Urban construction soil operation management system based on cloud platform

Abstract

The invention discloses an urban construction soil operation management system based on a cloud platform, and belongs to the field of urban construction soil operation management. Land event data are collected in real time through a sensor network, an ordered event sequence is generated in combination with an event-driven architecture, and space-time association and causal relationship between events are analyzed by using an association rule mining algorithm; and the linkage influence of the planning change and the development progress is accurately identified. And when the influence exceeds a threshold value, triggering a block chain smart contract to execute right and interest circulation verification, generating a credible right and interest change log, and synchronizing the log to a related node through a distributed account book technology to ensure information consistency. If the possibility of ownership disputes is higher than a threshold value, an encrypted hash chain is adopted to generate a complete auditing track, key right transfer nodes are marked in combination with digital signatures, a final right ownership state is defined, and a land state database is updated. According to the invention, the transparent management of the full life cycle of the land and the efficient solution of ownership disputes are realized.

Description

Technical Field

[0001] This invention belongs to the field of urban construction land management technology, and particularly relates to an urban construction land management system based on a cloud platform. Background Technology

[0002] Urban land management is a crucial pillar of urban development, encompassing the entire process from land acquisition and planning to development and delivery, directly impacting the efficiency of urban resource allocation and the quality of economic development. Efficient and transparent land management not only ensures the rational use of urban space but also plays a vital role in safeguarding public interests and promoting market fairness. However, current land management still faces numerous challenges, urgently requiring technological innovation to address complex and ever-changing management needs.

[0003] Existing land management methods often rely on manual records and fragmented systems, leading to information silos and data inconsistencies. Many solutions lack the ability to dynamically track the entire lifecycle of land events, making it difficult to reflect changes in land status in real time. Furthermore, insufficient transparency and standardization in the transfer of rights often result in disputes due to information asymmetry or complex processes. These limitations make land management ill-suited to the rapidly changing needs of urban development. Event-driven dynamic tracking has become a core technical challenge in the full lifecycle management of land operations. Land management involves multiple stages, including transfer, planning, development, and acceptance, each generating numerous events such as the signing of transfer contracts, adjustments to planning schemes, and updates to development progress. The timing and interrelationships of these events are complex, making it difficult for existing systems to capture and integrate these dynamic changes in real time, resulting in managers being unable to accurately grasp the current state of the land. For example, in a city's land development, a dispute arose between the developer and regulatory authorities regarding land use due to planning changes not being updated to the system in a timely manner, delaying the project's progress. Standardized management of rights transfer is another key technical challenge. When land use rights, development rights, and other rights are transferred between different entities, it is essential to ensure that the process is transparent and compliant. However, current technology struggles to automatically verify and register changes in rights, making it prone to disputes due to manual operations or lack of transparency. For example, in the transfer of land revenue rights, the lack of a unified and reliable digital certificate can lead to disputes between the transacting parties regarding ownership, affecting the efficiency of the transfer.

[0004] Therefore, how to dynamically capture and integrate events throughout the entire lifecycle of land management, and how to ensure the transparency and automation of rights transfer, have become key issues in urban land management. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a cloud-based urban construction land management system to resolve the issues present in the prior art.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a cloud-based urban construction land management system, comprising: The event sequence generation module is used to collect real-time event data from the land site through a sensor network, process the real-time event data using an event-driven architecture, obtain a dynamic record of changes throughout the land's entire life cycle, and generate an ordered event sequence. The causal relationship analysis module is used to analyze the causal relationships between events based on the spatiotemporal correlation of each event in the event sequence, and to determine the linkage effect between planning changes and development progress. The verification result generation module is used to trigger the execution of the smart contract on the blockchain network and obtain the verification result of the transfer of rights if the determined linkage effect exceeds the preset threshold. The rights information registration module is used to extract rights change logs from the verification results and synchronize them to all relevant nodes through distributed ledger technology to obtain unified and reliable rights registration information. The ownership dispute judgment module is used to determine the possibility of ownership disputes by matching the rights registration information with the event sequence and using a consensus algorithm to confirm the consistency of the information. The audit trajectory generation module is used to obtain a complete audit trajectory by linking historical event data through an encrypted hash chain if the probability of ownership disputes is determined to be higher than a threshold, in order to resolve information asymmetry. The equity ownership module is used to extract key equity transfer nodes from the audit trail, mark key equity transfer nodes using a digital signature mechanism, and determine the final equity ownership status. The land management module is used to update the land status database based on the final ownership status, resulting in optimized urban resource allocation records.

[0007] In a second aspect, the present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0008] Compared with the prior art, the present invention has the following advantages and technical effects: This invention discloses a cloud-based urban construction land management system, proposing an integrated solution to address business scenarios in land management such as insufficient real-time data collection, unclear causal relationships of events, opaque rights transfer, and difficulty in tracing ownership disputes. This invention collects land event data in real time through a sensor network, generates ordered event sequences using an event-driven architecture, and analyzes the spatiotemporal correlations and causal relationships between events using association rule mining algorithms to accurately identify the linkage between planning changes and development progress. When the impact exceeds a threshold, a blockchain smart contract is triggered to verify the rights transfer, generating a reliable rights change log, which is synchronized to relevant nodes through distributed ledger technology to ensure information consistency. If the probability of ownership disputes exceeds the threshold, this invention uses an encrypted hash chain to generate a complete audit trail, combines digital signatures to mark key rights transfer nodes, clarifies the final rights ownership status, and updates the land status database.

