Administrative supervision and resource management system for whole life cycle of urban land block

Abstract

The application relates to the technical field of urban resource management, and discloses an administrative supervision and resource management system for the whole life cycle of urban plots, which comprises a data set collection module, a geometric identification upper chain module, an index generation module, a resource matching module and a constraint linkage solving module.

Description

Technical Field

[0001] This invention belongs to the field of urban resource management technology, specifically involving an administrative supervision and resource management system for the entire life cycle of urban land parcels. Background Technology

[0002] With the acceleration of urbanization, the management of urban land parcels throughout the entire process, from planning and approval to development, construction, operation, and maintenance, is becoming increasingly complex. Traditional administrative supervision models rely heavily on static land parcel files and scattered departmental data, lacking cross-stage and cross-departmental information linkage mechanisms. This leads to problems such as inconsistent land parcel data, low resource allocation efficiency, and difficulties in regulatory traceability. Especially in large-scale urban projects with long construction cycles, the status of land parcels changes frequently during the planning, construction, and use phases, and existing systems cannot reflect the dynamic relationship between land parcel spatial form, construction progress, and resource utilization in real time.

[0003] While existing geographic information systems can achieve visualized management of land parcel spatial data, they lack the capability for full lifecycle data ownership confirmation and status verification. Administrative approval systems, on the other hand, typically focus only on a single stage of the business process, making continuous lifecycle supervision difficult. Furthermore, urban resource management suffers from inaccurate supply-demand matching and a lack of data support for allocation decisions. Resource allocation often relies on manual judgment or static planning, lacking intelligent control mechanisms based on real-time observation data. Summary of the Invention

[0004] This invention provides an administrative supervision and resource management system for the entire life cycle of urban land parcels, solving the technical problems in related technologies such as fragmented land parcel supervision information, untraceable life cycle data, low efficiency in matching resource supply and demand, and difficulty in dynamically inheriting cross-stage constraints.

[0005] This invention provides an administrative supervision and resource management system for the entire lifecycle of urban land parcels, including:

[0006] The dataset acquisition module is used to acquire the full lifecycle dataset of the target land parcel and generate evidence summary fingerprints;

[0007] The geometric identifier on-chain module is used to standardize the initial geometric data of the target plot, generate a unique plot identifier and geometric fingerprint, and form a state commitment record.

[0008] The indicator generation module is used to calculate the schedule deviation rate, actual floor area ratio, and usage consistency index based on the full life cycle dataset and on-site observation data, and form a regulatory result set.

[0009] The resource matching module is used to determine the resource demand and supply based on the full life cycle dataset, calculate the resource matching degree index, and form a resource matching result set.

[0010] The constraint linkage solution module is used to generate constraint versions and resource-side constraints through constraint inheritance and lateral propagation rule engine, and to perform feasible region calculation to obtain resource allocation results;

[0011] The same transaction solidification module is used to combine the resource allocation results into a verification receipt record, and submit it in the same transaction as the status commitment record of this stage in the time-series status ledger.

[0012] The Deviation Writeback Snapshot module is used to aggregate deviations in field observation data, trigger the constraint inheritance and lateral propagation rule engine to generate new stage constraint versions and write them back, and return the full-cycle state snapshot and associated evidence path.

[0013] Furthermore, the full lifecycle dataset includes: plot number, land use, net land area, approved floor area ratio, construction schedule, and surrounding population density;

[0014] By calculating hash fingerprints for each data field of the full lifecycle dataset, combining the hash fingerprints of each field in the field sequence order to form a hash fingerprint set, and constructing a Merkle tree, the root hash value of the Merkle tree is determined as the evidence digest fingerprint of the full lifecycle dataset.

[0015] Furthermore, the initial geometry of the target plot is normalized to generate a unique plot identifier and geometric fingerprint, and a state commitment record is formed, including:

[0016] Step 11: Obtain the initial geometric data of the target plot and perform integrity verification and spatial projection unification. The initial geometric data includes the plot boundary coordinate point set, boundary line segment topological relationship, plot center point coordinates, and plot outer rectangle.

[0017] Step 12: Perform vertex sorting, boundary direction correction and coordinate precision normalization on the initial geometric data obtained in Step 11 to generate normalized geometric data. Calculate the hash value as a geometric fingerprint based on the binary representation of the normalized geometric data. Then, concatenate the administrative division code and registration timestamp with the normalized geometric data and perform a hash operation to generate a unique identifier for the land parcel.

[0018] Step 13: Bind the unique identifier of the land parcel, the geometric fingerprint, and the evidence digest fingerprint of the full life cycle dataset to form a state commitment structure, calculate the hash result of the state commitment structure, generate a state commitment record, and submit it to the time-series state ledger.

[0019] Furthermore, it also includes cross-phase consistency verification mechanisms, including:

[0020] Using the unique identifier of the land parcel as an index, extract state snapshots of two consecutive stages from the time-series state ledger, and perform field-by-field hash comparison of the geometric fingerprint and constraint version number in the state snapshot to determine whether the hash values ​​of each field are completely consistent.

[0021] When the hash comparison result of any field is inconsistent, the abnormal transaction backtracking process is triggered, and the process backtracks step by step along the associated evidence path to the first consistent state snapshot. An abnormal verification record is generated in the time-series state ledger, and the abnormal field, the difference hash value and the backtracking stage number are recorded.

[0022] Furthermore, the schedule deviation rate, actual floor area ratio, and land use consistency index are calculated to form a regulatory result set, including:

[0023] Step 21: Using the unique identifier of the land parcel as an index, extract the project schedule and planned floor area ratio from the full life cycle dataset, extract the actual construction progress, total building volume and actual use data from the on-site observation data, and align them in time series.

