Blockchain-based lease user credit risk assessment method and system

Abstract

The application discloses a blockchain-based lease user credit risk assessment method and system, and relates to the technical field of blockchain management.The blockchain-based lease user credit risk assessment method comprises the following steps: blockchain call delay evaluation; data writing delay evaluation; off-chain transmission delay evaluation.The application performs stability delay evaluation based on link transmission data volume, simultaneously judges whether to perform block parameter optimization according to the stability delay evaluation result, performs data writing delay evaluation based on writing delay data, simultaneously judges whether to perform blockchain data call optimization, performs off-chain transmission delay evaluation based on transmission delay data, and simultaneously judges whether to perform data byte number optimization, so that the effect of improving the reliability of lease user credit level data transmission risk assessment is achieved, and the problem of low reliability of lease user credit level data transmission risk assessment caused by corresponding transmission delay in the data chaining process of the blockchain in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchain management, in particular to a leasing user credit risk assessment method and system based on a blockchain. BACKGROUND

[0002] Under the background of increasing industry competition, enterprises tend to convert non-core assets (such as office equipment) into a leasing mode to reduce fixed costs and improve liquidity. A leasing platform collects relevant data (such as leasing duration, rent payment, default records, etc.) of leasing users from multiple channels through an oracle machine, and securely and reliably transmits these data to a blockchain. The blockchain ensures the reliability and non-tamperability of the data source. Then, the collected data is cleaned, standardized, and de-noised and redundant, and key features (such as leasing frequency, leasing duration, and equipment type) are extracted. Then, an analytic hierarchy process or entropy weight method is used to assign appropriate weights to each key feature according to its impact on credit risk, and a credit risk assessment model is constructed using a decision tree algorithm. The processed data is input into the model to train the model. In the form of a consortium chain, the leasing platform, financial institutions, and regulatory authorities participate as nodes to maintain the blockchain network. New user data is input into the decision tree algorithm model to assess the credit risk of leasing users in real time. The credit score of the user is dynamically updated according to the assessment results, providing decision support for the leasing platform, and the evaluation process and results are stored on the chain.

[0003] For example, the invention patent with publication number CN110826903B discloses an enterprise credit evaluation method based on a blockchain, which includes: standardizing the chained enterprise credit data based on smart contract technology and packaging it into an initial block; evaluating the enterprise credit data in the initial block based on a dynamic weight voting consensus mechanism; if the model evaluation requirements are met, a new block is generated and linked into the blockchain; a standardized enterprise blockchain credit evaluation model is established based on the blockchain; and the credit value of the enterprise to be evaluated in the enterprise blockchain credit evaluation model is obtained.

[0004] For example, the invention patent with publication number CN115130883A discloses an address credit risk assessment method and system based on a blockchain, which includes: monitoring blacklisted addresses, analyzing the blacklisted addresses to establish an address blacklist database, and deploying the blacklisted addresses as decentralized blacklist blockchain nodes, while establishing a server node; requesting the server node to obtain blockchain transaction data, comparing the transaction data with the server node or the blacklist blockchain node, and obtaining abnormal transaction addresses; applying a machine learning model to analyze the upstream and downstream blockchain nodes related to the abnormal transaction addresses based on the abnormal transaction addresses, and performing transaction risk evaluation modeling.

[0005] The above-mentioned technology at least has the following technical problems:

[0006] In existing technologies, user rental risk assessment systems rely on data stored on the blockchain. This data must be uploaded to the blockchain through a consensus mechanism before it can be read. The consensus mechanism requires nodes to reach an agreement through computation, voting, or signing. Due to differences in the number of nodes, network bandwidth, and computing power, this process cannot be completed instantly and must take time. Data transmission from the source node to the target node often involves multiple intermediate nodes. As the number of nodes increases, the data transmission path becomes more complex, leading to a longer overall transmission time. Therefore, during this time, data cannot be transmitted to the target node in a timely manner, resulting in a transmission delay from the source node to the target node. If the assessment system initiates a request to retrieve user credit data stored on the blockchain to provide a basis for rental decisions, the target node may not have fully received the latest user credit information. This means the time required for transmission from the source node to the target node is longer, affecting the accuracy of the data correction mechanism in processing the credit assessment results. This presents a problem of low reliability in the data transmission risk assessment of rental users' credit ratings due to transmission delays during the data uploading process. Summary of the Invention

