House building data updating method and system based on sampling data

Abstract

The invention belongs to but not limited to the technical field of information technology service, and discloses a house building data updating method and system based on sampled data, and the method comprises the steps: classifying villages and towns in a region through analyzing the relation between regional house building data and local population, economy and other data, and coding each type of villages and towns; performing layered sampling investigation on house building data in the region by combining town classification in the region; the house building area data is divided into urban resident per capita housing area, agricultural resident per capita housing area and public building area, and the three building areas of each type of villages and towns are determined according to the sampling data; according to the village and town population data obtained every year, town population Pv1, agricultural population Pv2 and total population Pv, calculating to obtain the building area of each village and town house; and sorting out the percentage of the building area of each structure type of houses in each type of villages and towns according to the sampling data, and obtaining the building area data of each type of houses in villages and towns by combining the calculated building area of each type of houses in villages and towns.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of information technology service technology, and in particular relates to a method and system for updating house construction data based on sampling data. Background Art

[0002] House building data is an important data in the earthquake emergency basic database. It is the main disaster-bearing data of natural disasters such as earthquakes, mudslides, and landslides. Its timeliness and accuracy determine the accuracy of earthquake disaster rapid assessment and earthquake disaster risk assessment results. There are two main methods for updating house building data: the first is to fill in the statistics one by one by issuing a form; the second is to extract based on high-definition remote sensing data. The house building structure type extracted by this method is quite different from the actual situation. According to years of data collection experience, the first two methods are very labor-intensive, long-term and costly to update the building data of a province or a large region.

[0003] House building data is an important data in the earthquake emergency basic database. It is the main disaster-bearing data of natural disasters such as earthquakes, mudslides, and landslides. Its timeliness and accuracy determine the accuracy of earthquake disaster rapid assessment and earthquake disaster risk assessment results. There are two main methods for updating house building data: the first is to fill in the statistics one by one by issuing a form; the second is to extract based on high-definition remote sensing data. The house building structure type extracted by this method is quite different from the actual situation. According to years of data collection experience, the first two methods are very labor-intensive, long-term and costly to update the building data of a province or a large region.

[0004] In view of the above analysis, the technical problems that need to be solved urgently in the existing technology are: the existing methods for updating the building data of a province or a large area are very labor-intensive, long-term and costly. Specifically, the existing technology has the following technical problems in data processing:

[0005] 1. The manual reporting method is inefficient: The method of filling in statistics one by one by issuing forms requires a lot of manpower and time. Especially when the amount of data is huge, this method is prone to problems such as data omissions and incorrect reporting, making it difficult to ensure the timeliness and accuracy of the data.

[0006] 2. Insufficient accuracy of remote sensing data extraction: Although the method of extracting house building information based on high-definition remote sensing data reduces the workload of manual statistics, due to the resolution and shooting angle limitations of remote sensing images, there is a large gap between the extracted house building structure types and the actual situation, which makes it difficult to accurately reflect the actual conditions of the house buildings, thus affecting the accuracy of earthquake disaster assessment.

[0007] 3. The update cycle is long and the cost is high: Whether it is manual reporting or remote sensing data extraction, updating the building data of a province or a large region requires a lot of time and resources. The workload is huge, the cycle is long and the cost is high, which makes it difficult to meet the timeliness requirements of earthquake emergency response and disaster risk assessment.

[0008] 4. Complex data integration and analysis: Existing methods lack systematic and scientific means in the process of data integration and analysis, resulting in low data processing efficiency and difficulty in timely and accurate updating and obtaining the latest housing construction data. This seriously affects the timeliness and accuracy of emergency response and disaster assessment in the face of natural disasters.

[0009] In summary, there are significant technical problems in the existing technology on the updating method of building construction data, and there is an urgent need for a more efficient, accurate and low-cost data updating method to improve the accuracy and timeliness of rapid earthquake disaster assessment and risk assessment. Summary of the invention

[0010] In view of the problems existing in the prior art, the present invention provides a housing construction data updating method and system based on sampling data, which combines the relationship between regional housing construction data and local population, economic and other data to solve the problem of fast and relatively accurate updating of township housing construction data in a province or region.

[0011] The present invention is achieved by a method for updating housing construction data based on sampled data, comprising:

[0012] The present invention realizes the efficient operation of the housing construction data update method based on sampling data by combining software and hardware devices. First, in step 1, the housing construction data, population data and economic data in the area are obtained through data acquisition equipment (such as sensors or mobile acquisition tools). The collected data is input into the data analysis software, and the relationship between the housing construction and population and economic data in the area is analyzed using the software algorithm. Different towns are classified and coded through cloud servers or local computing devices. This process ensures the accuracy and automation of data classification.

