Multi-period forest investigation sample plot coding processing method and system

Abstract

The invention provides a multi-period forest survey sample plot coding processing method and system, and the method comprises the steps: S11, building a sample plot coding rule system, and employing a standard format of an original sample plot number-period code + change reason code; s12, identifying sample plot change conditions through a database query technology, wherein the sample plot change conditions comprise four change types of felling, tree missing, inter-period and coordinate inconsistency; step S13, performing automatic coding processing on the identified change sample plot based on DBH (diameter at breast height) square sum ratio calculation and a threshold judgment mechanism; and step S14, realizing consistent updating of multi-period data, and ensuring synchronous change of sample plot data and sample wood data. According to the invention, accurate identification, code identification and data correction of sample plot change conditions can be realized.

Description

Technical Field

[0001] This invention relates to the field of forest resource management technology, and in particular to a method and system for coding sample plots in multi-period forest surveys. Background Technology

[0002] Forest resource surveys hold a fundamental position in my country's forestry management system. Timely and accurate understanding of forest resource conditions is a crucial prerequisite for forestry production units to achieve scientific management and sustainable operation. Forest management surveys, as the core means for grassroots forestry units to obtain information on the quantity and quality of forest resources, directly affect the quality of forest management plan development, the rationality of logging quota setting, and the accuracy of resource record updates. With the deepening of forestry informatization, forest resource surveys are transforming from traditional manual operations to digital and intelligent methods, which places higher demands on data processing methods and quality control.

[0003] The application of computer technology in forestry surveys has undergone significant development and evolution. In the early 1980s, forestry survey data processing mainly relied on manual calculation and table lookup, resulting in high error rates and low efficiency. In the 1990s, with the improvement of computer hardware capabilities, stand-alone data processing systems began to emerge, such as the microcomputer processing system used in Xiaolongshan Forest Farm in Gansu Province, realizing the transition from purely manual to computer-aided processing. The computer transmission module for Class II survey data developed by Xiao Ling, Zheng Xilin, and others adopted graphical input and window operation methods, establishing a preliminary error detection mechanism. Since the 21st century, the state-level continuous inventory data statistics software developed by Zhang Jiabao and the data warehouse technology application explored by Yang Weimin's team at Central South University of Forestry and Technology mark a new stage of systematic and intelligent development in forestry survey data processing.

[0004] Processing multi-period forest survey data presents unique complexities and technical challenges. This type of data exhibits significant temporal sequence characteristics, requiring the maintenance of spatial continuity to reflect the dynamic changes in forest resources. In practice, complex logical relationships exist between survey data from different periods, including spatial correspondences of sample plots, continuity of tree growth, and records of human disturbance. These characteristics make quality control and processing of multi-period data more complex than single-period data, necessitating specialized technical methods.

[0005] In practice, due to long survey intervals (usually 5-10 years), the influence of human factors, and changes in the natural environment, multi-period survey data have the following problems: Inconsistent plot numbering: The same plot may be assigned different numbers in different survey periods; Coordinate position change: The coordinates of the center point of the sample plot change due to measurement errors or actual movement; Change of sample plot type: ordinary sample plot is changed to a re-established sample plot or an additional sample plot is added; Data missing anomalies: data quality issues such as missed sample tree measurements and unlabeled logging. Intertemporal phenomenon: The sample plot is missing in a certain period and then reappears; Traditional processing methods rely mainly on manual identification and modification, which suffers from low efficiency, inconsistent standards, and susceptibility to errors. The lack of systematic coding rules and automated processing mechanisms makes it difficult to guarantee the consistency and comparability of data from multiple periods, thus affecting the accuracy of dynamic monitoring of forest resources. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a systematic and automated method and system for coding and processing sample plots in multi-phase forest surveys, which can achieve accurate identification, coding, and data correction of sample plot changes.

[0007] The present invention solves the technical problem by adopting the following technical solution: A method for coding sample plots from multiple forest surveys includes the following steps: Step S11: Establish a sample plot coding rule system, using a standardized format of original sample plot number - period code + reason for change code; Step S12: Identify changes in the sample plots using database query technology, including four types of changes: logging, missing trees, spanning different periods, and inconsistent coordinates. Step S13: Based on the ratio of the sum of squares of diameter at breast height (DBH) and the threshold determination mechanism, the identified change plots are automatically coded. Step S14: Achieve consistent updates of data from multiple periods to ensure synchronized changes in plot data and sample tree data.

