediction model is used to evaluate and analyze the to-be-predicted data corresponding to the resource data prediction index related to the predicted value of the resource data in the region to be predicted, no longer targeting each region The predicted value of resource data is only based on manually collected specific data of the same type, combined with a single qualitative evaluation model to predict resource data, thereby achieving the purpose of comprehensive tracking and evaluating the predicted value of resource data in the area to be predicted. It can avoid the defect that the resource data prediction information obtained by the traditional resource data prediction method is prone to deviation, thereby improving the accuracy of the obtained resource data prediction information. At the same time, combining the pre-trained resource data prediction model to analyze the data to be predicted can further improve the accuracy of the obtained resource data prediction information.

In one of the embodiments, the above step S202, querying the database according to the resource data prediction index, and obtaining the to-be-predicted data corresponding to the resource data prediction index includes: extracting the known resource data prediction index from the database; Match with the known resource data forecast index; if the resource data forecast index matches the known resource data forecast index, obtain the data to be predicted corresponding to the known resource data forecast index from the database; compare the acquired data with the known resource data The to-be-predicted data corresponding to the forecast index is used as the to-be-predicted data corresponding to the resource data forecast index.

For example, the server separately calculates the matching degree between the resource data prediction index and each known resource data prediction index, and uses the data to be predicted corresponding to the known resource data prediction index with the largest matching degree as the resource data prediction index from the database. Corresponding to the data to be predicted, thereby obtaining the data to be predicted corresponding to each resource data prediction index. Through this embodiment, it is convenient for the subsequent server to determine the predicted value of the resource data corresponding to the area identifier according to the to-be-predicted data corresponding to each resource data prediction index, and realize the comprehensive tracking and evaluation of the predicted value of the resource data of the area identifier, thereby improving The obtained resource data predicts the accuracy of the information.

In addition, in order to avoid unnecessary information interference, the obtained data to be predicted can be converted into corresponding values through the server. In one of the embodiments, the above step S203, according to the preset data conversion instruction, transforms the acquired data to be predicted corresponding to the resource data predictive index to obtain the associated value corresponding to the resource data predictive index, including: extracting the predictive index Set the data conversion rule in the data conversion instruction; the data conversion rule is the conversion rule between the data and the associated value; according to the data conversion rule, the obtained data to be predicted corresponding to the resource data prediction index is converted to obtain the resource data The associated value corresponding to the predictor.

In this embodiment, the data conversion rule refers to a rule that converts the to-be-predicted data corresponding to the resource data predictive index into corresponding associated values; different resource data predictive indices have different corresponding data conversion rules.

The server obtains the identifier of the data conversion rule, and extracts the corresponding data conversion rule from the data conversion instruction according to the identifier of the data conversion rule; according to the data conversion rule, executes the prediction on the obtained data to be predicted corresponding to the resource data prediction index. Through conversion, the corresponding level to be predicted is obtained, and the obtained level to be predicted is identified as the associated value corresponding to the resource data prediction index. Through this embodiment, it is convenient for the subsequent server to predict the predicted value of the resource data based on the obtained correlation value, avoid unnecessary data interference, and improve the accuracy of the obtained resource data prediction information.

For digital data to be predicted, such as the data to be predicted corresponding to the Shanghai population growth index, the value in the data to be predicted corresponding to the resource data forecast index can be converted into the corresponding level to be predicted, and the obtained to be predicted The level is used as the associated value corresponding to the resource data prediction index; for example, the population growth number in the to-be-predicted data corresponding to the Shanghai population growth index is converted into the corresponding population growth level, and the population growth level obtained is regarded as the population growth level of Shanghai The associated value corresponding to the growth indicator. For theoretical data to be evaluated, such as the data to be predicted corresponding to the Guangzhou Commercial Bank's housing loan policy indicators, the data to be predicted can be transformed according to the degree of influence of the data to be predicted corresponding to the resource data prediction index on the increase in the predicted value of resource data The corresponding level to be predicted is used as the associated value corresponding to the resource data prediction index; for example, according to the degree of influence of the to-be-predicted data corresponding to the Guangzhou Commercial Bank’s housing loan policy index on the increase in the predicted value of the resource data, Convert the data to be predicted into the corresponding policy level, and use the obtained policy level as the associated value corresponding to the Guangzhou Commercial Bank's housing loan policy indicators.