[0007] This application provides a blockchain-based method and system for assessing the credit risk of rental users. This solves the problem in the prior art where the reliability of data transmission risk assessment of rental user credit ratings is low due to transmission delays during the data uploading process. It improves the reliability of data transmission risk assessment of rental user credit ratings during the data uploading process.

[0008] This application provides a blockchain-based method for assessing the credit risk of rental users, comprising the following steps: Step 1, during the call phase of the established consortium blockchain network, a data call latency assessment is performed on the process of calling rental user credit rating data, and it is determined whether there is a need for block parameter optimization; Step 2, during the data writing phase, based on the acquired write latency data, a data write latency assessment is performed on the process of writing qualified data by rental users, and it is determined whether there is a need for blockchain data call optimization; Step 3, during the off-chain transmission phase, based on the acquired transmission latency data, an off-chain transmission latency assessment is performed on the process of writing qualified data by rental users, and it is determined whether there is a need for data byte count optimization.

[0009] The embodiment of the application provides a blockchain-based rental user credit risk assessment system, comprising a data call delay evaluation module, a data write delay evaluation module and an off-chain transmission delay evaluation module; wherein the data call delay evaluation module is used for evaluating the data call delay of the rental user credit level data in the call stage of the constructed alliance chain network, and simultaneously judging whether there is a block parameter optimization requirement; the data write delay evaluation module is used for evaluating the data write delay of the rental user call qualified data in the data write stage based on the obtained write delay data, and simultaneously judging whether there is a block chain data call optimization requirement; and the off-chain transmission delay evaluation module is used for evaluating the off-chain transmission delay of the rental user write qualified data in the off-chain transmission stage based on the obtained transmission delay data, and simultaneously judging whether there is a data byte number optimization requirement.

[0010] The one or more technical solutions provided in the embodiment of the application have at least the following technical effects or advantages:

[0011] 1. The stability delay of the link transmission data amount is evaluated, whether the block parameter optimization is performed is judged according to the stability delay evaluation result, the link transmission data throughput is improved, the single data transmission pressure is reduced, then the data write delay is evaluated based on the write delay data, whether the block chain data call optimization is performed is judged, the block chain is avoided from being frequently called in the system high load, the redundant call is reduced, the response waiting time is reduced, finally the off-chain transmission delay is evaluated based on the transmission delay data, whether the data byte number optimization is performed is judged, the demand for the bandwidth of the block chain network is reduced, more effective data is transmitted under the same bandwidth cost, the transmission delay is reduced, and therefore the rental user credit level data transmission risk assessment reliability is improved.

[0012] 2. The prediction machine data grabbing frequency, the data consensus execution time length and the consensus verification number obtained at the end of the data write delay evaluation period are compared and analyzed with the preset write delay data in the database in terms of difference degree, the write delay data correction value is introduced to correct the comparison and analysis results, and the correction results are coupled to obtain the data write delay evaluation value. Compared with the prior art which only relies on a single influence data write delay index for evaluation, such as the average write delay, the method quickly identifies the problem dimension causing the delay through multi-dimensional attribution, dynamically updates the correction value by continuously comparing and analyzing the deviation trend, and thereby reduces the influence of the data write delay on the corresponding transmission delay in the data chaining process of the alliance chain.