[0024] Step 22: Based on the aligned project schedule and the actual construction progress, calculate the difference between the actual construction progress and the project schedule in the time series, divide it by the project schedule, and obtain the schedule deviation rate. Then, calculate the weighted average to obtain the total schedule deviation rate.

[0025] Step 23: Obtain the net land area of ​​the plot based on geometric fingerprint, obtain the actual plot ratio by the ratio of the total building volume to the net land area of ​​the plot, and calculate the difference between the actual plot ratio and the planned plot ratio by dividing the difference by the planned plot ratio to obtain the plot ratio deviation rate.

[0026] Step 24: Unify the coding of actual use data and planned use data in the field observation data. Obtain the use consistency index by dividing the number of intersection elements of actual use data and planned use data by the number of elements of planned use data. Then, summarize the unique identifier of the land parcel, the total progress deviation rate, the plot ratio deviation rate and the use consistency index to generate a regulatory result set.

[0027] Furthermore, the resource demand and supply are determined, the resource matching index is calculated, and a resource matching result set is formed, including:

[0028] Step 31: Using the unique identifier of the land parcel as an index, extract the resource demand parameters and supply parameters from the full life cycle dataset. By multiplying the original resource demand and the correction coefficient and adding the total schedule deviation rate and the plot ratio deviation rate, the corrected resource demand is obtained. By multiplying the original resource supply and the use consistency index and adding the original resource supply, the corrected resource supply is obtained.

[0029] Step 32: For each type of resource, calculate the ratio of the difference between the corrected resource demand and the corrected resource supply to the sum of the two, find the negative of the absolute value and add one to obtain the matching index of that type of resource; sum the matching indices of each resource by weight to obtain the comprehensive resource matching index.

[0030] Step 33: Summarize the unique identifier of the land parcel, each resource matching index, and the comprehensive resource matching index to form a resource matching result set.

[0031] Further, constraint versions and resource-side constraints are generated, and feasible region calculation is performed to obtain resource allocation results, including:

[0032] Step 41: Using the unique identifier of the land parcel as an index, read the constraint version and resource matching result set of the previous stage from the time-series status ledger, verify the effective time and version number of the constraint, and inherit the constraints within the validity period as the basic constraints; when the change of the comprehensive resource matching degree index exceeds the preset magnitude threshold, generate the resource-side constraints of adjacent land parcels according to the spatial adjacency relationship and change direction between land parcels, and form the constraint set of the current stage.

[0033] Step 42: Merge the basic constraints and resource-side constraints into a constraint version candidate set, and perform consistency processing according to constraint type, constraint target variable and constraint priority; when there are multiple constraints in the same resource category, take the constraint with the highest priority as the main constraint, sum the weighted values ​​of each constraint to generate additional constraint items and add them to the main constraint to form a unified constraint version structure.

[0034] Step 43: Using a unified constraint version structure and resource matching result set as input, establish a feasible domain calculation model, solve for the resource allocation amount of each plot, and form a resource allocation result set.

[0035] Furthermore, step 43 specifically includes:

[0036] Step 51: Establish a set of resource supply and demand equations, and correspond the supply variables, demand variables and equilibrium variables of each resource category to the resource supply, resource demand and resource equilibrium of the land parcel, respectively; take the difference between the supply variable and the demand variable as the constraint condition that it equals the equilibrium variable, set non-negativity constraints on the equilibrium variable, set upper limit constraints on the supply variable, and set lower limit constraints on the demand variable to form a resource supply and demand constraint system.

[0037] Step 52: Using the minimization of the sum of absolute values ​​of the supply and demand deviations of land resources as the objective function of linear programming, assign a penalty coefficient to the supply and demand deviation of each resource category, multiply the penalty coefficient by the absolute value of the deviation and sum them up, and use this as the optimization objective function to solve for the initial feasible solution that satisfies the global equilibrium condition.

[0038] Step 53: Using the initial feasible solution as input, calculate the rate of change of the comprehensive resource matching degree index with respect to the resource allocation amount in the second-level iterative optimization stage. Adjust the resource allocation amount according to the direction of the rate of change, and decrease the step size of each adjustment according to the preset decay factor. When the difference of the comprehensive resource matching degree index between two consecutive iterations is lower than the preset convergence threshold, the model is determined to have converged. Output the final resource allocation amount for each plot and summarize the plot unique identifier, the final resource allocation amount and the constraint satisfaction status to generate a resource allocation result set.

[0039] Furthermore, the resource allocation results are compiled into verification receipt records and committed in the same transaction as the status commitment record for this stage in the time-series status ledger, including:

[0040] Step 61: Based on the resource allocation result set, calculate the verification value for each record by subtracting the resource demand from the resource supply and then subtracting the resource balance. When the verification value is zero, generate a verification receipt record and bind the constraint version number, calculation timestamp, and comprehensive resource matching index.

[0041] Step 62: Perform hash calculation on the verification receipt record, input the record content sequence into the hash function to generate a hash digest, and use the hash digest as the verification receipt fingerprint, which is in the same format as the digest fingerprint of the status commitment record for comparison at the ledger layer.

[0042] Step 63: Using the unique identifier of the land parcel as the transaction primary key, combine the hash digest and the status commitment record into a transaction commit unit. Perform an integrity check on the transaction commit unit. After confirming that the hash matches and the timestamps are continuous, write the transaction unit into the time-series status ledger, generate a unique transaction number, and use this number to identify the corresponding resource allocation result.

[0043] Furthermore, the offset write-back snapshot module includes:

[0044] Step 71: Using the unique identifier of the land parcel as an index, match the on-site observation data with the verification receipt record of the previous stage, and perform time series difference calculation on the progress deviation rate, plot ratio deviation rate and land use consistency index of the same land parcel to obtain the progress deviation rate change rate, plot ratio deviation rate change rate and land use consistency index change rate, forming a set of deviation aggregation indicators.