[0008] Furthermore, in step S11, the period code is identified using a letter sequence: A represents the first phase of the baseline survey; B indicates the second follow-up examination; C indicates the third follow-up examination; D indicates the fourth follow-up examination; E indicates the fifth follow-up examination; The code for the reason for the change includes: 41: Indicates a missing tree sample plot, used to identify situations where forest records are abnormally missing; 40: Indicates a logging sample plot, used to identify situations where the logging intensity per unit area exceeds a threshold; 50: Indicates a cross-period sample plot, used to identify the resumption status after a survey was interrupted; 51: Indicates a plot of land with inconsistent coordinates, used to identify situations where the location of the plot has changed.

[0009] Furthermore, in step S12, the identification of missing tree plots includes: Extract all sample tree numbers within the current and next period's sample plots using an SQL query; Calculate the sum of squares of diameter at breast height (DBH) of sample trees that exist in the current period but disappear in the next period; Calculate the sum of squares of the total diameter at breast height (DBH) of the sample trees in this period; Missing tree plots were identified through ratio calculation and threshold determination. The threshold determination uses configurable parameters: The default threshold is 15%; Supports dynamic adjustment of thresholds based on different forest stand types and survey requirements; Set a minimum effective number of trees to avoid misjudgment in small samples; The calculation of the sum of squares of the diameter at breast height includes: Calculate the square of the diameter at breast height for each sample tree; Sum the squared diameter at breast height (DBH) values ​​of the target sample tree group; The COALESCE function is used to handle null values ​​to ensure computational stability.

[0010] Furthermore, the identification of logging plots includes: Obtain sample plot category information and screen sample plots for retesting; Calculate the ratio of the total cross-sectional area of ​​felled timber to the total cross-sectional area before felling; When the logging intensity is ≥15%, it is determined to be a logging sample plot; The coding update of logging plots adopts a recursive strategy: Immediately update the plot codes for the period in which logging occurred; Iterate through all subsequent periods and update the data for the same location number; Only update records where the plot category remains the same as the resurveyed plot.

[0011] Furthermore, it also includes step S15, quality control: Data existence verification confirms the existence status of sample plots in multiple data periods; Data rationality check to verify the logical rationality of changes in chest diameter and number; The processing results are verified by sampling checks to ensure the accuracy of the code updates.

[0012] An error handling mechanism is implemented, including a data rollback function to restore the original data when errors occur during processing; detailed operation logs are recorded, including processing time, operators, and change details; and an anomaly alarm mechanism is set up to promptly detect and handle system anomalies.

[0013] A multi-phase forest survey plot coding and processing system, used to implement the multi-phase forest survey plot coding and processing method described in any one of the above claims, includes: The database management module is used to store and manage forest survey data from multiple periods. The change recognition module is configured to automatically detect four types of changes in the sample plots. The coding module automatically codes the identified variation plots based on the ratio of the sum of squares of diameter at breast height (DBH) and a threshold determination mechanism. The data update module automatically updates the sample plot codes according to preset rules and verifies and checks the processing results.

[0014] Furthermore, the database management module includes: The data table management unit is used to manage the sample plot table, sample tree table, and code table; The Temporary Tables management unit is used to create and process temporary data tables; The connection management unit is responsible for establishing and maintaining database connections.

[0015] Furthermore, the change recognition module includes: The missing tree detection unit identifies missing trees based on the ratio of the sum of squares of diameter at breast height (DBH). The logging detection unit identifies logging based on the logging intensity of cross-sectional area. The inter-period detection unit identifies inter-period data based on the continuity of the sample plot's state. The coordinate detection unit identifies coordinate changes based on geographical location consistency.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for coding sample plots in a multi-phase forest survey as described in any of the preceding claims.

[0017] An electronic device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the method for coding sample plots of a multi-phase forest survey as described above.

[0018] The beneficial effects of this invention are: High degree of standardization: A unified sample plot coding rule system has been established, which has solved the problem of inconsistent codes between multiple periods of data; High degree of automation: It realizes automatic identification and coding update of sample plot changes, which greatly improves work efficiency; Improved accuracy: Change recognition based on scientific algorithms avoids subjective errors from human judgment; Strong traceability capability: Through a systematic coding system, the complete traceability of historical changes in the sample plots is achieved; Highly adaptable: Flexible threshold parameters and period settings to adapt to the survey needs of different regions and periods; Data quality improvement: Data logic errors were effectively identified and corrected, improving data reliability. Attached Figure Description