[0013] 3. By comparing the obtained data format conversion duration, data packet transmission delay fluctuation value and weighted score calculation duration with the preset transmission delay data in the database at the end of the off-chain transmission delay evaluation period, and introducing a transmission delay data correction value to correct the difference comparison analysis results, and coupling the corrected difference results, the off-chain transmission delay evaluation value is obtained. Compared with the prior art which relies on static threshold and cannot adapt to the fluctuation of alliance chain network or the change of node load, the method introduces a correction value to adjust the difference analysis results in real time and flexibly cope with fluctuations, thereby reducing the influence of data transmission delay on the corresponding transmission delay of the alliance chain in the data on-chain process. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 The flowchart of the blockchain-based rental user credit risk assessment method provided by the embodiment of the present application;

[0015] Figure 2 The logic framework diagram of the blockchain-based rental user credit risk assessment method provided by the embodiment of the present application;

[0016] Figure 3 The blockchain call delay evaluation flowchart provided by the embodiment of the present application;

[0017] Figure 4 The data write delay evaluation flowchart provided by the embodiment of the present application;

[0018] Figure 5 The off-chain transmission delay evaluation flowchart provided by the embodiment of the present application;

[0019] Figure 6 The structural schematic diagram of the blockchain-based rental user credit risk assessment system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0020] The embodiment of the application provides a blockchain-based lease user credit risk assessment method and system, solves the problem of low reliability of lease user credit level data transmission risk assessment caused by corresponding transmission delay in the data chaining process of the prior art, performs data call delay assessment on the call process of the lease user credit level data through a data call delay assessment module in the call stage of the constructed alliance chain network, simultaneously judges whether there is a block parameter optimization requirement, then performs data write delay assessment on the write process of the qualified data of the lease user through a data write delay assessment module in the data write stage, simultaneously judges whether there is a block chain data call optimization requirement, finally performs off-chain transmission delay assessment on the off-chain transmission process of the qualified data of the lease user based on acquired transmission delay data through an off-chain transmission delay assessment module in the off-chain transmission stage, simultaneously judges whether there is a data byte number optimization requirement, and the effect of improving the reliability of lease user credit level data transmission risk assessment is achieved, and the problem of low reliability of lease user credit level data transmission risk assessment caused by corresponding transmission delay in the data chaining process of the blockchain is solved.

[0021] The technical solution in the embodiment of the application is to solve the problem of low reliability of lease user credit level data transmission risk assessment caused by corresponding transmission delay in the data chaining process of the blockchain, and the general idea is as follows:

[0022] Based on the link transmission data amount, stability delay assessment is performed, and whether block parameter optimization is performed is judged according to the stability delay assessment result, then data write delay assessment is performed based on write delay data, whether block chain data call optimization is performed is judged, finally off-chain transmission delay assessment is performed based on transmission delay data, and whether data byte number optimization is performed is judged, and the effect of improving the reliability of lease user credit level data transmission risk assessment in the data chaining process of the blockchain is achieved.

[0023] In order to better understand the above technical solution, the above technical solution will be described in detail in combination with the drawings in the specification and specific embodiments.

[0024] As Figure 1As shown in the figure, a flowchart of the blockchain-based lease user credit risk assessment method provided by the embodiment of the application, the blockchain-based lease user credit risk assessment method provided by the embodiment of the application comprises the following steps: step one, in the constructed alliance chain network calling stage, the data calling delay evaluation of the calling process of the lease user credit level data is performed, and whether there is a block parameter optimization demand is judged, the data calling delay evaluation is used to measure the influence degree of the link transmission data volume in the alliance chain network calling stage on the corresponding transmission delay in the data chaining process of the blockchain, the block parameter optimization represents that the calling delay risk of the blockchain is reduced by adjusting the block size and the block generation time; step two, in the data writing stage, the data writing delay evaluation of the writing process of the qualified data of the lease user calling is performed based on the obtained writing delay data, and whether there is a blockchain data calling optimization demand is judged, the data writing delay evaluation is used to measure the influence degree of the writing delay data in the data writing stage on the corresponding transmission delay in the data chaining process of the blockchain, the blockchain data calling optimization represents that the writing delay risk of the blockchain is reduced by adjusting the concurrent calling quantity and the data re-adjusting frequency; step three, in the off-chain transmission stage, the off-chain transmission delay evaluation of the off-chain transmission process of the qualified data of the lease user writing is performed based on the obtained transmission delay data, and whether there is a data byte number optimization demand is judged, the off-chain transmission delay evaluation is used to measure the influence degree of the transmission delay data in the off-chain transmission stage on the corresponding transmission delay in the data chaining process of the blockchain, and the data byte number optimization represents that the off-chain transmission delay risk of the blockchain is reduced by adjusting the data packet byte number.

