Provincial land analysis and statistics method and device

Abstract

The invention discloses a provincial land analysis statistical method and device, and relates to the field of geographic information statistical analysis, and the method comprises the steps: obtaining a statistical task, basic statistical data and a plurality of index types of a to-be-counted provincial land; wherein the basic statistical data comprises a plurality of data layers; according to the statistical task and the basic statistical data, obtaining a data flow relationship corresponding to the multiple index types, and constructing multiple data analysis operators according to the data flow relationship; based on the data flow relationship, processing the plurality of data layers according to the plurality of data analysis operators to obtain a plurality of index data corresponding to the plurality of index types; and obtaining a statistical analysis result of the provincial land to be counted according to the statistical task and the various index data. According to the application, the problem that the analysis and statistics of the existing provincial land are lack of high efficiency and convenience can be solved.

Description

Technical Field

[0001] The present application relates to the field of geographic information statistical analysis, and in particular to a provincial land statistical analysis method and device. Background Art

[0002] National land space planning data has strong spatial and graphical characteristics. With the implementation and dynamic monitoring of the plans, large-scale spatial data needs to be analyzed and counted. The spatial data storage and computing framework of traditional relational databases can no longer meet current statistical needs.

[0003] At present, the analysis and statistics of national land space planning data, especially provincial land, are mainly based on manual combined tools, and data analysis is carried out in a step-by-step and line-by-line manner. There are many layers and statistical fields involved in the statistics, and the amount of data analysis is large; secondly, the business logic of statistics is complex, and the skills of the staff are high; at the same time, the current traditional method of step-by-step calculation by manual combined tools leads to the need for manual modification of models and parameters to the corresponding steps in each step, and the flexibility and adaptability of the model are very low, the degree of automation is also low, and it cannot be reused; furthermore, each step of the step-by-step calculation by manual combined tools requires the output of layers, statistical tables and other data as the input conditions of the next step, and the spatial calculation and drawing of the layers in each step requires a lot of time, resulting in unnecessary waste of time. Therefore, how to efficiently and conveniently analyze and count provincial land is still a technical problem that needs to be solved by existing technologies. Summary of the invention

[0004] The present application provides a provincial land analysis and statistics method and device to solve the technical problem that the existing provincial land analysis and statistics lack efficiency and convenience.

[0005] According to a first aspect of the implementation manner of the present application, a provincial land analysis and statistics method is provided, comprising: Obtaining statistical tasks, basic statistical data and multiple indicator types of provincial land to be counted; wherein the basic statistical data includes multiple data layers; According to the statistical task and the basic statistical data, a data flow relationship corresponding to the multiple indicator types is obtained, and a multiple data analysis operator is constructed according to the data flow relationship; Based on the data flow relationship, the multiple data layers are processed according to the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types; According to the statistical task and the multiple indicator data, the statistical analysis results of the provincial land to be counted are obtained.

[0006] The present application first obtains the statistical tasks, basic statistical data and various indicator types of the provincial land to be counted, and then obtains the data flow relationship corresponding to the various indicator types and constructs a variety of data analysis operators. Compared with the existing technology that manually performs data analysis and statistics step by step according to complex business logic, the present application establishes a data flow relationship and atomizes the instructions in the original complex business logic to obtain operators. Based on the plug-and-play and reusable characteristics of the operators, the convenience of business statistical analysis is improved; then, based on the data flow relationship, multiple data layers of the basic statistical data are processed according to a variety of data analysis operators to obtain a variety of indicator data, and then the statistical analysis results are obtained. Through the data analysis operators, the originally lengthy analysis and calculation chain can be shortened, thereby improving the efficiency of data analysis and statistics.

[0007] In certain embodiments of the present application, the data flow relationships corresponding to the multiple indicator types are obtained according to the statistical tasks and the basic statistical data, and multiple data analysis operators are constructed according to the data flow relationships, specifically including: According to the statistical task, analyzing and obtaining a plurality of data flows between the basic statistical data and the plurality of indicator types; Analyzing and obtaining data flow relationships corresponding to the multiple indicator types according to the multiple data flows; According to the logical operation between the data in the data flow relationship, a variety of data analysis operators are constructed.

[0008] This application first analyzes the basic statistical data and various indicator types according to the statistical task to obtain the corresponding data flow, and can accurately obtain the corresponding data flow relationship according to the data flow, and then construct various data analysis operators according to the logical operations between the data in the data flow relationship, thereby improving the matching degree between the data analysis operator and the current statistical task, thereby improving the accuracy of data analysis statistics, and relying on the plug-and-play and reusable characteristics of the data analysis operator itself to improve the convenience and efficiency of business statistical analysis.

[0009] In certain embodiments of the present application, the multiple data layers are processed based on the data flow relationship according to the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types, specifically including: Based on the data flow relationship and according to the basic statistical data, the multiple data analysis operators are used to perform distributed calculations on the multiple data layers to obtain multiple indicator data corresponding to the multiple indicator types.

[0010] This application first uses a variety of data analysis operators to perform distributed calculations on multiple data layers based on data flow relationships and basic statistical data to obtain a variety of indicator data. The data analysis operators can shorten the originally lengthy analysis and calculation chain and improve the efficiency of business statistical analysis. Combined with the high efficiency of distributed computing, the efficiency of business statistical analysis can be further improved.

[0011] In certain embodiments of the present application, based on the data flow relationship and according to the basic statistical data, the multiple data analysis operators are used to perform distributed calculations on the multiple data layers to obtain multiple indicator data corresponding to the multiple indicator types, specifically including: According to the data flow relationship, generating a plurality of parallel data processing tasks corresponding to the plurality of indicator types; According to the data flow relationship, a logical calculation relationship of the plurality of parallel data processing tasks is obtained; Based on the logical calculation relationship, distributed calculations are performed on the multiple parallel data processing tasks to obtain multiple indicator data corresponding to the multiple indicator types; wherein the input of each parallel data processing task is the basic statistical data; during distributed calculations, each parallel data processing task uses the multiple data analysis operators to process the multiple data layers.

[0012] This application first generates multiple parallel data processing tasks based on the data flow relationship, and then obtains the corresponding logical calculation relationship, which can avoid the untimely processing of parallel tasks due to the lack of logical calculation relationship, and then performs distributed calculation on multiple parallel data processing tasks to obtain multiple indicator data. The distributed calculation of parallel tasks can improve the efficiency of business statistical analysis.

[0013] In certain implementations of the present application, the statistical analysis results of the provincial land to be counted are obtained according to the statistical task and the multiple indicator data, specifically including: According to the statistical task and the preset business rules, a first business scenario is obtained; According to the first business scenario, a first statistical template is obtained by matching in a preset statistical template library; According to the preset business rules, the multiple indicator data are respectively inserted into the first statistical template to obtain the statistical analysis results of the provincial land to be counted.

