Information processing method and device and electronic equipment

Abstract

The invention provides an information processing method and device and electronic equipment, and relates to the technical field of data processing. The information processing method comprises the following steps: acquiring an index query request of a client, and determining a city to be queried according to the index query request; collecting total transaction data of different house types in each region in the city in N statistical periods; for the house type j, according to the total transaction data of the house type j, determining the global transaction area of the city, the regional transaction area of the region and the regional average price of the house type j in the t-th statistical period; according to the global transaction area, the regional transaction area and the regional average price, determining a price index of the house type j in the t-th statistical period, thereby determining a real estate index set of the city; the real estate index set is fed back to the client according to the real estate index set of the city, the accuracy of price index acquisition is improved, and the availability of the price indexes is greatly improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to an information processing method, device and electronic device. Background Art

[0002] The development of the real estate industry is currently the focus of attention, among which the real estate transaction price is an important economic indicator that has attracted much attention. The real estate index system is an indicator system and analysis method that reflects the long-term operating conditions and development trends of real estate in the country and major cities in the form of a price index. As an important indicator, the price index often attracts the attention of most people.

[0003] Currently, the average price of each period is usually obtained based on the city’s sales floor area and the price of each project in each period, and the price index is determined based on the average price of each period. Only price and floor area are used as the average price. The calculation model is relatively simple, resulting in the final price index may not conform to the actual situation and has low reference value. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] The first embodiment of the present disclosure provides an information processing method, including:

[0006] Obtaining an indicator query request from a client, and determining a city to be queried according to the indicator query request;

[0007] Collecting the full transaction data of different housing types in each area of ​​the city within N statistical periods, where N is a natural number greater than or equal to 1, and the N statistical periods include the current t-th statistical period;

[0008] For house type j, based on the full transaction data of house type j, determine the global transaction area of ​​the city in N statistical periods, the regional transaction area of ​​region i in N statistical periods, and the regional average price of house type j in region i in the t-th statistical period, where the values ​​of i and j are natural numbers greater than or equal to 1;

[0009] Performing a weighted operation on the regional average price according to the global transaction area and the regional transaction area, and combining the price association information of the housing type j in the t-1th statistical period, determining the price index of the housing type j in the tth statistical period;

[0010] According to the price indicators of different housing types in the t-th statistical period, a real estate indicator set of the city in the t-th statistical period is determined, and the real estate indicator set is fed back to the client.

[0011] The second aspect of the present disclosure provides an information processing device, including:

[0012] A city determination module, used to obtain an index query request from a client, and determine a city to be queried according to the index query request; a data collection module, used to collect full transaction data of different housing types in each area of ​​the city within N statistical periods, where N is a natural number greater than or equal to 1, and the N statistical periods include the current t-th statistical period;

[0013] A data analysis module is used to determine, for house type j, the global transaction area of ​​each city in N statistical periods, the regional transaction area of ​​each region i in N statistical periods, and the regional average price of house type j in region i in the t-th statistical period based on the full transaction data of house type j, wherein the values ​​of i and j are natural numbers greater than or equal to 1;

[0014] An index acquisition module, used to perform a weighted operation on the regional average price according to the global transaction area and the regional transaction area, and determine the price index of the housing type j in the t-th statistical period in combination with the price association information of the housing type j in the t-1th statistical period;

[0015] The set acquisition module is used to determine the real estate index set of the city in the t-th statistical period according to the price indicators of different house types in the t-th statistical period, and feed back the real estate index set to the client.

[0016] The third aspect of the present disclosure provides an electronic device, including:

[0017] at least one processor; and

[0018] a memory communicatively connected to at least one processor; wherein,

[0019] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute the information processing method provided in the embodiment of the first aspect of the present disclosure.

[0020] The fourth aspect embodiment of the present disclosure proposes a computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are used to enable a computer to execute the information processing method provided in the first aspect embodiment of the present disclosure.

[0021] The fifth aspect embodiment of the present disclosure proposes a computer program product, including a computer program, which implements the information processing method provided in the first aspect embodiment of the present disclosure when executed by a processor.

[0022] This application can obtain the global transaction area and regional transaction area of ​​house types through the full transaction data of different house types, and obtain price indicators based on the global transaction area, regional transaction area and regional average price, thereby improving the accuracy of price indicator acquisition, greatly increasing the availability of price indicators, and providing more accurate results for actual real estate market supervision and policy formulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of an information processing method provided by an embodiment of the present disclosure;

[0024] Figure 2 is a flow chart of obtaining a price index provided by an embodiment of the present disclosure;

[0025] Figure 3 This is a flow chart of collecting full transaction data provided by an embodiment of the present disclosure;

[0026] Figure 4 is a flow chart of another information processing method provided by an embodiment of the present disclosure;

[0027] Figure 5 is another trend chart of price index changes provided by an embodiment of the present disclosure;

[0028] Figure 6 is a structural block diagram of an information processing device provided by an embodiment of the present disclosure;

[0029] Figure 7 It is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0032] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of relevant laws and regulations.