[0019] Figure 1 This is a flowchart of the multi-phase forest survey plot coding and processing method of the present invention; Figure 2 The flowchart for adding and modifying the sample plot coding process is provided for this invention. Figure 3 This is a flowchart of the logging sample plot coding process of the present invention; Figure 4 This is a flowchart of the missing tree sample plot encoding process of the present invention; Figure 5 This is a flowchart of the cross-period sample plot coding process of the present invention; Figure 6 This is a flowchart of the coordinate inconsistency sample plot encoding process of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] refer to Figure 1 This invention provides a method for coding sample plots in multi-period forest surveys, comprising the following steps: Step S11: Establish a sample plot coding rule system, using a standardized format of original sample plot number - period code + reason for change code; Step S12: Identify changes in the sample plots using database query technology, including four types of changes: logging, missing trees, spanning different periods, and inconsistent coordinates. Step S13: Based on the ratio of the sum of squares of diameter at breast height (DBH) and the threshold determination mechanism, the identified change plots are automatically coded. Step S14: Achieve consistent updates of data from multiple periods to ensure synchronized changes in plot data and sample tree data.

[0022] To further optimize the technical solution, in step S11, the period code is identified by a letter sequence: A represents the first phase of the baseline survey; B indicates the second follow-up examination; C indicates the third follow-up examination; D indicates the fourth follow-up examination; E indicates the fifth follow-up examination; The code for the reason for the change includes: 41: Indicates a missing tree sample plot, used to identify situations where forest records are abnormally missing; 40: Indicates a logging sample plot, used to identify situations where the logging intensity per unit area exceeds a threshold; 50: Indicates a cross-period sample plot, used to identify the resumption status after a survey was interrupted; 51: Indicates a plot of land with inconsistent coordinates, used to identify situations where the location of the plot has changed.

[0023] Table 1 Encoding Instructions coding illustrate 12 Additional sample plots will be added (A12 indicates the first phase of additions). 13 Modified sample plots (A13 indicates the first phase of modification) 40 Logging sample plots (current logging area ≥ 15%, A40 indicates logging from the initial stage to the first phase). 41 Missing tree plots (the cross-sectional area where trees were present in the early stage but not in the later stage accounts for ≥15% of the total cross-sectional area in the early stage). 50 Inter-period sample plots (C50 indicates that there is no data for the third period, but data for the second and fourth periods) 51 Plots with inconsistent coordinates (inconsistent x and y coordinates between early and late stages). (1) Reference Figure 2 The steps for adding or modifying sample plot codes are as follows: Step 1: Target Processing and Initialization Preparation The main function of the sample plot addition / modification check and correction process is to handle two special types of sample plots: "modified sample plots" and "added sample plots". At the start of the program, the database connection and recordset objects are initialized to prepare for subsequent data processing.

[0024] Step 2: Identification and Screening of Modified Sample Plot Data The program first retrieves the category code corresponding to "redesigned sample plot" from the system configuration, then queries all records in the specified data table that are classified as redesigned sample plots, and establishes the target dataset to be processed.

[0025] Step 3: Determining the conditions for generating and updating new plot numbers For each modified sample plot, the program generates a new sample plot number according to the numbering rules. Before performing the update, the program checks whether the sample plot number has already been processed by the rules for the current period to avoid duplicate operations.

[0026] Step 4: Update sample plot data For eligible modified sample plots, the program calls the data update sub-process to replace the original sample plot number with a new sample plot number containing a period code and a change reason code, ensuring the uniqueness and accuracy of the data identification.

[0027] Step 5: Add sample plot data processing After completing the modification of the sample plot, the program uses the same logic to add sample plots, obtains the category code of the added sample plot, and filters the corresponding records for subsequent processing.

[0028] Step 6: Add sample plot data update execution For each newly added sample plot, the program generates a new sample plot number and performs an update operation to ensure that the newly added sample plot is correctly identified and processed.

[0029] Step 7: Resource cleanup and process wrap-up After all additions, modifications, and sample site setups have been completed, the program cleans up database connections and temporary objects, thus completing the entire processing flow.

[0030] (2) Reference Figure 3 The coding of logging sample plots includes the following steps: Step 1: Establishing the goals and principles The core objective of logging sample plot inspection and correction is to accurately record and track forest sample plots that have undergone significant logging activities through systematic coding and identification. The process follows three main principles: threshold triggering, full traceability, and data consistency.

[0031] Step 2: Calculation of Logging Intensity and Assessment of Conditions The program calculates logging intensity based on the diameter at breast height (DBH) cross-sectional area. When the total cross-sectional area of ​​the logged trees accounts for 15% or more of the total cross-sectional area in the early stage of logging, the coding change mechanism is triggered.