[0013] In step 2, based on the classification results of towns and villages, a sampling algorithm is used to select sample towns and villages in each classification level for sampling survey. In this process, mobile devices equipped with GPS and image recognition functions are used to collect data from the selected sample towns and villages, and record the building area and related attribute information of houses. The collected data is transmitted to the central server via a wireless network for storage and processing, ensuring the real-time and accuracy of data collection.

[0014] In step 3, the data processing system subdivides the housing construction data collected in the early stage into per capita housing area of ​​urban residents, per capita housing area of ​​agricultural residents, and public building area. The sampling data is modeled and calculated using software tools to determine the housing construction area of ​​each type of township. The system automatically calculates and outputs the three types of construction areas for each type of township through an integrated algorithm, providing accurate data support.

[0015] Finally, in steps 4 and 5, the system automatically obtains the latest township population data by interfacing with government departments or demographic systems. The data processing module calculates the total housing construction area of ​​each township based on the input township population data and the percentage of housing construction area obtained in the early stage, and further subdivides the construction area of ​​different types of houses. All calculations and analysis processes are automatically completed by the background server and displayed through data visualization tools, which is convenient for users to view and export updated housing construction data. Further, the housing construction area of ​​each township in step 4 is:

[0016] X v =∑T v ·P v1 +C v ·P v2 +PW v ·P v

[0017] Where X v is the building area of ​​township houses, T v is the per capita housing area of ​​urban residents; C v PW is the per capita housing area of ​​agricultural residents; v It is the per capita public housing construction area; public housing buildings mainly include: government and institution buildings, school buildings, hospital buildings, corporate office and factory buildings, shopping malls and shops buildings, guesthouses and other public buildings.

[0018] Furthermore, the building area data of each type of houses in the township in step 5 is:

[0019] Bm k =X v˙ P k

[0020] Among them, Bm k Data on the building area of ​​a type of houses in township buildings, Pe k The percentage of the building area of ​​Class A buildings in township buildings to the total building area.

[0021] Another object of the present invention is to provide a housing construction data updating system based on sampled data for realizing the housing construction data updating method based on sampled data, comprising:

[0022] Township classification module: by analyzing the relationship between regional housing construction data and local population, economic and other data, the townships in the region are classified and each type of township is coded;

[0023] Stratified sampling survey module: Combined with the classification of towns in the region, a stratified sampling survey of housing construction data in the region is conducted;

[0024] Three building area confirmation modules: Housing building area data are divided into per capita housing area of ​​urban residents, per capita housing area of ​​agricultural residents, and public building area. These three types of building areas are determined for each type of township based on sampling data;

[0025] Township housing construction area calculation module: According to the township population data obtained each year, the urban population P v1 , agricultural population v2 , total population P v , calculate the building area of ​​each township;

[0026] Housing construction area data acquisition module: Based on the sample data, the percentage of housing construction area of ​​each structural type in each type of township is sorted out, and combined with the calculated housing construction area of ​​each township, the housing construction area data of each type of township is obtained.

[0027] Another object of the present invention is to provide a computer device, the computer device includes a memory and a processor, the memory stores a computer program, when the computer program is executed by the processor, the processor executes the steps of the housing construction data updating method based on sampling data.

[0028] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the housing construction data updating method based on sampling data.

[0029] Another object of the present invention is to provide an information data processing terminal, which includes the house construction data updating system based on sampling data.

[0030] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0031] First, the workload of updating house and building data in the present invention is relatively small and the cost is low; the cycle of updating house and building data in the present invention is short and the accuracy is relatively high; the present invention can be used to review and verify other methods for updating house and building data.

[0032] The most critical technical problem of the present invention is to clarify the relationship between population and housing construction data. By using a GIS platform to perform vector data overlay analysis and field investigation related combination methods for analysis, implicit information between various data is extracted, the relationship between the distribution of data such as population, economy and residential areas and the distribution of local buildings is analyzed, and different areas with similar distribution of housing structure types and per capita area are divided, namely: townships within the jurisdiction of large cities, townships within the jurisdiction of general cities, townships within the jurisdiction of plateau cities, townships within the jurisdiction of ordinary districts and counties, and townships in ethnic minority areas; and further subdivided according to regional characteristics, the townships within the jurisdiction of large cities and the townships within the jurisdiction of general cities are subdivided into townships in the main urban area, townships in some areas located in the main urban area, townships around the main urban area and townships far away from the main urban area, the townships within the jurisdiction of plateau cities are subdivided into townships in the urban area, townships around the urban area and townships far away from the urban area, the townships within the jurisdiction of ordinary districts and counties are subdivided into townships resident in county towns, townships around county towns and ordinary townships, and the townships in ethnic minority areas are subdivided into townships resident in county towns and ordinary townships.