[0025] As Figure 2 shown, a logic framework diagram of the blockchain-based lease user credit risk assessment method provided by the embodiment of the application, the specific design logic is: first, the alliance chain network calling stage is evaluated, the whole process is divided into three stages of alliance chain network calling, data writing and off-chain transmission, the delay problem of each stage is accurately positioned, the delay evaluation is performed in the data calling and writing stage, the timeliness and accuracy of the lease user credit level data and the qualified data are ensured, the off-chain transmission delay evaluation and byte number optimization are performed, the integrity and efficiency of the data in the off-chain transmission process are ensured, the data loss or error caused by the transmission problem is reduced, the phased evaluation and optimization avoid the redundant operation possibly caused by the global optimization, and the resource utilization efficiency is improved.

[0026] As Figure 3As shown, the blockchain call delay evaluation flowchart provided by the embodiments of the present application has the following specific design logic: first, in the constructed alliance chain network call stage, the data call delay evaluation of the call process of the lease user credit level data is performed on the alliance chain network, the data call delay evaluation result is obtained, and it is determined whether there is a demand for block parameter optimization according to the data call delay evaluation result. If the data call delay evaluation result shows that there is such a demand, the block parameter optimization operation is immediately performed; if the data call delay evaluation result shows that there is no such demand, the data writing stage is evaluated.

[0027] Further, whether there is a demand for block parameter optimization is determined, and the specific process is as follows: the numerical change relationship between the obtained link transmission data amount and the preset link transmission data amount in the database is determined: if the obtained link transmission data amount is not less than the preset link transmission data amount, it is recorded as data call qualified, and the lease user credit level data after call qualification is recorded as lease call qualified data and enters the data writing stage. The link transmission data amount is used to quantify the corresponding data transmission rate of the alliance chain communication link in the obtained lease user credit level data block chain; if the obtained link transmission data amount is less than the preset link transmission data amount, it is recorded as data call unqualified and block parameter optimization is performed. The link transmission data amount is used to quantify the amount of data transmitted through the alliance chain communication link, and is used to measure the speed of data transmission in the alliance chain network.

[0028] Among them, the block parameter optimization has the following specific steps: based on the link transmission data amount deviation obtained in the database, a block size adjustment value is obtained, which is used to prompt the smart contract in the alliance chain to reduce the data transmission pressure in the alliance chain network based on the obtained block size adjustment value; at the same time, based on the link transmission data amount deviation obtained in the database, a block generation time extension amount is obtained, which is used to prompt the smart contract in the alliance chain to improve the transaction processing speed of the alliance chain network node based on the obtained block generation time extension amount; the link transmission data amount deviation represents the absolute value of the difference between the link transmission data amount obtained at the end of the data call delay evaluation period and the preset link transmission data amount. The preset link transmission data amount is represented by the result of summing and averaging the corresponding link transmission data amount at the end of the historical alliance chain network call stage in the database; if the re-obtained link transmission data amount is not less than the preset link transmission data amount after block parameter optimization, the block parameter optimization is completed and the data writing stage is entered, otherwise it is determined that there is a transmission delay risk of the alliance chain communication link and a communication link delay risk warning is performed. The block parameter optimization includes block size optimization and block generation time optimization.

[0029] In this embodiment, the alliance chain belongs to a specific type in the blockchain category, which can quickly complete the storage and verification of data, and the transaction processing speed can meet the real-time requirements of credit risk assessment in the leasing business; the obtained block size adjustment value and block generation time length extension are used as inputs of the least squares linear regression algorithm, and by minimizing the sum of squared errors between the predicted value and the actual value, the linear relationship between the link transmission data volume deviation and the block size, the block size adjustment value, and the link transmission data volume deviation and the block generation time length, the block generation time length extension is found, and the block size adjustment result and the block generation time length result are output, thereby speeding up the synchronization of user credit level data of the node, ensuring that the data update can quickly spread to each node, and improving the data quality and system performance.