[0032] Step 3: Identification and Screening of Logging Sample Plots The system queries plot records with the plot category of "retest plot" and the logging intensity reaching the threshold, and establishes a target set of logging plots that need to be processed.

[0033] Step 4: Generating New Sample Plot Codes According to the numbering rules, a new plot number containing the period code and the logging code is generated for the identified logging plots. For example, "A001-B40" indicates a plot where logging occurred in the second period.

[0034] Step 5: Consistent Update of Data Across Multiple Periods The program employs a recursive update strategy, updating not only the sample plot data for the period in which logging occurred, but also simultaneously updating records with the same plot number in all subsequent periods, ensuring the integrity of data traceability.

[0035] Step 6: Handling Special Circumstances For complex situations involving continuous logging or mixed variations, the program performs special processing according to preset rules to ensure the rationality and consistency of the coding logic.

[0036] (3) Reference Figure 4 The missing tree sample plot coding includes the following steps: Step 1: Clearly define the processing objectives The missing tree sampling inspection mainly deals with forest records that existed in the early stage but disappeared abnormally in the later stage, and focuses on the ecological importance rather than the simple quantity.

[0037] Step 2: Identification and Calculation of Missing Trees The program calculates the proportion of the sum of diameter at breast height (DBH) of the missing trees to the total sum of DBH of the previous period by comparing sample tree data from two adjacent periods, thus quantifying the severity of the missing tree situation.

[0038] Step 3: Threshold Determination and Decision When the percentage of missing trees reaches or exceeds the 15% threshold, the system determines that the sample plot has missing trees and needs to be processed, triggering the coding change process.

[0039] Step 4: Generation and updating of new sample plot codes A new code for the missing tree plot is generated according to the numbering rules. For example, "A001-B41" indicates a plot where a missing tree occurred during the second phase of the survey.

[0040] Step 5: Data Validation and Quality Control The program performs multiple quality controls on the processing results, including existence verification, quantity rationality check, and chest diameter validity verification, to ensure the accuracy of the processing results.

[0041] (4) Reference Figure 5 The coding of cross-period sample plots includes the following steps: Step 1: Target Processing and Initialization Preparation The cross-period sample plot inspection mainly deals with situations where sample plots were missing in a certain survey but reappeared in a subsequent survey. The program determines the starting period of the inspection based on the baseline year and initializes the status record array.

[0042] Step 2: Obtaining the list of target sample plots The program reads a list of all sample plot numbers from the baseline data table as the base dataset for intertemporal analysis.

[0043] Step 3: Intertemporal analysis of individual sample plots For each sample plot number, the program checks its existence status in each survey period in chronological order and records continuity information.

[0044] Step 4: Inter-period pattern recognition When a typical inter-period pattern is detected where the previous period is missing but the current period is present, the program determines that the sample plot has experienced an inter-period phenomenon.

[0045] Step 5: Generation and Update of New Plot Numbers After confirming the cross-period, the program generates a new plot number with a "50" code and updates the data record for the period in which the cross-period occurred.

[0046] Step 6: Loop Processing and Resource Cleanup After completing the cross-period inspection of all sample plots, the program clears temporary resources and ends the processing flow.

[0047] (5) Reference Figure 6 Coding of plots with inconsistent coordinates includes the following steps: Step 1: Target Processing and Initialization Preparation The coordinate inconsistency check aims to identify instances where the coordinates of the same plot of land change across different survey periods. The program initializes the coordinate data array and a temporary working environment.

[0048] Step 2: Coordinate Data Integration and Temporary Table Creation The program integrates the coordinate data of all sample plots from all periods into a temporary table, creating a complete historical coordinate history.

[0049] Step 3: Preliminary screening of suspected sample plots Statistical analysis methods were used to identify sample plots that appeared in multiple periods and may have inconsistent coordinates.

[0050] Step 4: Detailed analysis of coordinate history For each suspicious sample plot, the program obtains its complete coordinate change sequence in chronological order and loads it into a memory array for analysis.

[0051] Step 5: Coordinate Change Detection and Judgment The program compares the coordinate data of the sample plots period by period. When it finds that the coordinate values ​​change between adjacent periods, it determines that the coordinates are inconsistent.

[0052] Step 6: New Encoding Generation and Data Update After confirming the coordinate change, the program generates a new plot number with the code "51" and updates the data record for the period in which the change occurred.

[0053] Step 7: Temporary resource cleanup and process completion After processing all plots with inconsistent coordinates, the program deletes temporary tables and other working resources, ending the entire inspection and correction process.