[0045] Step 72: When any deviation aggregation index exceeds the preset deviation threshold, the constraint inheritance and lateral propagation rule engine is triggered, the constraint version and resource matching result set of the previous stage are read, the constraints within the validity period are inherited, and the propagation influence coefficient is calculated based on the spatial adjacency relationship between plots. The propagation influence coefficient is multiplied by the basic constraint weight and accumulated to generate a lateral propagation constraint set.

[0046] Step 73: Merge the inherited basic constraints and the propagated lateral constraints to form a new stage constraint version, and write the new stage constraint version into the time-series state ledger with the unique identifier of the land parcel as the transaction primary key, while generating a state snapshot and associated evidence path.

[0047] The beneficial effects of this invention are as follows: By introducing land parcel geometric normalization, state commitment records, time-series state ledgers, and constraint inheritance and propagation mechanisms, this invention achieves continuous supervision and reliable rights confirmation of land parcels throughout their entire lifecycle, including planning, construction, and operation. By integrating lifecycle data with on-site observation data, it can calculate progress deviation rates, plot ratio deviation rates, and land use consistency indices in real time, automatically generating resource matching degrees and constraint versions to achieve dynamic balance and optimized allocation of resource supply and demand. Through hash fingerprinting and same-transaction ledger submission mechanisms, it ensures the tamper-proof and traceability of land parcel status records. This invention effectively solves the problems of fragmented land parcel supervision, data isolation, and lack of dynamic feedback in resource allocation in existing technologies, realizing cross-stage data consistency verification and automatic evolution of spatial correlation constraints, providing intelligent, verifiable, and trustworthy technical support for administrative supervision and resource management of urban land parcels throughout their entire lifecycle. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the modules of the administrative supervision and resource management system for the entire life cycle of urban land parcels according to the present invention. Detailed Implementation

[0049] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0050] like Figure 1 As shown, the administrative supervision and resource management system for the entire life cycle of urban land parcels includes:

[0051] Data set acquisition module 1 is used to acquire the full life cycle dataset of the target land parcel and generate evidence summary fingerprints;

[0052] The geometric identifier on-chain module 2 is used to standardize the initial geometric data of the target plot, generate a unique plot identifier and geometric fingerprint, and form a state commitment record;

[0053] Indicator generation module 3 is used to calculate the schedule deviation rate, actual floor area ratio, and usage consistency index based on the full life cycle dataset and on-site observation data, forming a regulatory result set.

[0054] Resource matching module 4 is used to determine the resource demand and supply based on the full life cycle dataset, calculate the resource matching degree index, and form a resource matching result set;

[0055] The constraint linkage solution module 5 is used to generate constraint versions and resource-side constraints through constraint inheritance and lateral propagation rule engine, and to perform feasible domain calculation to obtain resource allocation results;

[0056] Same as Transaction Solidification Module 6, used to assemble the resource allocation results into a verification receipt record, and submit it in the same transaction as the status commitment record of this stage in the time-series status ledger;

[0057] The Deviation Writeback Snapshot Module 7 is used to aggregate deviations in field observation data, trigger the constraint inheritance and lateral propagation rule engine to generate new stage constraint versions and write them back, and return the full-cycle state snapshot and associated evidence path.

[0058] In one embodiment of the present invention, the full lifecycle dataset is used to cover the entire process information of a land parcel from planning approval, development and construction to operation and maintenance. The full lifecycle dataset includes: land parcel number, land use nature, net land area of ​​the land parcel, approved floor area ratio, project schedule, and surrounding population density. The land use nature represents the functional attribute of the land parcel in the planning, such as residential land, commercial land, or public facility land. The net land area of ​​the land parcel is the actual developable area after deducting public roads and green spaces. The approved floor area ratio is used to constrain the ratio between the total building area and the land area of ​​the land parcel. The project schedule records the phased time nodes and construction progress targets of the project. The surrounding population density is used to characterize the social load level of the area where the land parcel is located.

[0059] Hash fingerprints are calculated for each data field of the full lifecycle dataset. These hash fingerprints are then combined in sequence to form a hash fingerprint set, and a Merkle tree is constructed. The root hash value of this Merkle tree is determined as the evidence digest fingerprint of the full lifecycle dataset. The hash fingerprint is a fixed-length digital digest obtained by unidirectionally mapping the input data using a hash function, used to uniquely identify the original data content. The field sequence order is preset by the system. The Merkle tree is constructed using a bottom-up binary hash calculation structure. Each leaf node of the Merkle tree stores the hash fingerprint of a single data field, and each non-leaf node stores the combined hash result of its child node hash values. Through recursive calculation, a unique root hash value is finally generated at the root node of the tree.

[0060] This embodiment combines hash calculation with Merkle tree structure to enable structured evidence storage of urban land parcel data throughout its entire lifecycle. Through the hierarchical design of field-level hashing and root-level digest, it achieves integrity verification of multi-dimensional information of land parcels and establishes a reliable data evidence storage mechanism for the entire lifecycle of urban land parcels.

[0061] In one embodiment of the present invention, the initial geometry of the target plot is normalized to generate a unique plot identifier and geometric fingerprint, and a state commitment record is formed, including:

[0062] Step 11: Obtain the initial geometric data of the target plot and perform integrity verification and spatial projection unification. The initial geometric data includes the plot boundary coordinate point set, boundary line segment topological relationship, plot center point coordinates, and plot enclosing rectangle. The plot boundary coordinate point set represents the closed boundary structure of the plot outline; the boundary line segment topological relationship describes the connection method between adjacent boundary line segments; the plot center point coordinates are the spatial center position of the plot in the unified coordinate system; the enclosing rectangle represents the smallest rectangular range containing all the boundaries of the plot. Spatial projection unification refers to converting the above geometric data from the original surveying coordinate system to the system's preset standard projection coordinate system to ensure the comparability and consistency of geographic data from different sources.

