Method and system suitable for farmland quality grade evaluation data statistical analysis, storage medium and equipment

Abstract

The invention provides a method, a system, a storage medium and equipment suitable for cultivated land quality grade evaluation data analysis, and the method comprises the steps of data simplification extraction, data content formatting and data statistics, and comprises the steps of loading a mechanical configuration xml file, determining a processing mode of xlxs file data, then loading the xlxs file, and storing the xlxs file in a database; column data of the xlxs file is extracted according to configuration through support of an NPOI library in the C language on reading and analysis of the xlxs file. According to the method, the processing process of the time-consuming xlxs file data designation mode is solved, and the time-consuming manual work and the error-prone statistical workload are reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of big data statistics, and in particular relates to a method, system, storage medium and equipment suitable for statistical analysis of cultivated land quality grade evaluation data. Background Art

[0002] As a basic resource for agricultural production, the quality and ecological safety of cultivated land are directly related to national food security and sustainable development.

[0003] The existing results of arable land quality grade evaluation require the construction of a multi-scale and multi-dimensional arable land quality evaluation system, relying on remote sensing (RS), geographic information system (GIS), global positioning system (GPS) and Internet of Things sensing technology, integrating indicators such as soil physical and chemical properties (such as organic matter, pH value, heavy metal content), environmental conditions (climate, hydrology) and management measures, and realizing accurate classification and dynamic monitoring of arable land quality grades through spatial interpolation and machine learning models (such as random forests and neural networks). Finally, a basic database for arable land quality evaluation and arable land quality grade evaluation management unit data are formed for statistical analysis to complete the evaluation results.

[0004] This process, due to the large number of evaluation indicators (16 indicators for county-level cultivated land quality evaluation and 15 indicators for the three national censuses) and the massive amount of data (tens of thousands to hundreds of thousands of data units in a single county), results in a heavy workload, arduous tasks, and error-prone manual processing. Currently, evaluators primarily use Excel's "Pivot Table" function for manual statistics or ArcGIS software's "Summary Statistics" function for data processing. These traditional methods are time-consuming and labor-intensive, or require a high level of professional expertise, significantly reducing the efficiency of evaluation results.

[0005] Technical solution of prior art 1 PivotTable is a powerful data analysis tool in Excel that can quickly summarize, analyze, explore, and present data.

[0006] The steps are as follows: (1) Prepare the data source: Ensure that the data is in tabular format, with the first row containing column headers; there should be no blank rows or columns in the data, and each column should contain the same type of data.

[0007] (2) Create a PivotTable: Select any cell in the data area, click "Insert" → "PivotTable" on the menu bar; confirm the data range in the dialog box; select the placement: "New Worksheet" or "Existing Worksheet".

[0008] (3) Design a PivotTable: Drag fields to the four areas: Row area: Define row labels Columns area: Define column labels Value area: defines the values ​​to be calculated and summarized Filters: Define report-level filters Disadvantages of the prior art 1 (1) After the source data changes, the pivot table will not be automatically updated and needs to be refreshed manually. This is equivalent to only being able to count the results of one indicator at a time, or only being able to count the results of one county at a time. It cannot be set once and then batch counted. (2) It is necessary to distinguish between descriptive values ​​and indicator values. Descriptive values ​​can be directly counted, but indicator values ​​require secondary assignment and then use pivot tables for statistics because the results require hierarchical statistics. The process is complicated and prone to errors. (3) Evaluators need to perform repeated operations, which is time-consuming and labor-intensive.

[0009] Technical solution of existing technology 2 Sumary Statistics is a commonly used data analysis tool in ArcGIS, which is used to calculate statistical values ​​of tabular data.

[0010] The basic steps are as follows: (1) Open ArcMap or ArcGIS Pro and load the data layer containing the attribute table to be counted; (2) Open the Summary Statistics tool: In ArcMap: Open ArcToolbox → Analysis Tools → Statistical Analysis → Summary Statistics; In ArcGIS Pro: Analysis tab → Tools → Search for "Summary Statistics"; (3) Setting parameters: Input Table: Select the feature class or table containing the data to be analyzed Output Table: Specify the location and name of the output statistics table Statistics Field: Select the field for which you want to calculate statistics. Statistics type: Select the statistical method for each statistical field (such as SUM, MEAN, MAX, etc.) Case Grouping Field (optional): Select the field to use for grouping (4) Run the tool: Click "OK" to execute statistics Disadvantages of the second prior art (1) Only a single indicator can be counted separately, and the output result is an independent table. Therefore, it is necessary to repeat the operation many times to obtain the various statistical tables required for the evaluation results; (2) When the amount of data is large, it may take a long time to process, which increases the time cost; (3) This requires high professional skills from the operator. If the person writing the evaluation results does not know how to use ArcMap or ArcGIS Pro software, this operation cannot be performed. Summary of the Invention

[0011] The purpose of the present invention is to solve the defects of the above-mentioned prior art and provide a method, system, storage medium and equipment suitable for statistical analysis of cultivated land quality grade evaluation data. According to relevant technical standards or document requirements, the relevant indicators required in the achievement report are extracted from the cultivated land quality grade evaluation management unit data to form an original data table. Through software programming, an application gadget is generated, and the statistical results are directly output after importing the original data, which greatly simplifies the data statistics work.