[0054] Based on a thorough understanding of relevant forestry survey knowledge, we analyze the problems to be solved and conduct a detailed logical design of the database—including the establishment of table structures, primary keys, fields, and table relationships—to effectively maintain the integrity of data at all levels and enable fast and accurate querying and implementation of relevant algorithms. We divide the database tables into three main categories: data tables, code tables, and result tables. The underlying database metadata design is shown in Table 2. Table 2

[0055] Required fields for plot and tree sample tables The original data consists of three main types: plot data and sample tree data. The .dbf data is then imported into an Access database. The field names and data types are shown in Table 3.

[0056] Table 3

[0057] Further optimization of the technical solution, in step S12, the identification of missing tree plots includes: Extract all sample tree numbers within the current and next period's sample plots using an SQL query; Calculate the sum of squares of diameter at breast height (DBH) of sample trees that exist in the current period but disappear in the next period; Calculate the sum of squares of the total diameter at breast height (DBH) of the sample trees in this period; Missing tree plots were identified through ratio calculation and threshold determination. The threshold determination uses configurable parameters: The default threshold is 15%; Supports dynamic adjustment of thresholds based on different forest stand types and survey requirements; Set a minimum effective number of trees to avoid misjudgment in small samples; The calculation of the sum of squares of the diameter at breast height includes: Calculate the square of the diameter at breast height for each sample tree; Sum the squared diameter at breast height (DBH) values ​​of the target sample tree group; The COALESCE function is used to handle null values ​​to ensure computational stability.

[0058] Further optimization of the technical solution, including the identification of logging sample plots: Obtain sample plot category information and screen sample plots for retesting; Calculate the ratio of the total cross-sectional area of ​​felled timber to the total cross-sectional area before felling; When the logging intensity is ≥15%, it is determined to be a logging sample plot; The coding update of logging plots adopts a recursive strategy: Immediately update the plot codes for the period in which logging occurred; Iterate through all subsequent periods and update the data for the same location number; Only update records where the plot category remains the same as the resurveyed plot.

[0059] Further optimization of the technical solution also includes step S15, quality control: Data existence verification confirms the existence status of sample plots in multiple data periods; Data rationality check to verify the logical rationality of changes in chest diameter and number; The processing results are verified by sampling checks to ensure the accuracy of the code updates.

[0060] An error handling mechanism is implemented, including a data rollback function to restore the original data when errors occur during processing; detailed operation logs are recorded, including processing time, operators, and change details; and an anomaly alarm mechanism is set up to promptly detect and handle system anomalies.

[0061] Plot factor logic checks include: (1) Sample plot number check. The sample plot number is a unique number for a sample plot in a population. There must be no duplicate sample plots in the same population. If there are duplicate sample plots, the reason must be found out and corrected. At the same time, the sample plot number must be consistent with the previous sample plot number.

[0062] SELECT COUNT(DISTINCT sample_plot_number) AS unique_count, COUNT(sample_plot_number) AS total_count FROM table_name; (2) Check the plot number corresponding to the sample plot and the sample tree. Delete the plots that contain plot information in the plot data but do not contain such plot information in the sample tree data.

[0063] If YMRs.State=1 Then YMRs.Close YMRs.Open "Select Sample Plot Number From T1994220 Where Sample Plot Number='" & YDH & "'",ADOAccessConn, 1, 1 If YMRs.RecordCount < 1 Then 'Indicates that there is no data for a sample plot in the sample tree data, delete the data for that sample plot from the sample plot data.

[0064] ADOAccessConn.Execute "Delete From P1994220 Where plot number='" & YDH& End If (3) Check the horizontal and vertical coordinates. When checking, the vertical coordinate should be 4 Arabic numerals and the horizontal coordinate should be 5 Arabic numerals. Check whether the horizontal and vertical coordinates of the same plot number in each period are consistent. If they are inconsistent, it should be determined whether it is an input error or a change of plot setting.

[0065] First, input all plot numbers, x-coordinates, and y-coordinates from the plot data files into a single file. YearSQLStr = "Select Plot Number, x-axis, y-axis, " & YearStr & " As Survey Year From " & YDTableName Secondly, statistically analyze sample plots with the same plot number but different x- or y-axis coordinates, and mark them accordingly. SQLStr = "Select Sample Plot Number, Count(*) As CountValue From T_YDLongitudeGroup by Sample Plot Number Having Count(*) > 1" Next, using the x and y coordinates of the last sample plot as the standard, we find the sample plot number for different x and y coordinates.

