Intelligent contract-based device rental service management system and method

Abstract

The application discloses a device leasing service management system and method based on a smart contract, and relates to the technical field of smart contracts. An original state data is signed by a security chip on a device end, a correlation coefficient of a position data sequence and an electric quantity data sequence is calculated to obtain a cross-modal consistency score after a signature is verified by an edge node, and trusted feed price data is generated and chained. A smart contract layer adopts an architecture in which a logic contract, a data contract and a business rule version register are independent of each other, an influence domain is determined according to an intersection of a change clause set and a stock contract applicable clause set, a supplementary agreement is automatically stored, and a similarity comparison of semantic feature fingerprints and code logic fingerprints of contract clause texts and contract codes is stored. The application improves the off-chain data credibility, realizes smooth evolution of business rules, provides traceable evidence for the consistency of clauses and codes, and can be used for Internet of Things device leasing.

Description

Technical Field

[0001] This invention relates to the field of smart contract technology, specifically to a smart contract-based equipment rental service management system and method. Background Technology

[0002] In the IoT equipment leasing industry, such as construction machinery, high-precision instruments, and shared equipment, traditional leasing processes rely on manual inspection, manual calculation, and manual dispute resolution, which have problems such as high trust costs, low settlement efficiency, and difficulty in evidence preservation. The introduction of blockchain smart contract technology enables operations such as equipment billing and settlement and deposit refund to be executed automatically according to pre-coded rules. The execution of smart contracts depends on two key inputs: external objective data and internal business rules. The former is introduced onto the chain through the oracle mechanism, while the latter is solidified in the contract logic in the form of code.

[0003] The closest existing technology to this invention is: the device is equipped with IoT sensors to collect status data such as location, power consumption and usage time. The data is directly uploaded to the oracle node through the gateway. After being signed by the node, it is submitted to the blockchain. The smart contract calculates based on the received data and pre-coded fixed business rules and triggers operations such as deposit deduction or refund.

[0004] When business rules need to be adjusted, operators usually deploy new smart contracts and require existing users to re-sign, or introduce a proxy contract model to only replace the logic code as a whole, but do not make fine-grained determinations on the scope of application of the specific terms of the existing contracts.

[0005] Regarding the consistency between contract terms and code, existing technologies typically rely solely on manual review and comparison, failing to establish an automated, traceable, two-way verification evidence chain.

[0006] In the aforementioned existing technologies, oracle nodes only sign the data transmission process and do not verify the physical authenticity of the generated data, resulting in insufficient data credibility. In addition, the existing proxy contract upgrade methods only solve the technical problem of replacing the entire logic code, which can easily lead to unclear boundaries between the old and new rules on existing contracts when business rules are adjusted.

[0007] Meanwhile, there may be semantic discrepancies between natural language contract terms and the actual deployed smart contract code. Existing technologies lack automated quantitative comparison and on-chain evidence storage methods, making it difficult to provide direct and credible evidence of ownership in the event of equipment damage or default arbitration. Summary of the Invention

[0008] The technical problems to be solved by this invention include the following three aspects:

[0009] First, in existing oracle data collection schemes, raw state data is directly forwarded and uploaded by terminal devices controlled by the lessee. The lessee has the technical capability and economic motivation to tamper with sensor readings and forge location trajectories. However, existing oracle nodes only sign the data transmission process and do not verify the physical authenticity of the generated data, resulting in a defect in the credibility of off-chain data that triggers smart contract execution.

[0010] Second, once deployed, smart contract code cannot be tampered with. Existing agency contract upgrade solutions do not automatically determine the scope of application of new and old business rules in existing effective contracts. This can easily lead to unclear boundaries between the application of new and old rules in existing contracts when business rules are adjusted. Operators either have to sacrifice agility to re-sign contracts or bear the risk of disputes.

[0011] Third, there may be semantic discrepancies between the lease contract terms written in natural language and the smart contract code actually deployed and running. Existing technologies lack automated quantitative comparison and on-chain evidence storage methods, making it difficult to provide evidence that the code execution logic is consistent with the true intention of the contract in the event of equipment damage or default arbitration.

