Dynamic index accounting system for displacement of cultivated land resources and forest land resources

Abstract

The invention relates to the technical field of resource accounting, and discloses a dynamic index accounting system for displacement of cultivated land resources and forest land resources. The system comprises a resource data acquisition unit for acquiring spatial distribution data of cultivated land resources, types and coverage data of forest land resources and replacement constraint condition data and performing space-time alignment to generate a resource multi-dimensional data set; the replacement index accounting unit is used for executing resource adaptability reconstruction processing based on the resource multi-dimensional data set and separating target resource data from abnormal resource data; the replacement scheme generation unit is used for generating an accounting result containing a replacement area, a weight and a priority by combining a preset replacement rate and a development rate; and the dynamic correction unit is used for identifying an exception type based on the exception resource data and correcting an accounting result in real time. The system can comprehensively capture resource dynamics, improves the accuracy and timeliness of replacement accounting, and adapts to complex resource management requirements.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resource accounting, in particular to an index dynamic accounting system for replacement of arable land resources and forest land resources. BACKGROUND

[0002] Under the dual demands of land resource management and ecological protection, the balance between arable land protection and forest land utilization has become a practical problem to be solved. With the acceleration of urbanization and the adjustment of agricultural production scale, the quantity and quality of arable land resources are under double pressure, while the protection and rational development of forest land as an important ecological resource are also related to the stability of the ecological system. In order to realize the efficient allocation of land resources, the replacement of arable land resources and forest land resources has become an important adjustment means. Through scientific replacement, the total amount of arable land can be guaranteed while the ecological function of forest land is taken into account. The existing resource replacement accounting method has many shortcomings. Data collection is often limited to a single dimension or static information, lacking comprehensive capture of the dynamic changes of the spatial distribution of arable land resources and forest land resources, resulting in insufficient timeliness and accuracy of the data. In the accounting process, the consideration of replacement constraints is not sufficient, and key constraints such as ecological protection red line and land use planning are not effectively integrated into the accounting system, making the generated replacement scheme less feasible. In addition, the traditional accounting method lacks a dynamic correction mechanism. When resource data is updated or abnormal, the accounting results cannot be adjusted in time, which is difficult to adapt to the complex and changing resource management needs, and thus affects the scientificity and rationality of resource replacement. SUMMARY

[0003] The purpose of the present application is to provide an index dynamic accounting system for replacement of arable land resources and forest land resources to solve the problems raised in the background art.

[0004] To achieve the above purpose, the present application provides an index dynamic accounting system for replacement of arable land resources and forest land resources, which comprises: a resource data acquisition unit for acquiring spatial distribution data of arable land resources, type and coverage range data of forest land resources, and replacement constraint condition data, and performing spatio-temporal alignment on the spatial distribution data, type and coverage range data, and replacement constraint condition data to generate a resource multidimensional data set; a replacement index accounting unit for performing resource adaptability reconstruction processing based on the resource multidimensional data set, and separating out target resource data for replacement accounting and abnormal resource data for dynamic correction accounting; a replacement scheme generation unit for generating a replacement index accounting result based on the target resource data in combination with a preset arable land resource replaceable rate and a forest land resource developable rate, wherein the replacement index accounting result includes arable land resource replacement area, forest land resource replacement weight, and replacement priority; The cultivated land resource replacement area is a set of areas after conversion of multiple factors, and is not a single area. The dynamic correction unit identifies an abnormal type in resource data updating based on the abnormal resource data, and corrects the replacement index accounting result in real time according to the abnormal type.

[0005] Preferably, the replacement scheme generation unit performs the following operations: The cultivated land resource level, the forest land resource ecological value coefficient, and the geographic superimposed area data in the replacement constraint condition in the target resource data are integrated. According to the integrated data and the preset replacement rule, a matching relationship between the cultivated land resource replacement area and the forest land resource replacement weight is calculated, and a replacement index accounting result is output.

[0006] Preferably, the dynamic correction unit performs the following operations: The abnormal resource data is monitored, and the cultivated land resource change block or the forest land resource ownership change data causing the abnormality is located. Based on the monitoring result, the replacement priority and the replacement area affected are re-evaluated, and the cultivated land resource replacement area and the replacement priority in the replacement index accounting result are corrected.

[0007] Preferably, the dynamic correction unit further performs the following operations: The cultivated land resource change reason and the forest land resource development restriction condition corresponding to multiple abnormal resource data in the same administrative region are associated and analyzed, and whether a cross-regional replacement adjustment is triggered is judged. If the cross-regional replacement adjustment is triggered, a multi-regional replacement coordination strategy is executed, a standby cultivated land resource block is allocated, and the replacement priority is updated.

[0008] Preferably, the replacement index accounting unit performs resource adaptation reconstruction processing, which includes: The resource multidimensional data set after time-space alignment is converted into a resource adaptation matrix. The resource adaptation matrix is decomposed to obtain a resource core matrix and a resource abnormal matrix. The resource core matrix contains the spatial matching characteristics of cultivated land resources and forest land resources, and the resource abnormal matrix identifies abnormal items of block ownership conflicts or ecological protection red line overlaps.

[0009] Preferably, when the resource data collection unit generates the resource multidimensional data set, the block coordinates of the cultivated land resources, the forest land resource boundary coordinates, and the administrative region boundary in the replacement constraint condition are spatially superimposed to generate a resource space mapping matrix. The replacement index accounting unit further performs: Based on the resource anomaly matrix, an abnormal item is extracted and associated with the resource space mapping matrix, the spatial features of the abnormal item are clustered, and a first type of abnormal sub-matrix and a second type of abnormal sub-matrix are generated. Among them, the first type of abnormal sub-matrix represents the abnormality caused by the change of cultivated land resource ownership, and the second type of abnormal sub-matrix represents the abnormality caused by the ecological restriction of forest land resources.

[0010] Preferably, the replacement index accounting unit includes: A multi-dimensional feature vector is added to each abnormal record, and the multi-dimensional feature vector includes plot change time, administrative region code, and ecological protection area overlap area. Based on the development intensity threshold in the replacement constraint condition, the multi-dimensional feature vector is configured with a weight; The multi-dimensional feature vectors are grouped according to spatial proximity to generate the first type of abnormal sub-matrix and the second type of abnormal sub-matrix.

