Land reclamation survey monitoring closed-loop management and control method and system

Abstract

The invention discloses a land reclamation survey monitoring closed-loop management and control method and system, and relates to the technical field of land reclamation project digital management and survey monitoring closed-loop management and control, and the method comprises the steps: collecting land reclamation data, solidifying the land reclamation data and a space unit ledger, and building an index dictionary, a threshold version, a point location and sampling plan and an evidence index. Constructing a baseline data set and completing consistency check; an investigation monitoring task list is generated according to the baseline data set, dispatching and execution caliber locking are completed, on-site collection and evidence trace reserving, structured warehousing and quality control judgment are carried out according to the locked caliber, and effective index records used for benchmarking calculation are output; and performing benchmarking and risk grading on the effective index records, generating rectification and upgrading processing work orders according to grading results, and performing trace reserving management. According to the method, monitoring data can be traced and audited through baseline filing and caliber locking, benchmarking grading and work order closed-loop processing are linked, reworking and rechecking disputes are remarkably reduced, and the period is shortened.

Description

Technical Field

[0001] This invention relates to the field of digital management and closed-loop control of land consolidation projects, specifically a method and system for closed-loop control of land consolidation survey and monitoring. Background Technology

[0002] Land consolidation is rapidly advancing in scenarios such as high-standard farmland construction, mine reclamation, farmland compensation, and ecological restoration, placing higher demands on comprehensive, quantifiable, and traceable investigation and monitoring. With the development of 3S technologies (GIS / RS / GNSS), UAV aerial surveying, mobile data acquisition terminals, and laboratory testing systems, the ability to acquire data for land use status identification, engineering quality verification, and soil and water environment monitoring has significantly improved. Simultaneously, project management based on e-government and big data platforms is gradually achieving online reporting, process tracking, and statistical analysis, providing a technological foundation for dynamic control of the consolidation process.

[0003] Existing technologies often focus on single-point data collection or periodic reports, making it difficult to support closed-loop management across units and batches. Firstly, the lack of unified documentation and consistency verification for multi-source data, spatial boundaries, locations, and indicator definitions leads to inconsistencies in coordinate benchmarks, unit divisions, and indicator interpretations for the same plot of land across different monitoring batches, resulting in incomparable and unverifiable results. Secondly, target values, allowable deviations, and thresholds often rely on manual configuration or static documents, lacking versioning and source traceability, easily leading to disputes regarding unclear threshold bases and inconsistent definitions. Thirdly, task assignment and execution standards are not effectively locked in; the links between mobile data collection, sample transfer, image interpretation, and GIS calculation lack evidence indexes and tamper-proof verification, often leaving data quality control at a formality level, making it difficult to create auditable and effective indicator records. Fourthly, risk assessment and rectification are generally disconnected from evidence, clauses, and work orders; the rectification process lacks continuous record-keeping and consistent judgment based on retesting and verification, making it difficult to achieve the technical effects of refined supervision and accountability. As a result, when problems such as engineering quality defects, environmental exceedances, and schedule deviations occur, there are often delays in detection, inaccurate positioning, increased repeated sampling and rework, and it is difficult to form a unified acceptance support package. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing land consolidation survey, monitoring and process control methods have the following problems: data and spatial unit ledgers are difficult to file in a unified manner and lack consistency verification; indicator caliber, target value and threshold lack versioning and source traceability; task execution caliber, evidence record and quality control link are imperfect, resulting in difficulty in auditing and reviewing the results; and how to achieve closed-loop control of benchmarking and classification and rectification and upgrading work order record linkage.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a closed-loop management method for land consolidation survey and monitoring, comprising: collecting land consolidation data; solidifying land consolidation data and spatial unit ledgers; establishing an indicator dictionary, threshold versions, sampling points and plans, and evidence indexes; constructing a baseline dataset and completing consistency verification; generating a survey and monitoring task list based on the baseline dataset and completing task assignment and execution caliber locking; conducting on-site data collection and evidence recording, structured data storage and quality control judgment according to the locked caliber; outputting valid indicator records for benchmarking calculation; performing benchmarking and risk classification on the valid indicator records; generating rectification and upgrade disposal work orders according to the classification results and managing the records.

[0007] As a preferred embodiment of the closed-loop management and control method for land consolidation survey and monitoring described in this invention, the solidification of land consolidation data and spatial unit ledgers includes unified coordinate benchmark transformation and topological verification of project boundary results, current status survey results, design documents and drawings, construction organization records, and acceptance or supervision basis documents; topological verification includes boundary closure, overlap and gap checks, and consistency checks of shared edges between adjacent units; spatial units are divided according to parcels, map features, fields, or grid rules under a unified coordinate benchmark, and the division rule parameters are registered; a ledger field set is established for each spatial unit; the field set includes ownership or management entity, area, current land type, consolidation type, design corresponding index, construction section information and acceptance node information, and a list of associated point facilities is established.

[0008] As a preferred embodiment of the closed-loop management and control method for land consolidation survey and monitoring described in this invention, the establishment of an indicator dictionary, threshold version, sampling location and sampling plan, and evidence index includes registering the indicator name, unit, collection method, sampling or measurement method, data format, and evidence type requirements for each indicator; the collection methods include laboratory testing, on-site instrument readings, GNSS / RTK measurement, UAV or satellite image interpretation, and GIS calculation; establishing threshold entries for each indicator and managing them in a versioned manner according to version number and effective date, with threshold entries preferentially referencing the allowable range given by standards or acceptance clauses, or deriving the allowable range from the target value and allowable deviation given in the design documents; registering the point coordinates, point type, sampling frequency, and allowable time window for indicators that require point placement, and pre-setting alternative rules and triggering conditions when the point is not reached.

[0009] As a preferred embodiment of the closed-loop management method for land consolidation survey and monitoring described in this invention, the step of generating a survey and monitoring task list based on the baseline dataset and completing the assignment and execution caliber locking includes generating task entries based on the indicator set and point configuration associated with spatial units, and writing the indicator name, unit, collection method, sampling or measurement method, sampling frequency, allowed time window, required evidence type, and required fields into the task entries; breaking down the task entries into on-site sampling and measurement sub-tasks, sample transfer and laboratory testing sub-tasks, aerial flight and image processing sub-tasks, and interpretation and GIS calculation sub-tasks, and writing personnel qualification requirements, equipment list, and equipment calibration requirements for each sub-task; after the assignment is completed, the indicator caliber version, threshold version, point and sampling plan version, and evidence index rule version referenced in this batch are solidified into a task parameter set and locked, while the trigger conditions, recording fields, and approval fields for alternative points, alternative equipment, or alternative image sources are preset.

[0010] As a preferred embodiment of the closed-loop management method for land consolidation investigation and monitoring described in this invention, the following steps are included: conducting on-site data collection and evidence documentation, structured data storage, and quality control judgment according to the locked criteria. This includes generating a unique sample number for each sample during on-site sampling and recording the sampling timestamp, sampling coordinates, sampling depth, sampling volume or mass, parallel sample identification, and sample handover information, forming a flow record of samples from collection and transportation to laboratory reception; recording the coordinates, elevation, or geometric dimensions of the measurement points, as well as the type of measurement tool, equipment number, and calibration validity period during on-site measurements; recording the flight sortie, flight altitude, ground resolution, image product type, processing software, and parameter version during aerial flight and image acquisition, and submitting orthophotos, DSM / DEM products, and version numbers; registering metadata for photos, videos, aerial photographs, test reports, and result documents and generating a verification summary, which is then linked to the evidence index table.

[0011] As a preferred embodiment of the closed-loop management and control method for land consolidation survey and monitoring described in this invention, the structured data entry and quality control judgment include writing the data collection form fields, result file metadata, and evidence index information into indicator record entries; the indicator record entries include indicator name, unit, measured value, data collection timestamp, data collection coordinates, equipment number, sample number or result file number, and evidence citation field; the data entry is subjected to field integrity verification, data type and unit consistency verification, task scope consistency verification, and evidence consistency verification; the task scope consistency verification includes matching the data collection coordinates with the point configuration, the data collection time falling within the allowable time window, and the equipment number being within the calibration validity period; the evidence consistency verification includes the consistency of the correspondence between the test report number and the sample number, the consistency of the result file version number with the task form record, and the consistency of the evidence verification summary; the quality control conclusion field is output and the reason code for failure is recorded, and a supplementary data collection or review pending action is generated when failure occurs.