In one of the embodiments, the above step S204, according to the preset value conversion instruction, transforms the obtained associated value corresponding to the resource data predictive index to obtain the reference value corresponding to the resource data predictive index, including: extracting the preset value The numerical conversion rule in the conversion instruction; the numerical conversion rule is the conversion rule between the associated value and the reference value; according to the numerical conversion rule, the obtained associated value corresponding to the resource data predictive index is converted to obtain the corresponding resource data predictive index Reference value.

In this embodiment, the numerical conversion rule refers to a rule that converts the associated value corresponding to the resource data prediction index into a corresponding reference value; different resource data prediction indexes have different corresponding numerical conversion rules.

In specific implementation, the server obtains the identifier of the value conversion rule, and extracts the corresponding value conversion rule from the value conversion instruction according to the identifier of the value conversion rule; determines the corresponding relationship between the associated value and the reference value according to the value conversion rule; Correspondence between the associated value and the reference value, transform the obtained associated value corresponding to the resource data predictive index to obtain the reference value corresponding to the associated value, and identify the reference value as the reference value corresponding to the resource data predictive index. Through this embodiment, it is convenient for the subsequent server to predict the predicted value of the resource data based on the obtained reference value, avoiding redundant data interference, thereby improving the accuracy of the obtained resource data prediction information. Further, in order to improve the accuracy of the obtained resource data prediction information, the reference value corresponding to the resource data prediction index may be input into a pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the area identifier.

In one of the embodiments, as shown in FIG. 3, the step of inputting the reference value corresponding to the resource data prediction index into the pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the region identifier specifically includes:

Step S301, based on the resource data prediction model, obtain an information matching table corresponding to each resource data prediction index; the information matching table includes the corresponding relationship between the reference value and the resource data change ratio.

Step S302: Determine the resource data change ratio that matches each reference value according to the information matching table corresponding to each resource data prediction index.

Step S303: Perform a weighted calculation on the corresponding resource data change ratio according to preset weighting factors corresponding to each resource data prediction index to obtain the total resource data change ratio.

Step S304: Obtain the current value of the resource data corresponding to the area identifier, and generate the predicted value of the resource data corresponding to the area identifier according to the current value of the resource data and the total proportion of resource data changes.

In this embodiment, the information matching table refers to the matching table used to identify the correspondence between the reference value and the resource data change ratio, and is classified and stored in the first pre-established database according to the resource data prediction index; different resource data prediction indexes The corresponding information matching table is different. Both the resource data change ratio and the total resource data change ratio refer to the percentage change of resource data in the future, which can be positive, negative, or zero. The weighting factor refers to the degree of influence of the reference value of the resource data predictive index on the predicted value of the resource data; the larger the weighting factor, the greater the degree of influence on the predicted value of the resource data; the weighting factor corresponding to each resource data predictive index in different area identifiers Different. The current value of resource data refers to the market value of the real estate in the current time period of the buildings in the area and the land occupied by them, and is classified and stored in the second pre-established database according to the area identification.

For example, the server inputs the reference value corresponding to the resource data prediction index into the pre-trained resource data prediction model; queries the first database established in advance according to the resource data prediction index, and obtains the information matching table corresponding to each resource data prediction index; The information matching table corresponding to each resource data predictive index determines the resource data change ratio that matches each reference value; and the determined resource data change ratio that matches each reference value is used as the resource data corresponding to each resource data predictive index. Change ratio; combined with preset weighting factors corresponding to each resource data prediction index, weighted calculation is performed on the corresponding resource data change ratio to obtain the total resource data change ratio; for example, the resource data prediction index corresponding to the area identifier is a, b , C, d, the resource data change ratios corresponding to the resource data predictors a, b, c, d are y1, y2, y3, y4, and the weighting factors corresponding to the resource data predictors a, b, c, d, respectively If it is m1, m2, m3, m4, the obtained total resource data change ratio y=y1×m1+y2×m2+y3×m3+y4×m4. The server queries the pre-established second database according to the area identifier to obtain the current value of the resource data corresponding to the area identifier; and calculates the predicted value of the resource data corresponding to the area identifier according to the current value of the resource data and the total resource data change ratio.