[0011] Preferably, the replacement index accounting unit performs resource adaptability reconstruction processing further includes: A resource replacement coupling matrix is constructed to represent the supply of cultivated land resources and the demand of forest land resources across administrative regions, and the associated matching relationship between the two; When the resource adaptation matrix is decomposed, the collaborative constraint term of the resource replacement coupling matrix is introduced, combined with the resource anomaly matrix to iteratively solve, and identify parallel abnormalities of cross-regional resource supply imbalance or ecological protection conflicts; When the supply-demand correlation degree of any two regions in the resource replacement coupling matrix exceeds the preset threshold, and the value of the corresponding abnormal item in the resource anomaly matrix is out of limit, a multi-region replacement collaborative strategy is triggered.

[0012] Preferably, the dynamic correction unit corrects the replacement index accounting result, and calls the minimum ecological protection ratio in the replacement constraint condition to correct the forest land resource replacement weight.

[0013] Preferably, the dynamic correction unit further performs: Based on the calibrated forest land resource replacement weight, the qualified rate of the cultivated land resource replacement area is recalculated; When the qualified rate is lower than the preset threshold, the replacement scheme generation unit triggers to regenerate the replacement index accounting result.

[0014] Compared with the prior art, the beneficial effects of the present application are: By spatiotemporal alignment of multiple types of data through the resource data acquisition unit, a resource multidimensional dataset is generated, which can comprehensively and accurately reflect the actual situation and dynamic changes of the two types of resources. This multidimensional data integration method breaks the limitations of traditional data collection and provides a more reliable basis for subsequent replacement accounting. The resource adaptability reconstruction processing performed by the replacement index accounting unit can effectively separate target resource data and abnormal resource data, so that the accounting process is more focused on core resources that meet the replacement conditions, reducing the interference of irrelevant data. The replacement index accounting result generated in combination with the preset replaceable rate and developable rate covers key information such as replacement area, weight, and priority, providing a clear guide for the development of resource replacement schemes, which helps to improve the pertinence and operability of the schemes. The dynamic correction unit identifies abnormal types based on abnormal resource data and corrects the accounting result in real time, so that the entire accounting system has the ability to respond to data updates and sudden situations. This dynamic adjustment mechanism can ensure that the accounting result always coincides with the actual situation of the resources, avoiding decision-making bias caused by data lag or abnormalities. At the same time, the system naturally incorporates replacement constraints in the accounting process, so that the generated replacement scheme can better meet the requirements of ecological protection and land use planning, achieving efficient allocation of resources while maintaining the stability of the ecological system. The units of the system work together to form a complete closed loop from data collection, accounting processing to dynamic correction, improving the automation and intelligence level of resource replacement accounting. By reducing human intervention, the possibility of human error is reduced, and the accounting efficiency is also improved, which can quickly respond to the actual needs of resource management and provide timely and effective support for the decision-making of relevant departments. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 a timing diagram of the index dynamic accounting system for replacement of cultivated land resources and forest land resources described in the present invention; Figure 2 a flowchart for the replacement scheme generation unit accounting matching; Figure 3 a flowchart for the cross-regional replacement collaborative strategy trigger; Figure 4 a flowchart for abnormal item spatial feature clustering; Figure 5 a flowchart for abnormal item multidimensional feature clustering. DETAILED DESCRIPTION

[0016] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0017] With reference to Figure 1 The present application provides an index dynamic accounting system for replacement of arable land resources and forest land resources, which comprises: The resource data acquisition unit acquires spatial distribution data of arable land resources, type and coverage range data of forest land resources, and replacement constraint condition data. These data are processed by spatio-temporal alignment to generate a resource multidimensional data set containing spatial coordinates, resource attributes and constraint conditions. The replacement index accounting unit performs resource adaptability reconstruction processing on the resource multidimensional data set to separate target resource data and abnormal resource data. The target resource data is used for replacement accounting, and the abnormal resource data is used for dynamic correction. The replacement scheme generation unit outputs an accounting result containing replacement area, weight and priority based on the target resource data, combined with the preset arable land resource replaceable rate and forest land resource developable rate. The dynamic correction unit monitors the abnormal resource data, identifies the abnormal type and corrects the accounting result in real time to ensure the timeliness and accuracy of the replacement scheme.

[0018] Embodiment 1: With reference to Figure 2 The core function of the replacement scheme generation unit is to integrate the arable land resource grade in the target resource data, the ecological value coefficient of the forest land resource, and the geographic overlay area data in the replacement constraint condition, and generate the final replacement index accounting result based on the preset replacement rule. The classification of arable land resource grade includes soil quality, terrain slope, irrigation condition and surrounding infrastructure supporting condition. The soil quality is obtained by sampling detection to obtain indexes such as organic matter content, pH value, heavy metal pollution degree and arable land concentration degree. The terrain slope is calculated based on the digital elevation model, and the irrigation condition is comprehensively evaluated in combination with the distribution of water conservancy facilities and water source guarantee capacity. After normalization processing, the arable land resources are divided into three grades of high, medium and low by using the weighted scoring method, and different grades correspond to different replacement priorities and area conversion coefficients.

[0019] The calculation of the ecological value coefficient of forest land resources involves multiple dimensions such as vegetation type, biodiversity, carbon sink function, and ecological sensitivity. Vegetation type is determined through remote sensing image interpretation combined with field investigation, including categories such as arbor forest, shrub forest, and mixed forest. Different vegetation types have different contributions to ecological function. Biodiversity assessment is based on indicators such as species richness, distribution of rare and endangered species, and habitat integrity. Carbon sink function is estimated based on parameters such as vegetation biomass and soil carbon storage. Ecological sensitivity analysis considers factors such as soil erosion risk and geological disaster susceptibility. After integrating these data, a multi-index comprehensive evaluation method is used to calculate the ecological value coefficient of each forest land plot. The higher the coefficient, the greater the ecological protection value of the forest land, and the stricter the replacement restriction conditions.