[0063] Step 12 involves performing vertex sorting, boundary direction correction, and coordinate precision normalization on the initial geometric data obtained in Step 11 to generate normalized geometric data. A hash value is calculated based on the binary representation of the normalized geometric data as a geometric fingerprint. The administrative division code and registration timestamp are then concatenated with the normalized geometric data, and a hash operation is performed to generate a unique identifier for the land parcel. Specifically, vertex sorting is used to uniformly encode the boundary point set of the land parcel, ensuring that the point order of the same land parcel remains consistent when processed by different times or systems. Boundary direction correction is used to unify the directionality of the boundary ring, i.e., clockwise or counterclockwise, to avoid calculation errors. Coordinate precision normalization ensures that all coordinate data are consistent within the standard precision range by setting the number of decimal places and rounding rules. The geometric fingerprint is the result of an irreversible encrypted digest of the spatial geometry of the land parcel, used to uniquely identify the boundary shape and spatial structure of the land parcel.

[0064] Step 13: Bind the unique identifier of the land parcel, the geometric fingerprint, and the evidence digest fingerprint of the full lifecycle dataset to form a state commitment structure. Calculate the hash result of the state commitment structure, generate a state commitment record, and submit it to the time-series state ledger. The state commitment structure is a data structure composed of land parcel identification information, geometric fingerprint, and lifecycle evidence digest, used to record the complete state of the land parcel at a specific time. The hash result is the hash digest value obtained by performing a hash calculation, serving as the unique credential of the land parcel's current state. The state commitment record includes the unique identifier of the land parcel, geometric fingerprint, evidence digest fingerprint, and constraint version information, etc. The time-series state ledger is used to record the multi-stage state information of the land parcel throughout its entire lifecycle in chronological order.

[0065] This embodiment verifies the integrity of the initial geometric data of the land parcel, standardizes and generates geometric fingerprints, combines administrative divisions and time information to generate a unique identifier, and records the status commitment on the blockchain. This achieves the uniqueness and verifiability of the spatial form of the land parcel throughout its life cycle, avoids inconsistencies in supervision due to differences in spatial data from different departments or systems, and constructs a spatial data ownership confirmation mechanism for the entire life cycle of urban land parcels. This ensures that the entire process of land parcel supervision, resource allocation, and status evolution is verifiable, traceable, and tamper-proof.

[0066] In one embodiment of the present invention, a cross-stage consistency verification mechanism is further included to perform consistency verification and anomaly backtracking on the status data of a land parcel at continuous stages throughout its entire life cycle, so as to ensure the continuity and reliability of data in the process of administrative supervision and resource management, including:

[0067] Using the unique identifier of the land parcel as an index, state snapshots of two consecutive stages are extracted from the time-series state ledger. A field-by-field hash comparison is performed on the geometric fingerprint and constraint version number in the state snapshots to determine if the hash values ​​of each field are completely consistent. The state snapshot refers to a complete set of information recorded in the ledger that reflects the state of a land parcel at a specific moment, including the geometric fingerprint, constraint version number, resource allocation result hash digest, and corresponding timestamp. Hash comparison refers to the process of comparing the mapping results after using a hash function to perform a one-way mapping of the field content. Since hash functions are irreversible and collision-resistant, if the field content of the two stages is completely consistent, their hash values ​​will also be consistent; otherwise, it can be determined that there are data differences.

[0068] When the hash comparison result of any field is inconsistent, it is considered that an abnormal change has occurred in the life cycle status record of the land parcel, triggering an abnormal transaction backtracking process. In this process, the system backtracks step by step along the associated evidence path to the first consistent state snapshot, generates an anomaly verification record in the time-series status ledger, and records the abnormal field, the difference hash value, and the backtracking stage number. Specifically, the associated evidence path is a chain-like data reference structure used to record all hash link information from the initial state commitment record to the current stage status record. This structure enables the system to complete the complete tracing of the status path based on the hash index without relying on the original data.

[0069] This embodiment ensures the continuity and consistency of land parcel status data across multiple lifecycle stages through hash comparison-based verification logic; and achieves chain-like tracing from the current state to historical states through a backtracking mechanism of associated evidence paths, making the anomaly location process transparent and verifiable, and realizing continuous verification and anomaly self-checking of land parcel lifecycle status.

[0070] In one embodiment of the present invention, the schedule deviation rate, actual floor area ratio, and usage consistency index are calculated to form a regulatory result set, including:

[0071] Step 21: Using the unique identifier of the land parcel as an index, extract the project schedule and planned floor area ratio from the full life cycle dataset, extract the actual construction progress, total building volume and actual use data from the on-site observation data, and align them in time series to ensure that the project schedule and the actual construction progress have a one-to-one correspondence at the same time node to support the time consistency of deviation calculation.

[0072] Step 22: Based on the aligned project schedule and the actual construction progress, calculate the difference between the actual construction progress and the project schedule in the time series, divide it by the project schedule, and obtain the schedule deviation rate. Then, calculate the weighted average to obtain the total schedule deviation rate. That is, the schedule deviation rate of all time nodes is processed by weighted average to obtain the total schedule deviation rate. The weight coefficient of the time node can be determined according to the importance or duration of the construction stage. The total schedule deviation rate is used to quantify the overall deviation of the actual construction progress of the plot from the planned target.