In this embodiment, by analyzing the to-be-predicted data corresponding to multiple resource data prediction indicators related to the resource data predicted value of the region identification, the resource data predicted value can be analyzed and evaluated from multiple angles, thereby realizing the The purpose of comprehensive tracking and evaluation of resource data prediction values is to avoid deviations, thereby improving the accuracy of the resource data prediction information obtained.

In one of the embodiments, the above step S304, based on the current value of the resource data and the total resource data change ratio, generating the resource data predicted value corresponding to the area identifier, includes: multiplying the current value of the resource data by the total resource data change ratio , To obtain the resource data change value; add the current value of the resource data and the resource data change value to obtain the resource data change value corresponding to the area identifier. For example, if the current value of resource data is A and the total ratio of resource data change is y, the obtained resource data change value A1=A×y; add the current value of resource data A and the resource data change value A1, and the result is The predicted value of the resource data corresponding to the identifier is B=A+A1=A+A×y. It is convenient for the user to intuitively and comprehensively understand the predicted value of the resource data corresponding to the area identifier in a period of time in the future, which is beneficial for the user to fully track and evaluate the predicted value of the resource data corresponding to the area identifier.

In one of the embodiments, the above step S205, generating resource data prediction information according to the resource data prediction value corresponding to the area identifier, includes: determining the corresponding import position of each resource data prediction index in the preset resource data map template; The data to be predicted corresponding to each resource data prediction index is imported into the corresponding import position in the preset resource data map template to generate a resource data map; the resource data map and the predicted value of the resource data corresponding to the area identifier are imported into the preset information In the template, the corresponding resource data prediction information is generated.

In this embodiment, the server obtains the location identifier of each resource data predictive index in the resource data atlas template. The location identifier is used to identify the import position of the resource data predictive index in the resource data atlas template; the index is predicted based on each resource data. To determine the corresponding import position of each resource data prediction index in the preset resource data map template; import the to-be-predicted data corresponding to each resource data prediction index into the corresponding import in the preset resource data map template Position, generate a resource data map; import the resource data map and the predicted value of the resource data corresponding to the area identifier into the preset information template in turn to generate corresponding resource data prediction information. It is convenient to send the generated resource data prediction information to the terminal through the server, and display the resource data map and the resource data prediction value corresponding to the area identifier on the display interface of the terminal, so that users can intuitively and comprehensively understand the resource data in the area in the future The predicted value over a period of time is helpful for users to comprehensively track and evaluate the predicted value of resource data corresponding to the area identifier.

In addition, in order to further improve the resource data prediction accuracy of the resource data prediction model, the resource data prediction model can be trained multiple times. In one of the embodiments, the resource data prediction model is obtained by the following method: respectively obtaining sample prediction data and weighting factors corresponding to each resource data prediction index; treating according to the sample prediction data and weighting factors corresponding to each resource data prediction index The trained resource data prediction model is trained to obtain the trained resource data prediction model; the prediction error between the resource data prediction value output by the trained resource data prediction model and the corresponding resource data actual value is obtained; when the prediction error is greater than or When it is equal to the preset threshold, adjust the weighting factor corresponding to each resource data prediction index according to the prediction error, and according to the adjusted weighting factor, the resource data prediction model to be trained is repeatedly trained until the resource data prediction model is obtained according to the trained resource data The prediction error of is less than the preset threshold.

For example, when the prediction error is greater than or equal to the preset threshold, the server adjusts the weighting factor corresponding to each resource data prediction index according to the prediction error, and retrains the resource data prediction model to be trained according to the adjusted weighting factor; The prediction error between the resource data prediction value obtained by the resource data prediction model after retraining and the corresponding actual value of the resource data, and the weighting factor corresponding to each resource data prediction index is adjusted again according to the prediction error to the resource to be trained The data prediction model is retrained until the prediction error between the resource data prediction value obtained according to the trained resource data prediction model and the corresponding resource data actual value is less than the preset threshold; when the obtained prediction error is less than the preset threshold, Obtain the current resource data prediction model and use it as the trained resource data prediction model; and use the current weight factors as preset weight factors corresponding to the resource data prediction indicators.