[0009] This invention integrates sensors, blockchain, and consensus algorithms to achieve transparent management of the entire land lifecycle and efficient resolution of ownership disputes, significantly improving the optimization efficiency and credibility of urban resource allocation. Attached Figure Description

[0010] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a system schematic diagram according to an embodiment of the present invention; Figure 2 This is a flowchart of the event sequence generation module according to an embodiment of the present invention; Figure 3 This is a flowchart of the rights attribution module in an embodiment of the present invention. Detailed Implementation

[0011] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0012] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0013] Example 1 like Figure 1 As shown, this embodiment provides a cloud platform-based urban construction land management system, including: The event sequence generation module is used to collect real-time event data from the land site through a sensor network, process the real-time event data using an event-driven architecture, obtain a dynamic record of changes throughout the land's entire life cycle, and generate an ordered event sequence. like Figure 2 As shown, real-time event data is acquired from the land site via a sensor network, and each event is timestamped to obtain a raw event dataset. Based on the raw event dataset, an event-driven architecture is used to segment the data stream, determine event triggering conditions, and identify the valid event set. If the valid event set contains consecutive timestamps, a time series analysis algorithm is used to extract dynamic change features and obtain land change trends. Based on the land change trends, a clustering algorithm is used to group the change features, generating a phase division of the land's entire life cycle. Using the phase division results and the timestamps of the event sequences, an ordered event record sequence is generated.

[0014] For example, real-time event data can be acquired from the land using a sensor network. Soil moisture sensors, temperature sensors, and light sensors can be deployed in farmland to collect data in real time and add a timestamp to each event. Assuming a farmland records soil moisture once per hour, the generated data format would be "2025-09-23 01:00, Moisture 30%". This data constitutes the raw event dataset, providing the foundation for subsequent processing. The high-frequency acquisition by the sensor network ensures real-time data, helping to promptly detect changes in land conditions.

[0015] In one possible implementation, data streams are sharded based on an event-driven architecture. This can be achieved by using a message queue system (such as Kafka) to shard data by time windows, for example, shards every 10 minutes. When determining event trigger conditions, rules can be set, such as humidity below 20% or temperature above 35℃ being considered anomalous events, thus filtering out the valid event set. For example, if a shard records "2025-09-23 02:00, humidity 18%", then that event is marked as valid. This architecture improves data processing efficiency, reduces latency, and ensures that critical events are quickly identified.

[0016] Specifically, for consecutive timestamps in a valid event set, time series analysis algorithms can be used to extract dynamic change features.

[0017] For example, using a sliding window to analyze 48 consecutive hours of humidity data and calculate the rate of change, it was found that humidity continuously decreased from 30% to 15% over a certain period. This trend indicates that the land may face drought risk. Time series analysis can effectively capture dynamic changes and provide data support for subsequent decision-making.

[0018] Preferably, based on land change trends, the K-means clustering algorithm can be used to group the change features. Assuming the analysis yields three trends: continuously decreasing humidity, stable humidity, and increasing humidity, the corresponding feature vectors can be generated based on the rate of change and duration, such as "decreasing rate 0.5% / hour, lasting 24 hours". The clustering results divide farmland into different life cycle stages, such as "drought period", "stable period", and "recovery period". This grouping method intuitively reflects the evolution of land conditions, facilitating precise management.

[0019] For example, by combining the phase division results and timestamps, an ordered sequence of event records can be generated. Suppose a certain farmland was classified as being in a dry period from "2025-09-23 01:00 to 2025-09-24 01:00". The record sequence would be: "2025-09-23 01:00, humidity 18%, dry period begins; 2025-09-23 12:00, humidity 15%, dry period continues." This sequence clearly records the timeline of land condition changes, helping to trace historical conditions and predict future trends.

[0020] Understandably, the combined application of the above methods significantly improves land management efficiency. Event-driven architecture ensures real-time response, time-series analysis reveals patterns of change, clustering algorithms provide a basis for stage division, and ordered record sequences facilitate long-term monitoring. This combination of technologies not only enhances the automation level of data processing but also provides a scientific basis for precision agriculture, optimizes resource utilization, and reduces management costs.

[0021] The causal relationship analysis module is used to analyze the causal relationships between events based on the spatiotemporal correlation of each event in the event sequence, and to determine the linkage effect between planning changes and development progress. Spatiotemporal attributes are extracted from event sequences, and events are classified using timestamps and spatial distribution to obtain a set of categorized events. Based on this set, association rule mining algorithms are used to analyze causal relationships between events, determining a set of causal relationships. If strongly correlated events exist in the causal relationship set, the event triggering sequence is obtained by comparing and analyzing timestamps. Based on the event triggering sequence, the impact of planning changes on development progress is analyzed, and schedule deviations are grouped using clustering algorithms to obtain a set of deviation groups. If significant schedule deviations exist in the deviation group set, affected areas are identified through spatial distribution analysis, obtaining a set of regional impacts. Based on the set of regional impacts, data flow is adjusted to generate optimized linkage relationship records. The timestamps of the event sequences are updated using these linkage relationship records to obtain an updated ordered event sequence.

[0022] For example, in the field of land management, real-time event data collected through sensor networks can be further analyzed for its spatiotemporal attributes to achieve precise classification and causal analysis. The extraction of spatiotemporal attributes is based on timestamps and spatial distribution. Suppose a farmland is equipped with soil moisture and temperature sensors, and the data record is "2025-09-23 01:00, humidity 30%, location A1". The timestamp is accurate to the minute, and the spatial distribution is identified using farmland grid coordinates, such as A1 representing the northwest corner area. This method binds events to specific time and space, facilitating subsequent classification. During classification, event types can be divided according to humidity range and regional location. For example, "drought event" is defined as records with humidity below 20%, generating a set of classified events, such as "2025-09-23 02:00, humidity 18%, A1, drought event".