[0066] To further optimize the technical solution, this invention supports parameterized configuration: The threshold parameters can be dynamically adjusted to adapt to the requirements of different regions and forest stand types; The period code can be configured extensibly to support survey data for more periods; The coding rules can be customized and modified to meet specific business needs.

[0067] Parameterized configuration is achieved through system parameter tables: Create a SystemParameters table to store all configurable parameters; Parameters include threshold parameters, period mapping, encoding rules, etc. It supports dynamically reading and updating parameter values ​​at runtime.

[0068] This invention employs batch processing optimization technology: Process each sample plot sequentially to avoid memory overflow; Use temporary tables to store intermediate results to improve query efficiency; Transaction processing mechanisms are used to ensure data consistency.

[0069] The batch processing of this invention includes: Process large amounts of data in batches and set reasonable batch sizes; Displays processing progress in real time and provides user feedback; Supports the function of resuming interrupted processes.

[0070] The method of this invention is applicable to: continuous forest resource inventory data processing; forestry management plan preparation support; dynamic monitoring and analysis of forest resources; and forestry scientific research data processing.

[0071] The coding results generated by this invention can be used for: forest resource change trend analysis; logging activity impact assessment; forest management effectiveness evaluation; and forestry policy formulation support.

[0072] The present invention also provides a multi-phase forest survey plot coding processing system for implementing the multi-phase forest survey plot coding processing method described in any of the above claims, comprising: The database management module is used to store and manage forest survey data from multiple periods. The change recognition module is configured to automatically detect four types of changes in the sample plots. The coding module automatically codes the identified variation plots based on the ratio of the sum of squares of diameter at breast height (DBH) and a threshold determination mechanism. The data update module automatically updates the sample plot codes according to preset rules and verifies and checks the processing results.

[0073] The database management module includes: The data table management unit is used to manage the sample plot table, sample tree table, and code table; The Temporary Tables management unit is used to create and process temporary data tables; The connection management unit is responsible for establishing and maintaining database connections.

[0074] Furthermore, the change recognition module includes: The missing tree detection unit identifies missing trees based on the ratio of the sum of squares of diameter at breast height (DBH). The logging detection unit identifies logging based on the logging intensity of cross-sectional area. The inter-period detection unit identifies inter-period data based on the continuity of the sample plot's state. The coordinate detection unit identifies coordinate changes based on geographical location consistency.

[0075] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for encoding sample plots in a multi-phase forest survey as described in any one of the preceding claims.

[0076] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the method for encoding sample plots of a multi-phase forest survey as described in any of the preceding claims.

[0077] This invention boasts several advantages: High standardization: It establishes a unified sample plot coding rule system, resolving inconsistencies in codes across multiple periods; High automation: It enables automatic identification and coding updates of sample plot changes, significantly improving work efficiency; Enhanced accuracy: Change identification based on scientific algorithms avoids subjective errors from manual judgment; Strong traceability: Through a systematic coding system, it achieves complete tracing of historical changes in sample plots; Good adaptability: Flexible threshold parameters and period settings adapt to the survey needs of different regions and periods; Improved data quality: It effectively identifies and corrects logical errors in the data, improving data reliability. Example

[0078] like Figure 1 As shown, the overall processing flow of the present invention includes the following steps: Step 101: System Initialization Configure database connection parameters, initialize the processing environment, and load the numbering rule configuration.

[0079] Step 102: Data Preprocessing The original sample plot data underwent quality checks, format standardization, and outlier handling.

[0080] Step 103: Change Detection and Analysis The following steps are performed sequentially: addition / renovation detection, logging detection, missing tree detection, cross-period detection, and coordinate detection.

[0081] Step 104: Data Processing Execution Based on the test results, new sample plot numbers are generated according to the numbering rules, and data update operations are performed.

[0082] Step 105: Result Verification Perform integrity verification and logical consistency checks on the processing results.