[0012] To address the aforementioned technical problems, this invention provides a device leasing service management method based on smart contracts. The method includes: the device performs a signature operation on the raw state data collected by the sensor inside a security chip to generate signed state data and send it to the edge node.

[0013] Edge nodes verify the signed status data. After successful verification, they calculate the displacement sequence of the location data sequence and the power consumption sequence of the power data sequence, and calculate the Pearson correlation coefficient between the two as the cross-modal consistency score.

[0014] When the cross-modal consistency score is not less than the preset consistency threshold, the data that has been mutually verified for consistency is signed to generate trusted price feed data and submitted to the blockchain data contract for storage.

[0015] The blockchain smart contract layer executes automated billing and settlement based on trusted price feed data and the currently effective business rule version. The smart contract layer has a logic contract, a data contract, and a business rule version registry that is independent of the aforementioned two. The three are associated through a proxy scheduling contract for delegated invocation.

[0016] When the operator submits a new version of the business rules and a set of change clause identifiers, the system iterates through the set of existing lease contracts recorded in the data contract, calculates the intersection of the set of applicable clauses and the set of change clause identifiers for each existing lease contract, includes existing lease contracts with non-empty intersections into the set of contracts in the affected domain, determines the adaptation mode based on the remaining lease term and the number of settlements executed, automatically generates a supplementary agreement and records it on the blockchain, and updates the current applicable rule version number field of the contract.

[0017] Based on the above method, the present invention also provides a two-way semantic hash consistency verification mechanism for natural language terms and smart contract code, specifically including: extracting semantic features from the lease contract terms text written in natural language and performing dimensionality reduction hash mapping to obtain semantic feature fingerprints;

[0018] The code snippet of the smart contract corresponding to the clause text is parsed to generate an abstract syntax tree, and the code logic fingerprint is obtained by hashing its node sequence.

[0019] Calculate the similarity between the semantic feature vector and the code logic feature vector. When the similarity is not less than the preset consistency judgment threshold, submit both fingerprints and the similarity score to the data contract for evidence storage. When the similarity is less than the threshold, prevent the deployment of the corresponding code segment and return a deviation clause location prompt.

[0020] Accordingly, the present invention provides a smart contract-based equipment rental service management system for implementing the above method.

[0021] Compared with the prior art, the beneficial effects of the present invention by adopting the above technical solution are as follows:

[0022] 1. This invention combines device-side hardware-level signature with cross-modal data consistency verification at edge nodes, which can block single-point forgery from the physical source of data generation. It also filters logically contradictory forged data by calculating the correlation between displacement sequences and power consumption sequences. When necessary, it combines vibration and power on / off status for secondary comprehensive credibility scoring, so that the credible price feed data on the chain has verifiable physical authenticity, thereby improving the credibility of the data on which on-chain execution is based.

[0023] 2. This invention, through its decoupled architecture of logical contracts, data contracts, and business rule version registry, and its adaptive adaptation algorithm for the influence domain, can ensure the immutability of smart contracts while automatically identifying the influence domain of existing contracts after changes in business rules, determining the adaptation mode, and automatically generating supplementary agreements for on-chain storage, thus eliminating the risk of a dual-track system in the transition between old and new rules.

[0024] 3. This invention, through bidirectional anchoring similarity comparison of semantic feature fingerprints and code logic fingerprints, can form a chain of evidence of consistency between terms and code at the contract deployment stage, providing direct evidence of the consistency between code logic and the true intention of the contract in case of subsequent arbitration disputes. Attached Figure Description

[0025] Figure 1 This is a general process flow diagram of the overall system business in this embodiment of the invention;

[0026] Figure 2 This is a flowchart of the multidimensional cross-verification algorithm in an embodiment of the present invention;

[0027] Figure 3 This is a flowchart illustrating the adaptive adaptation process of the business rule influence domain in this embodiment of the invention. Detailed Implementation

[0028] The following is in conjunction with the appendix Figure 1-3 The specific embodiments of the present invention will be further described below. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0029] To make the technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments. The embodiments provided by the present invention are not intended to limit the scope of protection of the present invention.