[0023] In one possible implementation, association rule mining algorithms are used to analyze causal relationships in a set of categorized events. Suppose that the Apriori algorithm reveals frequent co-occurrence of "high temperature events (temperatures above 35°C)" and "drought events" in region A1, with a support of 0.8 and a confidence of 0.9, indicating that high temperature may be a contributing factor to drought, thus forming a causal association set. For strongly correlated events, the triggering order is analyzed by comparing timestamps.

[0024] For example, “2025-09-23 01:00, temperature 36℃, A1” precedes “2025-09-23 02:00, humidity 18%, A1”, generating an event trigger sequence and confirming that high temperature triggers drought.

[0025] Specifically, based on the event trigger sequence, the impact of planning changes on development progress is analyzed. It is assumed that irrigation plans are adjusted due to a drought event, resulting in delayed planting in some areas. The K-means clustering algorithm is used to group progress deviations into "minor delays" and "severe delays."

[0026] For example, planting in region A1 was delayed by 3 days due to continuous drought, and it was classified as "severely delayed," forming a biased group set. If a significant bias exists, the affected areas are determined through spatial distribution analysis; for example, region A1 and the neighboring region A2 constitute a regional impact set.

[0027] Preferably, the data flow is adjusted based on the regional influence set. Assuming sensor data from region A1 is preferentially transmitted to the irrigation control system, optimized linkage records are generated, such as "2025-09-23 03:00, A1 prioritizes irrigation". These records are used to update the timestamps of the event sequences, generating ordered event sequences, such as "2025-09-23 03:00, humidity 25%, A1, irrigation initiated". This method, through spatiotemporal analysis, causal mining, and schedule adjustment, forms a closed-loop management system, ensuring accurate data-driven decision-making.

[0028] The verification result generation module is used to trigger the execution of the smart contract on the blockchain network and obtain the verification result of the transfer of rights if the determined linkage effect exceeds the preset threshold. If the detected linkage effect data exceeds a preset threshold, real-time linkage effect data is acquired through the data acquisition module to determine if the triggering condition is met. If the triggering condition is met, the smart contract of the blockchain network is activated via an interface call to obtain the initialization state of the contract execution. Based on the initialization state, the input linkage effect data is processed using the predefined rules of the smart contract to obtain the intermediate result of the equity transfer. The intermediate result is then validated using verification logic to determine whether the equity transfer complies with the contract rules. If the equity transfer complies with the contract rules, the transfer operation is completed through the execution mechanism of the smart contract, yielding the final verification result.

[0029] For example, in the field of land management, the processing of cascading effect data and its integration with blockchain smart contracts can optimize farmland resource management decisions. Cascading effect data refers to resource allocation adjustments triggered by environmental changes, such as changes in irrigation or planting plans. Acquiring real-time data through a data acquisition module is crucial.

[0030] For example, a soil moisture sensor deployed in farmland detects that the humidity in a certain area has dropped to 15%, below a preset threshold of 20%, triggering data collection. The collection module records the data, such as "2025-09-23 04:00, humidity 15%, location B1," and transmits the data to the processing system. This real-time collection ensures that the data reflects the latest environmental conditions, providing a basis for subsequent decision-making. If the triggering conditions are met, the system calls a smart contract on the blockchain network through an interface.

[0031] For example, a humidity level below 20% triggers an "irrigation priority" contract, initially set to "pending allocation." The smart contract processes data according to predefined rules, such as allocating water resources based on humidity levels and regional priorities. Assuming region B1 has high priority, the contract generates an intermediate result: allocating 1000 liters of water. Verification logic checks if this allocation complies with the rules, such as whether the water volume is within the available resource range. Assuming the total available water volume is 5000 liters, the allocation is valid, and the contract continues execution. The intermediate results of the rights transfer must be verified for compliance.

[0032] For example, a smart contract stipulates that water resource allocation must consider the demand balance of neighboring areas. If B1 is allocated 1000 liters, and the humidity in the neighboring B2 area is 25%, no irrigation is needed, the verification passes. Finally, the contract executes the flow operation, generating the record "2025-09-23 04:30, B1 irrigated 1000 liters". This mechanism ensures that resource allocation is transparent and tamper-proof.

[0033] Specifically, the execution mechanism of smart contracts can optimize scheduling through timestamps and spatial distribution.

[0034] For example, irrigation records for area B1 are updated to the blockchain, forming an irreversible log to ensure traceability. If the humidity in B1 remains below 20% for three consecutive days, the contract automatically adjusts to allocate 1200 liters per day, generating a new record: "2025-09-24 04:00, B1 irrigated 1200 liters". This approach optimizes resource utilization efficiency through data-driven methods.

[0035] In one embodiment, the verification logic can be extended to multi-dimensional verification.

[0036] For example, in addition to water volume, the status of irrigation equipment must also be verified. If the equipment in area B1 is functioning normally, the process continues; if it malfunctions, the contract is suspended and maintenance is notified. This multi-dimensional verification improves system reliability.

[0037] Preferably, the contract can dynamically adjust its rules based on historical data. For example, if region B1 is found to be chronically arid, its priority is automatically increased, resulting in more flexible resource allocation outcomes. Through this mechanism, data collection, contract invocation, rule processing, and result verification form a closed loop, ensuring accurate and transparent resource allocation. The immutability of blockchain guarantees data trustworthiness, and dynamic rule adjustments enhance adaptability, providing efficient support for farmland management.