[0033] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0034] The current method for obtaining price indices usually includes the following steps:

[0035] (1) Sample selection: Samples for testing include commercial housing, villas, and affordable housing. All properties for sale that have obtained sales licenses issued by the government are included in the calculation scope. Project construction scale restrictions: not less than 30,000 square meters in first-tier cities, and not less than 10,000 square meters in second-, third-, and fourth-tier cities; when the total remaining housing stock of all phases of the project is less than 5%, the sample can be withdrawn; the price of each property project is the actual price of the project, that is, the price after deducting the discount from the project's external quotation.

[0036] (2) Calculation model: base period and base point setting. For example, December 2000 is taken as the base period, Beijing’s base point is 1,000 points, and the base points of other cities in the base period are obtained by comparing the average price at that time with Beijing; the newly built residential type is taken as the calculation sample; the calculation formula of the newly built residential real estate price index is as follows:

[0037]

[0038] in, represents the average price of the jth city in period t; represents the price of the i-th project (new residential type) in the j-th city in the t-th period; Q ij Represents the sales floor area of ​​the i-th project in the current period; and Represent the price index of the j-th city in period t and period t-1 respectively.

[0039] The current price index calculation model is relatively simple. It only relies on the city's sales floor area and sales price in each period to calculate prices, and directly obtains the price index using the average price, ignoring the impact of price changes in other districts and counties on the city. For example, in a certain month, only 100 new residential units were sold in Haidian District in Beijing, with an average price of 150,000 yuan per square meter. Then, the average price in Beijing for that month was 150,000 yuan per square meter. The next month, only 100 new residential units were sold in Mentougou District in Beijing, with an average price of 50,000 yuan per square meter. According to this formula, Beijing's new residential real estate index fell by 66%. It can be seen that Beijing's price index is more obviously affected by the sales volume of the two districts. Therefore, in some special cases, the reference value of the price index is not high.

[0040] The information processing method, device and electronic device of the embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0041] Figure 1 is a flow chart of an information processing method provided by an embodiment of the present disclosure, such as Figure 1 As shown, the method comprises the following steps:

[0042] S101, obtaining an index query request from a client, and determining a city to be queried according to the index query request.

[0043] Optionally, the client may be a home buyer, a home seller, or a person from other real estate public institutions.

[0044] In some implementations, the index query request is used to query the real estate price index of a city, and the index query request includes the city to be queried.

[0045] S102, collecting full transaction data of different housing types in various areas within the city within N statistical periods.

[0046] Optionally, the housing types can be divided into newly built urban residences, shops, office buildings, villas, second-hand houses and other types, and the scope includes all properties for sale that have obtained sales licenses issued by the government, and the details are not elaborated here.

[0047] In some implementations, each area within the city can be divided according to the geographical location within the city, for example, the various areas of the city are determined based on actual urban area divisions, such as different areas such as Haidian District and Chaoyang District in Beijing.

[0048] Optionally, the statistical period may be a period such as one month or one quarter. In this embodiment, one month is selected as a statistical period. In other embodiments, a longer or shorter period may be selected as a statistical period, and the specific period is not limited thereto.

[0049] It can be understood that the full transaction data refers to all transaction data, which at least includes data such as sales area, sales price, and sales time for each housing type in each region; where N is a natural number greater than or equal to 1, and N statistical periods include the current t-th statistical period. For example, in this embodiment, N is 12, and all sales online signing data of newly built residential buildings in Haidian District, Beijing within 12 months are collected as full transaction data.

[0050] S103, for house type j, based on the full transaction data of house type j, determine the global transaction area of ​​each city in N statistical periods, the regional transaction area of ​​each region i in N statistical periods, and the regional average price of house type j in region i in the tth statistical period.

[0051] The values ​​of i and j are natural numbers greater than or equal to 1.

[0052] Optionally, the global transaction area refers to the total transaction area of ​​the city in each statistical period, that is, the sales area; the regional transaction area is the sales area of ​​each region, and the regional average price refers to the average sales price of the region in the tth statistical period.

[0053] For example, assuming that house type j is a newly built residential building, area i is Haidian District, and t is 3, then based on the full transaction data of newly built residential buildings, determine the global transaction area of ​​all newly built residential buildings in Beijing within 12 months, the regional transaction area of ​​Haidian District, Beijing within 12 months, and the regional average price of newly built residential buildings in Haidian District, Beijing in the third statistical period.