[0020] Geographical overlay area data is generated through spatial overlay analysis. The specific process is to overlay the vector boundaries of cultivated land resources plots with the vector boundaries of forest land resources, calculate the spatial proximity range and relative position relationship between them. Overlay analysis is implemented using geographic information system software, and the output results include spatial proximity area, proximity ratio, and spatial distribution characteristics of associated areas. These data are used to judge the potential replaceability between cultivated land resources and forest land resources. Plots with larger overlay areas should be given priority in replacement accounting to reduce ecological damage and improve land use efficiency.

[0021] The integrated data is input into the pre-set replacement rule model. The core algorithm of this model is based on linear programming method, with the objective function of maximizing ecological benefit and land use efficiency, while considering the constraint conditions of replaceable rate of cultivated land resources and developable rate of forest land resources. The replaceable rate of cultivated land resources is set according to plot grade and regional policy requirements, with lower replaceable rate for high-grade plots to ensure sustainable use of high-quality cultivated land. The developable rate of forest land resources is dynamically adjusted according to the ecological value coefficient and ecological protection red line range, with strict restrictions on forest land development rate with high ecological value coefficient. The replacement rule model uses an iterative optimization algorithm to gradually adjust the replacement area and weight allocation when calculating the matching relationship, until all constraint conditions are met and the optimal solution is reached.

[0022] The output results include cultivated land resource replacement area, forest land resource replacement weight, and replacement priority list. Replacement area is allocated by plot, grade, and matching degree, with low-grade plots with high matching degree being given priority for replacement. Replacement weight reflects the relative importance of forest land resources in replacement, and plots with high weight need to take more stringent ecological protection measures in subsequent development. The replacement priority list is sorted according to matching degree, ecological benefit, and administrative region demand, and is used to guide the implementation sequence of actual replacement work. All output data are presented in the form of structured tables and spatial distribution maps, which are easy for decision-makers to understand and apply to actual management.

[0023] In the data integration and calculation process, the system uses a distributed computing framework to handle large-scale spatial data, improving operation efficiency. The data storage uses a spatio-temporal database, supporting historical version management and real-time update query. The user interaction interface provides parameter configuration, result visualization and report generation functions, allowing administrators to adjust model parameters and view replacement schemes under different scenarios. The entire implementation process emphasizes data accuracy and algorithm transparency, with all intermediate results and calculation logic traceable for auditing and optimization.

[0024] The operation of the replacement scheme generation unit depends on high-quality data input and reasonable parameter settings, so strict verification of the data source is required before implementation to ensure the integrity and consistency of spatial coordinates, attribute information and constraint conditions. Parameter settings need to be updated regularly to adapt to the dynamic changes of resource management needs, combined with regional characteristics and policy requirements. The system supports batch processing and real-time computing modes to meet the needs of different application scenarios. Batch processing is suitable for large-scale regional planning, while real-time computing is used to respond to sudden resource changes or policy adjustments. Through continuous optimization of algorithms and enhancement of computing power, the replacement scheme generation unit can efficiently and accurately support the replacement decision of cultivated land resources and forest land resources.

[0025] Example 2: see Figure 3 The core function of the dynamic correction unit is to monitor abnormal resource data, locate the cultivated land resource change plot or forest land resource ownership change data that causes the anomaly, and make real-time corrections to the replacement index accounting results according to the type of anomaly. The operation mechanism of this unit is based on the continuous updating of data streams, through real-time data docking with real estate registration systems, national land survey databases, ecological protection red line management platforms and forestry systems to obtain the latest resource change information. The system uses incremental updating, only processing data entries that have changed, significantly reducing computational load and improving response speed.

[0026] The identification of cultivated land resource change plots is mainly achieved by comparing historical data with real-time updated spatial boundaries. The system maintains a versioned spatial database that records the boundary coordinates, ownership information and utilization status of each plot at different time points. When new survey data or approval files are entered into the system, the change detection algorithm is automatically triggered. The algorithm first performs overlay analysis on the spatial boundaries, calculating the difference area between the current version and the historical version, then combines attribute information to determine the change type, such as plot splitting, merging, use adjustment, etc. For detected change plots, the system extracts their spatial location, change time, change reason and new attribute data as important components of abnormal resource data.

[0027] The acquisition of forest resource ownership change data relies on data sharing mechanisms with forestry management departments. The system regularly receives official documents such as forest right registration, use right transfer, and ecological compensation area adjustment, and extracts key information such as changed plot number, new ownership subject, and change effective date through natural language processing technology. At the same time, the system accesses remote sensing monitoring data to identify changes in forest coverage types such as logging sites and afforestation areas through image interpretation. These change data are cross-verified with ownership registration information to ensure the accuracy of anomaly detection. When a plot that has changed ownership but is not reflected in the replacement plan is found, the system automatically marks it as an abnormal item.

[0028] The positioning of abnormal resource data uses a combination of spatial indexing and attribute filtering. The system establishes an R-tree spatial index to quickly retrieve surrounding plots affected by changes. Attribute filtering is based on pre-set abnormality determination rules, such as sudden changes in cultivated land resource level and abnormal fluctuations in forest ecological value coefficient. After positioning, the system generates a detailed impact assessment report for each abnormal item, including affected replacement plan number, original replacement area, original replacement priority, and comparison of key parameters before and after the change. These information provides data support for subsequent re-evaluation.

[0029] The re-evaluation process focuses on the affected replacement priority and replacement area. The system first calculates the deviation of the changed plot from the original replacement plan, which integrates spatial position offset, attribute difference, and ecological impact change. Spatial position offset is obtained by calculating the distance between the geometric centers of the plot before and after the change; attribute difference is based on the change in key parameters such as soil quality and slope; and ecological impact change compares the ecological value coefficient before and after the change. Abnormal items with a deviation exceeding the threshold enter the correction process.

[0030] The correction process adopts differentiated strategies according to the type of change. For cultivated land resource ownership changes, the system re-searches for alternative plots that meet the conditions and calculates replacement parameters according to the same matching rules as the original plan. The selection of alternative plots considers spatial proximity, level similarity, and administrative region consistency, and preferentially selects plots within the same administrative unit that are of the same or higher level. For forest resource ownership changes, the system re-evaluates the ecological value coefficient. If the change results in an increase in the coefficient, the system reduces the replacement weight of the plot or removes it from the replacement plan; if the coefficient decreases, the system carefully assesses whether to increase the replacement weight.