[0083] Step 106: Log Recording and Output Record processing logs, generate processing reports, and output the final results. Example

[0084] like Figure 2 As shown, the specific implementation steps for adding or modifying sample plots are as follows: Step 201: Obtain the category code Query the category code of the added / modified sample plot from the system parameter table: SELECT code value FROM system parameter table WHERE parameter name = 'Add sample plot code' SELECT code value FROM system parameter table WHERE parameter name = 'Change sample plot code' Step 202: Select target sample plots Execute a database query to filter the sample plot records that need to be processed: SQL Copy and download --Refactored Sample Plot Query: SELECT Sample Plot Number, Survey Year, Horizontal Axis, Vertical Axis FROM Sample Plot Data Table WHERE Sample Plot Category = 13 AND Survey Year IN('2004','2009','2014') --Add sample plot query: SELECT sample plot number, survey year, x-axis, y-axis FROM sample plot data table WHERE sample plot category = 12 AND survey year IN('2004','2009','2014') Step 203: Generate new sample plot number Generate new sample plot numbers according to the following rules: Modify the sample plot: Original sample plot number + "-" + period code + "13" Adding a sample plot: Original sample plot number + "-" + period code + "12" The period code is automatically determined based on the survey year: 1999 → A 2004 → B 2009 → C 2014 → D Step 204: Perform data update Call the stored procedure to update data, and batch modify sample plot data and sample tree data: EXEC update sample plot number process @original location number='1001', @New Sample Plot Number='1001-B13', @Survey Year='2004', @Update type='Redesign sample plot' Step 205: Verify the processing results Check the number of updated records, verify data integrity, and ensure consistency between plot and sample tree data.

[0085] Example 3: Treatment of logging sample plots like Figure 3 As shown, the specific implementation steps for processing logging sample plots are as follows: Step 301: Calculate the cross-sectional area of ​​the previous stage Query the previous sample wood data and calculate the total cross-sectional area: SQL Copy and download SELECT SUM(π * POWER(Diameter at breast height / 200, 2)) AS Previous Fiscal Area FROM Sample Tree Data Table WHERE Sample Plot Number='1001' AND Survey Year='2004' Step 302: Calculate the cross-sectional area of ​​the felled timber Query the current logging data and calculate the logging area: SQL Copy and download SELECT SUM(π*POWER(DBH / 200, 2)) AS Logging FROM Sample Tree Data Table WHERE Sample Plot Number='1001' AND Survey Year='2009' AND Tree Status='Logging' Step 303: Calculate logging intensity vb Copy and download Logging intensity = (Logging fracture area / Previous fracture area) * 100 If logging intensity >= 15, then The plot was identified as a significant logging site. End If Step 304: Generate logging sample plot codes Generate new numbers for sample plots that have reached the threshold: original sample plot number + "-" + period code + "40" Step 305: Update logging sample plot data Update current and subsequent data to maintain data continuity.

[0086] Example 4: System Architecture Implementation The system architecture of this invention includes the following modules: Numbering rule management module implementation: Python Copy and download class NumberingRuleManager: def __init__(self): self.period_codes = {'1999':'A', '2004':'B', '2009':'C', '2014':'D'} self.change_codes = {'Add':12, 'Reset':13, 'Logging':40, 'Missing Tree':41, 'Spanning Periods':50, 'Inconsistent Coordinates':51} def generate_new_number(self, original_no, survey_year, change_type): period_code = self.period_codes.get(survey_year, 'X') change_code = self.change_codes.get(change_type, 99) return f"{original_no}-{period_code}{change_code:02d}" Change detection and analysis module implementation: Python Copy and download class ChangeDetectionEngine: def detect_harvest(self, plot_data): "Logging Detection Algorithm" if plot_data['harvest_intensity'] >= 0.15: return True, 'logging sample plot' return False, None def detect_missing_trees(self, tree_data): """Leaky Tree Detection Algorithm""" missing_ratio = self.calculate_missing_ratio(tree_data) if missing_ratio >= 0.15: return True, 'Leaky Tree Sample' return False, None Example 5: Processing Threshold Configuration The threshold parameters of this invention can be configured according to actual needs: xml Copy, download, and run. <processing_config> <thresholds> <harvest_intensity>0.15< / harvest_intensity> <missing_tree_ratio>0.15< / missing_tree_ratio> <coordinate_tolerance>10.0< / coordinate_tolerance> < / thresholds> <rules> <numbering_rule> Original land number - period code change reason coding< / numbering_rule> <period_mapping> <period year="1999" code="A" / > <period year="2004" code="B" / > <period year="2009" code="C" / > <period year="2014" code="D" / > < / period_mapping> < / rules> < / processing_config> The present invention effectively solves the key technical problems of processing multi-phase forest survey data through the above technical solution, and provides reliable technical support for precise management of forest resources and scientific decision-making.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for coding and processing sample plots in multi-phase forest surveys, characterized in that, Includes the following steps: Step S11: Establish a sample plot coding rule system, using a standardized format of original sample plot number - period code + reason for change code; Step S12: Identify changes in the sample plots using database query technology, including four types of changes: logging, missing trees, spanning different periods, and inconsistent coordinates. Step S13: Based on the ratio of the sum of squares of diameter at breast height (DBH) and the threshold determination mechanism, the identified change plots are automatically coded. Step S14: Achieve consistent updates of data from multiple periods to ensure synchronized changes in plot data and sample tree data.