[0054] S104, performing a weighted operation on the regional average price according to the global transaction area and the regional transaction area, and combining the price association information of the housing type j in the t-1th statistical period, determining the price index of the housing type j in the tth statistical period.

[0055] Optionally, the transaction weight can be determined based on the global transaction area and the regional transaction area of ​​region i. For example, the ratio of the regional transaction area of ​​region i to the global transaction area is used as the transaction weight of region i; further, the price index of house type j in the tth statistical period is determined based on the regional average price of region i and the transaction weight.

[0056] In some implementations, a weighted operation can be performed on the regional average price corresponding to each region based on the transaction weight of the region. For example, the sum of the product results of the transaction weight of each region and the regional average price can be calculated as the regional price index, and the regional price index can be combined with the price association information of house type j in the t-1th statistical period to determine the price index of house type j in the tth statistical period.

[0057] Optionally, the price association information of house type j in the t-1th statistical period can be the price index of house type j in the t-1th statistical period; it can be understood that the t-1th statistical period is a statistical period that has been processed, and therefore the price index of house type j in the t-1th statistical period is a statistically known value.

[0058] In some implementations, the price index of house type j in the t-1th statistical period can be used as a reference basis to calculate the product of the regional price index and the price index of house type j in the t-1th statistical period as the price index of house type j in the tth statistical period.

[0059] S105, determining a real estate index set of the city in the tth statistical period according to price indexes of different housing types in the tth statistical period, and feeding back the real estate index set to the client.

[0060] It is understandable that the real estate indicator set includes price indicators of all housing types in the tth statistical period, and the real estate indicator set is fed back to the client so that the client can view price indicators of different housing types based on the real estate indicator set.

[0061] In this embodiment, the global transaction area and regional transaction area of ​​the house type are obtained through the full transaction data of different house types, and the price index is weighted by the global transaction area and the regional transaction area against the regional average price. In combination with the price correlation information of the house type in the previous statistical period, the price index of the current statistical period is determined, which effectively avoids the influence of different regions and locations on the urban price index, improves the accuracy of price index acquisition, greatly increases the availability of price indicators, and provides more accurate results for the client.

[0062] Based on the above embodiment, the process of obtaining the price index is described. Figure 2 is a flow chart of obtaining a price index provided by an embodiment of the present disclosure, such as Figure 2 As shown, the method comprises the following steps:

[0063] S201, determining the weight of region i in the tth statistical period based on the regional transaction areas of region i in N statistical periods and the global transaction areas of cities in N statistical periods.

[0064] In some implementations, the regional transaction areas of region i in N statistical periods can be summed to obtain a first sum value; the global transaction areas of cities in N statistical periods can be summed to obtain a second sum value; the ratio of the first sum value to the second sum value is obtained to obtain the weight of region i in the tth statistical period.

[0065] Alternatively, weights can be expressed as:

[0066]

[0067] in, represents the weight of region i in the tth statistical period; represents the first sum value; represents the second sum; represents the transaction area of ​​region i in the tth statistical period; Q t Represents the global transaction area of ​​the city in the tth statistical period.

[0068] S202, based on the weight of each area in the tth statistical period, weight the regional average price of house type j in each area in the tth statistical period to obtain the global average price of house type j in the tth statistical period.

[0069] That is, calculate the weight of each area in the tth statistical period, multiply it by the regional average price of house type j in the area in the tth statistical period, and add up the multiplication results of house type j in all areas in the tth statistical period to get the global average price.

[0070] S203, determining a price index of house type j in the t-th statistical period according to the global average price and price association information of house type j in the t-th statistical period.

[0071] Optionally, the price association information of house type j in the t-1th statistical period may include the global average price of house type j in the t-1th statistical period and the price index of house type j in the t-1th statistical period.

[0072] In some implementations, the ratio of the global average price of house type j in the t-th statistical period and the t-1-th statistical period can be obtained as the adjustment coefficient; the product of the adjustment coefficient and the price index of house type j in the t-1-th statistical period is obtained as the price index of house type j in the t-th statistical period.

[0073] Alternatively, the calculation of the price indicator can be expressed as:

[0074]

[0075] Among them, I t represents the price index of house type j in the tth statistical period; and I represents the regional average price of region i in the t-th statistical period and the regional average price in the t-1th statistical period respectively; t-1represents the price index of house type j in the t-1th statistical period; represents the global average price of house type j in the tth statistical period; represents the global average price of house type j in the t-1th statistical period.

[0076] In some implementations, this embodiment can set a certain month of the city as the base period, for example, January 2018, and the corresponding base point is 100, that is, the first period price index is set to 100.