[0031] Cross-region anomaly processing is an important function of the dynamic revision unit. The system identifies systematic change patterns by correlating multiple abnormal resource data within the same administrative region, such as large-scale ecological restoration projects leading to concentrated adjustment of cultivated land resources, regional forestry policy changes causing batch changes in forest land ownership, etc. The analysis process uses spatial clustering methods to merge abnormal entries with similar change reasons and adjacent geographic locations into the same event. For identified systematic changes, the system further determines whether to trigger cross-region replacement adjustment.

[0032] The conditions for triggering cross-region replacement adjustment include: insufficient resource supply in a single administrative region, local replacement restrictions due to ecological protection requirements, and major infrastructure projects occupying replacement plots, etc. When the triggering conditions are met, the system starts the multi-region replacement coordination strategy. This strategy first constructs an inter-regional resource supply and demand network, with nodes representing administrative regions and edges representing resource flow relationships, and the weight of the edge reflecting the supply capacity and demand intensity. Then, the network flow algorithm is used to calculate the optimal resource allocation scheme, which maximizes the overall replacement benefit while meeting the basic needs of each region.

[0033] The specific implementation of the multi-region replacement coordination strategy includes two main steps: allocating standby cultivated land resource plots and updating replacement priorities. Standby plots are sourced from the system-maintained cross-region resource reserve library, which includes information on voluntarily provided replaceable plots from each administrative region. The allocation process considers indirect transportation costs related to resource allocation (indirect transportation and supporting costs related to standby plot development, production and operation, and ecological restoration in cross-region replacement), topographic continuity, management convenience, and other factors, and preferentially selects plots in adjacent regions. The update of replacement priority is based on new supply and demand relationships and ecological protection requirements, and uses a multi-criteria decision-making method to reorder. The system generates detailed adjustment instructions, recording the basis and impact range of each change decision.

[0034] The implementation of the dynamic revision unit relies on a high-performance spatial computing engine and a real-time data processing architecture. The computing engine uses parallel computing technology to accelerate large-scale spatial analysis and network flow calculation; the data processing architecture is based on an event-driven mode to ensure low latency for anomaly detection and revision. The user interface provides interactive revision review functions, allowing administrators to view automatic revision suggestions and manually adjust parameters. All revision operations are recorded in the audit log to support backtracking and responsibility tracing.

[0035] The system periodically conducts self-evaluation on the dynamic correction effect, and calculates the difference between the replacement schemes before and after correction, the abnormal processing response time, the cross-regional coordination success rate and other indicators. The evaluation results are used to optimize the abnormal judgment rules, deviation threshold and coordination strategy parameters. At the same time, the system establishes a feedback mechanism to allow local management departments to report the actual implementation of the correction scheme, and further calibrate the algorithm. This continuous improvement mechanism enables the dynamic correction unit to adapt to the changing resource management needs and policy environment.

[0036] The dynamic correction unit and the replacement scheme generation unit form a closed-loop system. The corrected replacement index accounting results are automatically fed back to the scheme generation unit, triggering the necessary recalculation. The coordinated work of the two units ensures that the replacement scheme maintains long-term stability and can flexibly respond to sudden changes. The whole implementation process emphasizes the timeliness of data, the adaptability of algorithm and the explainability of decision-making, providing dynamic and precise management support for the replacement of cultivated land resources and forest land resources.

[0037] In Example 3, the resource adaptability reconstruction process of the replacement index accounting unit is to convert the spatio-temporally aligned resource multi-dimensional data set into a structured mathematical representation form, facilitating subsequent analysis and calculation. The core of this process is to construct a resource adaptation matrix, which can fully express the spatial matching relationship and attribute compatibility between cultivated land resources and forest land resources. The row dimension of the matrix represents the cultivated land resource plots, the column dimension represents the forest land resource plots, and the matrix element value reflects the adaptation degree between a specific cultivated land plot and a forest land plot. The calculation of the adaptation degree considers multiple dimensions such as spatial proximity, soil property matching degree, slope compatibility, and ecological function complementarity. Spatial proximity is quantified by calculating the Euclidean distance between the geometric centers of the plots, soil property matching degree is based on the similarity score of the soil types of the two plots, slope compatibility examines whether the slope of the cultivated land resource meets the engineering requirements of forest development, and ecological function complementarity evaluates the stability of the overall ecological system after replacement.

[0038] The mathematical representation of the resource adaptation matrix is: ; Where: represents the adaptation score of the i-th cultivated land resource plot and the j-th forest land resource plot; is the spatial proximity score, which is inversely proportional to the distance between the centers of the two plots; is the soil property matching degree score, calculated according to the compatibility rules of the soil classification system; is the slope compatibility score, evaluated based on engineering feasibility standards; is the ecological function complementarity score, derived from the ecosystem service value evaluation model; , , , are the weight coefficients of each index, which are dynamically adjusted according to regional resource management policies. This formula ensures that the score of the adaptation degree can comprehensively reflect the overall conditions of resource replacement, avoiding the dominance of a single index in decision-making.

[0039] The matrix decomposition process uses an improved singular value decomposition method to decompose the resource adaptation matrix into a resource core matrix and a resource anomaly matrix. The resource core matrix retains data with an adaptation degree higher than a preset threshold, which represents the combination of cultivated land resources and forest land resources that meet the basic replacement conditions. The threshold is set according to regional development intensity and ecological protection requirements, and usually uses a dynamic adjustment mechanism to appropriately lower the threshold in resource-intensive areas and increase the threshold in ecologically sensitive areas. The resource anomaly matrix captures all entries below the threshold and identifies their anomaly types, such as land ownership conflicts, ecological protection red line overlaps, and data quality defects. Anomaly types are represented by additional marker fields, facilitating subsequent classification processing.