In this embodiment, by continuously adjusting the weighting factors corresponding to each resource data prediction index to train the resource data prediction model multiple times, it is beneficial to output more accurate resource data prediction values through the resource data prediction model, thereby improving the obtained The resource data predicts the accuracy of the information.

It should be understood that, although the various steps in the flowchart of FIGS. 2-3 are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless specifically stated in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least some of the steps in Figure 2-3 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. These sub-steps or stages The execution order of is not necessarily performed sequentially, but may be performed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

In one of the embodiments, as shown in FIG. 4, a device for processing resource data is provided, including: a request receiving module 410, a data obtaining module 420, a data conversion module 430, a numerical value conversion module 440, and a predicted value obtaining module 450 And information generating module 460, where:

The request receiving module 410 is configured to receive a query request sent by the terminal; the query request is used to obtain resource data prediction information of the area to be predicted, and the query request carries the area identifier of the area to be predicted.

The data acquisition module 420 is configured to acquire the resource data prediction index corresponding to the area identifier, query the database according to the resource data prediction index, and obtain the data to be predicted corresponding to the resource data prediction index.

The data conversion module 430 is configured to obtain a preset data conversion instruction, and according to the preset data conversion instruction, convert the acquired data to be predicted corresponding to the resource data prediction index to obtain an associated value corresponding to the resource data prediction index.

The value conversion module 440 is configured to obtain a preset value conversion instruction, and according to the preset value conversion instruction, convert the obtained associated value corresponding to the resource data prediction index to obtain a reference value corresponding to the resource data prediction index.

The predicted value obtaining module 450 is configured to input the reference value corresponding to the resource data prediction index into the pre-trained resource data prediction model to obtain the predicted value of the resource data corresponding to the area identifier.

The information generating module 460 is configured to generate resource data prediction information according to the resource data prediction value corresponding to the area identifier, and send the resource data prediction information to the terminal for display.

In one of the embodiments, the data acquisition module is also used to extract the known resource data prediction index from the database; match the resource data prediction index with the known resource data prediction index; if the resource data prediction index matches the known resource data For predictive index matching, the to-be-predicted data corresponding to the known resource data predictive index is obtained from the database; the acquired to-be-predicted data corresponding to the known resource data predictive index is used as the to-be-predicted data corresponding to the resource data predictive index.

In one of the embodiments, the data conversion module is also used to extract the data conversion rules in the preset data conversion instructions; the data conversion rules are the conversion rules between the data and the associated values; according to the data conversion rules, the obtained and resource The data to be predicted corresponding to the data prediction index is transformed to obtain the associated value corresponding to the resource data prediction index.

In one of the embodiments, the value conversion module is also used to extract the value conversion rule in the preset value conversion instruction; the value conversion rule is the conversion rule between the associated value and the reference value; according to the value conversion rule, the obtained value and the resource The associated value corresponding to the data prediction index is transformed to obtain the reference value corresponding to the resource data prediction index.

In one of the embodiments, the predicted value obtaining module is also used to obtain the information matching table corresponding to each resource data prediction index based on the resource data prediction model; the information matching table includes the corresponding relationship between the reference value and the resource data change ratio; The information matching table corresponding to each resource data prediction index determines the resource data change ratio that matches each reference value; respectively, according to the preset weighting factors corresponding to each resource data prediction index, the corresponding resource data change ratio is weighted and calculated To obtain the total resource data change ratio; obtain the current value of the resource data corresponding to the area identifier, and generate the resource data predicted value corresponding to the area identifier according to the current value of the resource data and the total resource data change ratio.

In one of the embodiments, the predictive value obtaining module is also used to multiply the current value of the resource data by the total resource data change ratio to obtain the resource data change value; add the current value of the resource data and the resource data change value to obtain The predicted value of the resource data corresponding to the area identifier.

In one of the embodiments, the information generation module is also used to determine the corresponding import position of each resource data predictive index in the preset resource data map template; import the to-be-predicted data corresponding to each resource data predictive index into the preset resource The corresponding import location in the data map template generates a resource data map; the resource data map and the predicted value of the resource data corresponding to the region identifier are imported into the preset information template to generate corresponding resource data prediction information.