[0077] In this embodiment, the weight of each region in the statistical period is determined based on the regional transaction area corresponding to each region and the global transaction area of ​​the city, and the regional average price is further weighted based on the weight to determine the global average price of each period. The ratio of the global average prices of two adjacent statistical periods is used as an adjustment coefficient, and the product of the adjustment coefficient and the price index of the previous statistical period is used as the price index of the current statistical period. The weight is obtained based on the transaction area of ​​each region and the global transaction area of ​​the city. The impact of the sales situation of each region on the city is comprehensively considered, and the global average price is used as the adjustment coefficient. The price index of the current statistical period is calculated in combination with the price index of the previous statistical period. The information considered for obtaining the price index is more comprehensive, the influence of the region on the city price index is avoided, and the reference value of the price index is increased.

[0078] Based on the above embodiment, the process of collecting full transaction data is described. Figure 3 is a flow chart of collecting full transaction data provided by an embodiment of the present disclosure, such as Figure 3 As shown, the method comprises the following steps:

[0079] S301, determining one or more data sources of transaction data.

[0080] A data source refers to the source or location that supplies or generates data, such as a physical device, software system, database, sensor, application program interface or file, which is used to store, generate or provide data; this embodiment can collect data files, raw data and standardized data from the city's application system and the regional real estate online transaction system.

[0081] S302: Determine a target access mode for transaction data according to the type of data source.

[0082] Optionally, the target access mode can be divided into two access modes: the front-end interface machine and the direct connection with the service bus. The front-end interface machine is suitable for data sharing with large quantities of fixed demands, which is realized through data transfer services. The direct connection with the service bus is suitable for collection scenarios with flexible supply and demand and small information volume, which is realized through the service bus. Different data sources can choose the corresponding target access mode or share the two access modes according to actual conditions.

[0083] S303: Collect transaction data from the data source according to the target access mode to obtain full transaction data.

[0084] In some implementations, the collection method in this embodiment may include but is not limited to: manual entry, online transactions, batch files, intermediate libraries and tables, and real-time collection; manual entry is manual entry of data or importing of data files through the data transaction background, online transactions are providing data access services through transaction interfaces, batch files are generated according to data specifications for batch import, intermediate libraries and tables are intermediate libraries provided by the source system for direct access by the collection program of the data resource center, and real-time collection is online real-time collection of business system data.

[0085] In some implementations, a cache pool of transaction data collected from the data source can be determined based on the data source, and the transaction data collected from the data source can be stored in the cache pool; optionally, the collected transaction data includes structured, unstructured and semi-structured data, and all transaction data are stored in the cache pool.

[0086] Optionally, the collected transaction data may be verified and processed to obtain standard transaction data; the collected transaction data may be stored in a first storage area in the cache pool; and the standard transaction data may be stored in a second storage area in the cache pool, wherein the full amount of transaction data is standard transaction data.

[0087] In this embodiment, the first storage area is the original data area, and the second storage area is the standard data area. The first storage area can retain transaction data for a longer period of time and provide archived data for review; the standard transaction data in the second storage area can quickly provide available standardized incremental and full data, making the data more universal.

[0088] In some implementations, data verification and processing of collected data may include but are not limited to inspection, cleaning, standardization, and source processing. It may also be a work process to ensure that data quality meets business requirements through a series of management such as data quality definition, process control, quality monitoring, problem analysis, problem rectification, and evaluation and assessment during the process of data creation, processing, use, migration, and provision. When analyzing data quality, formulate data quality management work procedures in the field of digital housing and construction, and clarify data quality measurement indicators, including standardization, completeness, uniqueness, consistency, accuracy, and timeliness. Determine data quality verification rules for various types of data in the housing and construction field, such as data format check, non-empty check, value range check, association check and other rules, establish a data quality rule library, configure data quality verification tasks according to various types of data, clarify data quality problem solving processes and solutions, solve problems such as missing data, low data correlation, and serious data null values, improve data governance efficiency, and thus obtain better quality full transaction data.

[0089] In this embodiment, transaction data is collected by acquiring multiple data sources and the target access mode corresponding to each data source, and the collected transaction data is processed and stored in partitions to meet different data requirements. The transaction data is pre-processed such as processed, cleaned, and standardized, and the data format, association rules, etc. are checked to improve the data governance effect and efficiency, so that the quality of the full amount of transaction data collected is better.

[0090] Based on the above embodiments, Figure 4 is a flowchart of another information processing method provided by an embodiment of the present disclosure, such as Figure 4 As shown, the method comprises the following steps:

[0091] S401, obtaining an index query request from a client, and determining a city to be queried according to the index query request.

[0092] In the embodiments of the present disclosure, the implementation method of step S401 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0093] S402, determining one or more data sources of transaction data.