[0038] The rights information registration module is used to extract rights change logs from the verification results and synchronize them to all relevant nodes through distributed ledger technology to obtain unified and reliable rights registration information. The process involves obtaining a verification result set, validating equity change records according to data verification rules, and obtaining a legitimate equity change dataset. This dataset is then processed using a distributed consensus algorithm to generate update instructions for the distributed ledger. A node communication mechanism is used to broadcast the update instructions to each node, and the node's reception status is determined. If the node's reception status is successful, the update instructions are written into the distributed ledger using the ledger update protocol, resulting in updated ledger data. A node synchronization mechanism is used to compare the ledger data across nodes to determine data consistency. If the data consistency check passes, the update time is recorded using a synchronization timestamp, generating equity registration data.

[0039] For example, the topic of obtaining verification result sets and verifying equity change records through data verification rules can be understood as conducting a preliminary screening of the legality of equity changes within the blockchain network. Suppose a company manages asset transactions through blockchain and needs to verify the compliance of each transaction. Data verification rules might include checking whether the identities of both parties to the transaction are valid, whether the transaction amount is within the prescribed range, and whether the transaction time meets the system's timestamp requirements.

[0040] Specifically, the system collects a set of transaction records, such as 100 asset transfer records. Each record includes a transaction ID, initiator, recipient, amount, and timestamp. Verification rules check these records one by one, eliminating those exceeding 10 million yuan or those with unregistered identities, ultimately generating a dataset of legitimate changes in rights, such as 80 qualified records. This method ensures that all data processed subsequently is compliant, laying the foundation for updates to the distributed ledger.

[0041] In one embodiment, for the topic of processing a dataset of legitimate changes in interests and generating update instructions through a distributed consensus algorithm, a consensus process based on a proof-of-work mechanism can be envisioned. The goal of the distributed consensus algorithm is to ensure that all nodes in the network reach a consensus on the data state.

[0042] For example, 80 verified legitimate transaction records are input into the consensus algorithm module, where nodes calculate and confirm the order and content of these transactions. The consensus algorithm generates a unique hash value for each transaction and organizes these hash values ​​into a block. Assuming a block contains 10 transactions, the system generates an update instruction to append that block to the ledger. This mechanism ensures fairness and consistency in data processing, preventing a single node from manipulating the data.

[0043] For example, considering a topic that uses a node communication mechanism to broadcast update commands and determine the nodes' reception status, a blockchain network with 50 nodes can be envisioned. After the update command is generated, the master node broadcasts the command to the other 49 nodes via a peer-to-peer communication protocol. Each node, upon receiving the command, returns an acknowledgment signal.

[0044] For example, 48 nodes return a "successfully received" status within 5 seconds, while 1 node fails to respond due to network latency. The system records these statuses to ensure that the majority of nodes have received the instruction. This approach improves network robustness and ensures that update instructions are propagated efficiently.

[0045] In one embodiment, for the topic of writing update instructions to a distributed ledger and obtaining updated ledger data via a ledger update protocol, a time-priority ledger update protocol can be envisioned. Assuming the instruction is successfully broadcast, nodes will write the new block to the ledger in timestamp order.

[0046] For example, if a block contains 10 transactions, the total number of transactions in the ledger increases from 1000 to 1010 after the block is written. The updated ledger data includes the new block hash and transaction details, ensuring data traceability. This method guarantees the integrity and transparency of the ledger.

[0047] For example, regarding the topic of comparing ledger data and determining data consistency through a node synchronization mechanism, a synchronization comparison process can be envisioned. After writing a new block, 50 nodes will compare the hash values ​​of their respective ledgers through the synchronization mechanism.

[0048] For example, the system randomly selects ledger data from 10 nodes and calculates whether their hash values ​​are consistent. If all nodes have the same hash value, the consistency check passes. This method ensures that the data state of all nodes remains consistent, preventing data tampering.

[0049] In one embodiment, a timestamp allocation mechanism can be envisioned for topics that record update times and generate staking registration data via synchronized timestamps. Assuming the ledger update is complete, the system assigns a timestamp to the new block, such as 2025-09-23 01:41:00. The staking registration data records the final status of each transaction, such as "confirmed" or "registered," and generates a registration table containing the transaction ID and timestamp. This approach provides a reliable time basis for subsequent auditing.

[0050] The ownership dispute judgment module is used to determine the possibility of ownership disputes by matching the rights registration information with the event sequence and using a consensus algorithm to confirm the consistency of the information. The process involves acquiring rights registration information and event sequence data, parsing the data structure using a standardized protocol to obtain a structured dataset. Using this structured dataset, rights registration information and event sequences are compared using data matching rules to identify records with consistent matching. For these consistent records, a Byzantine fault-tolerant consensus algorithm is used to verify the credibility of the information source, resulting in a credibility score. Based on the credibility score, if the score is below a preset threshold, cross-validation is performed on the event timestamp records to determine the data integrity status. The data integrity status is then used to analyze the correlation between rights ownership identifiers and dispute triggering conditions, resulting in a set of potential dispute factors. For this set of potential dispute factors, a logistic regression algorithm is used to calculate the probability of ownership disputes, determining the dispute risk level. Based on the dispute risk level, a quantitative assessment result of the ownership dispute probability is generated, and the final judgment conclusion is output.

[0051] For example, regarding the acquisition of rights registration information and event sequence data, a standardized protocol can be used to parse the data structure. Imagine a company managing real estate transaction rights registration via blockchain. The standardized protocol could be a JSON-based data parsing rule used to extract key fields from the transaction data. Suppose the system collects 100 real estate transaction records, each containing fields such as transaction ID, owner, transferee, asset number, and transaction time. Through the parsing protocol, the system converts these records into a structured dataset with a unified format, such as a data table containing 80 complete records, where each record's fields clearly correspond, facilitating subsequent processing. This approach ensures data format consistency, providing a foundation for matching and verification.