In one of the embodiments, the device for generating resource data prediction information further includes a model training module, which is used to obtain sample prediction data and weighting factors corresponding to each resource data prediction index; predict according to the sample corresponding to each resource data prediction index Data and weighting factors train the resource data prediction model to be trained to obtain the trained resource data prediction model; obtain the prediction error between the resource data predicted value output by the trained resource data prediction model and the corresponding actual value of the resource data; When the prediction error is greater than or equal to the preset threshold, the weighting factor corresponding to each resource data prediction index is adjusted according to the prediction error, and the resource data prediction model to be trained is repeatedly trained according to the adjusted weighting factor, until it is trained according to the trained The prediction error obtained by the resource data prediction model is less than the preset threshold.

In each of the foregoing embodiments, the resource data processing device achieves the purpose of comprehensive tracking and evaluation of the resource data prediction value of the area to be predicted, which can avoid the defect that the resource data prediction information obtained by the traditional resource data prediction method is prone to deviation, thereby improving The accuracy of the obtained resource data prediction information.

For the specific definition of the resource data processing device, please refer to the above definition of the resource data processing method, which will not be repeated here. Each module in the above-mentioned resource data processing device can be implemented in whole or in part by software, hardware, and a combination thereof. The foregoing modules may be embedded in the form of hardware or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the foregoing modules.

In one of the embodiments, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. 5. The computer equipment includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide calculation and control capabilities. The memory of the computer device includes a non-volatile or volatile storage medium and internal memory. The non-volatile or volatile storage medium stores an operating system, computer readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the non-volatile or volatile storage medium. The database of the computer equipment is used to store the data to be predicted corresponding to the resource data prediction index. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer-readable instructions are executed by the processor to implement a method for processing resource data.

Those skilled in the art can understand that the structure shown in FIG. 5 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may Including more or fewer parts than shown in the figure, or combining some parts, or having a different arrangement of parts.

A computer device, including a memory and one or more processors, in which computer readable instructions are stored, and when the computer readable instructions are executed by the processor, the steps of the resource data processing method provided in any one of the embodiments of the present application are implemented .

One or more computer-readable storage media storing computer-readable instructions. The computer-readable storage media may be nonvolatile or volatile. When the computer-readable instructions are executed by one or more processors , Enabling one or more processors to implement the steps of the resource data processing method provided in any embodiment of the present application.

Persons of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented by instructing relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage. In the medium, when the computer-readable instructions are executed, they may include the processes of the foregoing method embodiments. Wherein, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and/or volatile memory. Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not a limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction between the combinations of these technical features, they should It is considered as the range described in this specification.

The above-mentioned embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can be made, and these all fall within the protection scope of this application. Therefore, the scope of protection of the patent of this application shall be subject to the appended claims.

Claims (20)

1. A method for processing resource data, including:

   Receiving a query request sent by a terminal; the query request is used to obtain resource data prediction information of the area to be predicted; the query request carries the area identifier of the area to be predicted;

   Obtaining a resource data prediction index corresponding to the area identifier, querying a database according to the resource data prediction index, and obtaining data to be predicted corresponding to the resource data prediction index;

   Acquiring a preset data conversion instruction, and converting the acquired data to be predicted corresponding to the resource data prediction index according to the preset data conversion instruction to obtain an associated value corresponding to the resource data prediction index;

   Obtaining a preset value conversion instruction, and according to the preset value conversion instruction, convert the obtained associated value corresponding to the resource data prediction index to obtain a reference value corresponding to the resource data prediction index;

   Input the reference value corresponding to the resource data prediction index into a pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the area identifier; and

   Generate resource data prediction information according to the resource data prediction value corresponding to the area identifier, and send the resource data prediction information to the terminal for display.
2. The method according to claim 1, wherein the querying a database according to the resource data prediction index to obtain the data to be predicted corresponding to the resource data prediction index comprises:

   Extract the known resource data prediction index from the database;

   Matching the resource data prediction index with the known resource data prediction index;

   If the resource data prediction index matches the known resource data prediction index, obtain the data to be predicted corresponding to the known resource data prediction index from the database; and

   The acquired data to be predicted corresponding to the known resource data prediction index is used as the data to be predicted corresponding to the resource data prediction index.
3. The method according to claim 1, wherein, according to the preset data conversion instruction, the acquired data to be predicted corresponding to the resource data predictive index is converted to obtain the corresponding resource data predictive index The associated value of includes:

   Extracting the data conversion rule in the preset data conversion instruction; the data conversion rule is a conversion rule between data and associated values; and

   According to the data conversion rule, the acquired data to be predicted corresponding to the resource data prediction index is converted to obtain the associated value corresponding to the resource data prediction index;

   The converting the obtained associated value corresponding to the resource data prediction index according to the preset value conversion instruction to obtain the reference value corresponding to the resource data prediction index includes:

   Extracting the numerical value conversion rule in the preset numerical value conversion instruction; the numerical value conversion rule is a conversion rule between an associated value and a reference value; and

   According to the numerical conversion rule, the obtained associated value corresponding to the resource data prediction index is converted to obtain the reference value corresponding to the resource data prediction index.
4. The method according to claim 1, wherein the inputting the reference value corresponding to the resource data prediction index into a pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the area identifier comprises:

   Obtaining an information matching table corresponding to each of the resource data prediction indicators based on the resource data prediction model; the information matching table includes a correspondence relationship between a reference value and a resource data change ratio;

   Respectively, according to the information matching table corresponding to each of the resource data prediction indicators, determine the resource data change ratio that matches each of the reference values;

   Weighting the corresponding resource data change ratios according to preset weighting factors corresponding to each of the resource data prediction indicators to obtain the total resource data change ratio; and

   Obtain the current value of the resource data corresponding to the region identifier, and generate the predicted value of the resource data corresponding to the region identifier according to the current value of the resource data and the total proportion of changes in the resource data.
5. The method according to claim 4, wherein the generating the predicted value of the resource data corresponding to the area identifier according to the previous value of the resource data and the total change ratio of the resource data comprises:

   Multiplying the current value of the resource data by the total resource data change ratio to obtain the resource data change value; and

   The current value of the resource data and the change value of the resource data are added to obtain the predicted value of the resource data corresponding to the area identifier.
6. The method according to any one of claims 1 to 5, wherein the generating resource data prediction information according to the resource data prediction value corresponding to the area identifier comprises:

   Determine the corresponding import position of each resource data prediction index in the preset resource data map template;

   Import the to-be-predicted data corresponding to each of the resource data prediction indicators into the corresponding import position in the preset resource data map template to generate a resource data map; and

   Import the resource data map and the resource data predicted value corresponding to the area identifier into a preset information template to generate corresponding resource data predicted information.
7. The method according to claim 6, wherein the resource data prediction model is obtained by the following method:

   Obtaining sample prediction data and weighting factors corresponding to each of the resource data prediction indicators;

   Training the resource data prediction model to be trained according to the sample prediction data and weighting factors corresponding to each of the resource data prediction indicators to obtain a trained resource data prediction model;

   Obtaining the prediction error between the predicted value of the resource data output by the resource data prediction model after the training and the actual value of the corresponding resource data; and

   When the prediction error is greater than or equal to a preset threshold, the weighting factor corresponding to each resource data prediction index is adjusted according to the prediction error, and the resource data prediction model to be trained is performed according to the adjusted weighting factor Repeated training is performed until the prediction error obtained from the trained resource data prediction model is less than the preset threshold.
8. A device for processing resource data includes:

   The request receiving module is configured to receive a query request sent by the terminal; the query request is used to obtain resource data prediction information of the area to be predicted; the query request carries the area identifier of the area to be predicted;

   A data acquisition module, configured to acquire a resource data prediction index corresponding to the region identifier, query a database according to the resource data prediction index, and obtain data to be predicted corresponding to the resource data prediction index;

   The data conversion module is configured to obtain a preset data conversion instruction, and according to the preset data conversion instruction, convert the obtained data to be predicted corresponding to the resource data predictive index to obtain the corresponding resource data predictive index The associated value;

   The numerical value conversion module is configured to obtain a preset value conversion instruction, and according to the preset value conversion instruction, convert the obtained associated value corresponding to the resource data prediction index to obtain a reference corresponding to the resource data prediction index value;

   A prediction value acquisition module, configured to input the reference value corresponding to the resource data prediction index into a pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the area identifier; and