[0030] This example automatically adjusts the block parameters according to the real-time alliance chain network state, adapts to different load network scenarios, reduces the amount of data transmission per time through block size optimization, relieves link bandwidth occupation, after dynamic adjustment, the block size matches the actual data traffic, which can reduce invalid transmission overhead and improve link utilization. Through the adjustment of the generation time, the node resource contention is avoided, the transaction throughput is improved, and the node can synchronize the block data in time, while reducing the consensus failure probability, thereby improving the overall stability and transaction confirmation speed of the alliance chain network.

[0031] As shown in Figure 4 The data write delay evaluation flowchart provided by the embodiment of the application is shown in the figure, and the specific design logic is: the process starts with data write delay evaluation of the alliance chain network for the write process of the qualified data called by the leasing user in the data write stage, obtains the data write delay evaluation result, and judges whether there is a blockchain data call optimization requirement based on the data write delay evaluation result. If the data write delay evaluation result shows that there is such a requirement, the blockchain data call optimization operation is performed; if the data write delay evaluation result shows that there is no such requirement, the data transmission stage is evaluated.

[0032] Further, the write delay data obtained is used to evaluate the write process of the qualified data called by the leasing user, and the specific process is: at the end of the data write delay evaluation period, the obtained write delay data is compared and analyzed with the preset write delay data in the database, and the write delay data correction value is introduced to correct the comparison and analysis results, and the correction results are coupled to obtain the data write delay evaluation value. The write delay data includes the prediction machine data grabbing frequency, the data consensus execution time length and the consensus verification times.

[0033] Among them, the oracle data fetching frequency is used to quantify the interval at which the oracle obtains user credit rating data from external APIs. It is calculated using a time window counting method after being recorded by a counter. The data consensus execution time represents the time interval between nodes in the consortium blockchain network reaching consensus and completing block generation. It is calculated by using the generation timestamp corresponding to the block height to calculate the block generation interval. The consensus verification count represents the number of rounds required for nodes to reach consensus through message interaction. It is calculated by counting the number of operations in which nodes participate in verification through the verification mechanism in the consensus protocol. The write latency data correction value includes the oracle data fetching frequency correction value, the data consensus execution time correction value, and the consensus verification count correction value. The data write latency evaluation value represents the quantitative data of the degree of data call latency caused by the write latency data. It is the result of the coupled processing of the oracle data fetching frequency coefficient, the data consensus execution time coefficient, and the consensus verification count coefficient. The preset write latency data includes the preset oracle data fetching frequency, the data consensus execution time, and the consensus verification count. The write latency data correction value includes the oracle data fetching frequency correction value, the data consensus execution time correction value, and the consensus verification count correction value.

[0034] Specifically, the specific constraint expression for the oracle data fetching frequency coefficient W1 is as follows: In the formula, W1 represents the oracle data fetching frequency coefficient corresponding to the end of the data write latency evaluation period for the consortium blockchain communication link, a1 represents the oracle data fetching frequency correction value, T1 represents the oracle data fetching frequency corresponding to the end of the data write latency evaluation period for the consortium blockchain communication link, and T10 represents the preset oracle data fetching frequency. The preset oracle data fetching frequency is obtained by summing the historical oracle data fetching frequencies corresponding to the end of the data write latency evaluation period for the consortium blockchain communication links, then calculating the average value of this sum, and finally using this average value. The value is used to represent this. It should be noted that the oracle data fetching frequency in the formula for calculating the oracle data fetching frequency coefficient (e.g., multiple times per second) is greater than the preset oracle data fetching frequency (e.g., once per second). In this case, the higher the oracle data fetching frequency, the higher the corresponding latency. This is because high-frequency fetching will increase the load on the consortium blockchain communication link, leading to data write queuing and resource contention, thereby increasing latency. Calculating the oracle data fetching frequency coefficient in this situation can avoid link overload, ensure that the data write latency is stable within an acceptable range, prevent data inconsistency or system lag caused by excessive latency, and ensure the collaborative efficiency and reliability of the oracle and the consortium blockchain.