[0052] For example, regarding the topic of comparing equity registration information with event sequences using data matching rules on structured datasets, a system could be envisioned that compares property rights change records with transaction event sequences. Data matching rules might include checking whether the transaction ID is unique and whether the property owner matches the initiator in the event sequence. Suppose the system compares 80 records and finds that the transaction ID and event timestamp of 78 records match perfectly, generating a consistency record table. This comparison method can quickly filter out data anomalies, ensuring the accuracy of subsequent analysis.

[0053] For example, regarding the topic of verifying the credibility of information sources using a Byzantine fault-tolerant consensus algorithm, a blockchain network with 32 nodes can be envisioned. The system inputs 78 consistent records into the Byzantine fault-tolerant algorithm module, and the nodes confirm the data source through a voting mechanism. Assuming a credibility score threshold of 0.8, the system calculates that 75 records have a score higher than 0.8, while 3 records have a score lower than the threshold due to inconsistencies reported by the nodes. This mechanism improves data credibility through multi-node verification.

[0054] For example, in a scenario involving cross-validation of event timestamp records with scores below a threshold, the system could be designed to further examine three low-scoring records. The cross-validation rules would involve comparing the timestamps with records from an external trusted time server. If two records' timestamps match the server's, they would be considered complete; one record would be marked as incomplete due to time discrepancies. This approach ensures data reliability through multi-source verification.

[0055] For example, by analyzing the correlation between ownership attribution markers and dispute triggering conditions, and obtaining a set of potential dispute factors, we can envision a system analyzing whether the property owner's identity and transaction amount comply with regulations. Suppose the system finds an incomplete record involving an amount exceeding limits, potentially triggering a dispute, generating a factor set including "abnormal amount" and "identity not verified." This analytical approach can identify potential risk points in advance.

[0056] For example, regarding the topic of calculating the probability of ownership disputes using logistic regression algorithms, one could envision a system that calculates the dispute probability based on a set of factors. Assuming that factors with abnormally high monetary values ​​have a higher weight in the factor set, the system might assess a dispute probability of 0.7, classifying it as high-risk. This quantitative approach provides data support for risk management.

[0057] For example, for a topic involving generating a quantitative assessment of the probability of ownership disputes and outputting a judgment conclusion, the system could generate a report showing that records with a dispute probability of 0.7 require manual review. The report would include the transaction ID, risk level, and recommended measures, such as supplementary identity verification. This approach provides a clear basis for decision-making.

[0058] The audit trajectory generation module is used to obtain a complete audit trajectory by linking historical event data through an encrypted hash chain if the probability of ownership disputes is determined to be higher than a threshold, in order to resolve information asymmetry. If the probability of ownership disputes exceeds a preset threshold, historical event data is analyzed using a machine learning classification algorithm to obtain a dispute probability assessment result. The historical event data is then processed using a cryptographic hash algorithm to generate unique hash values, ensuring the integrity of the data chain. Based on the generated hash values, a cryptographic hash chain is constructed, linking all relevant historical events to obtain a continuous event sequence. For this continuous event sequence, timestamps are used to mark the occurrence time of each event, resulting in a timestamped event chain. Data consistency checks are performed on the timestamped event chain to determine the integrity of the audit trail. If the audit trail is complete, the cryptographic hash chain is stored in a distributed database to obtain persistent data records. Based on these persistent data records, a traceable audit log is generated to eliminate information asymmetry.

[0059] For example, considering a scenario where the probability of ownership disputes exceeds a preset threshold, a blockchain system for managing real estate transactions could be envisioned, using machine learning classification algorithms to analyze historical event data. The system collects 1000 transaction records from the past year, each containing fields such as transaction ID, property owner, transaction amount, and time. Using a random forest classification algorithm, the system trains a model based on historical dispute cases, extracting features such as abnormal transaction amounts and incomplete property owner identities. Assuming a threshold of 0.6, the analysis reveals that 50 records have a dispute probability exceeding 0.6, marking them as high-risk. This approach identifies potential problems through data-driven analysis.

[0060] Specifically, the system generates unique hash values ​​for each transaction record using a cryptographic hash algorithm. For example, the system could apply the SHA-256 algorithm to each transaction record. Suppose a transaction record contains a transaction ID "TX123", the property owner "Zhang", and the amount is 5 million, generating the hash value "HASH789". This hash value ensures the data has not been tampered with. The system generates hashes for each of the 1000 records, building the foundation for data integrity.

[0061] For example, regarding the topic of constructing a cryptographic hash chain linking historical events, one could envision a system that links 1000 hash values ​​sequentially by transaction time to form a hash chain. The hash value of each record is associated with the hash of the previous record, generating a continuous link. If a hash mismatch is found, the system marks it as an anomaly, ensuring the integrity of the chain.

[0062] Specifically, for topics that use timestamp technology to mark event times, the system can be designed to add a trusted timestamp to each record, referencing an external time server. Assuming the transaction "TX123" is marked as 2025-09-23 10:00:00, the system generates a timestamped event chain containing 1000 records. This method ensures the traceability of event order.

[0063] For example, regarding the topic of verifying the integrity of audit trails through data consistency checks, one could envision a system comparing timestamps and hash chains to check for time discrepancies or hash mismatches in 1000 records. Assume 998 records pass the verification, while 2 records are marked due to timestamp anomalies. This verification ensures the reliability of the audit trail.