[0012] As a preferred embodiment of the closed-loop management method for land consolidation investigation and monitoring described in this invention, the following steps are included: Benchmarking and risk grading of effective indicator records, generating rectification and escalation work orders based on the grading results, and maintaining a record-keeping system. This includes binding the indicator record with the standard or acceptance clause number, design document number, or statistical batch number from the allowable range when the quality control conclusion of the indicator record is "pass," and using this binding for benchmarking judgment; outputting the pass, rectification, or escalation status based on the grading thresholds. Both the rectification and escalation thresholds are output based on the sorting quantile after filtering by a sample library with established acceptance conclusions according to stratified conditions, and the sample time window, sample size, quantile coefficient, algorithm version, and threshold version are fixed and archived; when the output is a rectification or escalation status, a work order is generated, binding the trigger indicator list, boundary exceedance details, evidence number and evidence verification summary, clause number, and version information to the work order, and recording the work order status transition and overdue escalation rules.

[0013] Another objective of this invention is to provide a closed-loop management and control system for land consolidation survey and monitoring. This system can generate a list of survey and monitoring tasks based on a baseline dataset, complete task assignment and execution criteria locking, conduct on-site data collection and evidence recording, structured data entry and quality control judgment according to the locked criteria, and output effective indicator records for benchmarking calculations. This solves the problem that current land consolidation survey, monitoring and process control methods have imperfect task execution criteria, evidence recording and quality control links, which makes the results difficult to audit and verify.

[0014] As a preferred embodiment of the closed-loop management and control system for land consolidation survey and monitoring described in this invention, the system includes: a baseline filing module, a task acquisition and quality control module, and a benchmarking and hierarchical dispatching module; the baseline filing module is used to collect land consolidation data and solidify spatial unit ledgers, indicator dictionaries, threshold versions, sampling plans and evidence indexes, and output baseline datasets; the task acquisition and quality control module is used to arrange survey and monitoring tasks based on the baseline datasets and lock in the execution criteria, organize on-site collection and evidence recording, complete structured data entry and quality control judgment, and output valid indicator records; the benchmarking and hierarchical dispatching module is used to benchmark and classify the valid indicator records according to risks, generate rectification / upgrade disposal work orders and record management, forming a disposal entry point for closed-loop management and control.

[0015] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement a closed-loop management and control method for land consolidation survey and monitoring.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a closed-loop management and control method for land consolidation survey and monitoring.

[0017] The beneficial effects of this invention are as follows: The land consolidation survey and monitoring closed-loop management method provided by this invention collects and solidifies project data and spatial unit ledgers, simultaneously establishes indicator dictionaries, threshold versions, point locations and sampling plans, and evidence indexes, and performs consistency checks. This achieves versioned binding of object boundaries, indicator definitions, threshold sources, and sampling rules within the same baseline dataset, eliminating the incomparability and traceability issues caused by inconsistencies in coordinate benchmarks, unit divisions, and indicator calibers. By generating a task list based on the baseline dataset and locking the execution caliber, it organizes the verification of multi-source data such as on-site collection, sample transfer, measurement, and image interpretation. Based on the data recording and structured data entry, and through deterministic quality control judgments, a chain constraint is implemented to ensure that the data collection and processing process is verifiable and retestable, reducing rework and supplementary data collection caused by missing fields, caliber drift, and inconsistent evidence. By benchmarking and risk classification of effective indicator records, rectification and upgrade disposal work orders are generated according to the classification results and managed with a record, consistent linkage between monitoring and disposal actions is achieved. This is used to bind trigger indicators, clause sources, evidence numbers, and version information to work orders, supporting unified retesting and verification criteria and closing judgments, thereby improving the efficiency of problem location, the accountability of disposal, and the completeness of acceptance support materials. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 The first embodiment of the present invention provides an overall flowchart of a closed-loop management and control method for land consolidation investigation and monitoring. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0021] Example 1, referring to Figure 1 As an embodiment of the present invention, a closed-loop management and control method for land consolidation survey and monitoring is provided, comprising: S1: Collect land consolidation data, solidify land consolidation data and spatial unit ledgers, establish indicator dictionaries, threshold versions, point locations and sampling plans and evidence indexes, construct baseline datasets and complete consistency verification.

[0022] Furthermore, the process begins with compiling original data generated during the project initiation, design, construction, and acceptance phases of land consolidation projects, followed by structured data entry and element extraction. This original data includes at least: project boundary results (vector range or coordinate list), current status survey results (current land types, topography, soil and hydrological data), design documents and drawings (leveling, roads, canal systems, field / plot layout, irrigation and drainage facilities, etc.), construction organization and quality control records, and acceptance or supervision documents (national / industry / local standards, acceptance clauses, contract technical requirements). For paper or unstructured documents, a standardized format and metadata registration are implemented, recording at least the document name, source unit, issuance date, version number, scope of application, and key clause index. Key fields (such as design dimensions, material specifications, target indicators, and allowable deviations) are extracted from drawings and tables.

[0023] Subsequently, a unified coordinate benchmark and spatial unit division were established for spatial objects within the remediation area. Spatial layers such as project boundaries, plot boundaries, linear elements of roads and canals, and facility locations were converted to the same coordinate system, and the boundary topology was checked, including at least: boundary closure checks, overlap and gap checks, consistency checks of shared edges between adjacent plots, and spatial relationship checks between linear and area elements. For plot units requiring the smallest management granularity, they were divided according to parcel / plot / field or gridding rules, and the division rules (grid side length, canal-lined division rules, road-lined division rules, priority of ownership boundaries, etc.) were written into the configuration items to ensure that the same project uses a consistent spatial unit caliber in different batches of monitoring and remediation re-measurement.

[0024] After determining the spatial units, a unique identifier is generated for each unit, and a ledger record is established for the spatial unit, element attributes, and data index. The ledger fields must include at least: ownership / management entity information, area and perimeter, current land type, remediation type (e.g., leveling, soil improvement, reclamation and revegetation, canal system improvement, etc.), corresponding design drawing page number / component number, construction section information, planned and actual start and completion dates, acceptance milestones, and a list of associated linear elements (roads, ditches) and point facilities (gates, pumping stations, water measuring facilities, etc.) related to the unit. For each ledger record, an additional data source identifier and update timestamp are recorded, ensuring that any field can be traced back to the corresponding data or on-site records.

[0025] Simultaneously establish evidence indexes and traceability links in the ledger records. For evidence documents such as images, reports, and records, register the evidence type (photographs, aerial photographs, inspection reports, construction logs, supervision records, measurement results, etc.), collection / generation time, collector or issuing unit, and document summary information. Generate verification summaries (such as hash digests or signature verification information) for each document to facilitate consistency verification. Establish a one-to-many relationship between the evidence index and spatial units, and establish a reference path using evidence number, clause number, and field item, so that when calculating deviation, triggering rectification, or reviewing item cancellation, the specific evidence document and corresponding clause source can be located.

[0026] After completing the spatial object and data index archiving, configure the indicator dictionary and indicator collection criteria. The indicator dictionary registers for each indicator: indicator name, unit, collection method (laboratory testing, field instrument readings, GNSS / RTK measurement, remote sensing / UAV interpretation, GIS calculation), sampling or measurement method (e.g., sampling depth, number of sampling points, parallel sample setup, flight altitude and resolution, DEM source and raster resolution, interpretation model or classification rule version, etc.), data format and allowable range (for database verification), and evidence type requirements (e.g., sample number and test report number are required, measurement point trajectory and coordinate result files are required, orthophoto and interpretation result version number are required). When the same indicator has different criteria in different remediation types (e.g., the requirements for coverage or slope differ between reclaimed land and farmland remediation), the applicable conditions and criterion differences are registered separately.

[0027] Based on the indicator dictionary, the control baseline is further solidified, including the source and version of target values, allowable ranges, or permissible deviations. For indicators whose upper and lower limits can be directly given by standards or acceptance clauses, the lower and upper allowable limits are recorded, along with the clause number, document version number, and effective date. For indicators whose target values ​​and permissible deviations are given by design documents, the target value, permissible deviation, and derived allowable range are recorded, along with the drawing / specification number and page / entry index. For indicators whose tolerances are not clearly defined by clauses or designs, the statistical caliber of baseline surveys or similar historical samples (sample time window, sample size, stratification conditions, statistical methods) is recorded, along with constraints related to the achievable accuracy of the measurement method (instrument model, calibration number, test method standard number), ensuring that such thresholds have recalcible data basis. The above threshold entries are managed using version numbers paired with effective dates, forming a switchable but not interchangeable threshold version record.