[0035] The specific constraint expression for the data consensus execution time coefficient W2 is as follows: , wherein W2 represents a data consensus execution duration coefficient corresponding to the alliance chain communication link in the alliance chain at the end of the data write delay evaluation period, a2 represents a data consensus execution duration correction value, T2 represents a data consensus execution duration corresponding to the alliance chain communication link in the alliance chain at the end of the data write delay evaluation period, and T20 represents a preset data consensus execution duration, which is obtained by accumulating each historical data consensus execution duration corresponding to the alliance chain communication link in the historical alliance chain at the end of the data write delay evaluation period and then calculating the average value of the accumulated sum.

[0036] The specific restriction expression of the consensus verification times coefficient W3 is: , wherein W3 represents a consensus verification times coefficient corresponding to the alliance chain communication link in the alliance chain at the end of the data write delay evaluation period, a3 represents a consensus verification times correction value, T3 represents a consensus verification times corresponding to the alliance chain communication link in the alliance chain at the end of the data write delay evaluation period, and T30 represents a preset consensus verification times, which is obtained by accumulating each historical consensus verification times corresponding to the alliance chain communication link in the historical alliance chain at the end of the data write delay evaluation period and then calculating the average value of the accumulated sum.

[0037] The specific restriction expression of the data write delay evaluation value W is: , wherein W represents a data write delay evaluation value corresponding to the alliance chain communication link in the blockchain at the end of the data write delay evaluation period.

[0038] The preset correction values closely related to the data write delay evaluation value are pre-stored in the database. These correction values and the oracle data grabbing frequency, data consensus execution duration and consensus verification times have an explicit mapping rule according to business requirements and system characteristics. This mapping can be a one-to-one correspondence between a single parameter and a compensation value, or a combination relationship in which multiple parameters correspond to a single correction value. For example, during the data write delay evaluation period, the system can obtain the actual values of the oracle data grabbing frequency, data consensus execution duration and consensus verification times in real time, and call the preset mapping rule to accurately match and extract the corresponding correction value.

[0039] It is particularly important to ensure the smooth progress of the evaluation process and ensure the comparability of the evaluation results in different scenarios. In this example, the value range of the oracle data grabbing frequency correction value, the data consensus execution duration correction value and the consensus verification times correction value is uniformly limited: all of them are controlled within the interval of 0 to 1, and the sum of the three is always equal to 1.

[0040] In the embodiment, the data write delay evaluation value increases with the increase of the oracle data grabbing frequency, the data consensus execution time length and the consensus verification times. When the oracle data grabbing frequency increases, the consensus process needs to be performed after each data grabbing, and frequent grabbing will increase the cumulative time of the consensus process, thereby lengthening the data consensus execution time length. The increase of the data consensus execution time length usually means that the oracle needs to process more data and consensus tasks. Because the increase of the consensus verification will increase the burden of the system, the system can not process all the verification tasks in time. At this time, the oracle data grabbing frequency is reduced. When the data consensus execution time length increases, if multiple data grabbing and consensus tasks need to be completed within a certain time, it can cause the delay of subsequent tasks, thereby reducing the actual number of consensus verifications completed within the same time.

[0041] The example is based on the mutual influence between parameters, simulates the delay trend in different business scenarios, captures the causal transmission chain between parameters, for example, when the business demand requires to increase the data grabbing frequency, the model can simultaneously predict the lengthening range of the consensus time length and the decline degree of the verification times, and then output more accurate delay evaluation values, provide basis for resource scheduling, convert the parameter correlation into adjustment rules, make the system have self-adjusting ability, build a closed-loop feedback adjustment mechanism, enhance the fault tolerance of abnormal scenarios, and finally improve the stability, efficiency and predictability of the blockchain system in complex business scenarios.