[0030] System Overall Architecture

[0031] The device rental service management system based on smart contracts provided in this embodiment of the invention consists of a trusted data collection module for the device side, an edge node verification module, a blockchain smart contract module, and a business rule management module.

[0032] The device-side trusted acquisition module includes the device itself, a sensor module, and a security chip. The sensor module is used to collect raw status data such as the device's location, power level, vibration amplitude, and cumulative usage time. The security chip is used to perform signature operations on the above raw status data within the chip to generate signed status data. The edge node verification module is used to receive the signed status data and perform signature verification. After successful verification, it performs cross-modal data consistency mutual verification calculation to generate trusted price feed data. The blockchain smart contract module adopts an architecture in which logical contracts, data contracts, and business rule version registry are set up independently. A proxy scheduling contract is set up between the three to establish a delegated call association. The business rule management module is used to perform business rule version registration, influence domain determination, automatic generation of supplementary protocols, and bidirectional semantic hash verification of natural language terms and contract code.

[0033] Device-side hardware-level signature and cross-modal data consistency verification mechanism

[0034] The security chip built into the device is pre-installed with a unique asymmetric key pair at the factory. The private key in this key pair is permanently stored inside the security chip and cannot be exported, while the public key is registered on the blockchain for edge node verification. During device operation, the raw state data collected by the sensor module is signed within the security chip at the same time as it is generated, forming a data unit that is bound to the signature. This physically ensures that the data cannot be tampered with by external programs before leaving the device chip. The signed data unit is sent to the edge node via the wireless communication module. The edge node verification module verifies the signature, and data that fails the verification is discarded and does not enter the subsequent processing flow.

[0035] To further prevent lessees from tampering with a single sensor, the edge node verification module executes a cross-modal data consistency verification algorithm after the signature verification is passed. By cross-verifying multiple data streams that should logically have a physical connection, it identifies and filters abnormal data with logical contradictions. Taking the verification of the device location change trajectory and the power consumption curve as an example, under normal use, significant changes in device location are usually accompanied by corresponding power consumption. If the location data shows that the device has moved significantly but the power consumption curve is almost flat, it can be determined that at least one data stream is suspected of being forged.

[0036] Specifically, the edge node verification module continuously receives signature-verified location data sequences within a set sampling period. With power data sequence ,in This represents the total number of sampling points. The formula for calculating the positional displacement and power consumption between adjacent sampling points is as follows:

[0037] ;

[0038] in, Indicates the first The sampling point and the first A function for calculating the geographical distance between sampling points;

[0039] Indicates the first The displacement within each sampling period constitutes a displacement sequence;

[0040] Indicates the first The power consumption within each sampling period constitutes a power consumption sequence.

[0041] After standardizing the displacement and energy consumption sequences, the Pearson correlation coefficient between them is calculated as the cross-modal consistency score. The calculation formula is as follows:

[0042] ;

[0043] in, This represents the cross-modal consistency score, with a value range of [value missing]. ;

[0044] This represents the mean of the displacement sequence;

[0045] This represents the mean of the electricity consumption sequence.

[0046] When calculated Less than the preset consistency threshold If a logical contradiction is found in the data within the time window, an anomaly marker is triggered and the data is temporarily stored in the queue to be reviewed. The data in this window will not be submitted to the chain as trusted price feed data.

[0047] when Not less than When the data passes consistency verification, the edge node verification module signs the original data that has passed consistency verification with its edge node private key to form a trusted price feed data packet, which is then submitted to the data contract for storage and subsequent settlement. In this embodiment, the consistency threshold... The value is 0.6.

[0048] For data windows deemed abnormal, the edge node verification module further performs secondary cross-validation by combining the device's vibration amplitude data with power-on / off status records, and calculates a comprehensive reliability score. The calculation formula is as follows:

[0049] ;

[0050] in, This indicates the overall credibility score;

[0051] , , These represent the weight coefficients corresponding to the cross-modal consistency score, vibration characteristic scoring function, and power-on / off state characteristic scoring function, respectively. The sum of the three is 1, and in this embodiment, they are taken as 0.5, 0.3, and 0.2, respectively.