[0028] Simultaneously, it provides directly referable point configurations and sampling plans for survey and monitoring tasks. For indicators requiring monitoring point deployment (such as soil samples, water samples, engineering section verification points, checkpoints, etc.), the point coordinates, point type, sampling frequency, sampling window, the relationship between the point and spatial unit, and the point substitution rules (such as the substitution radius and substitution conditions when the point is inaccessible) are recorded in the ledger. For remote sensing / UAV indicators, the data acquisition frequency, allowable cloud cover or image quality requirements, image product type (orthophoto / DSM / DEM), interpretation classification system, and model / rule version are recorded; for field measurement indicators, the measurement point deployment density, measurement line / section layout rules, measurement tools, and calibration requirements are recorded. All the above configurations are fixed in the form of parameter tables and associated with threshold versions and indicator caliber versions to ensure that the same configuration caliber can be reused for different batches of data collection and retesting verification.

[0029] Finally, the consistency and completeness of the ledger data are checked. The check includes at least the following: whether the association between spatial units and evidence indexes is complete; whether the indicator dictionary covers the indicators selected for this project; whether there are any missing or conflicting threshold entries (such as lower limits exceeding upper limits, inconsistent units, or missing source documents); whether there are any duplicates or out-of-bounds location configurations; and whether the version field meets the constraints of uniqueness and non-overlapping effective dates. After the check is passed, the spatial unit ledger, indicator dictionary, threshold version table, location and sampling plan table, and evidence index table are packaged into a baseline dataset, and the version combination for this batch (indicator caliber version, threshold version, location configuration version, and document index version) is recorded in the baseline dataset.

[0030] S2: Generate a list of survey and monitoring tasks based on the baseline dataset, complete the assignment and execution criteria locking, carry out on-site collection and evidence recording, structured data entry and quality control judgment according to the locked criteria, and output effective indicator records for benchmarking calculation.

[0031] Furthermore, based on the indicator collection criteria and location configuration requirements in the baseline dataset, this batch of survey and monitoring work is broken down into an executable task list, and the task execution criteria are solidified into a set of parameters that cannot be changed arbitrarily, so that data collection, warehousing quality control, risk calculation, rectification dispatch and retesting verification all use the same criteria. In practice, the process begins by reading the associated indicator sets and location configurations for each spatial unit within the remediation area. For each spatial unit, a corresponding survey and monitoring task entry is generated, specifying the indicator name, unit, collection method, sampling or measurement method, sampling frequency, allowable time window, required evidence type, and required evidence fields. If an indicator falls under laboratory testing, the task entry also includes the sample type, sampling depth, number of sampling points, parallel sample setup, sample preservation and transportation conditions, testing method standard number, and report format requirements. If an indicator falls under field measurement, the task entry includes the measurement point density, measurement line or cross-section layout rules, measurement tool type and calibration requirements, coordinate reference, and result file format requirements. If an indicator falls under remote sensing or UAV interpretation, the task entry includes the image acquisition frequency, image product type, image quality constraints, interpretation classification system and model or rule version, and interpretation result output format requirements.

[0032] Subsequently, the resource requirements for each task were analyzed, breaking them down into sub-tasks such as on-site sampling and measurement, sample transfer and laboratory testing, aerial flight and image processing, and interpretation and GIS calculation. For each sub-task, the required personnel qualifications, equipment list and quantities, estimated working hours, and available execution time were specified. Personnel qualifications included at least the following: sampling qualifications or testing authorization; UAV flight qualifications or aerial surveying qualifications; and qualifications for submitting engineering surveying and mapping results. The equipment list included at least: GNSS / RTK, portable water quality analyzer, sampling tools and sample containers, camera or recording equipment, UAV and payload equipment, ranging or measurement tools, etc. The equipment serial numbers and calibration validity periods for key equipment were recorded, allowing quality control to directly verify whether the equipment specified in the task was used and within its calibration validity period.

[0033] After generating task entries and resource requirements, task scheduling and assignment are performed. During scheduling, personnel shifts and available time slots, equipment borrowing and occupancy status, and traffic and work period constraints are retrieved. Subtasks are assigned to specific personnel or work teams, provided that qualification matching and equipment availability are met, and a work sequence and route plan are generated. The route plan includes at least the arrival order, estimated arrival time, work window, and route guidance information between points. When there are restrictions on inaccessible time slots, closed areas, or weather windows, these restrictions are written into the task constraint field and fixed in the assignment results. For tasks involving sample transfer, a sample transfer plan is further generated, including the time limit for sample storage after sampling, transportation method, recipient, handover form fields, and laboratory testing scheduling requirements, ensuring a complete and traceable link from sample collection to report issuance.

[0034] After the dispatch results are generated, the task execution criteria are solidified into a task parameter set and locked. This task parameter set includes at least: the version of the indicator criteria referenced in this batch, the threshold version, the location and sampling plan version, the evidence index rule version, and the corresponding indicator set, location coordinates, sampling method, sampling frequency, allowed time window, required equipment number and calibration requirements, evidence type, and required fields for that task item. For situations requiring substitution (e.g., location inaccessible, flight restricted, temporary equipment failure), substitution rules and triggering conditions are predefined in the task parameter set, such as substitution radius, substitution location generation method, equivalent conditions and calibration requirements for substitution equipment, and substitution image source conditions. Furthermore, once a substitution occurs, the reason for the substitution, the differences in parameters before and after the substitution, and the approval fields must be recorded to avoid disputes arising from inconsistent retesting and verification criteria.

[0035] During the task assignment and execution preparation phase, a field collection form and data submission template are generated for each task item, enabling field collection to be recorded according to a unified set of fields. The form fields must include at least: collection timestamp, collection location coordinates, personnel identification, equipment number, sample number, sampling depth and volume, parallel sample marking, on-site environmental records, and metadata fields related to evidence documents (e.g., photo or image file name, shooting time, watermark information, test report number, etc.). For remote sensing and UAV tasks, the template must include at least the flight number, flight altitude, ground resolution, image product version number, processing software and parameter version, and interpretation rule or model version number. These forms and templates are bound to the task parameter set upon task assignment, forming a data collection entry point for task-based submission, caliber-based verification, and version-based archiving.

[0036] During task execution, the task status is recorded and constrained in a procedural manner. The task status includes at least the following states: not started, in progress, pending supplementary data collection, pending verification, and completed. When the status changes, the change time, the reason for the change, and related evidence are recorded. After the field-collected data is submitted, the system performs field integrity checks and format validation on the submitted data, and verifies whether the submitted data meets the location, time window, equipment number, and calibration requirements in the task parameter set. If it does not meet the requirements, the task is set to pending supplementary data collection or pending verification, and a list of missing or inconsistent items is returned.

[0037] When multiple subtasks are executed in parallel within the same spatial unit, the outputs of the subtasks are linked. For example, the test report number and sample number of the laboratory testing subtask must match the sample number of the field sampling subtask; the orthophoto and DEM product version number output by the UAV image processing subtask must match the flight record; and the input DEM version and interpretation product version of the GIS calculation subtask must match the output of the image processing subtask.

[0038] It should be noted that after the task list is issued, work assignments are arranged, and task execution criteria are finalized, on-site data collection and submission are organized according to the task items. The raw data and evidence documents generated during the collection process are simultaneously recorded, structured, and quality-verified. In practice, on-site personnel access the collection form corresponding to each task item and perform collection operations according to the locked-in indicators, collection points and sampling plans, sampling methods, sampling frequencies, and allowable time windows in the task parameter set. For indicators requiring laboratory testing, such as soil and water samples, a unique sample number is generated for each sample during on-site collection. The form records the sampling timestamp, sampling coordinates, sampling depth, sampling volume or mass, sample type, parallel sample identification, on-site environmental records, and the specifications of the sampling equipment and containers used. Simultaneously, handover information is registered according to the sample transfer plan, forming a transfer record including handover time, handover personnel, recipient, transportation method, and storage conditions, ensuring full traceability of the sample from collection and transportation to laboratory reception. For engineering measurement indicators, measurements should be taken on-site according to the measurement point layout density and survey line / section arrangement rules specified in the task parameter set. Measurement point coordinates, elevation or geometric dimensions, measurement tool type, equipment number, and calibration validity information should be recorded. Measurement records or result files should be uploaded simultaneously to ensure consistency between measurement results and measurement point layout. For remote sensing and UAV interpretation indicators, during flight operations and image acquisition, the number of flights, flight altitude, ground resolution, image product type, image quality constraint execution status, processing software, and parameter version should be recorded in the task form. After image processing, orthophotos, DSM / DEM, and other product files and their version numbers should be submitted. Subsequently, interpretation results and GIS calculation results should be submitted, and the interpretation classification system and model or rule version number used should be recorded to ensure that the interpretation chain can be traced back by version.