[0014] This application first obtains the business scenario according to the statistical task and preset business rules, then obtains the first statistical template, and then inserts multiple indicator data into the first statistical template to obtain the statistical analysis results of the provincial land to be counted. It can obtain more matching corresponding statistical analysis results for different business scenarios, improve the confidence of the statistical analysis results of the current statistical task, and thus improve the accuracy of data analysis statistics.

[0015] According to a second aspect of the implementation of the present application, a provincial land analysis and statistics device is provided, including a data acquisition module, an operator construction module, a data processing module and an analysis and statistics module; The data acquisition module is used to obtain the statistical tasks, basic statistical data and multiple indicator types of the provincial land to be counted; wherein the basic statistical data includes multiple data layers; The operator construction module is used to obtain data flow relationships corresponding to the multiple indicator types according to the statistical tasks and the basic statistical data, and to construct multiple data analysis operators according to the data flow relationships; The data processing module is used to process the multiple data layers based on the data flow relationship and according to the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types; The analysis and statistics module is used to obtain the statistical analysis results of the provincial land to be counted based on the statistical tasks and the multiple indicator data.

[0016] In certain embodiments of the present application, the operator construction module includes a data flow analysis submodule, a relationship analysis submodule and an operator construction submodule; The data flow analysis submodule is used to analyze and obtain multiple data flows between the basic statistical data and the multiple indicator types according to the statistical task; The relationship analysis submodule is used to analyze the multiple data streams to obtain data stream relationships corresponding to the multiple indicator types; The operator construction submodule is used to construct a variety of data analysis operators according to the logical operation between the data in the data flow relationship.

[0017] In certain embodiments of the present application, the data processing module includes a distributed computing submodule; The distributed computing submodule is used to perform distributed computing on the multiple data layers based on the data flow relationship and the basic statistical data using the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types.

[0018] In certain embodiments of the present application, the distributed computing submodule includes a parallel task generating unit, a logic relationship acquiring unit, and a distributed computing unit; The parallel task generating unit is used to generate a plurality of parallel data processing tasks corresponding to the plurality of indicator types according to the data flow relationship; The logic relationship acquisition unit is used to obtain the logic calculation relationship of the multiple parallel data processing tasks according to the data flow relationship; The distributed computing unit is used to perform distributed computing on the multiple parallel data processing tasks based on the logical computing relationship to obtain multiple indicator data corresponding to the multiple indicator types; wherein the input of each parallel data processing task is the basic statistical data; and each parallel data processing task uses the multiple data analysis operators to process the multiple data layers during distributed computing.

[0019] In certain embodiments of the present application, the analysis and statistics module includes a scene acquisition submodule, a template matching submodule and an analysis and statistics submodule; The scenario acquisition submodule is used to obtain a first business scenario according to the statistical task and preset business rules; The template matching submodule is used to match a first statistical template in a preset statistical template library according to the first business scenario; The analysis and statistics submodule is used to insert the multiple indicator data into the first statistical template respectively according to the preset business rules to obtain the statistical analysis results of the provincial land to be counted.

[0020] The present application first obtains the statistical tasks, basic statistical data and various indicator types of the provincial land to be counted, and then obtains the data flow relationship corresponding to the various indicator types and constructs a variety of data analysis operators. Compared with the existing technology that manually performs data analysis and statistics step by step according to complex business logic, the present application establishes a data flow relationship and atomizes the instructions in the original complex business logic to obtain operators. Based on the plug-and-play and reusable characteristics of the operators, the convenience of business statistical analysis is improved; then, based on the data flow relationship, multiple data layers of the basic statistical data are processed according to a variety of data analysis operators to obtain a variety of indicator data, and then the statistical analysis results are obtained. Through the data analysis operators, the originally lengthy analysis and calculation chain can be shortened, thereby improving the efficiency of data analysis and statistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 : A flow chart of a provincial land analysis and statistical method shown in certain embodiments of the present application; Figure 2 : A module structure diagram of a provincial land analysis and statistics device shown in certain embodiments of the present application. DETAILED DESCRIPTION

[0022] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below in conjunction with the accompanying drawings are exemplary and are only used to explain some embodiments of the present application, and should not be construed as limitations on the embodiments of the present application. Based on the embodiments shown in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0023] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, unless otherwise clearly and specifically defined, "multiple" and "several" mean two or more.

[0024] At present, the analysis and statistics of national land space planning data, especially provincial land, mainly rely on manual combination tools, and adopt a step-by-step and line-by-line statistical method to analyze data. The workload of data analysis is large, the business logic is complex, and the skills of the staff are high. At the same time, in the process of manual combination tool step-by-step analysis, each step requires manual modification of the model, resulting in low flexibility and adaptability of the model, making it difficult to reuse, and in each step, the layer and statistical table of the previous step are required for layer space calculation and drawing, resulting in unnecessary waste of time. Therefore, how to efficiently and conveniently analyze and count provincial land is still a technical problem that needs to be solved urgently in the existing technology.

[0025] Based on the above technical background, please refer to Figure 1 The embodiment of the present application provides a provincial land analysis and statistics method, including steps S101 to S104, each of which is as follows: Step S101: Obtain statistical tasks, basic statistical data and multiple indicator types of provincial land to be counted; wherein the basic statistical data includes multiple data layers.

[0026] Exemplarily, the statistical task is specifically the statistical analysis of the provincial permanent basic farmland area and planning; the multiple indicator types are specifically the types of indicator data related to the provincial permanent basic farmland.

[0027] Step S102: According to the statistical task and the basic statistical data, data flow relationships corresponding to the multiple indicator types are obtained, and multiple data analysis operators are constructed according to the data flow relationships.

[0028] In certain embodiments of the present application, the data flow relationships corresponding to the multiple indicator types are obtained according to the statistical tasks and the basic statistical data, and multiple data analysis operators are constructed according to the data flow relationships, specifically including: According to the statistical task, analyzing and obtaining a plurality of data flows between the basic statistical data and the plurality of indicator types; Analyzing and obtaining data flow relationships corresponding to the multiple indicator types according to the multiple data flows; According to the logical operation between the data in the data flow relationship, a variety of data analysis operators are constructed.

[0029] In certain embodiments of the present application, the various data analysis operators include selection, (pairwise) intersection, correlation, (pairwise) erasure, scattering, (pairwise) buffering, extraction, assignment, fusion, analysis, statistics, output, and hierarchical export.