[0040] Constructing the resource replacement coupling matrix is a key step in handling cross-administrative region resource replacement. The rows and columns of this matrix represent different administrative regions, and the matrix element values represent the matching degree of cultivated land resource supply and forest land resource demand between regions. The generation of the coupling matrix depends on the statistical analysis of regional resource endowments and demand prediction models. Supply data comes from cultivated land resource survey and evaluation results, considering the total amount of replaceable resources, quality grade distribution, and policy-allowed development scale in each region. Demand data is based on forest land resource protection planning, ecological restoration tasks, and economic development land demand. The dynamic updating mechanism of the coupling matrix enables it to reflect the latest trends of resource flow between regions.

[0041] Introducing the collaborative constraint term of the resource replacement coupling matrix in the matrix decomposition process can effectively identify parallel anomalies of cross-regional resource supply imbalance or ecological protection conflicts. The collaborative constraint term acts on the singular value decomposition algorithm in the form of boundary conditions, limiting the decomposition results to meet the basic balance of resource flow between regions. In specific implementation, the coupling matrix is converted into linear constraint conditions, which are integrated into the objective function through the Lagrange multiplier method. This processing makes the resource core matrix not only reflect the adaptability of resources, but also embody the overall requirements of regional coordination. When the supply-demand correlation degree of any two regions in the coupling matrix exceeds the preset threshold, and the value of the corresponding anomaly entry in the resource anomaly matrix is out of limit, the system determines that there is a cross-regional resource mismatch problem that needs to be intervened.

[0042] The condition detection of the multi-region replacement coordination strategy adopts a multi-level judgment mechanism. First, the correlation degree between supply and demand in different regions is calculated. This index considers factors such as the scale of resource flow, indirect transportation costs related to resource allocation (indirect transportation and supporting costs related to the development of standby land, production and operation, and ecological restoration in cross-region replacement), and policy compatibility. Then, the abnormal intensity of related items in the resource anomaly matrix is checked. The abnormal intensity is classified according to the magnitude of deviation from the normal value and the range of influence. When both conditions meet the preset trigger threshold, the system automatically starts the coordination strategy. The strategy execution process first establishes a cross-region negotiation platform to share resource data and replacement needs. Then, it formulates a resource allocation plan, clearly defining the supply responsibilities and assignment rights of each region. Finally, it adjusts the replacement priority to ensure that the protection needs of key ecological regions are given priority.

[0043] The implementation of resource adaptation reconstruction processing relies on a distributed matrix operation framework. The construction and decomposition of large-scale resource adaptation matrices are divided into multiple sub-matrices, which are distributed to different computing nodes for parallel processing. The intermediate results are synchronized through an efficient communication protocol, and finally aggregated into complete decomposition results. For operations involving cross-region data exchange, blockchain technology is used to ensure data security and operation traceability. The user interface provides matrix visualization tools to display resource adaptation relationships and regional coupling states in the form of heat maps and network diagrams, assisting management personnel in understanding complex resource associations.

[0044] The follow-up processing of abnormal items uses a classification approach. For data quality defects, the system automatically initiates a data verification request, requiring relevant departments to re-verify and submit accurate information. For ownership conflict anomalies, it triggers the ownership dispute resolution process to coordinate all parties to confirm the final effective ownership status. For ecological protection red line overlap anomalies, it initiates an ecological impact assessment, and based on the assessment results, decides whether to adjust the protection red line range or cancel the replacement plan. Each type of anomaly has a corresponding processing time limit requirement. The system tracks the processing progress through a workflow engine, and automatically escalates unresolved anomalies to higher decision levels if the time limit is exceeded.

[0045] The optimization of the resource replacement coupling matrix is a continuous iterative process. The system regularly collects the actual execution of resource replacement schemes in each region, and calculates indicators such as supply completion rate, demand satisfaction rate, and ecological protection compliance rate. These data are fed back to the coupling matrix generation algorithm to calibrate the inter-regional resource flow parameters. At the same time, the system monitors external environmental changes, such as major policy adjustments and natural disasters, and triggers emergency updates of the coupling matrix in a timely manner. This dynamic adjustment mechanism enables the system to adapt to the changing needs of resource management, maintaining the timeliness and applicability of decision-making recommendations.

[0046] The replacement index accounting unit and the dynamic correction unit form a close cooperative relationship. When the dynamic correction unit detects significant resource changes, it triggers partial reconstruction of the resource adaptation matrix, updating only the matrix blocks in the affected area rather than recalculating the entire matrix, significantly improving response efficiency. In turn, the resource adaptation reconstruction process discovers systematic abnormalities, providing data support for the rule optimization of the dynamic correction unit. The two units share an abnormal knowledge base and a coordination strategy library, accumulating and reusing processing experience, gradually improving the overall decision-making ability of the system.

[0047] The entire implementation process emphasizes the transparency and interpretability of mathematical models. All intermediate results of matrix operations and parameter adjustment records are saved completely, supporting auditing and review. Key decision points are set with manual review links to ensure the rationality of automated processing. The system provides detailed explanatory reports to explain the generation logic and basis data of each replacement suggestion, helping decision-makers understand and trust system output. This design not only fully utilizes the efficiency advantage of data-driven decision-making, but also retains necessary human supervision and judgment, achieving human-machine collaborative intelligent resource management.

[0048] Example 4: refer to Figure 4 and Figure 5 In the process of generating the resource multidimensional dataset in the resource data acquisition unit, the system first performs spatial overlay analysis on the coordinates of the cultivated land resource plots, the coordinates of the forest land resource boundaries, and the administrative region boundaries in the replacement constraint conditions. This process is implemented through a geographic information system platform, using vector overlay algorithms to calculate the intersection relationship of the three types of spatial data. For example, in a certain county-level implementation case, the system processed spatial data containing 356 cultivated land resource plots and 128 forest land resource plots, generating a resource spatial mapping matrix after overlaying. Each cell of the matrix records the spatial relationship between a specific cultivated land plot and a forest land plot, including overlapping area, relative position, and administrative jurisdiction information. The structure of the resource spatial mapping matrix is shown in Table 1.

[0049] Table 1: The structure of the resource spatial mapping matrix is as follows.

[0050]

[0051] The "ecological protection zone overlap flag" field in the table comes from the comparison results of the ecological protection red line database, marking the existence of overlapping land combinations in ecologically sensitive areas. This structured representation allows subsequent anomaly detection to directly locate resource combinations with potential conflicts.