[0039] During the data collection process, evidence preservation measures are implemented for images, reports, and deliverables. For evidence documents such as on-site photos, videos, or aerial images, watermark information is added during generation or uploading, and metadata such as timestamps and collection coordinates are bound. For documents such as laboratory test reports, supervision records, and construction logs, the issuing unit, date of issuance, report number, and associated sample number or associated measurement point number are recorded.

[0040] Once the field data is submitted to the system, it first undergoes structured data entry processing. Form fields, metadata from the output file, and evidence index information are written into the database according to predefined field sets, forming entry entries based on indicator records. Each indicator record must include at least: indicator name, unit, measured value, collection timestamp, collection coordinates, collection personnel or work group, equipment number, sample number or output file number, location information, task entry information, and reference fields related to the evidence file. For indicators with duplicate measurements or parallel samples, the duplicate measurement sequence number, parallel sample identifier, and corresponding measured value are written together during data entry. For indicators that need to be calculated from the output file (e.g., slope calculated from DEM, coverage obtained from image interpretation), the input output file version number and calculation parameter version number are written simultaneously during data entry to ensure consistency in calculation methods within the same batch.

[0041] After data is entered into the database, a quality check is performed. This check primarily uses deterministic rules and generates a traceable quality control log. The quality check includes at least the following: field completeness check, data type and unit consistency check, allowable range and format check, task caliber consistency check, and evidence consistency check. Field completeness check checks whether key fields such as sample number, report number, equipment number, coordinates, and timestamp are missing. Data type and unit consistency check checks whether the units of measured values ​​are consistent with the indicator dictionary and whether the numerical types conform to the specified format. Allowable range and format check checks whether the values ​​fall within a reasonable physical range or the basic range required by the specifications, filtering out obvious input errors. Task caliber consistency check checks whether the collected coordinates match the sampling location and sampling plan, whether the collection time falls within the allowable time window, and whether the equipment number used is an allowed device in the task parameter set and is within its calibration validity period. Evidence consistency check checks whether the verification summary of the evidence file is consistent with the summary generated during data entry, whether the correspondence between the report number and sample number is consistent, and whether the version number of the output file is consistent with the task form record.

[0042] When the quality control conclusion is deemed unsuccessful, the indicator record is set to a state where it cannot participate in benchmarking calculations, and a supplementary sampling or review pending task is generated. The supplementary sampling or review pending task includes the reason for failure, the fields that need to be supplemented, the suggested supplementary sampling method, and the corresponding task items and location information, enabling on-site personnel to complete the supplementary sampling according to the same criteria and eliminate defects in a closed loop. For indicator records exhibiting critical quality control failures such as location deviation, time window deviation, expired equipment calibration, inconsistent evidence summaries, or sample-report mismatch, it is required that data be re-collected or that compliant evidence documents be reissued. Furthermore, the supplementary sampling process must still use the indicator collection criteria, locations, and sampling plan locked by the original task parameter set to avoid data incomparability caused by changes in criteria.

[0043] When the quality control conclusion is deemed passed, the indicator is recorded as valid data and entered into the benchmarking calculation and risk classification process. The valid data, its evidence index, threshold source information, and task parameter set version reference relationship are also solidified.

[0044] A summary record is generated for all data and quality control results of this batch of tasks. The summary record includes at least the following: the completion rate of data collection for various indicators, the pass rate of quality control, the distribution of reasons for failure, the number of times data was collected / re-examined, the situation of missing key evidence, and the version reference relationship with the baseline dataset composed of the spatial unit ledger, indicator dictionary, threshold version table, point and sampling plan table, and evidence index table.

[0045] S3: Record effective indicators for benchmarking and risk classification, generate rectification and upgrade disposal work orders based on the classification results, and manage them with a record.

[0046] Furthermore, after completing the collection, storage, and quality verification of the preliminary survey and monitoring data, the land parcel unit is used as the smallest management unit. Based on a pre-established indicator library, a unified benchmarking calculation and risk classification output are performed on the set of indicators involved in this batch of surveys and monitoring for that land parcel unit. The indicator library is not subjectively set, but rather it is a structured management of indicators commonly used in land consolidation supervision and acceptance that can form an original chain of evidence. Each indicator is bound to a specific collection method, evidence type, unit of measurement, and sampling frequency, thereby ensuring that every input quantity in the calculation can be verified by on-site records, test reports, or interpretation products. The indicators include at least the following: engineering quality indicators (e.g., field flatness, slope or elevation difference, dimensional deviation of ditch or road sections, etc., whose measured values ​​can be obtained from GNSS / RTK measurement point results, ruler / laser ranging records or image measurement results and construction records); soil environment indicators (e.g., pH, organic matter, salinity, heavy metal content, etc., whose measured values ​​can be obtained from soil sample collection records, sample numbers and laboratory test reports); water environment and irrigation and drainage indicators (e.g., irrigation and drainage connectivity or accessibility, water turbidity / conductivity, etc., whose measured values ​​can be obtained from canal system data and on-site verification records, portable instrument readings or sampling test reports); topographic and geomorphological indicators (e.g., slope, erosion sensitivity level, etc., whose measured values ​​can be obtained from DEM products or remote sensing image interpretation results and GIS calculations); and land cover / utilization indicators (e.g., vegetation cover or NDVI, bare land rate / greening rate, etc., whose measured values ​​can be obtained from satellite or UAV image interpretation results and combined with sampling verification records). To ensure that the definition of indicators remains consistent during the rectification and review phase, the indicator database also records the standard clause number, acceptance clause number, design document number, or statistical batch number associated with the indicator, and archives them along with the calculation results as threshold version information.

[0047] Furthermore, the target value and allowable deviation of each indicator are determined using a design-priority, baseline statistical fallback rule, and the source is solidified in an auditable manner: when the project acceptance clauses clearly specify the allowable range for the indicator, this allowable range is directly used as the benchmark boundary; when the clauses do not directly specify the allowable range but the target value and allowable deviation are given in the design documents, technical specifications, or project indicator table, the allowable range is derived from the target value and allowable deviation; when neither the clauses nor the design specify the allowable deviation, the verifiable allowable range is determined by the statistical distribution of the project baseline survey or similar historical acceptance samples, and this allowable range is required to meet the constraint of not being less than the achievable accuracy of the measurement / testing method used, to avoid situations where the allowable deviation is less than the instrument error or there is no basis for it. Simultaneously, the sample range, sample size, statistical caliber, instrument model, and calibration information are solidified and archived for auditing purposes. Based on the above allowable ranges and measured values ​​of the indicators, a normalized deviation is calculated for each indicator to eliminate the incomparability caused by different dimensions, and the degree of deviation is uniformly mapped to a dimensionless deviation score, expressed as:

[0048] in, Indicates the index sequence number; Indicates the first The measured values ​​of each indicator are derived from on-site measurement records, laboratory test reports, remote sensing / UAV image interpretation results, or GIS calculation results; Indicates the first The lower limit of each indicator is determined following the rule of prioritizing standards / acceptance clauses, followed by design documents, and then baseline statistics as a fallback: if the standard or acceptance clauses directly provide an allowable range, then... Take the lower limit given in the clause; if the design documents give the target value and allowable deviation, then If neither the terms nor the design are clearly defined, then The version is determined and solidified by baseline statistics or historical acceptance sample distribution, while meeting the constraint of not less than the accuracy achievable by the measurement method; Indicates the first The deviation of an indicator represents the relative degree to which the measured value of that indicator deviates from the boundary of the allowable range; when the measured value falls within the allowable range... When exceeding the allowable range Proportional to the excess range, and through Perform normalization; Indicates the first The allowable upper limit for each indicator, and the rules for determining it are... Symmetry: The clause gives an upper limit; or by Derived; or determined and solidified from baseline statistics / historical distribution; Indicates the first The boundary values ​​of each indicator are used to represent the benchmark boundary to be selected when the measured value exceeds the limit: if ,but ;like ,but .