[0030] This application first analyzes the basic statistical data and various indicator types according to the statistical task to obtain the corresponding data flow, and can accurately obtain the corresponding data flow relationship according to the data flow, and then construct various data analysis operators according to the logical operations between the data in the data flow relationship, thereby improving the matching degree between the data analysis operator and the current statistical task, thereby improving the accuracy of data analysis statistics, and relying on the plug-and-play and reusable characteristics of the data analysis operator itself to improve the convenience and efficiency of business statistical analysis.

[0031] Step S103: Based on the data flow relationship, the multiple data layers are processed according to the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types.

[0032] In certain embodiments of the present application, the multiple data layers are processed based on the data flow relationship according to the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types, specifically including: Based on the data flow relationship and according to the basic statistical data, the multiple data analysis operators are used to perform distributed calculations on the multiple data layers to obtain multiple indicator data corresponding to the multiple indicator types.

[0033] This application first uses a variety of data analysis operators to perform distributed calculations on multiple data layers based on data flow relationships and basic statistical data to obtain a variety of indicator data. The data analysis operators can shorten the originally lengthy analysis and calculation chain and improve the efficiency of business statistical analysis. Combined with the high efficiency of distributed computing, the efficiency of business statistical analysis can be further improved.

[0034] In certain embodiments of the present application, based on the data flow relationship and according to the basic statistical data, the multiple data analysis operators are used to perform distributed calculations on the multiple data layers to obtain multiple indicator data corresponding to the multiple indicator types, specifically including: According to the data flow relationship, generating a plurality of parallel data processing tasks corresponding to the plurality of indicator types; According to the data flow relationship, a logical calculation relationship of the plurality of parallel data processing tasks is obtained; Based on the logical calculation relationship, distributed calculations are performed on the multiple parallel data processing tasks to obtain multiple indicator data corresponding to the multiple indicator types; wherein the input of each parallel data processing task is the basic statistical data; during distributed calculations, each parallel data processing task uses the multiple data analysis operators to process the multiple data layers.

[0035] For example, to describe in detail the specific meaning of distributed computing in this application, assuming that the current task is statistical analysis of permanent basic farmland and high-standard farmland, the distributed computing is performed on the multiple parallel data processing tasks based on the logical computing relationship to obtain multiple indicator data corresponding to the multiple indicator types, including steps S103 (1) to S103 (36), and each step is specifically as follows: Step S103 (1): According to the “land type map” data in the basic statistical data, the “cultivated land” layer is selected, and the province, city and county are used as statistical units to obtain the cultivated land area and the proportion of cultivated land to the national territory.

[0036] Step S103 (2): According to the “land type map” data in the basic statistical data, the “stable cultivated land” layer is selected, and the province, city and county are used as statistical units to summarize and obtain the stable cultivated land area and the proportion of stable cultivated land to cultivated land.

[0037] Step S103 (3): According to the "land type map" data in the basic statistical data, the "unstable cultivated land" layer is selected, and the province, city and county are used as statistical units to summarize and obtain the unstable cultivated land area, the proportion of unstable cultivated land to cultivated land, the rocky desertification cultivated land area, the 25-degree slope cultivated land area, the river and lake area cultivated land area, the neighboring area cultivated land area, the pastoral area cultivated land area and the sandy desertification cultivated land area.

[0038] Step S103 (4): Based on the “land type map” data in the basic statistical data, the “permanent basic farmland” layer is selected, and the province, city, and county are used as statistical units to summarize and obtain the permanent basic farmland area and the proportion of permanent farmland area to the national land.

[0039] Step S103 (5): Based on the “land type map” and “high-standard farmland” data in the basic statistical data, the current land type data in the high-standard farmland is intersected and counted, and its ellipsoid area is calculated. Then, based on provinces, cities and counties as statistical units, the area of ​​high-standard farmland, the proportion of high-standard farmland in the national territory, the area of ​​built high-standard farmland, the proportion of built high-standard farmland in the high-standard farmland, the area of ​​high-standard farmland under construction, and the proportion of high-standard farmland under construction in the high-standard farmland.

[0040] Step S103 (6): Extract the “traffic convenience basics” data based on the “land type patch” data in the basic statistical data, and analyze the data under the buffering conditions of 500m, 1km and 2km based on the paired buffer operator to obtain the traffic convenience basic buffer data.

[0041] Step S103 (7): Based on the "land type map" and "farm irrigation" data in the basic statistical data, the irrigation area type data is extracted and fused to obtain the fused farm irrigation data; based on the relevant operators, the "land type map" and the "fused farm irrigation data" are operated to obtain the farm irrigation development plan and calculate its ellipsoid area, to obtain the irrigated farmland area, the total area of ​​large and medium-sized irrigation areas, the irrigated farmland area in the planned large and medium-sized irrigation areas, the irrigated farmland area in the current large and medium-sized irrigation areas, and the area of ​​non-large and medium-sized irrigation areas.

[0042] Step S103 (8): Based on the “land type patches” and “agricultural reclamation areas” data in the basic statistical data, a fused agricultural reclamation area layer is obtained; based on the intersection operator, the “land type patches” and the “fused agricultural reclamation areas” are operated to obtain an agricultural reclamation area range layer, and the ellipsoid area is summarized and counted based on provinces, cities and counties as statistical units to obtain agricultural reclamation area area data.

[0043] Step S103 (9): Based on the “land type map” and “nature reserve” data in the basic statistical data, the intersected nature reserve data are obtained by intersecting them, and the areas of nature reserves, core protection areas, and general control areas are obtained by summarizing the statistics based on provinces, cities, and counties as statistical units.

[0044] Step S103 (10): Based on the “river and lake management scope of 50 to 200 square kilometers” and “river and lake management scope of more than 200 square kilometers” layers in the basic statistical data, merge and obtain the layer data of the river and lake management scope of more than 50 square kilometers.

[0045] Step S103 (11): Based on the current land classification data of the high-standard farmland in S103 (5), with provinces, cities and counties as statistical units and land classification codes as classification basis, the classified statistics are summarized to obtain the ellipsoidal area and net surface area of ​​the high-standard farmland, agricultural land within the high-standard area, cultivated land within the high-standard agricultural land, garden land within the high-standard agricultural land, forest land within the high-standard agricultural land, grassland within the high-standard agricultural land, wetland within the high-standard agricultural land, water area and water conservancy facility land within the high-standard agricultural land, other land within the high-standard agricultural land, construction land area within the high-standard area, urban and rural areas and industrial and mining land within the high-standard construction land, transportation land within the high-standard construction land, hydraulic construction land area within the high-standard construction land, unused land within the high-standard area, grassland within the unused land, water area and water conservancy facility land within the unused land, wetland within the unused land, and other land within the unused land.