[0033] In the present invention, after analyzing and summarizing the housing construction area data obtained through sampling survey, the housing construction area data are divided into three categories: urban residential housing, rural residential housing and public housing. All housing buildings except urban and rural residential housing are classified as public housing, and a simplified table of changes in building areas for different purposes corresponding to the housing construction areas of towns and villages in different regions is obtained. As shown in Table 2. By sampling surveys of each type of towns and villages, the per capita area of ​​the three types of housing buildings, namely urban residential housing, agricultural residential housing and public housing, and the proportion of houses of various structural types are obtained. For towns and villages without sampling surveys, the total housing construction area data and the housing construction area data of various structural types of each town and village can be calculated by substituting the population data obtained from the Statistics Bureau each year into the formula for calculating housing construction data.

[0034] Second, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following important aspects:

[0035] (1) The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0036] The housing construction data updating method and system based on sampling data in the present invention, combined with a small amount of housing construction sampling data and local population data, can realize fast and relatively accurate updating of housing construction data in a certain province or region, provide an effective building data updating method for the Sichuan earthquake emergency basic database, improve the effectiveness of building data in the Sichuan earthquake emergency basic database, better support the Sichuan earthquake emergency command technical system, and improve the accuracy of its earthquake damage assessment; the present invention can be used for housing construction data statistics work in cities (prefectures), districts (counties), and can greatly save manpower and material resources; the present invention can be used for data review and verification work in detailed census work of housing construction data in a large area, and screen out data with low credibility and large errors, so the present invention has great social benefits and commercial value.

[0037] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0038] Through the investigation of domestic and foreign housing construction data update methods, it is found that the existing research is mainly on the data update mechanism, and there is basically no method for updating specific housing construction data; the housing construction data update method and system based on sampling data in the present invention, combined with a small amount of housing construction sampling data and local population data, can realize fast and relatively accurate updating of housing construction data in a province or a region, filling the technical gap in the industry at home and abroad.

[0039] Third, the technical solution of the present invention solves several key problems in the prior art in industrial applications and has achieved significant technical progress:

[0040] 1. Existing technical problems: housing construction data is not updated in a timely manner and the accuracy is insufficient

[0041] Traditional housing construction data update methods usually rely on a long-term comprehensive census, resulting in poor timeliness of data and difficulty in timely reflecting changes in housing construction conditions in the region. Such methods are also prone to insufficient accuracy in data collection and analysis due to human and resource constraints, especially in the uneven update of housing construction data in urban and rural areas.

[0042] The present invention uses a stratified sampling survey method combined with the latest township population data to efficiently update regional housing construction data in a relatively short period of time. By using classified townships for sampling, the representativeness of the data and the accuracy of the collection are improved, while ensuring that fewer samples can reflect the actual situation in a large area, greatly shortening the data update cycle.

[0043] 2. Problems of existing technologies: low efficiency of data collection and analysis, and strong dependence on manual work

[0044] The existing housing construction data update method mainly relies on manual collection and analysis. The data collection efficiency is low, and it is easily subject to manpower and resource constraints. The processing process is cumbersome and prone to data errors or inconsistencies, which affects the accuracy of the final data.

[0045] The present invention realizes the automatic collection and analysis of housing construction data by combining mobile devices, GPS and data analysis software. The data is transmitted to the cloud server in real time for processing, and the relationship between housing construction and population and economic data is automatically analyzed, which greatly improves the efficiency of data processing. The automatic and real-time update mechanism reduces manual intervention and significantly reduces human errors in the process of data collection and analysis.

[0046] 3. Problems with existing technologies: Difficulty in reflecting the multidimensional characteristics of housing construction data

[0047] Existing methods for updating housing construction data are usually difficult to break down to the level of various types of building areas, especially the difficulties in data classification and statistics of different building types such as urban residents, agricultural residents, and public buildings, resulting in incomplete data analysis results.

[0048] The present invention can accurately calculate the area of ​​different types of buildings by subdividing the housing construction area into per capita housing area of ​​urban residents, per capita housing area of ​​agricultural residents, and public building area, and combining the sampling data with the results of township classification. This method effectively solves the problem that multi-dimensional building data is difficult to collect and analyze, and provides more detailed and accurate housing construction data, which is convenient for policy formulation, planning and management.

[0049] 4. Existing technical problems: lack of intelligent and dynamic data update mechanism

[0050] Traditional methods rely on regular census data and cannot dynamically track changes in housing construction data in the context of rapid urbanization. As a result, existing construction data often cannot reflect new population and economic changes in a timely manner, affecting the timeliness of decision-making.