[0064] Specifically, by storing the topics of the cryptographic hash chain in a distributed database, one could envision a system using a distributed ledger to store a hash chain of 1000 records, deployed across 10 nodes. Each node holds a complete copy, and even if one node fails, the system can still recover data from the other nodes. This approach improves data persistence.

[0065] For example, regarding the topic of generating traceable audit logs, one could envision a system that generates logs based on hash chains and timestamps, recording the ID, time, hash value, etc., of each transaction. Suppose a user queries transaction "TX123," the system returns the complete audit path, displaying the entire process from initiation to confirmation. This approach eliminates information asymmetry through transparent recording.

[0066] The equity ownership module is used to extract key equity transfer nodes from the audit trail, mark key equity transfer nodes using a digital signature mechanism, and determine the final equity ownership status. like Figure 3As shown, original transfer records are obtained from audit trajectory data. Key transfer nodes are filtered using node extraction rules to obtain a set of key nodes. For the set of key nodes, an elliptic curve digital signature algorithm is used to generate digital signature markers, determining the set of marked nodes. The digital signature markers in the set of marked nodes are verified through a signature verification mechanism to obtain a set of verified nodes. Transfer node sequences are extracted from the set of verified nodes, and a logical order between nodes is constructed based on node relationships to obtain an ordered node sequence. Based on the ordered node sequence, state tracing path analysis is used to determine the evolution trajectory of the ownership status, obtaining a state evolution sequence. For the state evolution sequence, data integrity verification is performed, and a hash algorithm is used to verify the integrity of the audit trajectory data to obtain the final ownership status. If the final ownership status is consistent with the preset ownership rules, an ownership status confirmation record is generated through the state tracing path to determine the final ownership status.

[0067] In one possible implementation, the original transfer records are obtained from the audit trajectory data, but the reliability of the data source must be ensured.

[0068] For example, a blockchain-based distributed ledger stores asset transfer records, with each record containing information such as the transfer initiator, recipient, time, and asset type. Suppose a company maintains digital asset transfer records involving 1000 units of digital copyright transfers. The original records need to be extracted from a distributed database to ensure the data has not been tampered with. During extraction, a complete dataset containing transfer details can be obtained by querying a specific time period or asset ID, such as "On January 1, 2025, User A transferred 100 units of copyright to User B."

[0069] For example, key transfer nodes can be selected using node extraction rules based on criteria such as transaction amount, frequency, or participant identity. Suppose the rules are set as "single transfer exceeding 500 units" or "involving core users," 10 key nodes could be selected, such as "a 600-unit transfer from user A to user B." These nodes constitute a set of key nodes, reflecting the main asset flow path.

[0070] In one possible implementation, an elliptic curve digital signature algorithm is used to generate digital signature tokens for key nodes. Each node's data, such as the transfer amount and time, is hashed and combined with the private key to generate a unique signature.

[0071] For example, user A's transfer record generates a hash value H1, which is then signed with their private key to obtain the token S1. The signature token ensures the non-repudiation of node data; any tampering will invalidate the signature.

[0072] For example, signature verification mechanisms are used to validate the digital signatures of a set of tagged nodes. During verification, the signature is decrypted using the corresponding public key, and the decrypted hash value is compared with the current hash value of the node's data. Assuming that signature verification is performed on 10 nodes, 9 will pass, and 1 will fail due to data inconsistency. This failed node is then removed, resulting in the set of nodes that passed verification. This mechanism ensures data trustworthiness and excludes invalid nodes.

[0073] In one possible implementation, a sequence of transition nodes is extracted from the set of verified nodes, and a logical order is constructed based on the relationships between nodes, such as temporal or causal relationships.

[0074] For example, a node sequence shows the transfer path from "user A to B, B to C", with the logical order being A→B→C. Such an ordered node sequence clearly reflects the asset transfer process.

[0075] For example, state tracing path analysis is used to determine the evolution trajectory of ownership status. By analyzing an ordered sequence of nodes, the state changes of an asset from its initial to its final ownership can be traced. Assume the initial state is "User A owns 1000 units," and after multiple transitions, the final state is "User C owns 600 units." The state evolution sequence records each step of the change, such as "A decreases by 600, B increases by 600, B decreases by 600, C increases by 600."

[0076] In one possible implementation, data integrity verification uses a hash algorithm to check the audit trail.

[0077] For example, the overall hash value of the state evolution sequence is calculated and compared with the expected hash value stored to confirm that the data has not been tampered with. If they match, an attribution status confirmation record is generated.

[0078] For example, the final record shows that "User C is the rightful owner of 600 copyright units" and is stored in a distributed database to ensure traceability and transparency.

[0079] For example, if the final ownership status matches the preset rules, such as "only registered users are allowed to hold copyrights," a confirmation record is generated, clearly defining the ownership result. This record can serve as a basis for subsequent dispute resolution, improving audit efficiency and credibility.

[0080] The land management module is used to update the land status database based on the final ownership status, resulting in optimized urban resource allocation records.

[0081] The system retrieves the latest ownership status data from an external system via an ownership status query interface and stores it in a temporary data table to obtain an initial ownership dataset. Data cleaning techniques are used to remove duplicates and fill in missing values ​​in the initial ownership dataset, generating a standardized ownership status dataset. If the standardized ownership status dataset differs from existing records in the land status database, a status association analysis is triggered to calculate the matching degree between ownership status and land use classification, yielding a status matching result. Based on the status matching result, the land use classification field in the land status database is updated, generating an updated land status dataset. A resource allocation efficiency evaluation model is used to analyze the updated land status dataset, calculating the optimization coefficient for urban resource allocation and obtaining an optimized resource allocation scheme. A database consistency verification algorithm is used to compare the optimized resource allocation scheme with the land status database to determine if there are any conflicts, obtaining a consistency verification result. Based on the consistency verification result, the optimized resource allocation scheme is written into the configuration record storage module, generating optimized urban resource allocation records.