[0046] Step S103 (12): Based on the current land classification data of the high-standard farmland in S103 (5), the analyzed land classification code is used as the classification basis to extract the cultivated land data of the high-standard farmland; with provinces, cities and counties as statistical units, the net area of ​​the cultivated land within the high-standard farmland that has been built and the cultivated land within the high-standard farmland that is under construction is summarized and classified.

[0047] Step S103 (13): Based on the “high-standard farmland” and “stable cultivated land” data in the basic statistical data, the data of stable cultivated land in the high-standard farmland are intersected and obtained, and the net area of ​​stable cultivated land in the built high-standard farmland and the stable cultivated land in the high-standard farmland under construction are summarized and classified according to the province, city and county as statistical units.

[0048] Step S103 (14): Based on the current land classification data of the high-standard farmland in S103 (5) and the fused agricultural reclamation area layer data in S103 (8), the current land classification data of the high-standard farmland in the reclamation area are intersected and the ellipsoid area and net surface area are calculated. The province, city and county are used as statistical units. According to the corresponding high-standard farmland, agricultural land within high-standard, cultivated land within high-standard agricultural land, garden within high-standard agricultural land, forest land within high-standard agricultural land, grassland within high-standard agricultural land, and high-standard agricultural land in the land classification code are analyzed. The area of ​​high-standard farmland in the reclamation area is obtained by obtaining the coding codes for wetlands within the high-standard agricultural land, water areas and water conservancy facilities land within the high-standard agricultural land, other land within the high-standard agricultural land, construction land area within the high-standard land, urban and rural areas and industrial and mining land within the high-standard construction land, transportation land within the high-standard construction land, water conservancy building land area within the high-standard construction land, unused land within the high-standard land, grassland within the high-standard unused land, water areas and water conservancy facilities land within the high-standard unused land, wetlands within the high-standard unused land, and other land within the high-standard unused land.

[0049] Step S103 (15): Based on the high-standard farmland area in the reclamation area in S103 (14), the statistical units are summarized by province, city and county, and combined with the "project progress" classification in the "high-standard farmland" in the basic statistical data, the ellipsoid area of ​​the high-standard farmland in the built reclamation area and the ellipsoid area of ​​the high-standard farmland in the reclamation area under construction are obtained.

[0050] Step S103 (16): Based on the current land classification data in the high-standard farmland of S103 (5) and the agricultural reclamation area scope data of S103 (8), based on the paired erasure operator, the agricultural reclamation area data after fusion is erased from the current land classification data in the high-standard farmland to obtain the high-standard farmland data outside the reclamation area, and the province, city and county are used as statistical units and the project progress is used as the classification basis, and the ellipsoid area of ​​the high-standard farmland outside the built reclamation area and the ellipsoid area of ​​the high-standard farmland outside the reclamation area under construction are obtained by classification statistics.

[0051] Step S103 (17): Based on the current land classification data of the high-standard farmland in S103 (5) and the "urban development boundary" data in the basic statistical data, the high-standard farmland data within the urban development boundary is obtained based on the pairwise intersection operator, and the ellipsoid area of ​​the high-standard farmland within the built urban development boundary and the ellipsoid area of ​​the high-standard farmland within the urban development boundary under construction are obtained by classification statistics based on the province, city and county as statistical units and the project progress as the classification basis.

[0052] Step S103 (18): Based on the current land classification data within the high-standard farmland in S103 (5) and the "urban development boundary" data in the basic statistical data, the high-standard farmland data outside the urban development boundary is obtained based on the paired erasure operator, and the ellipsoid area of ​​the high-standard farmland outside the built urban development boundary and the ellipsoid area of ​​the high-standard farmland outside the urban development boundary under construction are obtained by classification statistics based on provinces, cities and counties as statistical units and project progress as classification basis.

[0053] Step S103 (19): Based on the current land classification data within the high-standard farmland in S103 (5) and the "ecological protection red line" data in the basic statistical data, the high-standard farmland within the ecological protection red line is obtained based on the pairwise intersection operator, and the ellipsoid area of ​​the high-standard farmland within the ecological protection red line is obtained by classification statistics based on the province, city and county summary and the nature reserve zoning.

[0054] Step S103 (20): Based on the current land classification data in the high-standard farmland in S103 (5) and the "ecological protection red line" data in the basic statistical data, the high-standard farmland outside the ecological protection red line is obtained based on the paired erasure operator, and the ellipsoid area of ​​the high-standard farmland outside the ecological protection red line is obtained by summarizing it by province, city and county and classifying it by nature reserves.

[0055] Step S103 (21): Based on the current land classification data of the high-standard farmland in S103 (5) and the "nature reserve" data in the basic statistical data, the high-standard farmland data in the nature reserve is obtained based on the pairwise intersection operator, and the high-standard farmland data in the nature reserve is summarized by province, city and county and classified according to natural function zoning, and the high-standard area in the core protection area of ​​the nature reserve and the high-standard area in the general control area of ​​the nature reserve are obtained by classification statistics.

[0056] Step S103 (22): Based on the current land classification data within the high-standard farmland in S103 (5) and the "nature reserve" data in the basic statistical data, based on the paired erasure operator, the high-standard farmland data outside the nature reserve is obtained, and summarized by province, city and county to obtain the area of ​​high-standard farmland outside the nature reserve.

[0057] Step S103 (23): Based on the current land classification data in the high-standard farmland of S103 (5) and the stable cultivated land data in the high-standard farmland of S103 (13), the current land classification data in the high-standard farmland and the nature reserve data are intersected based on the paired intersection operator to obtain the permanent agricultural land data in the high-standard farmland, and the net area is summarized and counted by province, city and county as statistical units to obtain the permanent agricultural land area in the high-standard farmland; based on the paired erasing operator, the current land classification data and permanent basic farmland in the high-standard farmland are erased to obtain the data of permanent basic farmland not included in the high-standard farmland, and the net area is summarized and counted by province, city and county as statistical units to obtain the non-permanent agricultural land area in the high-standard farmland; based on the paired erasing operator, the stable cultivated land data and permanent basic farmland in the high-standard farmland are erased to obtain the non-permanent agricultural land data in the stable cultivated land in the high-standard farmland, and the net area is summarized and counted by province, city and county as statistical units to obtain the non-permanent agricultural land in the stable cultivated land in the high-standard farmland.