[0051] The present invention can achieve dynamic acquisition of township population data by connecting with the statistical system of government departments or other population data interfaces, and establish an intelligent data update mechanism in combination with real-time updated building data. The system automatically calculates the building area of ​​each township, reducing the need to rely on large-scale censuses and achieving continuous updating and dynamic management of housing construction data.

[0052] In summary, the present invention solves the problems of low data updating efficiency, low precision and low intelligence in the prior art by introducing a data collection, analysis and automatic updating mechanism combining software and hardware, realizes efficient, accurate and dynamic updating of housing construction data, and provides strong technical support for policy formulation, planning and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a housing construction data updating method based on sampling data provided by an embodiment of the present invention;

[0054] Figure 2 It is a rendering of a housing construction data updating method based on sampling data provided by an embodiment of the present invention;

[0055] Figure 3 is a rendering of a housing construction data updating system based on sampling data provided by an embodiment of the present invention; a is township population data; b is township per capita housing construction area data; c is township various housing construction area percentage data; d is township various housing construction area data calculated by the system;

[0056] Figure 4 It is a workflow diagram of a housing construction data updating system based on sampling data provided by an embodiment of the present invention;

[0057] Figure 5 It is a structural diagram of a house building data updating system based on sampling data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with implementation examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] The method for updating building construction data based on sampling data provided by an embodiment of the present invention is characterized in that the building construction data in a region is updated through stratified sampling and data analysis, and specifically comprises the following steps:

[0060] Step 1: Regional housing construction data analysis and township classification

[0061] By analyzing the relationship between regional housing construction data and local population, economic and other data, the towns in the region are classified and coded for each type of town. The specific process is as follows:

[0062] Data collection: Collect housing construction data in the area (such as building area, use, etc.) as well as local population data (such as population density, urban population ratio, etc.) and economic data (such as GDP, per capita income, etc.).

[0063] Data analysis: Use data analysis software (such as Python's Pandas, NumPy and other libraries) to analyze the collected data and identify the relationship between housing construction data and population and economic data.

[0064] Township classification: Based on the analysis results, the townships are classified into different types (such as urban type, agricultural type, etc.), and each type of township is coded.

[0065] Step 2: Stratified sampling survey

[0066] Combined with the classification of towns and villages in the region, a stratified sampling survey of housing construction data in the region was conducted. The specific process is as follows:

[0067] Determine the sampling stratum: Determine the sampling stratum based on the township classification results, that is, each type of township is regarded as a sampling stratum.

[0068] Sampling design: Using sampling methods (such as stratified sampling), a number of sample towns are selected in each sampling stratum for investigation.

[0069] Data collection: Use survey tools (such as questionnaires, on-site measurements, etc.) to collect housing construction data from selected sample towns.

[0070] Step 3: Classification and determination of building area data

[0071] The housing construction area data is divided into the per capita housing area of ​​urban residents, the per capita housing area of ​​agricultural residents, and the public building area. The three types of construction areas of each type of township are determined based on the sample data. The specific process is as follows:

[0072] Data collation: Sort the collected housing construction data by category.

[0073] Data analysis: Statistical analysis software (such as SPSS, R, etc.) was used to analyze the sorted data to determine the per capita housing area of ​​urban residents, per capita housing area of ​​agricultural residents, and public building area for each type of township.

[0074] Step 4: Calculate the building area

[0075] Based on the township population data obtained each year (urban population Pv1, agricultural population Pv2, total population Pv), the building area of ​​each township is calculated. The specific process is as follows:

[0076] Data collection: Collect the latest township population data.

[0077] The formula is used to calculate the housing construction area of ​​each township: Township housing construction area = per capita housing area of ​​urban residents × Pv1 + per capita housing area of ​​agricultural residents × Pv2 + public building area x (Pv1 + Pv2) / total population;

[0078] Step 5: Arrangement of building area by structure type

[0079] Based on the sampling data, the percentage of building area of ​​each structural type of houses in each type of township is sorted out. Combined with the calculated building area of ​​houses in each township, the building area data of each type of house in the township is obtained.

[0080] The specific process is as follows:

[0081] Data collation: collate the sample survey data and calculate the percentage of building area of ​​each structural type of houses.

[0082] Area allocation: Utilize the calculated total housing building area of ​​each township and allocate it to each structural type according to percentage to obtain the building area data of each type of housing in the township.

[0083] Combining hardware and software devices and algorithms:

[0084] Hardware

[0085] Data collection equipment: measurement tools used for field surveys (such as laser rangefinders, GPS devices, etc.).