[0052] The vibration characteristic scoring function is calculated as follows: the measured vibration amplitude is compared with a preset vibration amplitude range. A value of 1 is assigned when the measured value falls within the range, and a value decreasing linearly proportionally when the measured value exceeds the range. The value range is... ;

[0053] The status characteristic scoring function is calculated as follows: it represents the proportion of times the change trends of the power on / off status records, location data sequences, and power consumption data sequences match within the corresponding time window, out of the total number of windows. The value range is... ,when When the data in the window is less than the preset comprehensive credibility threshold, it is judged as fake data and discarded, and an alert for abnormal behavior of the tenant is triggered.

[0054] when When the value is not less than the threshold, the window data is corrected and enters the trusted price feed data queue. In this embodiment, the comprehensive trust threshold is 0.7.

[0055] In another implementation, the trusted data acquisition process on the device side does not require a separately configured security chip. Instead, it utilizes the trusted execution environment built into the device's main control chip to perform isolated signature operations on the data. This also achieves the technical effect of blocking data tampering at the hardware level and is suitable for device scenarios where the main control chip has natively integrated trusted execution environment capabilities.

[0056] Two-layer contract architecture and influence domain adaptive adaptation algorithm

[0057] In the blockchain smart contract module, the data contract only stores the persistent state data of each lease contract, including the addresses of both parties, the deposit amount, the equipment number, and the currently applicable rule version number;

[0058] The logic contract stores the actual business processing code, including billing formulas and default judgment logic;

[0059] The business rule version registry independently records the detailed content, effective time, and applicable conditions of each business rule version. The three are associated through a proxy scheduling contract, which ensures that code upgrades of the logic contract do not affect the historical state data that has been persisted in the data contract. The logic contract can have multiple historical versions. Each existing lease contract instance records the currently applicable rule version number field, which reflects the correspondence between a single contract instance and the rule version.

[0060] When the operator adjusts the business rules, the business rules management module automatically identifies the actual impact of the rule change on existing lease contracts and automatically generates supplementary agreements for the transition between the old and new rules for the affected contracts. The specific implementation steps are as follows:

[0061] The operator submits a new version of the business rules to the business rules version registry. This version of the rules contains a set of changed clause identifiers. ,in Indicates the first The specific terms and conditions of the item that have been changed. This indicates the total number of clauses involved in this change;

[0062] The business rules management module iterates through the collection of existing lease contracts recorded in the data contracts. Read each existing lease contract The currently bound rule version number and its corresponding set of applicable terms. ,in This indicates the total number of existing lease contracts;

[0063] For each existing lease contract Calculate its set of applicable terms. With this set of changed terms intersection If the intersection is not empty, then the contract is determined. Contracts falling within the scope of this rule change will be included in the scope of affected contracts. ;

[0064] If the intersection is empty, then the contract is determined. Unaffected by this change, its rule version number remains unchanged;

[0065] For the set of contracts in the influence domain For each contract in the system, the business rules management module determines the applicability of the new rules based on the remaining lease term and the number of settlements executed for that contract. The adaptation mode is one of three preset modes: immediate application mode, remaining lease term application mode, and new contract only application mode. The determination is based on the adaptation strategy parameters pre-configured by the operator when submitting the new rule version.

[0066] For contracts deemed to require transitional processing, the business rules management module automatically generates a supplementary agreement text. This supplementary agreement includes the original rule version number, the new rule version number, the changed terms, the adaptation mode, and the effective time. The hash value of the supplementary agreement content, along with the above elements, is submitted to the data contract for on-chain storage. At the same time, the current applicable rule version number field recorded in the contract instance is updated.

[0067] The business rules management module uses a proxy scheduling contract to uniformly route subsequent settlement calls of the affected domain contract to the logical contract version that matches the new rule version number, thus completing a smooth transition of the rules. Historical settlement records are still completely retained in the data contract and remain unaffected.

[0068] In another implementation, the business rule version evolution architecture may not use the proxy scheduling contract combined with the version registry. Instead, it may use a multi-aspect contract architecture, which splits different business rule modules into independent aspect contracts. The independent upgrade of rules is achieved through a unified aspect lookup mechanism. This can also achieve the dynamic evolution of rules without losing existing contract status data. It is suitable for application scenarios where the business rule modules are divided in a more refined manner.