[0049] It should be noted that the quality control conclusion field, output from the preceding quality control process and entered into the database, is used as the objective criterion for whether or not a task is allowed to participate in the calculation. The quality control conclusion field is denoted as... Its value is either 0 or 1. When the data record corresponding to this indicator meets the preset quality control rules (e.g., the sampling point meets the specifications, the sampling time is within the allowable time window, repeated measurements meet stability requirements, the instrument calibration is within the validity period, the evidence document is consistent with the record and the fields are complete, etc.), it is judged as passing and assigned a value of 1; when any key quality control condition is not met, it is judged as failing and assigned a value of 0. For The indicator data is specified to be excluded from risk score calculation and to be subject to re-collection or review tasks triggered by the process.

[0050] Simultaneously, the weights of each indicator in the comprehensive calculation are determined. To avoid the weights being perceived as subjective judgments, they are defined as quantified results of clause importance and historical defect contribution: clause importance is derived from the acceptance clauses' classification of indicators (e.g., veto items, key items, general items, etc.), and converted into scores according to fixed mapping rules; historical defect contribution is derived from the statistical count of the number of times the indicator triggered rectification in the historical project database or the project's phased work order database. Considering the reality of insufficient sample size in the early stages of the project, a smoothing process is used to ensure that the weights are always calculable, expressed as:

[0051] in, Indicates the first Weighting coefficients for each indicator; Indicates the first The importance score of each indicator is obtained by converting the category of the indicator in the acceptance clause (e.g., veto item / critical item / general item) according to a fixed mapping rule. The mapping rule is fixed in the configuration table. Indicates the total number of indicators; Indicates the index number used for summation; For the first The historical defect contribution count of each indicator represents the number of times that indicator has appeared as a trigger for rectification in the historical project database or the phased work order database of this project. This count is obtained by aggregating and statistically analyzing the work order table according to the associated indicator / clause number / issue type. The statistical window and sample range can be recorded as statistical batch information. Represented as the first The historical defect contribution count for each indicator.

[0052] After obtaining the deviation, quality control pass criteria, and weights, the comprehensive risk score of the land parcel unit is output. and through To achieve the deterministic constraint that quality control failures are not included in the calculation, the comprehensive risk score is expressed as follows:

[0053] in, This represents the overall risk score of the land parcel unit; Indicates the first The quality control of each indicator is achieved by marking it with a value of 0 or 1, based on the deterministic rule judgment results from the preceding quality control process: This indicates that the data record corresponding to this indicator meets the preset quality control rules and is allowed to participate in risk calculation; : This indicates that the quality control was not passed. This indicator is not included in risk calculation and will not trigger re-collection / re-verification. The basis for this marking is derived from collectable fields (such as site records, timestamps, repeated measurement records, instrument calibration validity, evidence consistency and field completeness, etc.), and the quality control log can be archived with the results.

[0054] After completing the comprehensive risk score After calculation, to ensure consistency, traceability, and verifiability in the criteria for triggering rectification and escalating actions, the process of determining the tiered thresholds is incorporated into the same closed-loop management process. The sample range, statistical caliber, and algorithm version upon which the threshold calculation relies are also archived as threshold versions. In specific implementation, a threshold calculation sample library is first established. This library consists of existing projects or land parcels for which acceptance conclusions have been reached. Each sample must contain at least: the comprehensive risk score of that sample under the corresponding calculation caliber. The sample also includes the corresponding acceptance label (qualified / unqualified / requires rectification but not yet closed / unqualified by regulatory inspection, etc.). The acceptance label originates from the acceptance conclusion document or regulatory inspection conclusion, while the risk score comes from the calculation results of the same risk calculation caliber. When the weight version, deviation calculation version, or threshold caliber corresponding to the risk score of a historical sample is inconsistent with the current operating version, the original monitoring data and clause thresholds of the historical sample are recalculated according to the current version to generate a risk score consistent with the current version, thus avoiding threshold distortion due to version differences. To ensure that the statistical threshold can reflect the risk distribution of similar plots, samples can be stratified and screened according to remediation type, geomorphological zoning, or soil type when constructing the sample library. The stratification conditions are written into the threshold version record as applicable conditions for the threshold version, giving the threshold a clear scope of application.

[0055] After the threshold sample library is constructed, the samples are divided into qualified and unqualified sample sets according to the acceptance label: the qualified sample set is used to determine the rectification threshold, and the unqualified sample set is used to determine the upgrade threshold. To ensure the recalculation of the threshold calculation, a deterministic quantile algorithm of sorting, indexing, and value selection is used to calculate the threshold: the risk scores in the qualified sample set are sorted in ascending order, and the number of qualified samples is denoted as... Select the rectification quantile coefficient (For example, 0.95), the sorted number Each risk score serves as a rectification threshold. The selected quantile coefficient and index calculation method are then fixed and recorded. The technical meaning of this rectification threshold is that the risk scores of the vast majority of historically qualified samples do not exceed this threshold. Therefore, when the risk score of a new monitored object exceeds this threshold, a rectification order can be triggered while ensuring a low false alarm rate. Correspondingly, the risk scores in the set of unqualified samples are sorted in ascending order, and the number of unqualified samples is recorded as... Choose to upgrade quantile coefficient (e.g., 0.75), the sorted number Each risk score serves as the upgrade threshold. The technical meaning of this escalation threshold is as follows: among historical non-compliant samples, those with risk scores at a medium-to-high level can more consistently indicate significant defects or high-risk states. Therefore, when the risk score of a new monitored object exceeds this threshold, escalation measures are triggered to reduce underreporting. If there are clearly defined red-line conditions on the project management side, either by the competent authority or in the contract terms (e.g., veto triggers, regulatory red-line values, etc.), these red-line conditions will be treated as hard rules for escalation measures and written into the threshold version record, giving the escalation measures conditions an external authoritative source. In this case, It can coexist with the red line rule, meaning that any fulfillment of the red line rule will trigger an upgrade.

[0056] Considering the possibility of insufficient sample size at the initial stage of a project or when a new type of application is first used, a threshold activation and switching mechanism is set up to ensure the authenticity and reliability of the threshold source: when the qualified sample size Less than the preset minimum sample size When the number of non-compliant samples is 30 or 50, statistical rectification thresholds are not used. Instead, clause-based transitional rules are employed to trigger rectification. This means that rectification is triggered directly when any key indicator exceeds the limit and quality control passes. Less than the preset minimum sample size At this time, statistical upgrade thresholds are not used; instead, hard rules for veto items are employed to trigger upgrade actions. Specifically, if any veto item indicator exceeds its limit and quality control passes, a work stoppage, encrypted sampling inspection, or expert review is triggered. As the project progresses, the system continuously accumulates samples, and when both conditions are met... and When the conditions for activation are met, the system will automatically switch to the statistical threshold caliber and record the sample size, sample time window, old and new threshold parameters, calculation batch number and algorithm version number at the time of the switch.

[0057] After the threshold calculation is completed, a threshold version record is generated and archived. The threshold version record includes at least: threshold version number, applicable stratification conditions (such as remediation type / geomorphic zone / soil type), sample time window, qualified sample size and unqualified sample size, and quantile coefficient. and The quantile algorithm type (sorting-indexing-value retrieval or interpolation), index calculation rules, whether the red line rule is enabled, the weight version and deviation calculation version corresponding to the risk score used for calculation, and the generation time and generation batch number.