[0094] In the embodiments of the present disclosure, the implementation method of step S402 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0095] S403: Determine a target access mode for transaction data according to the type of data source.

[0096] In the embodiments of the present disclosure, the implementation method of step S403 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0097] S404: Collect transaction data from the data source according to the target access mode to obtain full transaction data.

[0098] In the embodiments of the present disclosure, the implementation method of step S404 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0099] S405, for house type j, based on the full transaction data of house type j, determine the global transaction area of ​​each city in N statistical periods, the regional transaction area of ​​each region i in N statistical periods, and the regional average price of house type j in region i in the tth statistical period.

[0100] In the embodiments of the present disclosure, the implementation method of step S405 can be implemented by any of the methods in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0101] S406, determining the weight of region i in the tth statistical period according to the regional transaction areas of region i in N statistical periods and the global transaction areas of cities in N statistical periods.

[0102] In the embodiments of the present disclosure, the implementation method of step S406 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0103] S407, based on the weight of each area in the tth statistical period, weight the regional average price of house type j in each area in the tth statistical period to obtain the global average price of house type j in the tth statistical period.

[0104] In the embodiments of the present disclosure, the implementation method of step S407 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0105] S408, determining a price index of house type j in the tth statistical period according to the global average price and price association information of house type j in the tth statistical period.

[0106] In the embodiments of the present disclosure, the implementation method of step S408 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0107] S409, determining a real estate index set of the city in the tth statistical period based on price indexes of different housing types in the tth statistical period, and feeding back the real estate index set to the client.

[0108] In some implementations, trend graphs of different housing types may be generated based on a set of real estate indicators of a city within N statistical periods, and the trend graphs of different housing types may be fed back to the client for display.

[0109] In some implementations, the real estate market of a city can be predicted based on the real estate index set of the city in N statistical periods to obtain the real estate prediction data of the city, so that the client can understand the price index of each house type; optionally, the transaction price of different house types in one or more subsequent statistical periods can be predicted based on the trend chart. Figure 5 As shown, it is a trend chart of the changes in the price index of a certain city from January 2018 to March 2022. According to the trend charts of different house types, the trend of the house type in each statistical period can be determined, and the pre-trained prediction model can be used to predict the transaction prices of different house types in one or more subsequent statistical periods, which is convenient for user analysis.

[0110] In some implementations, it is also possible to identify target house types with sudden trend changes among different house types based on the trend graph; obtain historical transaction data of the target house type; analyze the historical transaction data to obtain the cause of the sudden change in the target house type, and feedback the cause of the sudden change and / or the historical transaction data to the client for display; in this embodiment, a sudden trend change refers to a sudden and significant decrease or increase in the price index of a house type, so when a target house type with a sudden trend change occurs, analysis is performed based on the historical transaction data of the target house type to determine the cause of the sudden change, and the cause of the sudden change is fed back.

[0111] In some implementations, based on a set of real estate indicators of the city within N statistical periods, a comparison of multiple dimensions can be performed between different regions in a city to obtain a comparison result between regions; and / or, based on a set of real estate indicators of the city within N statistical periods, a comparison of multiple dimensions can be performed between different cities to obtain a comparison result between cities; the comparison results between regions and the comparison results between cities are fed back to the client for graphic display.

[0112] The price index of a single city can reflect the changes in the supply and demand relationship of the city's real estate market under the combined influence of multiple factors. The set of real estate indicators within N statistical periods can reflect the changes in the supply and demand relationship of the city's real estate market within N statistical periods. Multi-dimensional comparisons can be made between regions within the city, such as comparisons in price and transaction area dimensions. Based on the comparison results shown in the graph and the corresponding real estate indicator set of the city, it can be seen how changes in different regions affect the city's real estate market.

[0113] Comparing the price indices of two or more cities can not only show the differences in the average real estate prices of cities and the differences in real estate trends in different cities, but also illustrate the supply and demand conditions of various housing types in different cities and the direction of real estate development in different cities. Therefore, based on the comparison results shown in the graph, the differences and development directions of real estate between cities can be intuitively seen.

[0114] In the embodiments of the present disclosure, the implementation method of step S409 may be implemented in any manner in the embodiments of the present disclosure, which is not limited here and will not be described in detail.