[0052] When the substitution index accounting unit works based on the resource anomaly matrix, it first extracts abnormal entries and associates them with the resource space mapping matrix. In a practical application in a certain city, the system identified 42 abnormal records, including 19 records of changes in the ownership of cultivated land resources and 23 records of ecological restrictions on forest land resources. These abnormal records are divided into two categories through spatial feature clustering: the first category of abnormal sub-matrix focuses on changes in the ownership of cultivated land resources, typically represented by multiple changes in ownership registration of the same land parcel in adjacent time periods; the second category of abnormal sub-matrix focuses on changes in the ecological restrictions on forest land resources, mainly characterized by high-value forest land suddenly being included in the development range.

[0053] The construction of the multi-dimensional feature vector of abnormal entries is a key link in this implementation. The system extracts the following features for each abnormal record: land change timestamp, six-digit statistical division code of the administrative region, and overlapping area ratio with ecological protection zones. For example, the feature vector of a cultivated land ownership change record contains the "2023-05-12T08:30:00" time marker, the "330522" administrative division code, and the "0.15" overlap ratio. After standardization, these features enter the weighted calculation process. The weight configuration scheme refers to the development intensity threshold in the substitution constraint condition, for example, in ecologically sensitive areas, the weight of the overlap area ratio is set to three times that of the regular area to strengthen the influence of the ecological protection factor.

[0054] The spatial proximity clustering is implemented using an improved DBSCAN algorithm. This algorithm does not require pre-setting the number of clusters, but automatically discovers the clustering patterns of abnormal entries based on density reachability. In a provincial case, the system divided 25 abnormal entries into 4 spatial clusters, two of which were classified as the first category of abnormal sub-matrix, containing 11 records of concentrated changes in the ownership of cultivated land; the other two clusters were classified as the second category of abnormal sub-matrix, containing 14 records of ecological restriction anomalies. During the clustering process, the algorithm automatically identified a concentrated area of abnormal changes in the ownership of cultivated land in three townships at the eastern part of a county, and a concentrated area of abnormal development of forest land in the buffer zone of a nature reserve.

[0055] The processing of the first category of abnormal sub-matrix focuses on the verification of ownership stability. The system automatically generates ownership change chain analysis diagrams to display all registered changes of a specific land parcel within six months. For example, a land parcel was registered as village collective ownership, agricultural company lease, and state reserve land within three months, which triggered the ownership verification process. The system sends a verification request to the natural resource authority to provide the latest ownership identification documents, and updates the resource database based on the feedback.

[0056] The second type of abnormal sub-matrix focuses more on ecological compliance review. The system retrieves the historical data of the ecological value assessment of the abnormal forest land parcel, and compares the current development plan with the original protection requirements. For example, a parcel of original forest at the edge of the forest is newly included in the replacement range. The system retrieves and finds that the parcel belongs to the biodiversity maintenance area in the ecological function zoning, and triggers the ecological impact assessment process. The assessment report includes vegetation cover change simulation, species habitat connectivity analysis, and other content as the basis for decision-making whether to allow replacement.

[0057] The data quality control measures during implementation include: coordinate system conversion before spatial overlay analysis to avoid position deviation caused by projection differences; setting data freshness check during anomaly detection, historical data beyond the valid period needs to be re-verified; manual sampling review after clustering analysis to ensure the reliability of algorithm results. A regional implementation case shows that through these three layers of quality control, the construction accuracy of the spatial mapping matrix reaches 98.6%, and the false positive rate of anomaly recognition is controlled below 3%.

[0058] The system designs differentiated visualization schemes for different types of abnormal sub-matrices. The first type of anomaly displays the sequence of ownership changes in the form of a time axis, highlighting parcels with frequent changes; the second type of anomaly uses an ecological sensitivity heat map to represent the protection urgency of different areas with a color gradient. These visualization tools help managers quickly grasp the distribution patterns of anomalies and improve decision-making efficiency. For example, a city's natural resources bureau found through the heat map that the abnormal development of forest land was concentrated at the junction of two ecological corridors, and accordingly adjusted the regional replacement plan to avoid the fragmentation of the ecological network.

[0059] The anomaly handling workflow supports multi-department collaboration. When the system identifies a cluster of anomalies that cross administrative boundaries, it automatically creates a joint handling task and invites relevant regional managers to participate. The task tracking panel displays the completion status of each handling step, including data review, site investigation, consultation and decision-making, etc. In a cross-county case, the system completed the handling of 11 related anomalies involving three counties in 72 hours, significantly improving efficiency compared to the traditional paper document circulation method.

[0060] The update and maintenance of the resource space mapping matrix adopts an incremental processing strategy. In daily work, only the changed parcels are recalculated for spatial relationships, and the full matrix reconstruction is scheduled at night when the system load is low. This strategy effectively reduces the performance impact on daily business operations while maintaining data timeliness. The running data of a provincial platform shows that incremental updating reduces the time consumption of daily data processing by 67% and the peak server resource occupancy by 41%.

[0061] The implementation case shows that this method can effectively identify two types of key anomalies: 17 abnormal concentration areas of cultivated land ownership were found in a province in the east, and 9 of them were found to have registration errors after verification; 23 illegal development risk points were identified in a forest area in the west, and the replacement plan of 6 important ecological areas was successfully prevented. The abnormal analysis report generated by the system is included in the natural resource supervision clue library, providing data support for industry supervision. The entire implementation process embodies the technical route: through precise spatial correlation positioning, multi-dimensional feature classification anomaly, and targeted processing based on domain knowledge, a management closed loop is formed to continuously optimize. This mode not only plays the efficiency advantage of data-driven, but also maintains the core position of professional judgment, realizing the organic combination of technological innovation and institutional advantage.