[0042] Further, it is judged whether there is a blockchain data calling optimization demand, and the specific process is: judging the numerical change relationship between the obtained data write delay evaluation value and the preset data write delay evaluation value in the database: if the obtained data write delay evaluation value is not greater than the preset data write delay evaluation value, it is recorded as qualified data write, and the qualified data after calling is recorded as lease write qualified data and enters the off-chain transmission stage; if the obtained data write delay evaluation value is greater than the preset data write delay evaluation value, it is recorded as unqualified data write and the blockchain data calling optimization is performed.

[0043] The specific steps of the blockchain data call optimization are as follows: based on the obtained data write delay evaluation value deviation, a concurrent call quantity adjustment value is mapped in the database, which is used to prompt the resource management and scheduling controller to reduce contract call resource competition based on the obtained concurrent call quantity adjustment value; after the concurrent call quantity optimization, if the reacquired data write delay evaluation value is not greater than the preset data write delay evaluation value of the data, the concurrent call quantity optimization is completed and the off-chain transmission stage is entered, otherwise, data re-tuning frequency optimization is performed; the specific steps of the data re-tuning frequency optimization are as follows: based on the obtained data write delay evaluation value deviation, a re-call frequency adjustment value is mapped in the database, which is used to prompt the dynamic resource optimization controller to reduce system resource consumption based on the obtained re-call frequency adjustment value, the data write delay evaluation value deviation represents the difference between the obtained data write delay evaluation value and the preset data write delay evaluation value in the database, and the preset data write delay evaluation value is represented by the result of summing and averaging the corresponding data write delay evaluation values at the end of the historical data write stage in the database; after the data re-tuning frequency optimization, if the reacquired data write delay evaluation value is not greater than the preset data write delay evaluation value, the data re-tuning frequency optimization is completed and the off-chain transmission stage is entered, otherwise, it is determined that there is a transmission delay risk in the data write stage and data write delay risk warning is performed.

[0044] In the present embodiment, the PID (Proportional-Integral-Derivative) control algorithm in the alliance chain network scheduler takes the obtained concurrent call quantity adjustment value and re-call frequency adjustment value as input, and through the synergistic effect of the proportional, integral and differential three links, it quickly responds to real-time deviation and reduces resource preemption of contract execution between nodes; in the present example, the proportional term (P) of the concurrent call quantity adjustment value quickly responds to the deviation and reduces resource preemption of contract execution between nodes, the integral term (I) accumulates historical delay deviation and drives the re-call frequency adjustment value to converge to the "low energy consumption-high efficiency" interval, which reduces the consumption of invalid computing power of nodes while ensuring that the delay meets the standard, the differential term (D) predicts the trend of delay deviation and adjusts the concurrent and re-tuning parameters in advance, and finally realizes the elimination of redundant competition of call resources, dynamically balances resource competition and system energy consumption, reduces the processing time of contract calls, and improves the system response rate.

[0045] As Figure 5As shown, the off-chain transmission delay evaluation flowchart provided by the embodiment of the present application, the specific design logic is: the process starts with the off-chain transmission delay evaluation of the transmission process of the lease user writing qualified data in the off-chain transmission stage to the alliance chain network, obtains the off-chain transmission delay evaluation result, and judges whether there is a data byte number optimization requirement based on the off-chain transmission delay evaluation result. If the off-chain transmission delay evaluation result shows that there is such a requirement, the data byte number optimization operation is performed; if the off-chain transmission delay evaluation result shows that there is no such requirement, the evaluation process is ended.

[0046] Further, based on the obtained transmission delay data, the off-chain transmission delay of the off-chain transmission process of the lease user writing qualified data is evaluated, and the specific process is: at the end of the off-chain transmission delay evaluation period, the obtained transmission delay data is compared and analyzed with the preset transmission delay data in the database, the transmission delay data correction value is introduced to correct the comparison and analysis results of each difference, and the coupling operation is performed on the corrected difference results to obtain the off-chain transmission delay evaluation value. Transmission delay data includes data format conversion time, data packet transmission delay fluctuation value and weighted score calculation time. The data format conversion time and the weighted score calculation time are obtained by the system built-in timer. The data packet transmission delay fluctuation value represents the maximum delay time of the data packet in the same data stream in the transmission process, which is usually monitored by the network probe on the alliance chain.