2. The method for coding and processing sample plots in a multi-phase forest survey according to claim 1, characterized in that, In step S11, the period code is identified by a letter sequence: A represents the first phase of the baseline survey; B indicates the second follow-up examination; C indicates the third follow-up examination; D indicates the fourth follow-up examination; E indicates the fifth follow-up examination; The code for the reason for the change includes: 41: Indicates a missing tree sample plot, used to identify situations where forest records are abnormally missing; 40: Indicates a logging sample plot, used to identify situations where the logging intensity per unit area exceeds a threshold; 50: Indicates a cross-period sample plot, used to identify the resumption status after a survey was interrupted; 51: Indicates a plot of land with inconsistent coordinates, used to identify situations where the location of the plot has changed.

3. The method for coding and processing sample plots in a multi-phase forest survey according to claim 1, characterized in that, In step S12, the identification of missing tree plots includes: Extract all sample tree numbers within the current and next period's sample plots using an SQL query; Calculate the sum of squares of diameter at breast height (DBH) of sample trees that exist in the current period but disappear in the next period; Calculate the sum of squares of the total diameter at breast height (DBH) of the sample trees in this period; Missing tree plots were identified through ratio calculation and threshold determination. The threshold determination uses configurable parameters: The default threshold is 15%; Supports dynamic adjustment of thresholds based on different forest stand types and survey requirements; Set a minimum effective number of trees to avoid misjudgment in small samples; The calculation of the sum of squares of the diameter at breast height includes: Calculate the square of the diameter at breast height for each sample tree; Sum the squared diameter at breast height (DBH) values ​​of the target sample tree group; The COALESCE function is used to handle null values ​​to ensure computational stability.

4. The method for coding and processing sample plots in a multi-phase forest survey according to claim 1, characterized in that, Identification of logging plots includes: Obtain sample plot category information and screen retest sample plots; Calculate the ratio of the total cross-sectional area of ​​felled trees to the total cross-sectional area before felling; When the logging intensity is ≥15%, it is determined to be a logging sample plot; The coding update of logging plots adopts a recursive strategy: Immediately update the plot codes for the period in which logging occurred; Iterate through all subsequent periods and update the data for the same location number; Only update records where the plot category remains the same as the resurveyed plot.

5. The method for coding and processing sample plots in a multi-phase forest survey according to claim 1, characterized in that, It also includes step S15, quality control: Data existence verification confirms the existence status of sample plots in multiple data periods; Data rationality check to verify the logical rationality of changes in chest diameter and number; The processing results are verified by sampling checks to ensure the accuracy of the code updates; An error handling mechanism is implemented, including a data rollback function to restore the original data when errors occur during processing; detailed operation logs are recorded, including processing time, operators, and change details; and an anomaly alarm mechanism is set up to promptly detect and handle system anomalies.

6. A multi-phase forest survey plot coding and processing system, used to implement the multi-phase forest survey plot coding and processing method as described in any one of claims 1-5, characterized in that, include: The database management module is used to store and manage forest survey data from multiple periods. The change recognition module is configured to automatically detect four types of changes in the sample plots. The coding module automatically codes the identified variation plots based on the ratio of the sum of squares of diameter at breast height (DBH) and a threshold determination mechanism. The data update module automatically updates the sample plot codes according to preset rules and verifies and checks the processing results.

7. The multi-phase forest survey plot coding and processing system according to claim 6, characterized in that, The database management module includes: The data table management unit is used to manage the sample plot table, sample tree table, and code table; The Temporary Tables management unit is used to create and process temporary data tables; The connection management unit is responsible for establishing and maintaining database connections.

8. The multi-phase forest survey plot coding and processing system according to claim 7, characterized in that, The change recognition module includes: The missing tree detection unit identifies missing trees based on the ratio of the sum of squares of diameter at breast height (DBH). The logging detection unit identifies logging based on the logging intensity of cross-sectional area. The inter-period detection unit identifies inter-period data based on the continuity of the sample plot's state. The coordinate detection unit identifies coordinate changes based on geographical location consistency.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a method for coding sample plots in a multi-phase forest survey as described in any one of claims 1-5.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements a method for coding sample plots in a multi-phase forest survey as described in any one of claims 1-5.

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