[0073] Step 23: Obtain the net land area of ​​the plot based on geometric fingerprint, obtain the actual plot ratio by the ratio of the total building volume to the net land area of ​​the plot, and calculate the difference between the actual plot ratio and the planned plot ratio by dividing the difference by the planned plot ratio to obtain the plot ratio deviation rate. The plot ratio deviation rate reflects the degree of deviation of the actual development intensity from the planning target.

[0074] Step 24: Unify the coding of actual use data and planned use data in the field observation data to form a comparable attribute set. Calculate the use consistency index by dividing the number of intersection elements of actual use data and planned use data by the number of elements in planned use data. When the use consistency index is 1, it indicates that the actual use and planned use are completely consistent. When the use consistency index is less than 1, it indicates that there is a deviation in use. Finally, summarize the unique identifier of the land parcel, the overall progress deviation rate, the plot ratio deviation rate, and the use consistency index to generate a regulatory result set.

[0075] This embodiment, by introducing a time series alignment and deviation calculation mechanism, can quantify the actual progress deviation of land construction projects, ensuring that regulatory authorities can grasp the project construction status in real time; through the dynamic calculation of the plot ratio deviation rate, it realizes digital supervision of development intensity, preventing over-plot ratio development or inefficient land use; through the calculation of the land use consistency index, it establishes an automatic identification mechanism for land use changes, enabling closed-loop supervision and traceability verification of planning objectives, construction status and use behavior throughout the entire life cycle of the land plot under a unified technical framework.

[0076] In one embodiment of the present invention, determining resource demand and supply, calculating a resource matching index, and forming a resource matching result set includes:

[0077] Step 31: Using the unique identifier of the land parcel as an index, extract resource demand and supply parameters from the full lifecycle dataset. The corrected resource demand is obtained by multiplying the original resource demand by a correction coefficient and then adding the total schedule deviation rate and plot ratio deviation rate. Similarly, the corrected resource supply is obtained by multiplying the original resource supply by the land use consistency index and then adding the original resource supply. The resource demand parameters include the land parcel's construction stage, planned land use type, construction scale, and construction progress. The resource supply parameters include regional resource supply capacity, available supporting facilities, and surrounding environmental capacity. The formula for calculating the corrected resource demand is as follows: ,in, This indicates the revised resource requirement. Indicates the initial resource requirement. and These represent the overall schedule deviation rate and the plot ratio deviation rate, respectively. This represents a correction factor used to reflect the phased resource utilization efficiency of a land parcel;

[0078] Step 32: For each type of resource, calculate the ratio of the difference between the adjusted resource demand and the adjusted resource supply to their sum. Take the negative of the absolute value of this ratio and add one to obtain the matching index for that resource type. Then, sum the matching indices of all resources using a weighted average to obtain the comprehensive resource matching index. The formula for calculating the matching index is as follows: , This represents the matching degree index of the i-th type of resource. and ...

[0079] Step 33: Summarize the unique identifier of the land parcel, each resource matching index, and the comprehensive resource matching index to form a resource matching result set.

[0080] This embodiment incorporates schedule deviation rate, plot ratio deviation rate, and land use consistency index into the calculation, enabling the assessment results of resource demand and supply to reflect the dynamic changes in the actual state of the land parcel. By using the matching degree index and the comprehensive matching degree index, the traditional qualitative resource allocation is transformed into calculable numerical indicators, realizing a dynamic resource matching analysis mechanism for the entire life cycle of urban land parcels.

[0081] In one embodiment of the present invention, a constraint version and resource-side constraints are generated, and feasible region calculation is performed to obtain resource allocation results, including:

[0082] Step 41: Using the unique identifier of the land parcel as an index, read the constraint version and resource matching result set of the previous stage from the time-series status ledger, verify the effective time and version number of the constraints, and inherit the constraints within the validity period as basic constraints. The basic constraints refer to the set of conditions that are passed from the previous stage and still have timeliness and binding force, which are used to maintain the continuity of the land parcel status between life cycle stages. When the change range of the comprehensive resource matching degree index exceeds the preset range threshold, it is determined that the land parcel resource status has fluctuated. Based on the spatial adjacency relationship and change direction between land parcels, resource-side constraints of adjacent land parcels are generated, and the constraint set of the current stage is formed. Among them, the spatial adjacency relationship refers to the relationship between two or more land parcels located within a preset distance range in geographical space, the change direction refers to the change trend of resource matching degree in spatial distribution, that is, the change path from resource-excess land parcels to resource-scarce land parcels, and the resource-side constraint refers to the constraint conditions formed by the resource matching change of one land parcel being transmitted to the adjacent land parcels through the adjacency relationship, which are used to reflect the spatial coupling of resource consumption and supply between land parcels.

[0083] Step 42: Merge the basic constraints and resource-side constraints into a constraint version candidate set, and perform consistency processing according to constraint type, constraint target variable and constraint priority; when there are multiple constraints in the same resource category, the constraint with the highest priority is the main constraint, and the weight coefficients of other constraints are determined according to the source credibility and spatial relevance. The constraints are weighted and summed to generate additional constraint items and superimposed on the main constraint to form a unified constraint version structure. This structure fully records the current stage of land parcel constraints, weights and version numbers.

[0084] Step 43: Using a unified constraint version structure and resource matching result set as input, establish a feasible region calculation model, solve for the resource allocation amount for each plot, and form a resource allocation result set, specifically including:

[0085] Step 51: Establish a system of resource supply and demand equations. The supply variables, demand variables, and balance variables of each resource category are respectively mapped to the resource supply, resource demand, and resource balance of the land parcel. The resource balance represents the surplus or gap value formed by the supply and demand difference. With the difference between the supply variable and the demand variable equal to the balance variable as a constraint, a non-negative constraint is set on the balance variable to prevent the resource deficit from becoming negative. An upper limit constraint is set on the supply variable, and a lower limit constraint is set on the demand variable to form a resource supply and demand constraint system. This step provides the feasible region boundary for subsequent optimization solutions.