[0115] In this embodiment, transaction data is collected by acquiring multiple data sources and the target access mode corresponding to each data source, and the transaction data is pre-processed such as processing, cleaning and standardization, so that the quality of the collected full transaction data is better, and then the global transaction area and regional transaction area of ​​the house type are obtained according to the full transaction data, and the weight of each region in the statistical period is determined based on the regional transaction area corresponding to each region and the global transaction area of ​​the city, and the global average price of each period is further determined based on the weight, and the ratio of the global average prices of two adjacent statistical periods is used as the adjustment coefficient. The price index is determined according to the adjustment coefficient, and the impact of the sales situation of each region on the city is combined. The information considered for obtaining the price index is more comprehensive, the impact of the region on the city price index is avoided, and the reference value of the price index is increased; the real estate forecast data is obtained with a more reliable price index, and mutation analysis and comparison between regions or cities can be performed according to the trend chart of the real estate price index change, which greatly increases the availability of price indicators and provides more accurate results for actual real estate market supervision and policy formulation.

[0116] The above embodiments can also be applied to an information processing system. The information processing system has a containerized base for providing a containerized big data platform and supporting flexible resource management and elastic deployment methods. The information processing system includes a storage computing module, a data acquisition module, a data integration module, a task scheduling module, a data service module and a data application module. The storage computing module is used to provide storage and computing capabilities, and the data acquisition module is used to support various forms of data acquisition, including batch acquisition, real-time acquisition and manual entry, etc.; unified acquisition development is achieved in the form of a unified platform plus a client, and a flexible deployment architecture supports data acquisition across different networks; the data integration module supports processing capabilities such as data loading, data amplitude, data verification, source processing and data unloading; the task scheduling module can provide distributed task scheduling capabilities, support high concurrency, low latency, and flexible configuration of more than 100,000 daily average task scheduling capabilities; the data service module can provide data service management capabilities, including data application development and release, registration, etc.; the matrix application module is used to substitute the processed data into the calculation formula to obtain the price index.

[0117] Figure 6 A structural block diagram of an information processing device provided in an embodiment of the present disclosure, such as Figure 6 As shown, the information processing device 600 of the embodiment of the present disclosure includes:

[0118] The city determination module 601 is used to obtain the index query request from the client and determine the city to be queried according to the index query request;

[0119] The data collection module 602 is used to collect the full transaction data of different housing types in various areas within the city within N statistical periods, where N is a natural number greater than or equal to 1, and the N statistical periods include the current tth statistical period.

[0120] Data analysis module 603 is used to determine, for house type j, the global transaction area of ​​each city in N statistical periods, the regional transaction area of ​​each region i in N statistical periods, and the regional average price of house type j in region i in the tth statistical period based on the full transaction data of house type j, where the values ​​of i and j are natural numbers greater than or equal to 1.

[0121] The index acquisition module 604 is used to determine the price index of the housing type j in the t-th statistical period according to the global transaction area, the regional transaction area of ​​the region i and the regional average price.

[0122] The set acquisition module 605 is used to determine the real estate index set of the city in the tth statistical period according to the price indexes of different house types in the tth statistical period, and feed back the real estate index set to the client.

[0123] In some implementations, the indicator acquisition module 604 includes:

[0124] Determine the weight of region i in the tth statistical period according to the regional transaction area of ​​region i in N statistical periods and the global transaction area of ​​the city in N statistical periods;

[0125] Based on the weight of each region in the tth statistical period, the regional average price of house type j in each region in the tth statistical period is weighted to obtain the global average price of house type j in the tth statistical period;

[0126] According to the global average price and price correlation information of house type j in the tth statistical period, the price index of house type j in the tth statistical period is determined.

[0127] In some implementations, the indicator acquisition module 604 includes:

[0128] Sum the transaction areas of region i in N statistical periods to obtain the first sum value;

[0129] The global transaction areas of each city in N statistical periods are summed up to obtain the second sum value;

[0130] The ratio of the first sum value to the second sum value is obtained to obtain the weight of region i in the tth statistical period.

[0131] In some implementations, the price association information includes the global average price of housing type j in the t-1th statistical period and the price index of housing type j in the t-1th statistical period; the apparatus 600 further includes:

[0132] Get the ratio of the global average price of house type j in the t-th and t-1-th statistical periods as the adjustment coefficient;

[0133] The product of the adjustment coefficient and the price index of house type j in the t-1th statistical period is obtained as the price index of house type j in the tth statistical period.

[0134] In some implementations, the data collection module 602 further includes:

[0135] determining one or more data sources for the transaction data;

[0136] Determine the target access mode for transaction data based on the type of data source;

[0137] According to the target access mode, transaction data is collected from the data source to obtain the full transaction data.

[0138] In some implementations, the apparatus 600 further includes:

[0139] According to the data source, a cache pool of transaction data collected from the data source is determined, and the transaction data collected from the data source is stored in the cache pool.

[0140] In some implementations, the data collection module 602 includes:

[0141] Verify and process the collected transaction data to obtain standard transaction data;

[0142] Storing the collected transaction data in a first storage area in the buffer pool;

[0143] The standard transaction data is stored in the second storage area in the cache pool, wherein the full amount of transaction data is the standard transaction data.