[0058] Step S103 (24): Based on the permanent basic farmland data of S103 (4) and the "high-standard farmland" data in the basic statistical data, the permanent basic farmland data that have not been built with high standards are obtained based on the paired erasure operator, and the net area is summarized and counted based on provinces, cities and counties as statistical units to obtain the proportion of permanent basic farmland that have not been built with high standards and the proportion of permanent basic farmland that have been built with high standards.

[0059] Step S103 (25): Based on the Yongnong data of unbuilt high-standard in S103 (24), the fragmentation data of Yongnong without built high-standard is obtained based on the "multi-component to single-component" disintegration operator, and classified into within 5 mu, 5-10 mu, and more than 15 mu, and the ellipsoid area and net surface area are summarized and counted by province, city, and county as statistical units.

[0060] Step S103 (26): Based on the "land type map" data in the basic statistical data and the Yongnong data without elevated buildings in S103 (24), the slope data of Yongnong without elevated buildings are obtained based on the paired intersection operator, and the net area of ​​Yongnong without elevated buildings is calculated by taking provinces, cities and counties as statistical units, and the slope level is analyzed as the classification basis, and the net area of ​​Yongnong without elevated buildings is obtained by summary statistics.

[0061] Step S103 (27): Based on the data of Yongnong farms without high-standard farmland in S103 (24) and the data of “nature reserves” in the basic statistical data, the data of Yongnong farms intersecting with nature reserves without high-standard farmland are obtained based on the pairwise intersection operator, and then the data of Yongnong farms without high-standard farmland in nature reserves are obtained based on the “multiple components to single component” dispersal operator. The data are classified into less than 5 mu, 5-10 mu, and more than 15 mu, and the ellipsoid area and net surface area are summarized and counted according to provinces, cities, and counties as statistical units.

[0062] Step S103 (28): Based on the Yongnong data of unbuilt high-standard farmland in S103 (24) and the “nature reserve” data in the basic statistical data, the Yongnong erased nature reserve data of unbuilt high-standard farmland is obtained based on the paired erasure operator, and then based on the “multiple components to single component” dispersal operator, the Yongnong data of unbuilt high-standard farmland outside nature reserves is obtained, and the data is classified into less than 5 mu, 5-10 mu, and more than 15 mu, and the ellipsoid area and net surface area are summarized and counted by province, city, and county as statistical units.

[0063] Step S103 (29): Based on the layer data of the river and lake management scope of more than 50 square kilometers in S103 (10) and the Yongnong data without high-standard farmland in S103 (24), based on the paired intersection operator, the river and lake management scope data of more than 50 square kilometers without high-standard farmland are obtained after the intersection and fusion of the Yongnong data. Then, based on the "multi-component to single-component" disintegration operator, the Yongnong data without high-standard farmland within the river and lake management scope is obtained, and classified into within 5 mu, 5-10 mu, and more than 15 mu, and the ellipsoid area and net surface area are summarized and counted according to provinces, cities and counties as statistical units.

[0064] Step S103 (30): Based on the layer data of the river and lake management scope of more than 50 square kilometers in S103 (10) and the Yongnong data of unbuilt high-standard farmland in S103 (24), based on the paired erasing operator, the river and lake management scope data of more than 50 square kilometers without built high-standard farmland is obtained after erasing and merging. Then, based on the "multi-component to single-component" scattering operator, the Yongnong data of unbuilt high-standard farmland outside the river and lake management scope is obtained, and classified into within 5 mu, 5-10 mu, and more than 15 mu, and the ellipsoid area and net surface area are summarized and counted according to provinces, cities and counties as statistical units.

[0065] Step S103 (31): Based on the fragmentation data of Yongnong without high-rise buildings in S103 (25), the long integer field identifier is calculated to obtain the convenient transportation conditions of Yongnong without high-rise buildings, and the convenient transportation conditions of Yongnong without high-rise buildings and the buffered 500m data of the convenient transportation foundation of S103 (6) are intersected based on the paired intersection operator to obtain the Yongnong data without high-rise buildings within 500m of the transportation. Then, the convenient transportation conditions of Yongnong without high-rise buildings are connected to the convenient transportation conditions of Yongnong without high-rise buildings through the identifier and the corresponding value of the field "convenient transportation conditions" is assigned to be less than 500m from the transportation. Based on the paired intersection operator, the convenient transportation conditions of Yongnong without high-rise buildings and the buffered 1km data of the convenient transportation foundation of S103 (6) are intersected. The intersection operation is performed to obtain the Yongnong data of areas within 1km of transportation without elevated buildings. Then, the data is connected to the Yongnong transportation convenience conditions without elevated buildings through the label and the Yongnong data of areas within 500m-1km of transportation without elevated buildings is calculated according to the "Transportation Convenience Conditions" field. Based on the paired intersection operator, the Yongnong transportation convenience conditions without elevated buildings and the buffered 2km data of the transportation convenience basis of S103 (6) are intersected to obtain the Yongnong data of areas within 2km of transportation without elevated buildings. Then, the data is connected to the Yongnong transportation convenience conditions without elevated buildings through the label and the Yongnong data of areas within 1km-2km of transportation without elevated buildings and the Yongnong data of areas more than 2km away from transportation without elevated buildings are calculated according to the "Transportation Convenience Conditions" field.

[0066] Step S103 (32): Based on the Yongnong fragmentation data of S103 (25) where high standards have not been built, the long integer field identifier is calculated to obtain the coverage of the Yongnong irrigation area where high standards have not been built, and the "planned large and medium-sized irrigation areas", "current large and medium-sized irrigation areas" and "large and medium-sized irrigation areas" data are derived from the farmland irrigation development plan of S103 (7) according to the irrigation area type field. Based on the paired intersection operator, the coverage of the Yongnong irrigation area where high standards have not been built and the current large and medium-sized irrigation area data are intersected to obtain the intersecting current large and medium-sized irrigation areas and connected to the coverage of the Yongnong irrigation area where high standards have not been built through the identifier, and attribute values ​​are assigned in the intersecting irrigation area type field; Based on the paired intersection operator, the coverage of the Yongnong irrigation area where high standards have not been built and the large and medium-sized irrigation area data are intersected to obtain the intersecting large and medium-sized irrigation areas and connected to the coverage of the Yongnong irrigation area where high standards have not been built through the identifier. The data of large and medium-sized irrigation areas in the coverage of Yongnong irrigation areas without high-standard irrigation are erased based on the paired erasure operator to obtain the erased large and medium-sized irrigation area data and connected to the coverage of Yongnong irrigation areas without high-standard irrigation through identification and attribute assignment is performed in the irrigation area location field; in the coverage of Yongnong irrigation areas without high-standard irrigation, the relationship with the irrigation area is calculated according to the irrigation area location field, and the net area is summarized and counted based on the province, city and county as statistical units and the relationship field with the irrigation area as the classification basis to obtain the Yongnong area without high-standard irrigation that is entirely located in the existing large and medium-sized irrigation areas, the Yongnong area without high-standard irrigation that is entirely located in the planned large and medium-sized irrigation areas, the Yongnong area that is partially located in the existing large and medium-sized irrigation areas, the Yongnong area that is partially located in the planned large and medium-sized irrigation areas, and the Yongnong area that is entirely located outside the large and medium-sized irrigation areas.