[0069] Natural language terms and smart contract code bidirectional semantic hash consistency verification mechanism

[0070] During the lease agreement terms configuration phase, the business rules management module extracts features from the contract terms text written in natural language and the compiled smart contract code, and performs a two-way anchoring comparison of the feature summaries of the two to form consistent evidence that can be stored on the blockchain. The specific implementation steps are as follows:

[0071] The business rules management module receives a collection of lease contract terms written in natural language. ,in Indicates the first The specific text of each clause, Indicates the total number of clauses;

[0072] Utilizing a pre-trained semantic encoding model for each clause text Perform semantic feature extraction to generate semantic feature vectors. and to Perform a dimensionality reduction hash mapping to generate the semantic feature fingerprint of the clause. ;

[0073] Regarding the terms and conditions Corresponding smart contract code snippet Perform syntax parsing to generate an abstract syntax tree structure for the code snippet, and perform hash calculation on the node sequence of the abstract syntax tree to generate a code logic fingerprint. ;

[0074] Calculation Terms The similarity between the semantic feature fingerprint and the logical fingerprint of its corresponding code segment is used as the bidirectional anchoring comparison score, and the calculation formula is as follows:

[0075] ;

[0076] in, Terms and Conditions With code snippets The semantic consistency similarity score between them, with a value range of 100%. ;

[0077] Terms and Conditions semantic feature vectors;

[0078] Code snippet The logical feature vector obtained after transformation by the unified mapping space;

[0079] and These represent the magnitudes of the two vectors, respectively.

[0080] when Not less than the preset consistency judgment threshold When determining that a clause is semantically consistent with the corresponding code snippet, its semantic feature fingerprint is generated. Code logic fingerprint The similarity score and other data are packaged together and submitted to the data contract for on-chain storage.

[0081] When the similarity is less than If a semantic discrepancy is found between the clause and the code snippet, the final deployment of the contract clause snippet is prevented, and the specific location of the discrepancy clause is returned to the contract configuration personnel for manual review and correction. In this embodiment, the consistency determination threshold is... The value is 0.85.

[0082] When equipment damage or breach of contract arbitration is required, the business rules management module retrieves the semantic feature fingerprint, code logic fingerprint, and similarity score records of the lease contract stored on the blockchain during the deployment phase. These records are submitted to arbitration or litigation proceedings as evidence of the consistency between the terms and code logic, enabling traceable evidence of the consistency between the terms and code logic.

[0083] Overall process closed-loop description of the method

[0084] Based on the above technical solutions, the overall process of the equipment leasing service management method provided by the present invention is as follows: the device continuously collects hardware-signed status data and uploads it to the edge node;

[0085] Edge nodes execute cross-modal consistency mutual verification algorithms to generate trusted price feed data and upload it to the blockchain;

[0086] Smart contracts execute automated billing and deposit processing based on trusted price feed data and the currently effective version of business rules;

[0087] When business rules are adjusted, the system automatically determines the adaptation method of existing lease contracts through the influence domain adaptive adaptation algorithm and generates supplementary agreements for on-chain storage.

[0088] During the contract configuration phase and subsequent arbitration phase, the system ensures the integrity and verifiability of the evidence chain between the clause text and the code logic through a two-way semantic hash consistency verification mechanism. The three mechanisms act on the data input end, the rule evolution end, and the clause confirmation end, respectively, and together form a complete closed-loop technical solution. This can solve the problems of insufficient data credibility, contradiction between business rule evolution and contract immutability, and difficulty in confirming rights due to semantic discrepancies between clauses and code in existing technologies.