[0012] The problems solved by the present invention include: (1) a fixed template for writing evaluation results reports based on relevant technical standards or document requirements, which can be repeatedly applied to different projects with just one programming operation; (2) all required statistical tables can be directly output at once after inputting the original data, without the need for repeated operations; (3) the problem of long software runtimes for large amounts of data is solved, achieving the effect of outputting statistical results within 1 minute after inputting the original data. (4) The present invention directly generates an application widget, which is simple to operate and can be directly used by anyone.

[0013] The present invention adopts the following technical solutions: A statistical method for evaluating cultivated land quality grades, including Step 1. Load the configuration XML file and parse the node configuration in the configuration; Step 2. Load the xlxs file into the Iworkbook type data, extract the loaded data according to the parsed Project node configuration and store the extracted data into the new target Iworkbook type data, then perform statistics and format operations on the target data according to the parsed statistics configuration and store it into the Dictionary type data until the data processing is completed; Step 3. Write the data into the target xlxs file.

[0014] Furthermore, the Project node serves as a configuration node for a separate data extraction and statistical processing operation. Each Project node supports one data extraction and statistical processing of the original data.

[0015] Furthermore, operations on the Project node include: (1) Operations on the Simplify node include: a. Use SheetName to generate the Sheet name in the result file; b. Operations on Column child nodes include: Generate the column name of the data in the original xlxs file through SrcName; Generate the column name in the result file through Name; (2) Operations on the Statistics node include: a. Use RowKey to specify the column data content of the Name of the streamlined data as the row name of the statistical result; b. Use ColumnKey to specify the column data content of the Name of the simplified data as the column name of the statistical result; c. Perform specified operation data statistics on the data content of the specified column of the Name attribute of the streamlined data through DataKey; d. Complete addition, subtraction, multiplication, division, and square root arithmetic logic and statistical processing through Operation.

[0016] Furthermore, the absence of SrcName indicates that the column names in the original file and the result file are the same.

[0017] Furthermore, operations on Column child nodes also include: The TranslateEnum node is used to enumerate and format the specified column data, and the column data content under the specified logic is changed to other content through judgment; The Condition node is used to perform logical AND, OR, and NOT judgments on multiple child nodes. Configure the filtering and processing logic for column data through the Fileter node.

[0018] Furthermore, operations on the RowKey node also include configuring the row data sorting logic through Order.

[0019] Furthermore, the loaded xlxs file is obtained through the following steps: extracting relevant indicators required in the achievement report from the cultivated land quality grade evaluation management unit data to form an original data table.

[0020] A system suitable for statistical analysis of cultivated land quality grade evaluation data, comprising: Load configuration module, used to load XML file and determine how to process the data in XLXS file; Data processing module, extracts loaded xlxs files, formats data content, and performs data statistics operations; The data output module outputs the processed data to a file.

[0021] A storage medium stores one or more programs, which, when executed by a processor, implement the above-mentioned method for statistical analysis of cultivated land quality grade evaluation data.

[0022] A computer device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to implement the above-mentioned method for statistical analysis of cultivated land quality grade evaluation data when executing the computer program stored in the memory.

[0023] Beneficial effects of the present invention: (0) According to relevant technical standards or document requirements, the evaluation report writing template and data type are fixed. Users only need to develop or configure the logic for the first time, and subsequent projects can directly call it without rewriting the code. This saves more than 90% of repetitive programming time and is particularly suitable for long-term or multi-batch data analysis scenarios.

[0024] (1) After inputting the original data, all required statistical tables can be directly output at one time without repeated operations, avoiding manual distribution operations in traditional software and eliminating human operational errors. The data is more scientific, the quality is guaranteed, and manpower is greatly saved.

[0025] (2) The present invention does not require high software operating capabilities and can be completed on an ordinary office computer. After the original data is input, the statistical results can be obtained within 1 minute. It solves the dependence of large-scale statistical software (such as Arcgis) on high-performance hardware. It is suitable for small and medium-sized teams with limited resources. It saves resources and greatly improves work efficiency.