[0082] In one possible implementation, when obtaining the latest data from an external system through the ownership status query interface, an API-based real-time data retrieval mechanism can be designed.

[0083] For example, the urban land management system connects to the national land and resources database via a RESTful interface, retrieving the latest land ownership data daily. This data includes fields such as land parcel number, owner, and rights type, and is stored in a temporary data table. The initial rights dataset may contain 1,000 records, each including land parcel ID, ownership status, and timestamp. This approach ensures data timeliness and facilitates subsequent processing.

[0084] For example, data cleaning for the initial equity dataset can employ deduplication and missing value imputation techniques.

[0085] Specifically, when deduplicating records, duplicate records can be removed by combining the land parcel ID and the owner field.

[0086] For example, if a record with the ID A001 appears twice, the system retains the record with the latest timestamp. Missing value imputation can be based on business rules, such as missing rights type fields, which can be inferred as "residential" or "commercial" based on the land use. After cleaning, a standardized rights ownership status dataset is generated, potentially reducing the number of records to 950, with more standardized fields. This method improves data quality and provides a reliable foundation for subsequent analysis.

[0087] In one possible implementation, if there is a difference between the standardized dataset and the land state database, state association analysis can be triggered.

[0088] For example, a plot of land might be displayed as "commercial land" in a standardized dataset, while the land status database records it as "residential land." By calculating the matching degree, combining features such as land use, area, and location, a matching degree of 85% is obtained. If the matching degree is below 90%, a discrepancy is considered to exist, and the database needs to be updated. This analysis ensures data consistency and reduces attribution errors.

[0089] For example, when updating the land status database, the land use classification field can be adjusted based on the status matching results. If a city has 500 land parcels that need updating, the system can change the classification of 100 parcels from "residential" to "commercial" based on the matching results. The updated land status dataset reflects the latest classification, facilitating urban planning. This approach improves data accuracy and supports precise decision-making.

[0090] In one possible implementation, the resource allocation efficiency assessment model can analyze an updated land status dataset to calculate the optimization coefficients for urban resource allocation.

[0091] For example, the model assesses the land use distribution, taking into account factors such as traffic and population density, and arrives at an optimization coefficient of 0.92, indicating that resource allocation is close to optimal. The optimization plan might suggest increasing the proportion of commercial land. This assessment optimizes the efficiency of urban resource utilization.

[0092] For example, database consistency verification algorithms can compare optimized solutions with land status databases.

[0093] For example, an optimization plan might suggest converting a plot of land to industrial use, while the database shows it as commercial use; a conflict is detected during verification. The system records the conflict and generates a consistency check result to ensure the feasibility of the plan. This verification reduces the risk of execution errors.

[0094] In one possible implementation, when the resource allocation optimization scheme is written to the configuration record storage module, a record containing the plot ID, optimization purpose, and timestamp can be generated.

[0095] For example, a city generates 1,000 optimization records, stores them in the module, and allows for subsequent queries and audits. This storage method facilitates tracking and backtracking, improving management efficiency.

[0096] Example 2 This embodiment also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the system described in Embodiment 1.

[0097] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A cloud platform-based urban construction land management system, characterized in that, The method comprises the following steps: An event sequence generation module is configured to collect real-time event data from a land site through a sensor network, process the real-time event data using an event-driven architecture, obtain a dynamic change record of a land full life cycle, and generate an ordered event sequence; A causal relationship analysis module is configured to analyze the causal relationship between events in the event sequence using an association rule mining algorithm based on the spatiotemporal correlation of each event in the event sequence, and determine the linkage influence of planning changes and development progress; A verification result generation module is configured to trigger the execution of a smart contract of a blockchain network to obtain a verification result of the flow of rights and interests if the determined linkage influence exceeds a preset threshold; A rights and interests information registration module is configured to extract a rights and interests change log from the verification result, synchronize the rights and interests change log to all relevant nodes through a distributed ledger technology, and obtain uniform and reliable rights and interests registration information; A rights and interests dispute judgment module is configured to determine the possibility of a rights and interests dispute based on the matching of the rights and interests registration information and the event sequence, and confirm the consistency of the information using a consensus algorithm; An audit trail generation module is configured to obtain a complete audit trail by linking historical event data through an encryption hash chain if the possibility of a rights and interests dispute is higher than a threshold, so as to resolve information asymmetry; A rights and interests attribution module is configured to extract key rights and interests transfer nodes from the audit trail, mark the key rights and interests transfer nodes using a digital signature mechanism, and determine the final rights and interests attribution state; A land management module is configured to update a land state database based on the final rights and interests attribution state, and obtain an optimized urban resource allocation record.

2. The system of claim 1, wherein, The event sequence generation module specifically comprises: Real-time event data is obtained from a land site through a sensor network, and each event is marked with a timestamp to obtain an original event data set; The original event data set is processed using an event-driven architecture to determine an effective event set by judging event trigger conditions; If the effective event set contains continuous timestamps, a time series analysis algorithm is used to extract dynamic change characteristics to obtain a land change trend; The change characteristics are grouped using a clustering algorithm based on the land change trend to generate stage division of the land full life cycle; An ordered event record sequence is generated based on the stage division result and the timestamps of the event sequence.