[0062] In the process of modifying the replacement index calculation results, the dynamic correction unit first calls the minimum ecological protection ratio parameter specified in the replacement constraint condition. This parameter is formulated by the provincial natural resource authority according to the regional ecological function orientation, and usually represents the proportion of protected areas in forest resources that must remain unchanged. After receiving the correction instruction, the system automatically retrieves the forest resource plots involved in the current calculation results, calculates the ratio of the proposed replacement area to the total area for each plot, and compares it with the minimum ecological protection ratio. When the replacement ratio of a certain plot exceeds the allowed threshold, the system starts the compliance calibration program, and reduces the replacement area of the plot according to the difference ratio. The calibration process uses a stepwise adjustment strategy, preferentially reducing the replacement area of plots with lower ecological value coefficients to preserve the integrity of high ecological value areas.

[0063] Compliance calibration not only considers the replacement ratio of a single plot, but also needs to evaluate the ecological balance of the administrative region as a whole. The system aggregates the replacement of all forest resources in the region, calculates the weighted average protection ratio, and automatically triggers a regional level protection warning when the overall protection ratio of some administrative units approaches the minimum requirement. This warning mechanism prompts the system to re-examine the replacement weight distribution of each plot in the region, and adjusts the replacement area of adjacent plots to meet local needs while ensuring overall ecological safety. The regional balance algorithm uses an iterative approximation method, recalculates the protection ratio after each adjustment until all administrative units meet the requirements. This hierarchical calibration mechanism not only ensures the rigid constraints of policy red lines, but also retains the flexibility of resource allocation.

[0064] After the forest resource replacement weight calibration is completed, the system immediately calculates the qualified rate of the associated cultivated land resource replacement area. The qualified rate index reflects the percentage of the cultivated land resource area that meets the ecological protection requirements in the modified replacement plan. The calculation process integrates multiple dimensional constraint conditions: whether the soil quality level meets the tillage standard, whether the slope is within the suitable tillage range, whether the buffer distance from the ecological protection zone is sufficient, whether the surrounding infrastructure meets the agricultural demand, etc. Each condition sets a binary decision rule, and the plot that meets all the conditions is included in the qualified area. The system dynamically generates a qualified rate change curve to intuitively display the impact trend of the previous modifications on the scheme quality.

[0065] When the qualified rate is monitored to be lower than the preset threshold, the system sends a re-calculation request to the replacement scheme generation unit. The request contains detailed modification reason explanation and key parameter change record. The re-calculation process is not a simple repetition of the original process, but a targeted optimization focusing on problem areas. The system first locks the core factors that cause the qualified rate to decrease, such as the soil quality not meeting the standard in a specific area or the slope restriction being too strict, and then adjusts the screening conditions or replacement rules in that area. For example, for soil quality problems, soil improvement feasibility assessment can be introduced to allow some improved land to be included in the replacement range; for slope restrictions, terracing engineering measures can be considered to appropriately relax the replacement threshold for steep slopes. This problem-oriented re-calculation mechanism effectively avoids "one-size-fits-all" resource waste.

[0066] After the re-calculation scheme is generated, the system performs multi-version comparison analysis to highlight the main differences between the new and old schemes. The comparison dimensions include the increase and decrease of replacement area in each administrative region, the change of forest resource protection ratio, and the distribution of cultivated land resource quality, etc. Management personnel can intuitively understand the actual impact of the modification, especially the balance between ecological protection and resource development. The system also provides a difference reason tracing function, which allows you to view the specific parameter adjustment and calculation logic that caused the change by clicking on any change indicator. This transparent decision-making process greatly enhances the acceptability and execution efficiency of the scheme.

[0067] The interaction between the dynamic modification unit and the replacement scheme generation unit adopts an asynchronous communication mechanism. The modification request is placed in a high-priority task queue, and the scheme generation unit returns the result through a callback interface after processing is completed. This design avoids direct coupling between the two units, ensuring stable operation of the system under high load. The communication content uses a structured message format, containing complete context information, so that interrupted tasks can be accurately restored. All interaction records are stored persistently, forming a complete audit trail to support post-tracing and responsibility identification.

[0068] The system periodically evaluates the effectiveness of the correction mechanism, focusing on two indicators: correction response time and correction accuracy. Response time statistics are from the entire process from anomaly detection to completion of correction, and accuracy measures the degree of fit between the corrected scheme and actual management needs. Evaluation data comes from two parts: system automatically recorded operation log, and manually filled satisfaction survey. The analysis results are used to optimize the parameter settings of the correction strategy, such as adjusting the trigger sensitivity of the qualified rate threshold, improving the convergence speed of the regional balance algorithm, etc. This continuous self-optimization enables the system to adapt to changing policy environment and management requirements.

[0069] In a specific implementation case, a mountainous county found that the original replacement scheme involved 3 important water conservation forests. The system reduced the replacement area from 125 hectares to 82 hectares through compliance calibration, and supplemented 2 qualified farmland resource plots through re-calculation. The entire process took less than 4 hours, and the corrected scheme not only met the county's economic development land needs, but also ensured the integrity of the ecological function of the water source area. Another correction case in a plain area shows that the system improved the regional qualified rate from 68% to 89% through three iterations, including optimizing soil detection sampling scheme and redefining ecological buffer distance.

[0070] The implementation of the dynamic correction unit fully considers the operation habits of users at different levels. Provincial managers can view macro correction statistics and trend analysis, city-level users focus on correction details and cross-regional coordination requirements within their jurisdiction, and county-level operators mainly handle specific land correction tasks. The system interface dynamically configures display content and operation permissions according to user roles to ensure that all types of users can efficiently complete their work. At the same time, the system provides detailed correction guidance documents and online training resources to help grassroots personnel quickly master the operation points.

[0071] The design philosophy of the entire correction process is: taking ecological protection red line as the rigid constraint, taking resource utilization efficiency as the optimization target, and realizing scientific decision-making through data-driven dynamic adjustment. On the technical implementation, it emphasizes the adaptability and interpretability of the algorithm, and on the management process, it focuses on the uniformity of the specification and the convenience of the operation. This implementation not only takes advantage of the automation of information systems, but also respects the complexity of natural resource management, and has shown strong practical value and promotion potential in practice. With the accumulation of system running time and continuous optimization of algorithm models, the accuracy and efficiency of dynamic correction will be further improved, providing more intelligent support for farmland and forest resources replacement management.