[0026] (3) The template is fixed according to relevant requirements, and the application gadgets generated directly are easy to operate. Non-professionals can also complete professional analysis independently, reducing the cost of cross-departmental collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a technical framework diagram of the present invention; Figure 2 It is a flowchart of the program operation of the present invention; Figure 3 The directory structure of the system of the present invention; Figure 4 The content of Run.bat; Figure 5 This is an example of a system configuration file of the present invention; Figure 6 This is the data example of the management unit for the evaluation of cultivated land quality grade I; Figure 7 This is the data example of the management unit for the evaluation of the quality level of cultivated land II; Figure 8Extract the required indicators (part) from the original data table; Figure 9 The location of the original data table; Figure 10 To configure the path; Figure 11 Configuration examples; Figure 12 Edit the save path; Figure 13 The program running status; Figure 14 Output statistical results of the present invention is shown in Example I; Figure 15 Example II of outputting statistical results of the present invention; Figure 16 This is Example III of the output statistical results of the present invention. DETAILED DESCRIPTION

[0028] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0029] The technical solution of the present invention aims to provide a universal software system on the Windows computer platform to solve the relatively time-consuming processing process of the xlxs file data specification method and reduce the manual time-consuming and error-prone statistical workload.

[0030] The present invention is a solution for performing data simplification extraction and partial data statistics on the contents of a specified format xlxs file.

[0031] A method for statistical analysis of cultivated land quality grade evaluation data, including data simplification and extraction, data content formatting, and data statistics.

[0032] like Figure 1 As shown, the first step of data streamlining and extraction requires loading the parsing configuration xml file to determine how to process the xlxs file data. Then, the xlxs file is loaded. Through the support of the NPOI library in C# for reading and parsing xlxs files, its column data can be extracted according to the configuration. The second step is to count the streamlined data and export the xlxs file.

[0033] like Figure 2As shown, the first step includes: loading the configuration XML file and parsing the node configuration in the configuration. Then, loading the .xlxs file into the IWorkbook type data. According to the parsed configuration, the loaded .xlxs data is extracted using the Project configuration as the node. After extraction, the extracted data is stored in a target IWorkbook type data. Then, statistical operations are performed on the extracted data according to the statistical configuration and stored in the Dictionary type data. After all data processing is completed, the data is written to the target .xlxs file, thus completing the data processing.

[0034] like Figure 3 As shown in the figure, the Config directory is the configuration file directory. By modifying the configuration file, users can support data simplification, extraction, and statistics for .xlxs files of different formats under specified configurations. The Excel directory is the storage location for the original .xlxs files to be processed and the resulting .xlxs files after processing. net5.0 is the tool logic library file. After the user has configured it, they can double-click Run.bat to start data processing.

[0035] Figure 4 For the Run.bat content, users can modify the paths of the configuration xml and original xlxs files to implement different configuration logic processing for different files.

[0036] like Figure 5 As shown, the configuration file primarily uses a Project node as a configuration node for a single data extraction and statistical processing operation. Each Project node supports a single data extraction and statistical processing operation on raw data. By configuring multiple Project nodes, you can perform various data processing operations on raw data. Project node configuration is mainly divided into two parts: the Simplify node is mainly responsible for streamlining the raw data and performing simple content processing; the Statistics node is mainly responsible for performing statistical processing on the data after the streamlining and extraction.

[0037] (1) Simplify node: The sheetName attribute in the Simplify node is the name of the sheet in the generated result file. This tool extracts column data from xlxs files. The Column child node under this node is the configuration for the refined extraction process of column data in the file. Taking <Column Name=“Area - mu” SrcName=“Area” / > as an example, the SrcName attribute in the Column node refers to the column name of the data in the original xlxs file, and the Name attribute is the column name in the generated result file. If there is no SrcName attribute, it means the column names in the original file and the result file are the same. Through this attribute configuration of Column, the column data in the original file can be extracted targeted. The Column node supports simple processing operations on column data by configuring different types of child nodes.

[0038] <translateenum target="一等地"> <condition type="Or"> <fileter Type="Equal" Value="1" / > <fileter Type="Equal" Value="2" / > < / condition> The above formula shows the configuration of three seed nodes. The Translateenum node performs enumeration and formatting of specified column data, and performs logical AND, OR, and NOT judgment processing on specified columns. The Fileter node configures the filtering processing logic for column data. By configuring the filtering processing logic for specified column data, data streamlining is achieved by judging the filtering conditions of the specified column data. These three nodes are generally used in combination to achieve more complex data streamlining and content change processing.

[0039] (2) Statistics node The Statistics node mainly performs statistical processing on the data extracted by the Simplify node. Currently, it only supports one specified column data as the row name and another specified column data as the column name, and performs statistics on the target specified column data.