[0086] Step 52: Using the minimization of the sum of absolute values ​​of supply and demand deviations for land resources as the objective function, a penalty coefficient is assigned to the supply and demand deviation for each resource category. The penalty coefficient is multiplied by the absolute value of the deviation and summed to obtain the initial feasible solution that satisfies the global equilibrium condition. The formula for calculating the objective function is as follows: Z represents the value of the objective function, which is the weighted sum of the supply and demand deviations of land resources. This represents the deviation penalty coefficient for the i-th type of resource, used to reflect the importance of the resource type. and Let represent the corrected supply and demand of resource of type i, respectively.

[0087] Step 53: Using the initial feasible solution as input, calculate the rate of change of the comprehensive resource matching degree index with respect to the resource allocation amount in the second-level iterative optimization stage. Adjust the resource allocation amount according to the direction of the rate of change, and decrease the step size of each adjustment according to the preset decay factor. When the difference between the comprehensive resource matching degree index of two consecutive iterations is lower than the preset convergence threshold, the model is determined to have converged. Output the final resource allocation amount of each plot and summarize the plot unique identifier, the final resource allocation amount and the constraint satisfaction status to generate a resource allocation result set.

[0088] It should be noted that the resource allocation amount corresponds to the solution result of the resource supply amount in the model. Based on the constraints in step 51, the system optimizes the objective function and dynamically adjusts the resource supply amount. When the model converges, the final resource supply amount is the resource allocation amount of the plot at the current stage.

[0089] This embodiment ensures the continuity of land parcel regulatory conditions across different lifecycle stages by effectively constraining the temporal state ledger inheritance; it links spatial adjacency and resource status changes between land parcels through lateral propagation rules, reflecting the resource synergy characteristics of urban land parcels in the spatial dimension; and it automatically solves for the optimal resource allocation result by constructing a constraint-driven feasible domain model, enabling the system to have adaptive control capabilities, realizing dynamic optimization of land parcel resource allocation and cross-stage consistent constraint management, thereby improving the intelligence and data credibility of administrative supervision.

[0090] In one embodiment of the present invention, the resource allocation results are compiled into a verification receipt record and submitted in the same transaction as the current stage status commitment record in the time-series status ledger, including:

[0091] Step 61: Based on the resource allocation result set, perform supply and demand balance verification on each record. Specifically, calculate the verification value for each record by subtracting the resource demand from the resource supply and then subtracting the resource balance. When the verification value is zero, it means that the resource supply, demand and balance constraints are completely matched in the ledger logic. The system generates a verification receipt record and binds the constraint version number, calculation timestamp and comprehensive resource matching index.

[0092] Step 62: Perform hash calculation on the verification receipt record, input the record content sequence into the hash function to generate a hash digest, and use the hash digest as the verification receipt fingerprint, which is in the same format as the digest fingerprint of the status commitment record for comparison at the ledger layer.

[0093] Step 63: Using the unique identifier of the land parcel as the transaction primary key, combine the hash digest and the status commitment record into a transaction commit unit. Perform an integrity check on the transaction commit unit to ensure hash matching, timestamp continuity, and uniqueness. Hash matching means that both the verification receipt fingerprint and the status commitment record fingerprint can be found in the ledger index with a valid mapping. Uniqueness constraint means that the identifier of the same land parcel can only generate one transaction unit at the same time. After confirming hash matching and timestamp continuity, write the transaction unit into the time-series status ledger, generate a unique transaction number, and use this number to identify the corresponding resource allocation result.

[0094] This embodiment establishes a joint mechanism for generating verification receipts, calculating fingerprints, and submitting ledger transactions, thereby achieving a reliable binding between land resource allocation results and status commitment records. This enables the administrative supervision and resource management data of the entire life cycle of land parcels to achieve a reliable registration, automatic verification, and time-series evidence storage in an integrated closed loop at the ledger level.

[0095] In one embodiment of the present invention, the offset write-back snapshot module includes:

[0096] Step 71: Using the unique identifier of the land parcel as an index, match the on-site observation data with the verification receipt record of the previous stage, and perform time series difference calculation on the progress deviation rate, plot ratio deviation rate and land use consistency index of the same land parcel to obtain the progress deviation rate change rate, plot ratio deviation rate change rate and land use consistency index change rate, forming a set of deviation aggregation indicators.

[0097] Step 72: When any deviation aggregation index exceeds a preset deviation threshold, it is considered that the land parcel has undergone a significant state change during its life cycle evolution. This triggers the constraint inheritance and lateral propagation rule engine, which reads the constraint version and resource matching result set from the previous stage, inherits the constraints within their validity period, and calculates the propagation impact coefficient based on the spatial adjacency relationship between land parcels. The propagation impact coefficient is then multiplied by the basic constraint weights and accumulated to generate a lateral propagation constraint set. Specifically, the formula for calculating the propagation impact coefficient is: , This represents the propagation influence coefficient of plot i on plot j. This represents an element in the adjacency matrix. It is set to 1 when plot i is adjacent to plot j, and 0 otherwise. This represents the spatial distance between plot i and plot j. It represents the spatial attenuation coefficient, which controls the rate at which the propagation intensity decreases with distance;

[0098] Step 73: Merge the inherited basic constraints and the propagated lateral constraints to form a new stage constraint version. Using the unique identifier of the land parcel as the transaction primary key, write the new stage constraint version into the time-series state ledger. Simultaneously, generate a state snapshot and associated evidence paths. The state snapshot refers to the structured data record of the constraint status, resource allocation results, and deviation aggregation indicators of the land parcel in the current stage, used to quickly reconstruct the state of this stage. The associated evidence path refers to the set of hash links connecting the state commitments, verification receipts, and constraint versions of the current stage and the previous stage, used for lifecycle backtracking and verification. Together, they ensure the verifiability and cross-stage traceability of the state of each stage.