[0058] Based on the solidified threshold version, a hierarchical status is output for each land parcel unit, and a closed-loop response is initiated: when the risk score... Output the pass or watch status at any time; when The system outputs the rectification status in real time and generates a rectification work order. The rectification work order includes a list of indicators causing risks, details of the deviation of each indicator, corresponding clause numbers, and sampling and testing report numbers or image evidence numbers. When a red-line hard rule is triggered, an escalation status will be output, triggering actions such as work stoppage, encrypted spot checks, or expert review. Simultaneously, the triggering reason, triggering clause, and evidence chain will be solidified. After rectification, during the retesting and review phase, the same indicator library, weight version, and threshold version will be used to recalculate the risk score for the retest data. After completing the indicator benchmarking and comprehensive risk score After calculating and outputting the status of "pass," "rectification," or "upgrade" based on the fixed threshold, a rectification work order is automatically generated for the land parcels in the rectification or upgrade status. The work order is then bound to the triggering indicator details, evidence index, clause source, threshold version, and weight version information, forming an executable and verifiable closed-loop disposal entry point. In specific implementation, a rectification work order is first generated for the land parcels in the rectification status. The rectification work order includes the corresponding indicator list and its deviation details for that land parcel, recording the measured value of each indicator, the allowable interval boundary, the direction of the boundary crossing, and the magnitude of the boundary crossing. It also references the corresponding evidence document number and evidence verification summary, and includes the standard or acceptance clause number, design document number, or statistical batch number that triggered the allowable interval configuration to ensure the traceability of the rectification basis. For land parcels in an upgraded status, in addition to generating rectification work orders, an upgraded status record is also generated. The record contains the triggering condition type that triggered the upgraded status (e.g., the comprehensive risk score reaches or exceeds the upgrade threshold, or the red line hard rule is triggered). The risk score, threshold version, weight version at the time of triggering, as well as the clause number and evidence index on which the trigger is based, are also fixed to ensure that the upgraded status has a clear and tamper-proof triggering basis.

[0059] After a work order is generated, the rectification content is broken down into actionable steps. The problem list in the work order is divided into actionable rectification items according to indicator categories and construction procedures. Each rectification item includes rectification requirements and acceptance criteria. Rectification requirements must include at least: a description of the rectification object, the rectification location range (expressed through spatial unit boundaries or point ranges), the type of rectification measures (e.g., backfilling and leveling, ditch cross-section repair, road overlay, irrigation and drainage connection repair, soil improvement, revegetation, etc.), the required rectification materials and equipment, and the type of process evidence to be provided after rectification. Acceptance criteria must include at least: the set of indicators that must be re-collected or reviewed after rectification, the sampling or measurement methods and frequencies, the allowable time window, and the required evidence fields, ensuring that the review after rectification is comparable to the initial monitoring in terms of criteria. For rectification items requiring laboratory testing verification, the work order must clearly specify the sample type, sampling depth, number of sampling points, and parallel sample setup, and include the sample number generation rules and report format requirements. For rectification items requiring on-site measurement verification, the work order must clearly specify the measurement point layout rules, cross-sectional location, and measurement tool type. For rectification items requiring remote sensing or UAV verification, the work order must clearly specify the image product type, interpretation classification system, and model or rule version to avoid disputes caused by changing the scope during the verification stage.

[0060] Regarding rectification work orders and responsibility allocation, rectification work orders are assigned to specific responsible units and individuals based on the project organizational structure, bid section division, and contractor responsibility boundaries, with rectification deadlines, stage milestones, and overdue rules simultaneously included. Rectification deadlines can be configured according to problem severity or escalation requirements, with the latest completion time, milestone acceptance time, and overdue escalation strategies fixed in the work order. Contact information, work period constraints, and site access permits for responsible units are also included. To ensure traceability of the rectification process, continuous documentation is required: responsible units must submit process evidence at each key construction milestone, including pre-construction status photos or videos, construction process photos or videos, material arrival slips or inspection slips, equipment shift records, construction logs, or supervisor signature records, etc. The issuing unit, issuance time, and evidence verification summary are registered for each evidence document, and an evidence index is linked to the work order number. For rectification items involving long-term processes such as soil improvement and revegetation, stage-specific evidence can be submitted according to stage milestone requirements, recording construction parameters such as amendment dosage, tillage depth, irrigation frequency, and seedling specifications and density.

[0061] When a rectification action is upgraded to an escalation status, a more stringent handling process is triggered simultaneously with the dispatch of the work order. The escalation process includes at least: setting stop-work or restricted construction markers on the relevant work areas; increasing the sampling rate or review frequency for the site unit and its adjacent areas; setting mandatory review requirements for key indicators; and potentially triggering expert review or regulatory reporting processes based on project management requirements. During the escalation process, process documentation must still be maintained according to the work order mechanism, recording the change time, reason for change, approval fields, and related evidence for each status change. For situations triggering red-line hard rules, the trigger clause number and evidence number must be referenced in the escalation record, and the risk score and threshold version information at the time of triggering must be solidified to ensure the legality, compliance, and accountability of the escalation.

[0062] During the rectification process, the execution status of work orders is managed systematically. Work order statuses include at least: pending, in progress, pending review, closed, overdue, and upgraded. The responsible person, time, and basis for each status transition are recorded. If adjustments to rectification measures, changes to the scope of rectification, or changes to the review criteria are required during the rectification process, they must be registered through a change process. The change record must specify the reason for the change, the content of the change, the differences in criteria before and after the change, the approval fields, and the effective date. Furthermore, the changed records must maintain consistent criterion references with the indicator dictionary, threshold version table, and sampling plan table. For overdue work orders, reminders and upgrades are automatically triggered according to the overdue rules fixed in the work order. Upgrades can increase the review frequency, increase the sampling ratio, or raise the handling level. The triggering conditions and time for overdue upgrades are fixedly recorded.

[0063] Once the responsible unit submits a rectification completion application, the work order status is set to pending review, and a pending review list is output. The pending review list includes the set of indicators that must be re-collected or reviewed, the review criteria and evidence requirements, as well as the threshold version and weight version information that should be used for the review.

[0064] After completing the rectification assignment, process documentation, and escalation handling, the retesting and review process is initiated for rectification work orders that have entered the pending review status. Upon successful review, the work orders are closed and archived. All data from the retesting, review, and closure processes are uniformly summarized to form an auditable, traceable, and reusable project closed-loop archive and performance evaluation results. In specific implementation, the mandatory review indicator set, review points or scope, sampling or measurement methods, review frequency, allowed time windows, and evidence field requirements for each work order are first retrieved from the pending review list. It is clarified that the review must adhere to the versions defined in the fixed indicator dictionary, threshold version table, point and sampling plan table, and evidence index table to ensure consistency between the retesting / review and the initial monitoring in terms of collection methods, point layout, evidence types, and threshold sources. For work orders that have alternative rule triggering conditions, before reviewing, read the change records and approval fields in the rectification process to confirm that the alternative location, alternative equipment or alternative image source meets the established triggering conditions, and include the differences before and after the substitution and the approval information in the review basis to avoid disputes over the cancellation of items due to inconsistent review standards.

[0065] After the retesting and verification task is issued, on-site data collection and submission will be organized according to the same procedures as the initial monitoring. For laboratory testing verification indicators such as soil and water samples, the sampling depth, number of sampling points, parallel sample setup, and sample preservation and transportation conditions specified in the original caliber will continue to be used during the retesting. Sample numbers will be regenerated, and key fields such as sampling timestamp, sampling coordinates, sampling depth, and sampling volume or mass will be recorded. At the same time, sample handover and transfer records will be created to ensure that the retested samples correspond one-to-one with the test reports. For engineering measurement verification indicators, measurements will be taken according to the measurement point layout density, measurement line or cross-section location specified in the original caliber during the retesting. Measurement point coordinates, elevation or geometric dimensions, measurement tool type, equipment number, and calibration validity information will be recorded to ensure that the retested data is comparable to the initial measurement data. For remote sensing or UAV verification indicators, image products will be acquired and processed according to the image acquisition frequency, image product type, interpretation classification system, and model or rule version specified in the original caliber during the retesting. Flight sorties or image sources, product version numbers, and processing parameter versions will be recorded to ensure that the interpretation and calculation results are traceable. During the retesting process, photos, videos, aerial photographs, test reports, and result documents are all registered with metadata and generated with verification summaries in accordance with the requirements for evidence retention. Evidence indexes are linked with work order numbers so that the retesting results can cite a clear chain of evidence.

[0066] After the retest data is submitted and stored, a quality verification process consistent with the initial test is executed. This quality verification includes at least field integrity verification, data type and unit consistency verification, task caliber consistency verification, and evidence consistency verification. A quality control conclusion field and reason code are written to each retest indicator record. When a retest record has a key quality control failure (e.g., location deviation, sampling time exceeding the allowable time window, expired equipment calibration, sample and report mismatch, inconsistent evidence verification summary, or missing key fields), the retest record is deemed unusable for exit criteria, and a supplementary sampling or review pending task is automatically generated. The record must be supplemented according to a unified standard before proceeding to exit criteria. For retest records that pass quality control, they are associated with the corresponding rectification items, rectification process evidence, initial test record, threshold source document number, and version information in the work order to form a review data package usable for exit criteria.