[0086] Computers and Servers: Computers and servers used for data storage and processing.

[0087] Software tools and algorithms:

[0088] Data analysis software: such as Python's Pandas, NumPy, SciPy libraries, R language, etc., for data organization and analysis.

[0089] Statistical analysis software: such as SPSS, SAS, etc., used for data statistical analysis.

[0090] Sampling design tools: Online survey tools such as SurveyMonkey and Qualtrics.

[0091] Algorithm models: such as regression analysis model, stratified sampling algorithm, area calculation formula, etc.

[0092] Through the combination of the above hardware and software equipment and algorithm models, house construction data can be updated and analyzed efficiently and accurately.

[0093] The method for updating housing construction data based on sampling data provided by the embodiment of the present invention aims to efficiently and accurately update the housing construction data in a region through sampling data. The specific steps include regional township classification, stratified sampling survey, determination of building area type, calculation of township housing construction area, and obtaining building area data of each type of housing.

[0094] 1. Township classification and coding: First, by analyzing the relationship between regional housing construction data and local population, economic and other data, the townships in the region are classified and each type of township is coded for subsequent data processing.

[0095] 2. Stratified sampling survey: Based on the classification of towns and villages in the region, a stratified sampling survey is conducted on housing construction data to ensure the representativeness and breadth of the sample.

[0096] 3. Determine the type of building area: divide the housing building area data into three categories: per capita housing area of ​​urban residents, per capita housing area of ​​agricultural residents and public building area. Determine these three types of building areas for each type of township based on the sampling data.

[0097] 4. Calculate the building area of ​​township houses: According to the township population data obtained each year (urban population P v1 , agricultural population v2 , total population P v ), combined with the various building areas determined in step 3, calculate the building area of ​​each township.

[0098] 5. Obtain data on the building area of ​​each type of house: Based on the sample data, sort out the percentage of building area of ​​each structural type of house in each type of township, and combine it with the calculated building area of ​​each township to further obtain data on the building area of ​​each type of house in the township.

[0099] Example 1: Urban Renewal Planning

[0100] In a rapidly developing city, the government plans to carry out urban renewal planning to optimize the urban spatial layout and improve the quality of life of residents. By using this method, the urban planning department can quickly obtain the housing construction area data of each township, including the per capita housing area of ​​urban residents, the per capita housing area of ​​agricultural residents, and the public building area. Based on these data, the planning department can formulate a more accurate and reasonable urban renewal strategy to ensure that the renewal project can meet the needs of different resident groups and optimize the allocation of public resources.

[0101] Example 2: Post-disaster reconstruction assessment

[0102] In an area hit by a natural disaster, post-disaster reconstruction is imminent. By using this method, disaster management departments can quickly assess the housing losses in various towns and villages. By obtaining the housing building area data of each town and village, combined with field surveys, the specific conditions of house collapse and damage can be accurately calculated. Based on these data, management departments can formulate more effective post-disaster reconstruction plans, give priority to repairing and rebuilding severely damaged towns and buildings, and ensure that affected residents can resume normal life as soon as possible. At the same time, these data can also be used to evaluate the progress and effectiveness of reconstruction work and provide support for subsequent decision-making.

[0103] The housing construction data updating method based on sampling data provided by the embodiment of the present invention includes the following detailed signal and data processing process:

[0104] Step 1: Classification and coding

[0105] By analyzing the relationship between regional housing construction data and local population, economic and other data, the towns in the region are classified. The specific operation includes collecting housing construction data, population data, economic data, etc. in the region, and then using data analysis techniques (such as regression analysis, cluster analysis, etc.) to determine the relationship between these variables. According to the analysis results, the towns are divided into several categories, and each category of towns is coded. This step ensures the scientificity and rationality of subsequent sampling and analysis.

[0106] Step 2: Stratified sampling survey

[0107] Combined with the classification results of towns and villages in the region, a stratified sampling survey of housing construction data in the region is conducted. The specific operation includes randomly selecting a certain number of samples in each type of town and village to ensure that the sampling is representative. The sampling survey should cover key indicators such as per capita housing area of ​​urban residents, per capita housing area of ​​agricultural residents, and public building area. The collected data will be used for subsequent analysis and calculations.

[0108] Step 3: Determine the building area

[0109] The housing construction area data are divided into three categories: per capita housing area of ​​urban residents, per capita housing area of ​​agricultural residents, and public building area. Based on the sample data obtained from the stratified sampling survey, the average values ​​of these three types of construction areas are calculated for each type of township. These average values ​​will serve as the benchmark data for each type of township, reflecting the housing construction characteristics of this type of township.