[0072] It is to be understood that the terminology used herein such as first and second, and the like, is only used to distinguish one entity or action from another entity or action, and does not necessarily require or imply any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0073] While embodiments of the present application have been shown and described with reference to particular embodiments thereof, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the application. The scope of the application is defined by the appended claims and their equivalents.

Claims

1. An index dynamic accounting system for the replacement of cultivated resources and forest resources, characterized in that, The method comprises the following steps: a resource data acquisition unit acquires spatial distribution data of cultivated land resources, type and coverage range data of forest land resources, and replacement constraint condition data, and performs spatio-temporal alignment on the spatial distribution data, type and coverage range data, and replacement constraint condition data to generate a resource multidimensional data set; a replacement index accounting unit performs resource adaptability reconstruction processing based on the resource multidimensional data set, and separates out target resource data for replacement accounting and abnormal resource data for dynamic correction accounting; a replacement scheme generation unit generates a replacement index accounting result based on the target resource data, in combination with a preset cultivated land resource replaceable rate and a forest land resource developable rate, the replacement index accounting result comprising a cultivated land resource replacement area, a forest land resource replacement weight, and a replacement priority; the cultivated land resource replacement area is a non-single area, being a set of areas after multi-factor area conversion; a dynamic correction unit identifies an abnormal type in resource data updating based on the abnormal resource data, and corrects the replacement index accounting result in real time according to the abnormal type.

2. The index dynamic accounting system for the replacement between cultivated resources and forest resources according to claim 1, characterized in that, The replacement scheme generation unit performs the following operations: integrating cultivated land resource grades in the target resource data, forest land resource ecological value coefficients, and geographic overlay region data in the replacement constraint condition; calculating a matching relationship between the cultivated land resource replacement area and the forest land resource replacement weight according to the integrated data and a preset replacement rule, and outputting the replacement index accounting result.

3. The index dynamic accounting system for the replacement between cultivated resources and forest resources according to claim 1, characterized in that, The dynamic correction unit performs the following operations: monitoring the abnormal resource data, and locating a cultivated land resource change plot or forest land resource ownership change data that causes the abnormality; based on the monitoring result, re-evaluating the replacement priority and the replacement area affected, and correcting the cultivated land resource replacement area and the replacement priority in the replacement index accounting result.

4. The index dynamic accounting system for the replacement between the cultivated resources and the forest resources according to claim 3, characterized in that, The dynamic correction unit further performs the following operations: correlation analysis of cultivated land resource change reasons and forest land resource development restriction conditions corresponding to multiple abnormal resource data in the same administrative region, to determine whether a cross-regional replacement adjustment is triggered; if the cross-regional replacement adjustment is triggered, a multi-region replacement coordination strategy is executed to allocate standby cultivated land resource plots and update the replacement priority.

5. The index dynamic accounting system for the replacement between cultivated resources and forest resources according to claim 1, characterized in that, The replacement index accounting unit performs resource adaptability reconstruction processing, which comprises the following steps: converting the spatio-temporally aligned resource multidimensional data set into a resource adaptation matrix; decomposing the resource adaptation matrix to obtain a resource core matrix and a resource abnormal matrix; wherein the resource core matrix contains spatial matching characteristics of cultivated land resources and forest land resources, and the resource abnormal matrix identifies abnormal entries of plot ownership conflicts or ecological protection red line overlaps.

6. The index dynamic accounting system for the replacement between cultivated resources and forest resources according to claim 5, characterized in that, When the resource data acquisition unit generates the resource multidimensional data set, the plot coordinates of cultivated land resources, the boundary coordinates of forest land resources, and the administrative region boundaries in the replacement constraint condition are spatially overlaid to generate a resource space mapping matrix; The replacement index accounting unit further performs the following operations: based on the resource abnormal matrix, extracting abnormal entries and correlating the resource space mapping matrix, clustering the spatial characteristics of the abnormal entries to generate a first type of abnormal sub-matrix and a second type of abnormal sub-matrix; The first type of abnormal sub-matrix represents an abnormality caused by a change in the ownership of cultivated land resources, and the second type of abnormal sub-matrix represents an abnormality caused by ecological restrictions on forest land resources.

7. The index dynamic accounting system for the replacement between cultivated resources and forest resources according to claim 6, characterized in that, The clustering of the spatial features of the abnormal items by the replacement index accounting unit includes: adding a multi-dimensional feature vector to each abnormal record, the multi-dimensional feature vector including a time of change of the land plot, an administrative region code to which the land plot belongs, and an overlapping area of an ecological protection zone; configuring a weight for the multi-dimensional feature vector based on a development intensity threshold in the replacement constraint condition; grouping the multi-dimensional feature vectors according to spatial proximity to generate the first type of abnormal sub-matrix and the second type of abnormal sub-matrix.

8. The index dynamic accounting system for the replacement between cultivated resources and forest resources according to claim 5, characterized in that, The resource adaptability reconstruction processing performed by the replacement index accounting unit further includes: constructing a resource replacement coupling matrix to represent a supply of cultivated land resources and a demand for forest land resources across administrative regions; when the resource adaptation matrix is decomposed, introducing a cooperative constraint term of the resource replacement coupling matrix, combining resource abnormal matrices to iteratively solve, and identifying parallel abnormalities of cross-regional resource supply imbalance or ecological protection conflicts; when the supply-demand correlation degree of any two regions in the resource replacement coupling matrix exceeds a preset threshold, and the value of the corresponding abnormal item in the resource abnormal matrix is out of limit, triggering a multi-region replacement cooperative strategy.

9. The index dynamic accounting system for the replacement between cultivated resources and forest resources according to claim 1, characterized in that, When the replacement index accounting result is corrected by the dynamic correction unit, the minimum ecological protection ratio in the replacement constraint condition is called to calibrate the compliance of the corrected forest land resource replacement weight.

10. The index dynamic accounting system for the replacement between cultivated resources and forest resources according to claim 9, characterized in that, The dynamic correction unit also performs: based on the calibrated forest land resource replacement weight, recalculating the qualified rate of the cultivated land resource replacement area; when the qualified rate is lower than a preset threshold, triggering the replacement scheme generation unit to regenerate the replacement index accounting result.

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