[0089] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of the present invention. Therefore, any modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for managing equipment rental services based on smart contracts, characterized in that, include: The device acquires raw status data collected by the sensor and performs a signature operation inside the security chip to generate signed status data, which includes a signed location data sequence and a signed power data sequence. The signed status data is verified by signature. After verification, the position displacement between adjacent sampling points in the position data sequence is calculated to obtain the displacement sequence. The power consumption between adjacent sampling points in the power data sequence is calculated to obtain the power consumption sequence. Calculate the Pearson correlation coefficient between the displacement sequence and the power consumption sequence to obtain the cross-modal consistency score; If the cross-modal consistency score is not less than a preset consistency threshold, the location data sequence and the power data sequence that have undergone consistency verification are signed to generate trusted price feed data, and the trusted price feed data is submitted to the data contract on the blockchain for storage. The blockchain smart contract layer performs automated billing and settlement based on the trusted price feed data and the currently effective business rule version. The smart contract layer includes a logic contract, a data contract, and a business rule version registry that is independently set up with the logic contract and the data contract. The data contract stores persistent state data of the lease contract, the logic contract stores business processing code, and the business rule version registry records the content, effective time, and applicable conditions of each business rule version. The logic contract, the data contract, and the business rule version registry are associated through a proxy scheduling contract for delegated invocation. Receive the new business rule version and the corresponding set of change clause identifiers submitted by the operator, traverse the set of existing lease contracts recorded in the data contract, and read the rule version number currently bound to each existing lease contract and the set of applicable clauses. Calculate the intersection of the set of applicable terms for each existing lease contract and the set of change terms identifiers, and record the existing lease contracts with non-empty intersections into the set of contracts in the influence domain. For each existing lease contract in the affected domain contract set, the adaptation mode of the new business rule version for the existing lease contract is determined based on whether the remaining lease term of the existing lease contract is greater than the preset lease term threshold and whether the number of settlements executed is zero. Specifically, if the remaining lease term is greater than the preset lease term threshold and the number of settlements executed is zero, it is determined to be an immediately applicable mode; if the remaining lease term is greater than the preset lease term threshold and the number of settlements executed is greater than zero, it is determined to be an applicable mode for the remaining lease term; and if the remaining lease term is not greater than the preset lease term threshold, it is determined to be an applicable mode for newly signed contracts only. A supplementary agreement is generated, the hash value of the supplementary agreement is submitted to the data contract for notarization, and the current applicable rule version number field recorded in the existing lease contract is updated.

2. The method according to claim 1, characterized in that, If the cross-modal consistency score is less than a preset consistency threshold, the vibration amplitude data and power-on / off status record data of the device are acquired. A vibration feature score is calculated based on the deviation of the vibration amplitude data from the preset vibration amplitude range corresponding to the expected usage state. A status feature score is calculated based on the consistency of the power-on / off status record data with the location data sequence and the power data sequence in the corresponding time period. The cross-modal consistency score, the vibration feature score, and the status feature score are multiplied by their respective weighting coefficients and then summed to obtain a comprehensive reliability score, where the sum of the weighting coefficients is 1. If the comprehensive reliability score is less than a preset comprehensive reliability threshold, the data in the corresponding window is discarded and an abnormal behavior warning is triggered. If the overall credibility score is not less than the preset overall credibility threshold, the data in the corresponding window is corrected and added to the credible price feed data queue.

3. The method according to claim 1, characterized in that, The adaptation mode is one of the following three modes: immediate application mode, remaining lease term application mode, and new contract only application mode; the supplementary agreement includes the original rule version number, the new rule version number, the changed terms, the adaptation mode, and the effective time.

4. The method according to claim 1, characterized in that, After updating the current applicable rule version number field recorded in the existing lease contract, the subsequent settlement calls of the existing lease contract are routed to the logical contract version that matches the new business rule version number through the proxy scheduling contract, and the historical settlement records are retained in the data contract.

5. The method according to claim 1, characterized in that, Also includes: Obtain the text of the lease contract terms written in natural language, extract semantic features from the text using a semantic coding model to obtain a semantic feature vector, and perform a dimensionality reduction hash mapping on the semantic feature vector to obtain a semantic feature fingerprint; The code fragment of the smart contract corresponding to the term text is parsed to generate an abstract syntax tree. The node sequence of the abstract syntax tree is hashed to obtain a code logic fingerprint. The code logic fingerprint is transformed through a unified mapping space to obtain a logical feature vector. The similarity between the semantic feature vector and the logical feature vector is calculated to obtain a semantic consistency similarity score.