[0067] Step S103 (33): Based on the permanent agricultural data of high-standard farmland that has not been built outside the nature reserves in S103 (28) and the "high-standard farmland" data in the basic statistical data, the permanent basic farmland data and the river and lake management scope of more than 50 square kilometers after fusion are erased based on the paired erasing operator to obtain the permanent agricultural data of high-standard farmland that has not been built outside the nature reserves and the river and lake management scope of more than 50 square kilometers after fusion. Then, based on the "multi-component to single-component" scattering operator, the permanent agricultural data of high-standard farmland that has not been built outside the nature reserves and river and lake management scope is obtained, and classified into within 5 mu, 5-10 mu, and more than 15 mu, and the ellipsoid area and net surface area are summarized and counted according to provinces, cities and counties as statistical units.

[0068] Step S103 (34): Based on the permanent agricultural data of the unbuilt high-standard farmland outside the nature reserves and river and lake management scope in S103 (33), the long integer field identifier is calculated to obtain the permanent agricultural situation of the unbuilt high-standard farmland outside the nature reserves and river and lake management scope, and the permanent agricultural situation of the unbuilt high-standard farmland outside the nature reserves and river and lake management scope is intersected with the buffer 500m data of the convenient transportation basis in S103 (6) based on the paired intersection operator to obtain the data of the area within 500m of the transportation. The Yongnong data of the farmland without high standards built within the natural protection area and the management scope of rivers and lakes are connected to the Yongnong data of the farmland without high standards built outside the natural protection area and the management scope of rivers and lakes through the mark, and the corresponding value of the field "convenient transportation situation" is assigned to the distance from the traffic less than 500m; based on the paired intersection operator, the Yongnong data of the farmland without high standards built outside the natural protection area and the management scope of rivers and lakes are intersected with the buffer 1km data of the convenient transportation basis of S103 (6), and the Yongnong data of the farmland without high standards built within 1km of the traffic are obtained, and then the Yongnong data of the farmland without high standards built within 1km of the traffic are connected through the mark. The data of permanent farmers without high-standard farmland outside the scope of nature reserves and rivers and lakes that have not been built are connected and the data of permanent farmers without high-standard farmland within 500m-1km of transportation outside the scope of nature reserves and rivers and lakes that have not been built are calculated according to the "convenient transportation conditions" field; based on the paired intersection operator, the data of permanent farmers without high-standard farmland outside the scope of nature reserves and rivers and lakes that have not been built are intersected with the buffer 2km data of the convenient transportation basis of S103 (6) to obtain the data of permanent farmers without high-standard farmland within 2km of transportation; then, the data of permanent farmers without high-standard farmland outside the scope of nature reserves and rivers and lakes that have not been built are connected through the label and the data of permanent farmers without high-standard farmland within 1km-2km of transportation outside the scope of nature reserves and rivers and lakes that have not been built and the data of permanent farmers without high-standard farmland within 2km of transportation outside the scope of nature reserves and rivers and lakes that have not been built are calculated according to the "convenient transportation conditions" field; the net area is summarized and statistically analyzed by taking provinces, cities and counties as statistical units and convenient transportation conditions as classification basis to obtain the area and proportion of the above indicators.

[0069] Step S103 (35): Based on the Yongnong data of the unbuilt high-standard farmland outside the nature reserves and river and lake management scope in S103 (33), the long integer field identifier is calculated to obtain the coverage of the Yongnong irrigation area outside the nature reserves and river and lake management scope that has not been built with high standards, and based on the paired intersection operator, the coverage of the Yongnong irrigation area outside the nature reserves and river and lake management scope that has not been built with high standards is intersected with the current large and medium-sized irrigation area data in S103 (32) to obtain the intersecting current large and medium-sized irrigation area data and connect them to the Yongnong irrigation area coverage outside the nature reserves and river and lake management scope that has not been built with high standards through the identifier. The data of the Yongnong irrigation areas outside the nature reserves and rivers and lakes management scope that have not been built with high standards are intersected based on the paired intersection operator, and the large and medium-sized irrigation areas in S103 (32) are intersected to obtain the data of the intersecting large and medium-sized irrigation areas, and the data are connected to the Yongnong irrigation areas outside the nature reserves and rivers and lakes management scope that have not been built with high standards through the identifier, and the attributes are assigned in the irrigation area location field; the data of the large and medium-sized irrigation areas in the Yongnong irrigation areas outside the nature reserves and rivers and lakes management scope that have not been built with high standards are erased based on the paired erasure operator, and the data of the erased large and medium-sized irrigation areas are obtained. The coverage of Yongnong irrigation areas that have not been built with high standards outside the management scope of nature reserves and rivers and lakes is identified and attributes are assigned in the irrigation area location field; in the coverage of Yongnong irrigation areas that have not been built with high standards outside the management scope of nature reserves and rivers and lakes, the relationship with the irrigation area is calculated according to the irrigation area location field, and the net area is summarized and counted based on the province, city and county as statistical units and the relationship with the irrigation area field as the classification basis, and the Yongnong area of ​​farmland that has not been built with high standards and is located outside the management scope of nature reserves and rivers and lakes in large and medium-sized irrigation areas is obtained. The permanent farmland area of ​​​​high-standard farmland, the permanent farmland area that is entirely located in nature reserves and outside the management scope of rivers and lakes in planned large and medium-sized irrigation areas, the permanent farmland area that has not been built with high-standard farmland, the permanent farmland area that is partially located in nature reserves and outside the management scope of rivers and lakes in large and medium-sized irrigation areas, the permanent farmland area that has not been built with high-standard farmland, the permanent farmland area that is partially located in nature reserves and outside the management scope of rivers and lakes in existing large and medium-sized irrigation areas, the permanent farmland area that has not been built with high-standard farmland, the permanent farmland area that is partially located outside nature reserves and outside the management scope of rivers and lakes in planned large and medium-sized irrigation areas, and the permanent farmland area that is entirely located in nature reserves and outside the management scope of rivers and lakes outside large and medium-sized irrigation areas.