[0110] Step 4: Calculate the floor area of ​​your home

[0111] According to the township population data obtained each year (including urban population P v1 , agricultural population v2 and the total population P v ), calculate the building area of ​​each township. The specific operation is as follows:

[0112] Urban residential housing construction area = urban residents per capita housing area * urban population P v1

[0113] Agricultural resident housing area = agricultural resident per capita housing area * agricultural population P v2

[0114] The public building area is calculated based on the proportion of the sample data.

[0115] These calculation results reflect the total housing construction area of ​​each township in different years, ensuring dynamic updating and accuracy of the data.

[0116] Step 5: Determine the type and area of ​​the house structure

[0117] According to the sampling data, the percentage of the building area of ​​each structural type of houses in each type of township (such as brick-concrete structure, steel structure, wooden structure, etc.) is sorted out. Then, combined with the calculated total building area of ​​each township, the building area data of each type of house is calculated. The specific operation is as follows:

[0118] Determine the percentage of building area of ​​each structural type in each township

[0119] These percentages were multiplied by the total floor area of ​​housing in each township to obtain the floor area of ​​housing by structural type.

[0120] Ultimately, these data provide the distribution of building area of ​​houses of different structural types in each township, ensuring the comprehensiveness and accuracy of the data. Through the above steps, the scientific update of regional housing construction data can be achieved, providing an important basis for relevant policy formulation and planning.

[0121] The process of the present invention is as follows Figure 1 As shown, taking a certain region as an example, the specific steps are as follows:

[0122] Step 1: Analyze the relationship between regional housing construction data and local population, economic and other data, and classify the towns in the region. The major categories are towns within the jurisdiction of large cities, towns within the jurisdiction of general cities, towns within the jurisdiction of plateau cities, towns within the jurisdiction of ordinary districts and counties, and towns in ethnic minority areas; towns within the jurisdiction of large cities and towns within the jurisdiction of general cities are subdivided into towns in the main urban area, towns in some areas located in the main urban area, towns around the main urban area, and towns far away from the main urban area. The towns in the six urban areas of Chengdu, namely Jinjiang, Qingyang, Jinniu, Wuhou, Chenghua, and High-tech Zone, and towns within the jurisdiction of general cities. Inner townships refer to townships within the urban areas of prefecture-level cities excluding ethnic minority areas; townships within the jurisdiction of plateau cities refer to townships within the urban areas of Ganzi Prefecture and Aba Prefecture, which are subdivided into urban townships, townships around urban areas, and townships far from urban areas; townships within the jurisdiction of ordinary districts and counties refer to townships excluding townships in large cities, general cities and townships in ethnic minority areas, which are subdivided into townships located in county towns, townships around county towns, and ordinary townships; townships in ethnic minority areas refer to townships in Liangshan Prefecture, Ganzi Prefecture and Aba Prefecture excluding townships within the jurisdiction of cities, which are subdivided into townships where county towns are located and ordinary townships.

[0123] Step 2: Code each type of township in the region according to its category. The code is a two-digit number, with the first digit being the major category code and the second digit being the minor category code.

[0124] Step 3: Organize the sampled housing area data and divide it into three types: urban residents’ housing area, agricultural residents’ housing area, and public building area. Combined with the local population, calculate the per capita housing area of ​​urban residents, per capita housing area of ​​agricultural residents, and per capita public building area for each type of township.

[0125] Step 4: According to the sample data, the percentage Pe of the building area of ​​each structural type in each township is sorted out. k , k is the number of housing structure types in each type of township, and the housing structure types can be divided into: civil structure, brick-wood structure, brick-concrete structure, steel-concrete structure, high-rise structure and other structures.

[0126] Step 5: Substitute the urban population (P v1 ), agricultural population (P v2 ), total population (P v ) and the per capita housing area of ​​urban residents in each type of township obtained through sampling (T v ), per capita housing area of ​​agricultural residents (C v ), per capita public building area (PW v ) into the following formula to obtain the building area of ​​all towns in the region.

[0127] F i (X v )=∑T vj ·P v1i +C vj ·P v2i +PW vj ·P vi

[0128] Among them, i is the number of all towns in the region where 1≤i≤, and j is the town code;

[0129] Step 6: Based on the calculated total building area X of each township v , combined with the percentage of building area of ​​each structural type in each township Pe k , calculate and obtain the building area data Bm of each type of house in each township in the region k .

[0130] F i (Bm k )=F i (X v )·Pe k

[0131] Among them, k is 1≤k≤the number of building structure types in each type of township in the region, F i (Bm k ) is the building area of ​​the k-type structure type in the ith township.