6. The method according to claim 5, characterized in that, If the semantic consistency similarity score is not less than the preset consistency judgment threshold, the semantic feature fingerprint, the code logic fingerprint, and the semantic consistency similarity score are submitted to the data contract for evidence storage; if the semantic consistency similarity score is less than the preset consistency judgment threshold, the deployment of the corresponding smart contract code fragment is blocked, and a deviation clause location prompt message is returned.

7. A smart contract-based equipment rental service management system, characterized in that, It includes a device-side trusted data collection module, an edge node verification module, a blockchain smart contract module, and a business rules management module; The trusted acquisition module on the device side includes a sensor and a security chip. The sensor is used to collect raw state data, and the security chip is used to perform a signature operation on the raw state data to generate signed state data. The signed state data includes a signed location data sequence and a signed power data sequence. The edge node verification module is used to verify the signature of the signed status data. After the verification is successful, the displacement between adjacent sampling points in the location data sequence is calculated to obtain a displacement sequence, the power consumption between adjacent sampling points in the power data sequence is calculated to obtain a power consumption sequence, and the Pearson correlation coefficient between the displacement sequence and the power consumption sequence is calculated to obtain a cross-modal consistency score. If the cross-modal consistency score is not less than a preset consistency threshold, the data that has passed the consistency verification is signed to generate trusted price feed data. The blockchain smart contract module includes a logic contract, a data contract, and a business rule version registry that is independently set up with the logic contract and the data contract. The data contract is used to store persistent state data of the lease contract and receive the trusted price feed for notarization. The logic contract is used to store business processing code and perform automated billing and settlement based on the trusted price feed and the currently effective business rule version. The business rule version registry is used to record the content, effective time and applicable conditions of each business rule version. A proxy scheduling contract is set up between the logic contract, the data contract and the business rule version registry. The proxy scheduling contract is used for delegated call association. The business rule management module is used to receive new business rule versions and corresponding change clause identifier sets submitted by the operator, traverse the set of existing lease contracts recorded in the data contract, read the rule version number currently bound to each existing lease contract and the corresponding set of applicable clauses, calculate the intersection of the set of applicable clauses of each existing lease contract and the set of change clause identifiers, record existing lease contracts with non-empty intersections into the impact domain contract set, and for each existing lease contract in the impact domain contract set, determine the adaptation mode of the new business rule version for the existing lease contract based on the remaining lease term and the number of settlements executed, generate a supplementary agreement, submit the hash value of the supplementary agreement to the data contract for notarization, and update the current applicable rule version number field recorded in the existing lease contract.

8. The system according to claim 7, characterized in that, The edge node verification module is further configured to, when the cross-modal consistency score is less than a preset consistency threshold, acquire the device's vibration amplitude data and power-on / off status record data, calculate a vibration feature score based on the degree of matching between the vibration amplitude data and the expected usage state, calculate a status feature score based on the degree of matching between the power-on / off status record data and the changes in the location data sequence and the power data sequence, multiply the cross-modal consistency score, the vibration feature score, and the status feature score by their respective weighting coefficients and then sum them to obtain a comprehensive credibility score, wherein the sum of the weighting coefficients is 1, and discard the data in the corresponding window and trigger an abnormal behavior warning when the comprehensive credibility score is less than a preset comprehensive credibility threshold.

9. The system according to claim 7, characterized in that, The business rule management module is also used to obtain lease contract terms written in natural language, extract semantic features from the terms using a semantic coding model to obtain a semantic feature vector, perform dimensionality reduction hash mapping on the semantic feature vector to obtain a semantic feature fingerprint, perform syntax parsing on the smart contract code fragment corresponding to the terms to generate an abstract syntax tree, perform hash calculation on the node sequence of the abstract syntax tree to obtain a code logic fingerprint, calculate the similarity between the semantic feature vector and the logical feature vector obtained by transforming the code logic fingerprint through a unified mapping space to obtain a semantic consistency similarity score, and submit the semantic feature fingerprint, the code logic fingerprint and the semantic consistency similarity score to the data contract for storage if the semantic consistency similarity score is not less than a preset consistency judgment threshold.

10. The system according to claim 7, characterized in that, The security chip has a built-in asymmetric key pair, and the private key in the asymmetric key pair is stored inside the security chip and cannot be exported.

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