[0099] This embodiment achieves adaptive state updates and multi-stage consistency maintenance of land parcels throughout their entire lifecycle through deviation aggregation calculation, constraint inheritance and lateral propagation rule engine triggering, and ledger write-back mechanism. It constructs a dynamic state evolution framework for the entire lifecycle of urban land parcels, providing technically feasible closed-loop support for urban administrative supervision and resource management.

[0100] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0101] The embodiments of the present invention have been described above, but the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of the present embodiments, all of which are within the protection scope of the present embodiments.

Claims

1. An administrative supervision and resource management system for the entire lifecycle of urban land parcels, characterized in that: include: The dataset acquisition module is used to acquire the full lifecycle dataset of the target land parcel and generate evidence summary fingerprints; The geometric identifier on-chain module is used to standardize the initial geometric data of the target plot, generate a unique plot identifier and geometric fingerprint, and form a state commitment record; The indicator generation module is used to calculate the schedule deviation rate, actual floor area ratio, and usage consistency index based on the full life cycle dataset and on-site observation data, and form a regulatory result set. The resource matching module is used to determine the resource demand and supply based on the full life cycle dataset, calculate the resource matching degree index, and form a resource matching result set. The constraint linkage solution module is used to generate constraint versions and resource-side constraints through constraint inheritance and lateral propagation rule engine, and to perform feasible region calculation to obtain resource allocation results, including: Step 41: Using the unique identifier of the land parcel as an index, read the constraint version and resource matching result set of the previous stage from the time-series status ledger, verify the effective time and version number of the constraint, and inherit the constraints within the validity period as the basic constraints; when the change of the comprehensive resource matching degree index exceeds the preset magnitude threshold, generate the resource-side constraints of adjacent land parcels according to the spatial adjacency relationship and change direction between land parcels, and form the constraint set of the current stage. Step 42: Merge the basic constraints and resource-side constraints into a constraint version candidate set, and perform consistency processing according to constraint type, constraint target variable and constraint priority; when there are multiple constraints in the same resource category, take the constraint with the highest priority as the main constraint, sum the weighted values ​​of each constraint to generate additional constraint items and add them to the main constraint to form a unified constraint version structure. Step 43: Using a unified constraint version structure and resource matching result set as input, establish a feasible region calculation model, solve for the resource allocation amount for each plot, and form a resource allocation result set, including: Step 51: Establish a set of resource supply and demand equations, and correspond the supply variables, demand variables and equilibrium variables of each resource category to the resource supply, resource demand and resource equilibrium of the land parcel, respectively; take the difference between the supply variable and the demand variable as the constraint condition that it equals the equilibrium variable, set non-negativity constraints on the equilibrium variable, set upper limit constraints on the supply variable, and set lower limit constraints on the demand variable to form a resource supply and demand constraint system. Step 52: Using the minimization of the sum of absolute values ​​of the supply and demand deviations of land resources as the objective function of linear programming, assign a penalty coefficient to the supply and demand deviation of each resource category, multiply the penalty coefficient by the absolute value of the deviation and sum them up, and use this as the optimization objective function to solve for the initial feasible solution that satisfies the global equilibrium condition. Step 53: Using the initial feasible solution as input, calculate the rate of change of the comprehensive resource matching degree index with respect to the resource allocation amount in the second-level iterative optimization stage. Adjust the resource allocation amount according to the direction of the rate of change, and decrease the step size of each adjustment according to the preset decay factor. When the difference of the comprehensive resource matching degree index between two consecutive iterations is lower than the preset convergence threshold, the model is determined to have converged. Output the final resource allocation amount for each plot and summarize the plot's unique identifier, the final resource allocation amount, and the constraint satisfaction status to generate a resource allocation result set. The same transaction solidification module is used to combine the resource allocation results into a verification receipt record, and submit it in the same transaction as the status commitment record of this stage in the time-series status ledger. The Deviation Writeback Snapshot module is used to aggregate deviations in field observation data, trigger the constraint inheritance and lateral propagation rule engine to generate new stage constraint versions and write them back, and return the full-cycle state snapshot and associated evidence path.

2. The administrative supervision and resource management system for the entire life cycle of urban land parcels as described in claim 1, characterized in that, The full lifecycle dataset includes: plot number, land use, net land area, approved floor area ratio, construction schedule, and surrounding population density. By calculating hash fingerprints for each data field of the full lifecycle dataset, combining the hash fingerprints of each field in the field sequence order to form a hash fingerprint set, and constructing a Merkle tree, the root hash value of the Merkle tree is determined as the evidence digest fingerprint of the full lifecycle dataset.

3. The administrative supervision and resource management system for the entire life cycle of urban land parcels as described in claim 1, characterized in that, The initial geometry of the target plot is normalized to generate a unique plot identifier and geometric fingerprint, and a state commitment record is formed, including: Step 11: Obtain the initial geometric data of the target plot and perform integrity verification and spatial projection unification. The initial geometric data includes the plot boundary coordinate point set, boundary line segment topological relationship, plot center point coordinates, and plot outer rectangle. Step 12: Perform vertex sorting, boundary direction correction and coordinate precision normalization on the initial geometric data obtained in Step 11 to generate normalized geometric data. Calculate the hash value as a geometric fingerprint based on the binary representation of the normalized geometric data. Then, concatenate the administrative division code and registration timestamp with the normalized geometric data and perform a hash operation to generate a unique identifier for the land parcel. Step 13: Bind the unique identifier of the land parcel, the geometric fingerprint, and the evidence digest fingerprint of the full life cycle dataset to form a state commitment structure, calculate the hash result of the state commitment structure, generate a state commitment record, and submit it to the time-series state ledger.