[0040] <statistics> <rowkey name="质量等级" ordertype="Customize"> <order Key="一等地" Value="1" / > <order Key="二等地" Value="2" / > <order Key="三等地" Value="3" / > <order Key="四等地" Value="4" / > <order Key="五等地" Value="5" / > <order Key="六等地" Value="6" / > <order Key="七等地" Value="7" / > <order Key="八等地" Value="8" / > <order Key="未分类" Value="9" / > < / rowkey> <columnkey Name="地形部位" / > <datakey Name="面积-亩" Operation="Add" Prefix="求和项：" / > < / statistics> The Statistics configuration example shows how to configure data statistics. Currently, only this type of configuration is supported. The RowKey node specifies the column data content of the Name attribute of the streamlined data as the row name of the statistical result. The Order configuration under this node configures the sorting logic for the row data. OrderType="Customize" indicates that the display is based on the custom sorting logic below. The absence of the OrderType attribute indicates no sorting. The Column node specifies the column data content of the Name attribute of the streamlined data as the column name of the statistical result. This node configuration also supports Order sorting configuration. The DataKey node performs a specified operation on the column data content of the Name attribute of the streamlined data. Operation="Add" is the statistical method. Currently, addition, subtraction, multiplication, division, and square root arithmetic logic statistical processing are supported.

[0041] The present invention determines the content and form types required for the evaluation results report based on relevant documents and standard requirements, and fixes the arrangement of relevant indicators and data extracted from the cultivated land quality grade evaluation management unit data for configuration. The finally generated application gadget can be used directly by the evaluator. Its operation only requires extracting indicator data consistent with the instance from the cultivated land quality grade evaluation management unit data of the object to be evaluated and arranging them in order. By importing this xlxs file into the application gadget, the statistical tables required in the evaluation report can be directly output for evaluation and analysis.

[0042] Example like Figure 6-Figure 8 As shown in the figure, the relevant indicators required in the result report are extracted from the cultivated land quality grade evaluation management unit data to form an original data table.

[0043] like Figure 9 As shown, after obtaining the original data table, place the original data table in the gadget Excel directory and ensure that there is no Chinese in the data table directory path.

[0044] like Figure 10 、 Figure 11 As shown, create a new XML configuration file in the gadget Config directory, and ensure that the path does not contain Chinese characters. Then determine the configuration requirements and perform the configuration operations based on the statistical data required by the target.

[0045] like Figure 12 As shown, right-click Run.bat->Edit, modify the configuration and original data table path, save the changes, and then double-click Run.bat to run the program.

[0046] like Figure 13 As shown, run the process.

[0047] like Figure 14-16 As shown, the statistical result table is output at one time without sorting the results.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.< / translateenum>

Claims

1. A method for statistical evaluation of cultivated land quality grade data, characterized in that: include: Step 1. Load the configuration XML file and parse the node configuration in the configuration file; Step 2. Load the xlxs data into the Iworkbook type data, extract the loaded data according to the parsed project node configuration, store the extracted data into the new target Iworkbook type data, perform statistics and formatting on the target data according to the parsed statistics configuration and store it into the Dictionary type data until the data processing is completed; Step 3. Write the data into the target xlxs file.

2. The method according to claim 1, characterized in that Step 2: Operations on project nodes include: a. Generate the name in the result file; b. Generate column names of data in the original xlxs file; c. Generate column names in the result file; d. Specify the column data content as the row name of the statistical result; e. Specify the column data content as the statistical result column name; f. Perform specified operation data statistics on the specified column data content; d. Complete arithmetic logic and statistical processing of addition, subtraction, multiplication, division and square root.

3. The method according to claim 2, characterized in that Also includes: It is used to perform enumeration and formatting processing on the specified column data, and change the column data content under the specified logic to other content through judgment; Decision processing on logical AND, OR, and NOT of multiple child nodes; Configuring the filtering logic for column data.

4. The method according to claim 2, characterized in that Also includes configuration for row data sorting logic.

5. The method according to claim 1, characterized in that The xlxs file loaded in step 2 is obtained through the following steps: extract the relevant indicators required in the result report from the cultivated land quality grade evaluation management unit data to form an original data table.

6. A system suitable for statistical analysis of cultivated land quality evaluation data, characterized in that: include: Load configuration module, used to load XML file and determine how to process the data in XLXS file; The data processing module extracts the loaded xlxs file, formats the data content, and performs data statistics operations; The data output module outputs the processed data to a file.

7. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by the processor, implement the method for statistical analysis of cultivated land quality grade evaluation data as described in claims 1-6.

8. A computer device, characterized in that: The computer device includes a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement a method for statistical analysis of cultivated land quality grade evaluation data as described in claims 1-6.

Images