[0067] During the execution phase of the sales evaluation criteria, a comparison relationship is established for each work order, and a consistent evaluation is conducted according to the acceptance criteria fixed in the work order. The evaluation first verifies whether all mandatory retesting indicators required by the work order have been retested and passed quality control. Secondly, it verifies whether the retesting indicator records meet the allowable range or allowable deviation requirements, and whether the evidence chain is complete, including whether the retested sample number corresponds to the test report number, whether the retested measurement results include the specified measurement points or sections, whether the retested image product version and the interpretation result version meet the original criterion requirements, and whether the rectification process evidence covers key nodes. For situations requiring continuous and stable maintenance (e.g., requiring two consecutive retests to meet the requirements or requiring stability over a certain time span), multiple retests are performed according to the retesting frequency and evaluation rules fixed in the work order, and the time, evidence number, and evaluation conclusion of each retest are recorded in the sales evaluation record. If all conditions for item cancellation are met, the work order status will be updated to "Item Cancelled" and an item cancellation record will be generated. If there are any unmet conditions, the work order will be returned to the rectification stage or upgraded to a handling stage, and the reason for failure, the corresponding indicators and evidence cited, and the suggested supplementary measures will be stated in the return record.

[0068] After the completion of the item closing process, a closed-loop archiving and performance audit summary are performed. During archiving, all project data is solidified according to a unified directory structure, including spatial unit ledgers, indicator dictionaries, threshold version tables, point and sampling plan tables, evidence index tables, task lists and work assignment records, data collection forms and warehousing records, quality control logs, risk classification output records, rectification work orders and escalation handling records, rectification process evidence, retesting and verification data packages, and item closing records. Verification summaries are generated for key documents for consistency verification. To ensure traceability, the archived records also solidify the version information and scope of effect used in each stage, and key change records and approval fields are included in the archive, ensuring that any conclusion can be traced back to the standards and evidence used at the time.

[0069] Regarding performance audit summaries, quantifiable statistical results are generated for the closed-loop process and recorded in the audit summary table. The statistics include at least: on-time completion rate, first-pass rate, number of supplementary sampling or reviews, distribution of reasons for quality control failures, distribution of the number and type of rectification work orders, rectification cycle, number of overdue and upgrade times, number of retests, time spent on item completion, sampling hit rate, and work order completion quality indicators categorized by responsible unit. For costs and resource consumption, labor hours, testing fees, flight and image processing fees, and material and shift records are categorized by work order to form a cost list. Budget deviations and their corresponding cause classifications (e.g., rework, increased sampling, equipment failure, weather window impact) are recorded in the audit summary, enabling project management to formulate control strategies based on real process data. The above performance and audit results are archived along with the closed-loop documentation and used as an effective data source for threshold sample libraries and weighted statistics in similar projects, thus achieving continuous iteration and reusability without changing the established criteria.

[0070] Example 2, an embodiment of the present invention, provides a closed-loop management and control method for land consolidation survey and monitoring. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment.

[0071] First, three typical plots of land with independent structures and similar construction procedures from a high-standard farmland improvement project in a certain county were selected as experimental subjects, denoted as Plot A, Plot B, and Plot C, respectively. Each plot included field leveling, repair of field roads and irrigation systems, partial soil improvement, and greening, and construction and acceptance preparation were completed within the same construction season. The experiment was divided into two groups: the control group adopted common practices (data was stored in a decentralized manner, definitions relied on manual interpretation, data collection mainly relied on form submissions, and rectification mainly relied on textual descriptions); the experimental group adopted the closed-loop management method of this invention. First, baseline archiving was implemented in the experimental group: boundary results, current status surveys, design drawings, construction records, acceptance criteria, and other data underwent unified coordinate benchmark transformation and boundary topology verification. Spatial units were divided according to field / plot rules, and ledger fields were established. Simultaneously, an indicator dictionary was established, registering the collection methods, units, and evidence types for each indicator in the categories of engineering quality, soil environment, water environment and irrigation / drainage, topography, and land cover. The allowable ranges, target values, and permissible deviations in the standards / acceptance clauses or design documents were compiled into threshold versions, and the sampling locations and sampling plans (including sampling windows, point coordinates, and substitution rules) were solidified. Consistency checks were performed on the baseline dataset to eliminate issues such as boundary conflicts, missing thresholds, and point out-of-bounds errors. Subsequently, a survey and monitoring task list was generated based on the baseline dataset, and tasks were assigned. The indicator caliber versions, threshold versions, point and sampling plan versions, and evidence index rules were locked as task parameters. Tasks were organized and executed according to sub-tasks such as on-site measurement, sample collection and laboratory testing, aerial surveys and image processing, and interpretation and GIS calculations. During the on-site collection phase, samples were numbered according to rules, and handover and transfer records were created. Measurement results recorded the coordinates of measurement points and equipment calibration information, while image results recorded flight sorties and product versions. Photos, aerial photographs, test reports, and result documents all generated verification summaries and entered into the evidence index. After data feedback, structured data was stored and quality control was performed. Records failing quality control triggered supplementary collection / review pending processing, while records passing quality control formed valid indicator records and entered into benchmarking calculations. Subsequently, benchmarking and risk classification were performed on valid indicator records, and rectification work orders or upgraded disposal work orders were automatically generated based on the classification results, binding the trigger indicator details, evidence number, clause source, and version information. After rectification, a retest and review with the same caliber was organized. If the review passed, the item was closed and archived, forming a closed-loop file. The control group completed the same batch of monitoring and disposal according to the standard process, and the quantitative differences between the two groups in terms of consistency, quality control, timeliness, evidence completeness, and review closure were statistically analyzed.

[0072] Table 1 Experimental Data

[0073] The parallel data from the control and experimental groups show that, in terms of baseline consistency and caliber convergence, the number of spatial unit boundary consistency issues in the control group (plots A / B / C) was 7 / 6 / 8, respectively, while in the experimental group it decreased to 1 / 1 / 2. The number of indicator caliber conflicts decreased from 5 / 4 / 6 to 0 / 0 / 1, indicating that baseline archiving based on a unified coordinate benchmark, spatial unit ledger, and indicator dictionary can solidify previously scattered interpretations from drawings, reports, and personnel experience into reusable data calibers. Furthermore, consistency verification eliminates boundary topological conflicts and missing thresholds before monitoring begins. The threshold traceability rate was only 50%-60% in the control group, while it reached 100% in the experimental group. This reflects the solidification mechanism of threshold versioning and source indexing (standards / clauses / design documents or statistical batches), providing auditable basis for benchmarking and reducing the risk of review disputes due to unclear threshold basis. Secondly, regarding the quality of data collection and the output of effective data, the experimental group achieved a first-pass rate of 90%-94% in data quality control, significantly higher than the control group's 68%-72%. The number of re-collection / rework attempts decreased from 4-5 times to 1 time, and the number of deviations in task execution criteria was 0 in the experimental group. This indicates that the locking of execution criteria after task list generation, the constraint of evidence fields, and the structured data entry quality control judgment can suppress data inconsistencies caused by arbitrary changes in site locations, time windows, or sampling methods, making the effective indicator records for benchmarking calculations more stable and verifiable. In contrast, the control group, lacking criterion locking and evidence index constraints, often experienced problems such as unstable correlation between sample numbers and reports, inconsistent image versions, and missing fields, leading to frequent quality control failures and re-collection, directly extending the project cycle and increasing management costs. Secondly, regarding the efficiency of risk classification, linkage, and closed-loop management, the time delay from risk classification to work order generation in the experimental group was only 3-4 hours, while it was 36-48 hours in the control group. This reflects that the automatic linkage between benchmarking and work order generation significantly improves the problem response speed. The work order evidence completeness rate was 97%-99% in the experimental group, while it was only 58%-62% in the control group. This indicates that the evidence indexing and verification summary mechanism makes the rectification basis and process traceability more complete, thereby supporting the consistent verification in the retesting and review stages. Furthermore, the first-time clearance rate of retesting and review increased from 50%-55% in the control group to 80%-86% in the experimental group. This indicates that the solidification of mandatory review indicators, review criteria, and evidence requirements in the rectification work orders makes the review judgments after rectification more consistent, reducing repeated rectification and repeated retesting.The overall results show that the total monitoring and handling cycle has been shortened from 29-32 days to 17-19 days, the recurrence rate of problems found in regulatory spot checks has decreased from 24%-28% to 5%-7%, and the manual verification time has been reduced from 22-25 hours to 9-11 hours. This indicates that the present invention can improve the certainty and auditability of the discovery-handling-verification link while ensuring data traceability and consistency of standards. It also makes up for the inherent shortcomings of existing technologies in terms of inconsistent standards for multi-source data, weak quality control links, and inconsistent rectification and review standards, thus demonstrating the innovative advantages of the closed-loop management method in engineering implementation.