4. The administrative supervision and resource management system for the entire life cycle of urban land parcels as described in claim 3, characterized in that, It also includes cross-phase consistency verification mechanisms, including: Using the unique identifier of the land parcel as an index, extract state snapshots of two consecutive stages from the time-series state ledger, and perform field-by-field hash comparison of the geometric fingerprint and constraint version number in the state snapshot to determine whether the hash values ​​of each field are completely consistent. When the hash comparison result of any field is inconsistent, the abnormal transaction backtracking process is triggered, and the process backtracks step by step along the associated evidence path to the first consistent state snapshot. An abnormal verification record is generated in the time-series state ledger, and the abnormal field, the difference hash value and the backtracking stage number are recorded.

5. The administrative supervision and resource management system for the entire life cycle of urban land parcels as described in claim 1, characterized in that, Calculate the schedule deviation rate, actual floor area ratio, and usage consistency index to form a regulatory result set, including: Step 21: Using the unique identifier of the land parcel as an index, extract the project schedule and planned floor area ratio from the full life cycle dataset, extract the actual construction progress, total building volume and actual use data from the on-site observation data, and align them in time series. Step 22: Based on the aligned project schedule and the actual construction progress, calculate the difference between the actual construction progress and the project schedule in the time series, divide it by the project schedule, and obtain the schedule deviation rate. Then, calculate the weighted average to obtain the total schedule deviation rate. Step 23: Obtain the net land area of ​​the plot based on geometric fingerprint, obtain the actual plot ratio by the ratio of the total building volume to the net land area of ​​the plot, and calculate the difference between the actual plot ratio and the planned plot ratio by dividing the difference by the planned plot ratio to obtain the plot ratio deviation rate. Step 24: Unify the coding of actual use data and planned use data in the field observation data. Obtain the use consistency index by dividing the number of intersection elements of actual use data and planned use data by the number of elements of planned use data. Then, summarize the unique identifier of the land parcel, the total progress deviation rate, the plot ratio deviation rate and the use consistency index to generate a regulatory result set.

6. The administrative supervision and resource management system for the entire life cycle of urban land parcels as described in claim 1, characterized in that, Determine resource demand and supply, calculate resource matching index, and form a resource matching result set, including: Step 31: Using the unique identifier of the land parcel as an index, extract the resource demand parameters and supply parameters from the full life cycle dataset. By multiplying the original resource demand and the correction coefficient and adding the total schedule deviation rate and the plot ratio deviation rate, the corrected resource demand is obtained. By multiplying the original resource supply and the use consistency index and adding the original resource supply, the corrected resource supply is obtained. Step 32: For each type of resource, calculate the ratio of the difference between the corrected resource demand and the corrected resource supply to the sum of the two, find the negative of the absolute value and add one to obtain the matching index of that type of resource; sum the matching indices of each resource by weight to obtain the comprehensive resource matching index. Step 33: Summarize the unique identifier of the land parcel, each resource matching index, and the comprehensive resource matching index to form a resource matching result set.

7. The administrative supervision and resource management system for the entire life cycle of urban land parcels as described in claim 1, characterized in that, The resource allocation results are compiled into a verification receipt record and committed in the same transaction as the status commitment record for this stage in the time-series status ledger, including: Step 61: Based on the resource allocation result set, calculate the verification value for each record by subtracting the resource demand from the resource supply and then subtracting the resource balance. When the verification value is zero, generate a verification receipt record and bind the constraint version number, calculation timestamp, and comprehensive resource matching index. Step 62: Perform hash calculation on the verification receipt record, input the record content sequence into the hash function to generate a hash digest, and use the hash digest as the verification receipt fingerprint, which is in the same format as the digest fingerprint of the status commitment record for comparison at the ledger layer. Step 63: Using the unique identifier of the land parcel as the transaction primary key, combine the hash digest and the status commitment record into a transaction commit unit. Perform an integrity check on the transaction commit unit. After confirming that the hash matches and the timestamps are continuous, write the transaction unit into the time-series status ledger, generate a unique transaction number, and use this number to identify the corresponding resource allocation result.

8. The administrative supervision and resource management system for the entire life cycle of urban land parcels as described in claim 1, characterized in that, The offset write-back snapshot module includes: Step 71: Using the unique identifier of the land parcel as an index, match the on-site observation data with the verification receipt record of the previous stage, and perform time series difference calculation on the progress deviation rate, plot ratio deviation rate and land use consistency index of the same land parcel to obtain the progress deviation rate change rate, plot ratio deviation rate change rate and land use consistency index change rate, forming a set of deviation aggregation indicators. Step 72: When any deviation aggregation index exceeds the preset deviation threshold, the constraint inheritance and lateral propagation rule engine is triggered, the constraint version and resource matching result set of the previous stage are read, the constraints within the validity period are inherited, and the propagation influence coefficient is calculated based on the spatial adjacency relationship between plots. The propagation influence coefficient is multiplied by the basic constraint weight and accumulated to generate a lateral propagation constraint set. Step 73: Merge the inherited basic constraints and the propagated lateral constraints to form a new stage constraint version, and write the new stage constraint version into the time-series state ledger with the unique identifier of the land parcel as the transaction primary key, while generating a state snapshot and associated evidence path.

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