[0070] Step S103 (36): Based on the permanent agricultural situation of high-standard farmland that has not been built outside the natural protection area and river and lake management scope in S103 (34), extract the data with an area interval of more than 15 mu and a convenient transportation situation of less than 500m from the transportation to obtain the first process data; based on the processing logic of S103 (32), process the "relationship with irrigation district" field in each data of the first process data to obtain the second process data; extract the data in the second process data with the attribute of "relationship with irrigation district" being all located in the existing large and medium-sized irrigation districts, and obtain The third process data; based on the intersection operator, the third process data is intersected with the "land type map" data in the basic statistical data to obtain the fourth process data; the fourth process data is classified and extracted according to the analysis slope level as the classification basis to obtain the comprehensive analysis data of the unbuilt high-standard permanent farms, and the net area is summarized and statistically analyzed by province, city and county as statistical units to obtain the area data of the unbuilt high-standard permanent farms that are located outside the nature reserves and river and lake management areas, with a single map area of ​​more than 15 mu, a slope of less than 15 degrees, accessible by roads within 500 meters, and covered by existing large and medium-sized irrigation areas.

[0071] It should be understood that in order to reduce the redundancy of the expressions, some simplified expressions are adopted in the above steps. For example, "high standards" corresponds to "high-standard farmland" and "permanent farmland" corresponds to "permanent basic farmland". The above simplified expressions have complete and accurate interpretations in this field and correspond one-to-one with their complete expressions. There is no ambiguity or misuse, and they will not be explained in detail here.

[0072] This application first generates multiple parallel data processing tasks based on the data flow relationship, and then obtains the corresponding logical calculation relationship, which can avoid the untimely processing of parallel tasks due to the lack of logical calculation relationship, and then performs distributed calculation on multiple parallel data processing tasks to obtain multiple indicator data. The distributed calculation of parallel tasks can improve the efficiency of business statistical analysis.

[0073] Step S104: Obtain statistical analysis results of the provincial land to be counted according to the statistical task and the multiple indicator data.

[0074] In certain implementations of the present application, the statistical analysis results of the provincial land to be counted are obtained according to the statistical task and the multiple indicator data, specifically including: According to the statistical task and the preset business rules, a first business scenario is obtained; According to the first business scenario, a first statistical template is obtained by matching in a preset statistical template library; According to the preset business rules, the multiple indicator data are respectively inserted into the first statistical template to obtain the statistical analysis results of the provincial land to be counted.

[0075] This application first obtains the business scenario according to the statistical task and preset business rules, then obtains the first statistical template, and then inserts multiple indicator data into the first statistical template to obtain the statistical analysis results of the provincial land to be counted. It can obtain more matching corresponding statistical analysis results for different business scenarios, improve the confidence of the statistical analysis results of the current statistical task, and thus improve the accuracy of data analysis statistics.

[0076] The present application first obtains the statistical tasks, basic statistical data and various indicator types of the provincial land to be counted, and then obtains the data flow relationship corresponding to the various indicator types and constructs a variety of data analysis operators. Compared with the existing technology that manually performs data analysis and statistics step by step according to complex business logic, the present application establishes a data flow relationship and atomizes the instructions in the original complex business logic to obtain operators. Based on the plug-and-play and reusable characteristics of the operators, the convenience of business statistical analysis is improved; then, based on the data flow relationship, multiple data layers of the basic statistical data are processed according to a variety of data analysis operators to obtain a variety of indicator data, and then the statistical analysis results are obtained. Through the data analysis operators, the originally lengthy analysis and calculation chain can be shortened, thereby improving the efficiency of data analysis and statistics.

[0077] Corresponding to the previous method, see Figure 2 , the embodiment of the present application provides a provincial land analysis and statistics device, including a data acquisition module 210, an operator construction module 220, a data processing module 230 and an analysis and statistics module 240; The data acquisition module 210 is used to acquire the statistical tasks, basic statistical data and multiple indicator types of the provincial land to be counted; wherein the basic statistical data includes multiple data layers; The operator construction module 220 is used to obtain data flow relationships corresponding to the multiple indicator types according to the statistical tasks and the basic statistical data, and to construct multiple data analysis operators according to the data flow relationships; The data processing module 230 is used to process the multiple data layers based on the data flow relationship and according to the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types; The analysis and statistics module 240 is used to obtain the statistical analysis results of the provincial land to be counted according to the statistical task and the multiple indicator data.

[0078] In certain embodiments of the present application, the operator construction module 220 includes a data flow analysis submodule, a relationship analysis submodule, and an operator construction submodule; The data flow analysis submodule is used to analyze and obtain multiple data flows between the basic statistical data and the multiple indicator types according to the statistical task; The relationship analysis submodule is used to analyze the multiple data streams to obtain data stream relationships corresponding to the multiple indicator types; The operator construction submodule is used to construct a variety of data analysis operators according to the logical operation between the data in the data flow relationship.

[0079] In certain embodiments of the present application, the data processing module 230 includes a distributed computing submodule; The distributed computing submodule is used to perform distributed computing on the multiple data layers based on the data flow relationship and the basic statistical data using the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types.

[0080] In certain embodiments of the present application, the distributed computing submodule includes a parallel task generating unit, a logic relationship acquiring unit, and a distributed computing unit; The parallel task generating unit is used to generate a plurality of parallel data processing tasks corresponding to the plurality of indicator types according to the data flow relationship; The logic relationship acquisition unit is used to obtain the logic calculation relationship of the multiple parallel data processing tasks according to the data flow relationship; The distributed computing unit is used to perform distributed computing on the multiple parallel data processing tasks based on the logical computing relationship to obtain multiple indicator data corresponding to the multiple indicator types; wherein the input of each parallel data processing task is the basic statistical data; and each parallel data processing task uses the multiple data analysis operators to process the multiple data layers during distributed computing.

[0081] In certain embodiments of the present application, the analysis and statistics module 240 includes a scene acquisition submodule, a template matching submodule and an analysis and statistics submodule; The scenario acquisition submodule is used to obtain a first business scenario according to the statistical task and preset business rules; The template matching submodule is used to match a first statistical template in a preset statistical template library according to the first business scenario; The analysis and statistics submodule is used to insert the multiple indicator data into the first statistical template respectively according to the preset business rules to obtain the statistical analysis results of the provincial land to be counted.

[0082] The present application first obtains the statistical tasks, basic statistical data and various indicator types of the provincial land to be counted, and then obtains the data flow relationship corresponding to the various indicator types and constructs a variety of data analysis operators. Compared with the existing technology that manually performs data analysis and statistics step by step according to complex business logic, the present application establishes a data flow relationship and atomizes the instructions in the original complex business logic to obtain operators. Based on the plug-and-play and reusable characteristics of the operators, the convenience of business statistical analysis is improved; then, based on the data flow relationship, multiple data layers of the basic statistical data are processed according to a variety of data analysis operators to obtain a variety of indicator data, and then the statistical analysis results are obtained. Through the data analysis operators, the originally lengthy analysis and calculation chain can be shortened, thereby improving the efficiency of data analysis and statistics.