[0144] In some implementations, the apparatus 600 includes:

[0145] Generate trend graphs of different housing types based on a collection of real estate indicators of a city over N statistical periods;

[0146] Feedback trend charts of different house types to the client for display.

[0147] In some implementations, the apparatus 600 further includes:

[0148] According to the trend graph, identify the target housing types with sudden trend changes among different housing types;

[0149] Obtain historical transaction data of the target housing type;

[0150] Analyze historical transaction data, obtain the cause of the sudden change of the target house type, and feedback the cause of the sudden change and / or historical transaction data to the client for display.

[0151] In some implementations, the apparatus 600 further includes:

[0152] Based on the set of real estate indicators of the city within N statistical periods, multiple dimensions are compared between different regions in the city to obtain comparison results between regions; and / or, based on the set of real estate indicators of the city within N statistical periods, multiple dimensions are compared between different cities to obtain comparison results between cities; the comparison results between regions and the comparison results between cities are fed back to the client for image display.

[0153] In this embodiment, transaction data is collected by acquiring multiple data sources and the target access mode corresponding to each data source, and the transaction data is pre-processed such as processing, cleaning and standardization, so that the quality of the collected full transaction data is better, and then the global transaction area and regional transaction area of ​​the house type are obtained according to the full transaction data, and the weight of each region in the statistical period is determined based on the regional transaction area corresponding to each region and the global transaction area of ​​the city, and the global average price of each period is further determined based on the weight, and the ratio of the global average prices of two adjacent statistical periods is used as the adjustment coefficient. The price index is determined according to the adjustment coefficient, and the impact of the sales situation of each region on the city is combined. The information considered for obtaining the price index is more comprehensive, the impact of the region on the city price index is avoided, and the reference value of the price index is increased; the real estate forecast data is obtained with a more reliable price index, and mutation analysis and comparison between regions or cities can be performed according to the trend chart of the real estate price index change, which greatly increases the availability of price indicators and provides more accurate results for actual real estate market supervision and policy formulation.

[0154] Figure 7 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 7 As shown, the electronic device 700 includes:

[0155] The memory 710 and the processor 720, a bus 730 connecting different components (including the memory 710 and the processor 720), the memory 710 stores a computer program, and when the processor 720 executes the program, the information processing method described in the embodiment of the present disclosure is implemented.

[0156] Bus 730 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0157] The electronic device 700 typically includes a variety of electronic device readable media. These media can be any available media that can be accessed by the electronic device 700, including volatile and non-volatile media, removable and non-removable media.

[0158] The memory 710 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 740 and / or cache memory 750. The electronic device 700 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 760 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 not shown, usually called a "hard drive"). Although Figure 7 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 730 via one or more data medium interfaces. The memory 710 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present disclosure.

[0159] A program / utility 780 having a set (at least one) of program modules 770 may be stored, for example, in the memory 710, such program modules 770 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 770 generally perform the functions and / or methods of the embodiments described in the present disclosure.

[0160] The electronic device 700 may also communicate with one or more external devices 790 (e.g., keyboard, pointing device, display 791, etc.), one or more devices that enable a user to interact with the electronic device 700, and / or any device that enables the electronic device 700 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input / output (I / O) interface 792. Furthermore, the electronic device 700 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 793. Figure 7 As shown, the network adapter 793 communicates with other modules of the electronic device 700 via the bus 730. It should be understood that although Figure 7 Not shown, other hardware and / or software modules may be used in conjunction with electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0161] The processor 720 executes various functional applications and data processing by running the programs stored in the memory 710 .

[0162] It should be noted that the implementation process and technical principles of the electronic device of this embodiment refer to the aforementioned explanation of the information processing method of the embodiment of the present disclosure, and will not be repeated here.

[0163] In order to implement the above embodiments, the present disclosure also proposes a computer-readable storage medium.

[0164] When the instructions in the computer-readable storage medium are executed by the processor of the service server, the service server can execute the information processing method as described above. Optionally, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0165] In order to implement the above embodiments, the present disclosure further provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, the information processing method as described above is implemented.

[0166] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims. It should be understood that the present disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An information processing method, characterized in that: The method comprises: Obtaining an indicator query request from a client, and determining a city to be queried according to the indicator query request; Collecting the full transaction data of different housing types in each area of ​​the city within N statistical periods, where N is a natural number greater than or equal to 1, and the N statistical periods include the current t-th statistical period; For house type j, based on the full transaction data of house type j, determine the global transaction area of ​​the city in N statistical periods, the regional transaction area of ​​region i in N statistical periods, and the regional average price of house type j in region i in the t-th statistical period, where i and j are natural numbers greater than or equal to 1; Performing a weighted operation on the regional average price according to the global transaction area and the regional transaction area, and combining the price association information of the housing type j in the t-1th statistical period, determining the price index of the housing type j in the tth statistical period; According to the price indicators of different housing types in the t-th statistical period, a real estate indicator set of the city in the t-th statistical period is determined, and the real estate indicator set is fed back to the client.