[0074] Example 3, an embodiment of the present invention, provides a closed-loop management and control system for land consolidation survey and monitoring, including a baseline filing module, a task acquisition and quality control module, and a benchmarking and hierarchical task assignment module.

[0075] The baseline archiving module is used to collect land consolidation data and solidify spatial unit ledgers, indicator dictionaries, threshold versions, sampling plans and evidence indexes, and output baseline datasets. The task collection and quality control module is used to arrange survey and monitoring tasks based on the baseline dataset and lock in the execution criteria, organize on-site collection and evidence recording, complete structured data entry and quality control judgment, and output valid indicator records. The benchmarking and grading dispatching module is used to benchmark and grade the valid indicator records, generate rectification / upgrade disposal work orders and record management, forming a closed-loop management entry point.

[0076] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0078] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0079] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0080] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A closed-loop management and control method for land consolidation survey and monitoring, characterized in that, include: Collect land consolidation data, solidify land consolidation data and spatial unit ledgers, establish indicator dictionaries, threshold versions, point locations and sampling plans and evidence indexes, construct baseline datasets and complete consistency verification; Based on the baseline dataset, a list of survey and monitoring tasks is generated and the assignment and execution criteria are locked. On-site data collection and evidence preservation, structured data entry and quality control judgment are carried out according to the locked criteria, and effective indicator records for benchmarking calculation are output. Effective indicators are recorded for benchmarking and risk classification. Rectification and escalation work orders are generated based on the classification results and managed with a record.

2. The land consolidation survey and monitoring closed-loop management method as described in claim 1, characterized in that: The solidified land consolidation data and spatial unit ledger include unified coordinate benchmark conversion and topological verification of project boundary results, current status survey results, design documents and drawings, construction organization records, and acceptance or supervision basis documents. Topology checks include boundary closure, overlap and gap checks, and consistency checks of shared edges between adjacent cells; Under a unified coordinate datum, spatial units are divided according to parcels, map features, fields, or grid rules, and the division rule parameters are registered. Establish a ledger field set for each spatial unit; The field set includes ownership or management entity, area, current land type, remediation type, design corresponding index, construction section information and acceptance node information, and establishes an associated list of point facilities.

3. The land consolidation survey and monitoring closed-loop management method as described in claim 2, characterized in that: The establishment of the indicator dictionary, threshold version, sampling location and sampling plan and evidence index includes registering the indicator name, unit, collection method, sampling or measurement method, data format and evidence type requirements for each indicator; Data acquisition methods include laboratory testing, on-site instrument readings, GNSS / RTK measurements, UAV or satellite image interpretation, and GIS calculations; For each indicator, a threshold entry is established and versioned by version number and effective date. The threshold entry should first refer to the allowable range given by the standard or acceptance clause, or the allowable range should be derived from the target value and allowable deviation given in the design document. For indicators that require point locations, register the point coordinates, point type, sampling frequency, and allowed time window, and pre-set alternative rules and trigger conditions when the points are not available.

4. The land consolidation survey and monitoring closed-loop management method as described in claim 3, characterized in that: The process of generating a survey and monitoring task list based on the baseline dataset and completing the assignment and execution criteria locking includes generating task entries based on the indicator set and point configuration associated with spatial units, and writing the indicator name, unit, collection method, sampling or measurement method, sampling frequency, allowed time window, required evidence type and required fields into the task entries; The task items are broken down into on-site sampling and measurement sub-tasks, sample transfer and laboratory testing sub-tasks, aerial flight and image processing sub-tasks, and interpretation and GIS calculation sub-tasks. Personnel qualification requirements, equipment list and equipment calibration requirements are written for each sub-task. After the task assignment is completed, the version of the indicator caliber, threshold, location and sampling plan, and evidence index rule used in this batch will be fixed into a task parameter set and locked. At the same time, the trigger conditions, record fields and approval fields for alternative locations, alternative equipment or alternative image sources will be preset.

5. The land consolidation survey and monitoring closed-loop management method as described in claim 4, characterized in that: The process of conducting on-site collection and evidence preservation, structured storage and quality control judgment according to the locked criteria includes generating a unique sample number for each sample during on-site sampling and recording the sampling timestamp, sampling coordinates, sampling depth, sampling volume or mass, parallel sample identification and sample handover information, forming a flow record of the sample from collection and transportation to laboratory reception; Record the coordinates, elevation or geometric dimensions of the measuring points, as well as the type of measuring tool, equipment number and calibration validity period information during on-site measurements. During flight operations and image acquisition, record flight sorties, flight altitude, ground resolution, image product type, processing software and parameter version, and submit orthophotos, DSM / DEM products and version numbers; Metadata is registered for photos, videos, aerial photographs, test reports, and results documents, and verification summaries are generated. The verification summaries are then linked to the evidence index table.

6. The land consolidation survey and monitoring closed-loop management method as described in claim 5, characterized in that: The structured data entry and quality control judgment include writing the data collection form fields, result file metadata, and evidence index information into the indicator record entries; The indicator record entries include the indicator name, unit, measured value, collection timestamp, collection coordinates, equipment number, sample number or result document number, and evidence citation field; Perform field integrity checks, data type and unit consistency checks, task scope consistency checks, and evidence consistency checks on the data entering the database; The consistency verification of task specifications includes matching the acquisition coordinates with the point configuration, ensuring that the acquisition time falls within the allowable time window, and verifying that the device serial number is within the calibration validity period. The consistency verification of evidence includes the consistency between the test report number and the sample number, the consistency between the version number of the deliverables and the task form record, and the consistency of the evidence verification summary. Output the quality control conclusion field and record the reason code for failure. If it fails, generate a supplementary sampling or review pending task.

7. The land consolidation survey and monitoring closed-loop management method as described in claim 6, characterized in that: The process of benchmarking and risk classification of effective indicator records, generating rectification and upgrade disposal work orders based on the classification results, and managing them with a record includes binding the indicator record with the standard or acceptance clause number, design document number, or statistical batch number from the allowable range when the quality control conclusion of the indicator record is passed, and using it for benchmarking judgment. Based on the graded threshold, the status of the disposal is output as passed, rectified, or upgraded. The rectification threshold and the upgrade threshold are both output by filtering the sample library that has formed the acceptance conclusion according to the stratified conditions and based on the sorting quantile. The sample time window, sample size, quantile coefficient, algorithm version and threshold version are fixed and archived. When the output is in the rectification or upgrade status, a work order is generated. The work order is bound to a list of trigger indicators, details of the out-of-bounds range, evidence number and evidence verification summary, clause number and version information, and records the work order status flow and overdue upgrade rules.

8. A system employing the closed-loop management and control method for land consolidation survey and monitoring as described in any one of claims 1 to 7, characterized in that: Includes a baseline documentation module, a task data collection and quality control module, and a benchmarking and tiered task assignment module; The baseline archiving module is used to collect land consolidation data and solidify spatial unit ledgers, indicator dictionaries, threshold versions, point locations and sampling plans, and evidence indexes, and output baseline datasets. The task acquisition quality control module is used to arrange investigation and monitoring tasks based on the baseline dataset and lock the execution scope, organize on-site collection and evidence preservation, complete structured data entry and quality control judgment, and output effective indicator records. The benchmarking and grading dispatching module is used to benchmark and grade the effective indicator records, generate rectification / upgrade disposal work orders and keep records, forming a closed-loop management entry point.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the land consolidation investigation and monitoring closed-loop management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the land consolidation investigation and monitoring closed-loop management method as described in any one of claims 1 to 7.

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