   The information generating module is configured to generate resource data prediction information according to the resource data prediction value corresponding to the area identifier, and send the resource data prediction information to the terminal for display.
9. A computer device includes a memory and one or more processors. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the one or more processors, the one or more Each processor performs the following steps:

   Receiving a query request sent by a terminal; the query request is used to obtain resource data prediction information of the area to be predicted; the query request carries the area identifier of the area to be predicted;

   Obtaining a resource data prediction index corresponding to the area identifier, querying a database according to the resource data prediction index, and obtaining data to be predicted corresponding to the resource data prediction index;

   Acquiring a preset data conversion instruction, and converting the acquired data to be predicted corresponding to the resource data prediction index according to the preset data conversion instruction to obtain an associated value corresponding to the resource data prediction index;

   Obtaining a preset value conversion instruction, and according to the preset value conversion instruction, convert the obtained associated value corresponding to the resource data prediction index to obtain a reference value corresponding to the resource data prediction index;

   Input the reference value corresponding to the resource data prediction index into a pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the area identifier; and

   Generate resource data prediction information according to the resource data prediction value corresponding to the area identifier, and send the resource data prediction information to the terminal for display.
10. The computer device according to claim 9, wherein the processor further executes the following steps when executing the computer readable instruction:

    Extract the known resource data prediction index from the database;

    Matching the resource data prediction index with the known resource data prediction index;

    If the resource data prediction index matches the known resource data prediction index, obtain the data to be predicted corresponding to the known resource data prediction index from the database; and

    The acquired data to be predicted corresponding to the known resource data prediction index is used as the data to be predicted corresponding to the resource data prediction index.
11. The computer device according to claim 9, wherein the processor further executes the following steps when executing the computer readable instruction:

    Extracting the data conversion rule in the preset data conversion instruction; the data conversion rule is a conversion rule between data and associated values;

    According to the data conversion rule, the acquired data to be predicted corresponding to the resource data prediction index is converted to obtain the associated value corresponding to the resource data prediction index;

    Extracting the numerical value conversion rule in the preset numerical value conversion instruction; the numerical value conversion rule is a conversion rule between an associated value and a reference value; and

    According to the numerical conversion rule, the obtained associated value corresponding to the resource data prediction index is converted to obtain the reference value corresponding to the resource data prediction index.
12. The computer device according to claim 9, wherein the processor further executes the following steps when executing the computer readable instruction:

    Obtaining an information matching table corresponding to each of the resource data prediction indicators based on the resource data prediction model; the information matching table includes a correspondence relationship between a reference value and a resource data change ratio;

    Respectively, according to the information matching table corresponding to each of the resource data prediction indicators, determine the resource data change ratio that matches each of the reference values;

    Weighting the corresponding resource data change ratios according to preset weighting factors corresponding to each of the resource data prediction indicators to obtain the total resource data change ratio; and

    Obtain the current value of the resource data corresponding to the region identifier, and generate the predicted value of the resource data corresponding to the region identifier according to the current value of the resource data and the total proportion of changes in the resource data.
13. The computer device according to any one of claims 9 to 12, wherein the processor further executes the following steps when executing the computer readable instruction:

    Determine the corresponding import position of each resource data prediction index in the preset resource data map template;

    Import the to-be-predicted data corresponding to each of the resource data prediction indicators into the corresponding import position in the preset resource data map template to generate a resource data map; and

    Import the resource data map and the resource data predicted value corresponding to the area identifier into a preset information template to generate corresponding resource data predicted information.
14. The computer device according to claim 13, wherein the processor further executes the following steps when executing the computer readable instruction:

    Obtaining sample prediction data and weighting factors corresponding to each of the resource data prediction indicators;

    Training the resource data prediction model to be trained according to the sample prediction data and weighting factors corresponding to each of the resource data prediction indicators to obtain a trained resource data prediction model;

    Obtaining the prediction error between the predicted value of the resource data output by the resource data prediction model after the training and the actual value of the corresponding resource data; and

    When the prediction error is greater than or equal to a preset threshold, the weighting factor corresponding to each resource data prediction index is adjusted according to the prediction error, and the resource data prediction model to be trained is performed according to the adjusted weighting factor Repeated training is performed until the prediction error obtained from the trained resource data prediction model is less than the preset threshold.
15. One or more computer-readable storage media storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to perform the following steps:

    Receiving a query request sent by a terminal; the query request is used to obtain resource data prediction information of the area to be predicted; the query request carries the area identifier of the area to be predicted;

    Obtaining a resource data prediction index corresponding to the area identifier, querying a database according to the resource data prediction index, and obtaining data to be predicted corresponding to the resource data prediction index;

    Acquiring a preset data conversion instruction, and converting the acquired data to be predicted corresponding to the resource data prediction index according to the preset data conversion instruction to obtain an associated value corresponding to the resource data prediction index;

    Obtaining a preset value conversion instruction, and according to the preset value conversion instruction, convert the obtained associated value corresponding to the resource data prediction index to obtain a reference value corresponding to the resource data prediction index;

    Input the reference value corresponding to the resource data prediction index into a pre-trained resource data prediction model to obtain the resource data prediction value corresponding to the area identifier; and

    Generate resource data prediction information according to the resource data prediction value corresponding to the area identifier, and send the resource data prediction information to the terminal for display.
16. The storage medium according to claim 15, wherein the following steps are further performed when the computer-readable instructions are executed by the processor:

    Extract the known resource data prediction index from the database;

    Matching the resource data prediction index with the known resource data prediction index;

    If the resource data prediction index matches the known resource data prediction index, obtain the data to be predicted corresponding to the known resource data prediction index from the database; and

    The acquired data to be predicted corresponding to the known resource data prediction index is used as the data to be predicted corresponding to the resource data prediction index.
17. The storage medium according to claim 15, wherein the following steps are further performed when the computer-readable instructions are executed by the processor:

    Extracting the data conversion rule in the preset data conversion instruction; the data conversion rule is a conversion rule between data and associated values;

    According to the data conversion rule, the acquired data to be predicted corresponding to the resource data prediction index is converted to obtain the associated value corresponding to the resource data prediction index;

    Extracting the numerical value conversion rule in the preset numerical value conversion instruction; the numerical value conversion rule is a conversion rule between an associated value and a reference value; and

    According to the numerical conversion rule, the obtained associated value corresponding to the resource data prediction index is converted to obtain the reference value corresponding to the resource data prediction index.
18. The storage medium according to claim 15, wherein the following steps are further performed when the computer-readable instructions are executed by the processor:

    Obtaining an information matching table corresponding to each of the resource data prediction indicators based on the resource data prediction model; the information matching table includes a correspondence relationship between a reference value and a resource data change ratio;

    Respectively, according to the information matching table corresponding to each of the resource data prediction indicators, determine the resource data change ratio that matches each of the reference values;

    Weighting the corresponding resource data change ratios according to preset weighting factors corresponding to each of the resource data prediction indicators to obtain the total resource data change ratio; and

    Obtain the current value of the resource data corresponding to the region identifier, and generate the predicted value of the resource data corresponding to the region identifier according to the current value of the resource data and the total proportion of changes in the resource data.
19. The storage medium according to any one of claims 15 to 18, wherein the following steps are further performed when the computer-readable instructions are executed by the processor:

    Determine the corresponding import position of each resource data prediction index in the preset resource data map template;

    Import the to-be-predicted data corresponding to each of the resource data prediction indicators into the corresponding import position in the preset resource data map template to generate a resource data map; and

    Import the resource data map and the resource data predicted value corresponding to the area identifier into a preset information template to generate corresponding resource data predicted information.
20. The storage medium according to claim 19, wherein the following steps are further performed when the computer-readable instructions are executed by the processor:

    Obtaining sample prediction data and weighting factors corresponding to each of the resource data prediction indicators;

    Training the resource data prediction model to be trained according to the sample prediction data and weighting factors corresponding to each of the resource data prediction indicators to obtain a trained resource data prediction model;

    Obtaining the prediction error between the predicted value of the resource data output by the resource data prediction model after the training and the actual value of the corresponding resource data; and

    When the prediction error is greater than or equal to a preset threshold, the weighting factor corresponding to each resource data prediction index is adjusted according to the prediction error, and the resource data prediction model to be trained is performed according to the adjusted weighting factor Repeated training is performed until the prediction error obtained from the trained resource data prediction model is less than the preset threshold.

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