[0083] It should be understood that the device provided in the embodiment of the present application corresponds to the aforementioned method, and a provincial land analysis and statistics device provided in the embodiment of the present application can implement a provincial land analysis and statistics method provided in any embodiment of the present application.

[0084] Adaptively, the embodiments of the present application also provide a computer device and a computer-readable storage medium.

[0085] The computer device comprises: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor; Wherein, when the processor executes the computer program, a provincial land analysis and statistical method of the present application is implemented.

[0086] The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute a provincial land analysis and statistical method of the present application.

[0087] The above are some implementation methods of the present application, and the purpose, technical scheme and beneficial effects of the present application are further described in detail. It should be clear that the above-mentioned partial implementation methods of the present application cannot be understood as limiting the present application. It is particularly pointed out that for those skilled in the art, any changes, modifications, equivalent substitutions and variations made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A provincial land analysis and statistical method, characterized in that: include: Obtaining statistical tasks, basic statistical data and multiple indicator types of provincial land to be counted; wherein the basic statistical data includes multiple data layers; According to the statistical task and the basic statistical data, a data flow relationship corresponding to the multiple indicator types is obtained, and a multiple data analysis operator is constructed according to the data flow relationship; Based on the data flow relationship, the multiple data layers are processed according to the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types; According to the statistical task and the multiple indicator data, the statistical analysis results of the provincial land to be counted are obtained.

2. A provincial land analysis and statistical method according to claim 1, characterized in that: The step of obtaining data flow relationships corresponding to the multiple indicator types according to the statistical tasks and the basic statistical data, and constructing multiple data analysis operators according to the data flow relationships, specifically includes: According to the statistical task, analyzing and obtaining a plurality of data flows between the basic statistical data and the plurality of indicator types; Analyzing and obtaining data flow relationships corresponding to the multiple indicator types according to the multiple data flows; According to the logical operation between the data in the data flow relationship, a variety of data analysis operators are constructed.

3. A provincial land analysis and statistical method according to claim 1, characterized in that: The processing of the multiple data layers based on the data flow relationship and the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types specifically includes: Based on the data flow relationship and according to the basic statistical data, the multiple data analysis operators are used to perform distributed calculations on the multiple data layers to obtain multiple indicator data corresponding to the multiple indicator types.

4. A provincial land analysis and statistical method according to claim 3, characterized in that: Based on the data flow relationship and according to the basic statistical data, the multiple data analysis operators are used to perform distributed calculations on the multiple data layers to obtain multiple indicator data corresponding to the multiple indicator types, specifically including: According to the data flow relationship, generating a plurality of parallel data processing tasks corresponding to the plurality of indicator types; According to the data flow relationship, a logical calculation relationship of the plurality of parallel data processing tasks is obtained; Based on the logical calculation relationship, distributed calculations are performed on the multiple parallel data processing tasks to obtain multiple indicator data corresponding to the multiple indicator types; wherein the input of each parallel data processing task is the basic statistical data; during distributed calculations, each parallel data processing task uses the multiple data analysis operators to process the multiple data layers.

5. The provincial land analysis and statistical method according to claim 1 is characterized in that: The statistical analysis results of the provincial land to be counted are obtained according to the statistical tasks and the multiple indicator data, specifically including: According to the statistical task and the preset business rules, a first business scenario is obtained; According to the first business scenario, a first statistical template is obtained by matching in a preset statistical template library; According to the preset business rules, the multiple indicator data are respectively inserted into the first statistical template to obtain the statistical analysis results of the provincial land to be counted.

6. A provincial land analysis and statistics device, characterized in that: It includes data acquisition module, operator construction module, data processing module and analysis and statistics module; The data acquisition module is used to obtain the statistical tasks, basic statistical data and multiple indicator types of the provincial land to be counted; wherein the basic statistical data includes multiple data layers; The operator construction module is used to obtain data flow relationships corresponding to the multiple indicator types according to the statistical tasks and the basic statistical data, and to construct multiple data analysis operators according to the data flow relationships; The data processing module is used to process the multiple data layers based on the data flow relationship and according to the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types; The analysis and statistics module is used to obtain the statistical analysis results of the provincial land to be counted based on the statistical tasks and the multiple indicator data.

7. The provincial land analysis and statistics device according to claim 6, characterized in that: The operator construction module includes a data flow analysis submodule, a relationship analysis submodule and an operator construction submodule; The data flow analysis submodule is used to analyze and obtain multiple data flows between the basic statistical data and the multiple indicator types according to the statistical task; The relationship analysis submodule is used to analyze the multiple data streams to obtain data stream relationships corresponding to the multiple indicator types; The operator construction submodule is used to construct a variety of data analysis operators according to the logical operation between the data in the data flow relationship.

8. The provincial land analysis and statistics device according to claim 6, characterized in that: The data processing module includes a distributed computing submodule; The distributed computing submodule is used to perform distributed computing on the multiple data layers based on the data flow relationship and the basic statistical data using the multiple data analysis operators to obtain multiple indicator data corresponding to the multiple indicator types.

9. The provincial land analysis and statistics device according to claim 8, characterized in that: The distributed computing submodule includes a parallel task generating unit, a logic relationship acquiring unit and a distributed computing unit; The parallel task generating unit is used to generate a plurality of parallel data processing tasks corresponding to the plurality of indicator types according to the data flow relationship; The logic relationship acquisition unit is used to obtain the logic calculation relationship of the multiple parallel data processing tasks according to the data flow relationship; The distributed computing unit is used to perform distributed computing on the multiple parallel data processing tasks based on the logical computing relationship to obtain multiple indicator data corresponding to the multiple indicator types; wherein the input of each parallel data processing task is the basic statistical data; and each parallel data processing task uses the multiple data analysis operators to process the multiple data layers during distributed computing.

10. The provincial land analysis and statistics device according to claim 6, characterized in that: The analysis and statistics module includes a scene acquisition submodule, a template matching submodule and an analysis and statistics submodule; The scenario acquisition submodule is used to obtain a first business scenario according to the statistical task and preset business rules; The template matching submodule is used to match a first statistical template in a preset statistical template library according to the first business scenario; The analysis and statistics submodule is used to insert the multiple indicator data into the first statistical template respectively according to the preset business rules to obtain the statistical analysis results of the provincial land to be counted.

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