[0132] like Figure 2 As shown, the effect diagram of the housing construction data updating method based on sampling data provided by an embodiment of the present invention.

[0133] like Figure 5 As shown, the workflow diagram of the house building data updating system based on sampling data provided by the embodiment of the present invention includes:

[0134] Township classification module: by analyzing the relationship between regional housing construction data and local population, economic and other data, the townships in the region are classified and each type of township is coded;

[0135] Stratified sampling survey module: Combined with the classification of towns in the region, a stratified sampling survey of housing construction data in the region is conducted;

[0136] Three building area confirmation modules: Housing building area data are divided into per capita housing area of ​​urban residents, per capita housing area of ​​agricultural residents, and public building area. These three types of building areas are determined for each type of township based on sampling data;

[0137] Township housing construction area calculation module: According to the township population data obtained each year, the urban population P v1 , agricultural population v2 , total population P v , calculate the building area of ​​each township;

[0138] Housing construction area data acquisition module: Based on the sample data, the percentage of housing construction area of ​​each structural type in each type of township is sorted out, and combined with the calculated housing construction area of ​​each township, the housing construction area data of each type of township is obtained.

[0139] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of a method for updating house building data based on sampling data.

[0140] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a method for updating housing construction data based on sampling data.

[0141] An application embodiment of the present invention provides an information data processing terminal, which includes a house building data updating system based on sampling data.

[0142] The present invention is mainly used in the earthquake emergency command technology system based on housing construction data, the housing construction data update of the geological disaster risk assessment system, and the housing construction data statistics and census work of local governments such as cities (states), districts (counties), etc. The present invention is applied in software systems such as the "Sichuan Provincial Earthquake Bureau Lushan Earthquake Post-Disaster Recovery and Reconstruction-Earthquake Emergency Service System Construction Project" and the "Municipal and County Comprehensive Service System", solving the problem of timely updating of housing construction data.

[0143] The housing construction data results calculated by the method and system of the present invention are compared with the housing construction data collected from the census in all towns and villages in Jintang County of Chengdu City, Tongjiang Street of Central District of Leshan City, Yucheng District of Ya'an City (Dongcheng Street, Daxing Town), Jiulong County of Ganzi Prefecture (Xiaer Town, Sanyanlong Township), etc. The data comparison table results are shown in Table 1. From the table data, it can be seen that the housing construction data results calculated by the method and system of the present invention and the housing construction data collected from the census have an average error rate of less than 4%, and the accuracy is still relatively high.

[0144] Table 1: Test table of accuracy of calculation results of the method and system of the present invention

[0145]

[0146] The 2014 Earthquake Science and Technology Spark Plan "Study on Building Update of Sichuan Earthquake Emergency Basic Database" project aims to establish a relationship model between building data in different areas of Sichuan and local residential spatial distribution, population, economy and other data by analyzing the population, economic data and ethnic distribution characteristics of Sichuan Province. The model calculates local building data through economic and population changes in each region, so as to timely update the building data in the earthquake emergency database.

[0147] Application of project results:

[0148] 1) Application of earthquake emergency service system

[0149] The project results have been applied in the "Lushan Earthquake Post-disaster Recovery and Reconstruction of the Sichuan Provincial Seismological Bureau - Earthquake Emergency Service System Construction Project (Package 4)", especially in the earthquake emergency linkage and information sharing and disaster comprehensive assessment system upgrade and transformation project, which solved the problem that the building data in the Sichuan Provincial Earthquake Emergency Database could not be updated in time.

[0150] 2) Application of city and county comprehensive service system

[0151] The project results have been applied in the city and county comprehensive service systems of Badang County in Aba Tibetan and Qiang Autonomous Prefecture and Luding County, Batang County, and Daofu County in Ganzi Tibetan Autonomous Prefecture, solving the problem of timely updating of local building data.

[0152] 3) Application in other systems

[0153] The project results were also applied to Zigong's large and medium-sized cities earthquake emergency decision-making response system, helping to solve the problem of untimely updating of building data in the system.

[0154] By effectively utilizing Sichuan Province's economic, population and ethnic data, the project developed a model for the relationship between building data and local residential distribution, population and economy. It has been successfully applied in multiple earthquake emergency systems and city and county comprehensive service systems, significantly improving the real-time and accuracy of the earthquake emergency database.