2. The method according to claim 1, characterized in that The weighting operation on the regional average price according to the global transaction area and the regional transaction area, and combining the price association information of the housing type j in the t-1th statistical period, to determine the price index of the housing type j in the tth statistical period, includes: Determine the weight of region i in the tth statistical period according to the regional transaction areas of region i in N statistical periods and the global transaction areas of cities in N statistical periods; Based on the weight of each region in the t-th statistical period, the regional average price of housing type j in each region in the t-th statistical period is weighted to obtain the global average price of housing type j in the t-th statistical period; Determine a price index of the house type j in the tth statistical period according to the global average price of the house type j in the tth statistical period and the price association information.

3. The method according to claim 2, characterized in that The determining the weight of the region i in the tth statistical period includes: Sum the transaction areas of the region i in N statistical periods to obtain a first sum value; Summing the global transaction areas of the cities in N statistical periods to obtain a second sum value; The ratio of the first sum value to the second sum value is obtained to obtain the weight of the region i in the tth statistical period.

4. The method according to claim 2, characterized in that: The price association information includes the global average price of house type j in the t-1th statistical period and the price index of house type j in the t-1th statistical period; the method further includes: Obtaining the ratio of the global average price of house type j in the t-th statistical period and the t-1-th statistical period as an adjustment coefficient; The product of the adjustment coefficient and the price index of the house type j in the t-1th statistical period is obtained as the price index of the house type j in the tth statistical period.

5. The method according to any one of claims 1 to 4, characterized in that The collecting of the full transaction data of different housing types in each area of ​​the city within N statistical periods also includes: determining one or more data sources for the transaction data; Determining a target access mode for the transaction data according to the type of the data source; According to the target access mode, the transaction data is collected from the data source to obtain the full transaction data.

6. The method according to claim 5, characterized in that The method further comprises: According to the data source, a buffer pool of transaction data collected from the data source is determined, and the transaction data collected from the data source is stored in the buffer pool.

7. The method according to claim 6, characterized in that The storing the transaction data collected from the data source in the cache pool includes: Performing data verification and processing on the collected transaction data to obtain standard transaction data; Storing the collected transaction data in a first storage area in the buffer pool; The standard transaction data is stored in a second storage area in the cache pool, wherein the full transaction data is the standard transaction data.

8. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Generate trend graphs of different housing types based on a set of real estate indicators of the city in N statistical periods; The trend graphs of the different house types are fed back to the client for display.

9. The method according to claim 8, characterized in that After generating trend graphs of different house types, the method further includes: According to the trend graph, identifying target house types with sudden trend changes among different house types; Obtaining historical transaction data of the target house type; The historical transaction data is analyzed to obtain the cause of the sudden change of the target house type, and the cause of the sudden change and / or the historical transaction data are fed back to the client for display.

10. The method according to any one of claims 1 to 4, characterized in that After determining the real estate indicator set of the city in the t-th statistical period, the method further includes: Comparing different regions in the city in multiple dimensions based on a set of real estate indicators of the city in N statistical periods to obtain comparison results between regions; and / or, Based on the real estate index set of the city in N statistical periods, a comparison of different cities in multiple dimensions is performed to obtain a comparison result between the cities; The comparison results between the regions and the comparison results between the cities are fed back to the client for image display.

11. An information processing device, characterized in that: include: A city determination module, used to obtain an index query request from a client, and determine a city to be queried according to the index query request; A data collection module, used to collect the full transaction data of different housing types in each area of ​​the city within N statistical periods, where N is a natural number greater than or equal to 1, and the N statistical periods include the current t-th statistical period; A data analysis module is used to determine, for house type j, the global transaction area of ​​each city in N statistical periods, the regional transaction area of ​​each region i in N statistical periods, and the regional average price of house type j in region i in the t-th statistical period based on the full transaction data of house type j, wherein the values ​​of i and j are natural numbers greater than or equal to 1; An index acquisition module, used to perform a weighted operation on the regional average price according to the global transaction area and the regional transaction area, and determine the price index of the housing type j in the t-th statistical period in combination with the price association information of the housing type j in the t-1th statistical period; The set acquisition module is used to determine the real estate index set of the city in the t-th statistical period according to the price indicators of different house types in the t-th statistical period, and feed back the real estate index set to the client.

12. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 10 when executed by a processor.

14. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 10 when being executed by a processor.

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