3. The system of claim 1, wherein, The causal relationship analysis module specifically comprises: Temporal and spatial attributes are obtained from the event sequence, and events are classified using timestamp marking and spatial distribution to obtain a classified event set; The causal relationship between events is analyzed using an association rule mining algorithm based on the classified event set to determine a causal association set; If there are strongly associated events in the causal association set, the event trigger sequence is obtained by comparing and analyzing the event trigger order through timestamp marking; The influence of planning changes on development progress is analyzed based on the event trigger sequence, and a clustering algorithm is used to group progress deviations to obtain a deviation grouping set; If there are significant progress deviations in the deviation grouping set, the affected areas are determined through spatial distribution analysis to obtain a regional influence set; The data flow is adjusted based on the regional influence set to generate an optimized linkage relationship record; The timestamps of the event sequence are updated based on the linkage relationship record to obtain an updated ordered event sequence.

4. The system of claim 1, wherein, The verification result generation module specifically comprises: If the linkage effect data exceeds the preset threshold, real-time linkage effect data is acquired through the data acquisition module to determine whether the trigger condition is met; If the trigger condition is met, the smart contract of the blockchain network is activated through the interface call to obtain the initialization state of the contract execution; According to the initialization state, the input linkage effect data is processed using the predefined rules of the smart contract to obtain the intermediate result of the equity flow; The legality of the intermediate result is verified through the verification logic to determine whether the equity flow complies with the contract rules; If the equity flow complies with the contract rules, the flow operation is completed through the execution mechanism of the smart contract to obtain the final verification result.

5. The system of claim 1, wherein, The equity information registration module specifically comprises: The verification result set is acquired, the equity change record is verified through the data verification rule to obtain the legal equity change data set; The legal equity change data set is processed through the distributed consensus algorithm to generate the update instruction of the distributed ledger; The update instruction is broadcast to each node using the node communication mechanism to determine the node reception state; If the node reception state is successful, the update instruction is written into the distributed ledger through the ledger update protocol to obtain the updated ledger data; The data consistency is judged through the node synchronization mechanism by comparing the ledger data of each node; If the data consistency verification passes, the update time is recorded through the synchronization timestamp to generate the equity registration data.

6. The system of claim 1, wherein, The ownership dispute judgment module specifically comprises: The equity registration information and event sequence data are acquired, the data structure is parsed using the standardized protocol to obtain the structured data set; The structured data set is used to compare the equity registration information and event sequence using the data matching rule to determine the matching consistency record; The matching consistency record is verified using the Byzantine fault-tolerant consensus algorithm to obtain the credibility score; According to the credibility score, if the score is lower than the preset threshold, the event timestamp record is cross-verified to judge the data integrity state; Through the data integrity state, the relevance of the equity ownership identifier and the dispute trigger condition is analyzed to obtain the potential dispute factor set; The ownership dispute probability is calculated using the logistic regression algorithm for the potential dispute factor set to determine the dispute risk level; According to the dispute risk level, the quantitative evaluation result of the ownership dispute probability is generated to output the final judgment conclusion.

7. The system of claim 1, wherein, The audit trail generation module specifically comprises: If the ownership dispute probability exceeds the preset threshold, the historical event data is analyzed through the machine learning classification algorithm to obtain the dispute probability evaluation result; The historical event data is processed through the encryption hash algorithm to generate a unique hash value to determine the integrity of the data link; According to the generated hash value, an encryption hash chain is constructed to link all related historical events to obtain a continuous event sequence; For the continuous event sequence, the timestamp technology is used to mark the occurrence time of each event to obtain the event chain with timestamp; Through the event chain with timestamp, the data consistency verification is performed to judge the integrity of the audit trail; If the audit trail is complete, the encryption hash chain is stored through the distributed database to obtain the persistent data record; According to the persistent data record, a traceable audit log is generated to eliminate information asymmetry.

8. The system of claim 1, wherein, The equity attribution module specifically includes: The original transfer record is obtained from the audit trail data, the key transfer nodes are filtered through the node extraction rule, and the key node set is obtained; For the key node set, the digital signature mark is generated by using the elliptic curve digital signature algorithm to determine the marked node set; The digital signature mark in the marked node set is verified through the signature verification mechanism to obtain the node set that passes the verification; The transfer node sequence is extracted from the node set that passes the verification, and the logical order between nodes is constructed according to the node association relationship to obtain the ordered node sequence; According to the ordered node sequence, the state evolution sequence is obtained by using the state tracing path analysis to determine the evolution trajectory of the equity attribution state. For the state evolution sequence, the integrity of the audit trail data is verified by using the hash algorithm through the data integrity verification to obtain the final equity attribution state. If the final equity attribution state is consistent with the preset attribution rule, the attribution state confirmation record is generated by the state tracing path to determine the final equity attribution state.

9. The system of claim 1, wherein, The land management module specifically includes: Through the equity attribution state query interface, the latest equity attribution state data is obtained from the external system, stored in the temporary data table, and the initial equity data set is obtained; The data cleaning technology is used to remove duplicates and fill in missing values in the initial equity data set to generate a standardized equity attribution state data set; If there is a difference between the standardized equity attribution state data set and the existing record in the land state database, trigger the state association analysis, calculate the matching degree of the equity attribution state and the land use classification, and obtain the state matching result; According to the state matching result, the land use classification field in the land state database is updated to generate an updated land state data set; Through the resource allocation efficiency evaluation model, the updated land state data set is analyzed to calculate the optimization coefficient of urban resource allocation, and the resource allocation optimization scheme is obtained; The database consistency verification algorithm is used to compare the resource allocation optimization scheme with the land state database to determine whether there is a conflict, and the consistency verification result is obtained; According to the consistency verification result, the resource allocation optimization scheme is written into the configuration record storage module to generate the optimized urban resource allocation record.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the system of any one of claims 1-9.

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