[0155] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0156] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A housing construction data updating method based on sampling data, characterized in that: include: In step 1, the housing construction data, population data and economic data in the area are obtained through data acquisition equipment; The collected data is input into the data analysis software, and the relationship between housing construction and population and economic data in the region is analyzed using the software's algorithm. Different towns are classified and coded through cloud servers or local computing devices. This process ensures the accuracy and automation of data classification. In step 2, based on the classification results of towns and villages, a sampling algorithm is used to select sample towns and villages in each classification level for sampling survey. In this process, mobile devices equipped with GPS and image recognition functions are used to collect data from the selected sample towns and villages, and record the building area and related attribute information of houses. The collected data is transmitted to the central server through a wireless network for storage and processing, ensuring the real-time and accuracy of data collection. In step 3, the data processing system subdivides the housing construction data collected in the early stage into per capita housing area of ​​urban residents, per capita housing area of ​​agricultural residents, and public building area; the sampling data is modeled and calculated using software tools to determine the housing construction area of ​​each type of township; the system automatically calculates and outputs the three types of construction areas of each type of township through an integrated algorithm, providing accurate data support; Finally, in steps 4 and 5, the system automatically obtains the latest township population data through the interface with the government department or population statistics system; the data processing module calculates the total housing construction area of ​​each township based on the input township population data and the housing construction area percentage obtained in the previous period, and further subdivides the construction area of ​​different types of houses; All calculation and analysis processes are automatically completed by the background server and displayed through data visualization tools, making it convenient for users to view and export updated house construction data.

2. The method according to claim 1, characterized in that The data analysis and calculation steps are implemented by a computer system, which includes: A data storage module, used to store the collected housing construction data, population data and economic data; Data analysis module, used to analyze the stored data and identify the relationship between housing construction data and population and economic data; The sampling design module is used to determine the sampling level according to the township classification results and select sample townships for investigation; A data collection module is used to collect housing construction data from selected sample towns; A data calculation module is used to calculate the housing construction area of ​​each township based on township population data and sampling data; The result output module is used to output the building area data of each structural type of houses in each township. The computer system implements data analysis and calculation through the following algorithms and models: Data analysis algorithms, including regression analysis models and clustering algorithms, are used to identify relationships between housing construction data and demographic and economic data; Stratified sampling algorithm was used to select sample townships in each sampling stratum; Area calculation formula, used to calculate the building area of ​​each township; The result processing algorithm is used to sort out the percentage of building area of ​​each structural type of housing according to the sampling data, and output the result in combination with the calculated building area data of the housing.

3. The method for updating building construction data based on sampling data according to claim 1, characterized in that: In step 1, data analysis technology is used to determine the relationship between regional housing construction data and local population, economic and other data. In step 2, random sampling is used to ensure the representativeness of the sampling survey. In step 5, the percentage of the building area of ​​each structural type of housing is multiplied by the total building area of ​​each township, and finally the building area of ​​different structural types of housing in each township is calculated.

4. The method for updating building construction data based on sampling data according to claim 1, characterized in that: The building area of ​​each township in step 4 is: X v =∑T v ·P v1 +C v ·P v2 +PW v ·P v Where X v is the building area of ​​township houses, T v is the per capita housing area of ​​urban residents; C v PW is the per capita housing area of ​​agricultural residents; v It is the per capita public housing construction area; public housing buildings mainly include: government and institution buildings, school buildings, hospital buildings, corporate office and factory buildings, shopping malls and shops buildings, guesthouses and other public buildings.

5. The method for updating building construction data based on sampling data according to claim 1, characterized in that: The data of building area of ​​each type of houses in townships in step 5 are: Bm k =X v ˙Pe k Among them, Bm k Data on the building area of ​​a type of houses in township buildings, Pe k The percentage of the building area of ​​Class A buildings in township buildings to the total building area.

6. A system for updating building data based on sampled data for implementing the method for updating building data based on sampled data as described in any one of 1 to 5, characterized in that: include: Township classification module: by analyzing the relationship between regional housing construction data and local population, economic and other data, the townships in the region are classified and each type of township is coded; Stratified sampling survey module: Combined with the classification of towns in the region, a stratified sampling survey of housing construction data in the region is conducted; Three building area confirmation modules: Housing building area data are divided into per capita housing area of ​​urban residents, per capita housing area of ​​agricultural residents, and public building area. These three types of building areas are determined for each type of township based on sampling data; Township housing construction area calculation module: According to the township population data obtained each year, the urban population P v1 , agricultural population v2 , total population P v , calculate the building area of ​​each township; Housing construction area data acquisition module: Based on the sample data, the percentage of housing construction area of ​​each structural type in each type of township is sorted out, and combined with the calculated housing construction area of ​​each township, the housing construction area data of each type of township is obtained.

7. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for updating building construction data based on sampling data as described in any one of claims 1 to 4.

8. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for updating building construction data based on sampling data as claimed in any one of claims 1 to 4.

9. An information data processing terminal, comprising the house construction data updating system based on sampling data as claimed in claim 5.

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