[0047] Among them, the data format conversion time is used to quantify the time required to convert the data format on the alliance chain to a format that can be recognized by the off-chain evaluation system. The weighted score calculation time represents the total time consumed from reading data to outputting the final weighted score result. The off-chain transmission delay evaluation value represents the delay degree quantization data of the transmission delay data to the data off-chain transmission. The coupling processing result of the data format conversion time coefficient, the data packet transmission delay fluctuation value coefficient and the weighted score calculation time coefficient is represented. The preset transmission delay data includes the preset data format conversion time, the data packet transmission delay fluctuation value and the weighted score calculation time. The transmission delay data correction value includes the data format conversion time correction value, the data packet transmission delay fluctuation value correction value and the weighted score calculation time correction value.

[0048] Specifically, the specific limit expression of the data format conversion time coefficient D1 is: , wherein D1 represents a data format conversion duration coefficient corresponding to the qualified data at the end of the off-chain transmission delay evaluation period, b1 represents a data format conversion duration correction value, P1 represents a data format conversion duration corresponding to the qualified data at the end of the off-chain transmission delay evaluation period, and P10 represents a preset data format conversion duration, which is obtained by adding each historical data format conversion duration corresponding to the qualified data at the end of the off-chain transmission delay evaluation period and then calculating an average value of the sum.

[0049] The specific restriction expression of the data packet transmission delay fluctuation value coefficient D2 is: , wherein D2 represents a data packet transmission delay fluctuation value coefficient corresponding to the qualified data at the end of the off-chain transmission delay evaluation period, b2 represents a data packet transmission delay fluctuation value correction value, P2 represents a data packet transmission delay fluctuation value corresponding to the qualified data at the end of the off-chain transmission delay evaluation period, and P20 represents a preset data packet transmission delay fluctuation value, which is obtained by adding each historical data packet transmission delay fluctuation value corresponding to the qualified data at the end of the off-chain transmission delay evaluation period and then calculating an average value of the sum.

[0050] The specific restriction expression of the weighted score calculation duration D3 is: , wherein D3 represents a weighted score calculation duration coefficient corresponding to the qualified data at the end of the off-chain transmission delay evaluation period, b3 represents a weighted score calculation duration correction value, P3 represents a weighted score calculation duration corresponding to the qualified data at the end of the off-chain transmission delay evaluation period, and P30 represents a preset weighted score calculation duration, which is obtained by adding each historical weighted score calculation duration corresponding to the qualified data at the end of the off-chain transmission delay evaluation period and then calculating an average value of the sum.

[0051] The specific restriction expression of the off-chain transmission delay evaluation value D is: , wherein D represents an off-chain transmission delay evaluation value corresponding to the qualified data at the end of the off-chain transmission delay evaluation period.

[0052] The database pre-stores preset correction values closely related to the off-chain transmission delay evaluation value. The mapping rules between the data format conversion time, the data packet transmission delay fluctuation value and the weighted score calculation time are constructed according to the business requirements and system characteristics. The mapping can be a one-to-one correspondence between a single parameter and a compensation value, or a combination relationship between multiple parameters and a single correction value. For example, during the data transmission delay evaluation period, the system can obtain the actual values of the data format conversion time, the data packet transmission delay fluctuation value and the weighted score calculation time in real time, and call the preset mapping rule to accurately match and extract the corresponding correction value.

[0053] It is particularly important to ensure the smooth progress of the evaluation process and ensure the comparability of the evaluation results in different scenarios. The value range of the data format conversion time correction value, the data packet transmission delay fluctuation value correction value and the weighted score calculation time correction value is uniformly limited in this example: all of them are controlled in the interval of 0 to 1, and the sum of the three is equal to 1.

[0054] In this embodiment, the off-chain transmission delay evaluation value increases with the increase of the data format conversion time, the data packet transmission delay fluctuation value and the weighted score calculation time. When the data format conversion time increases, the CPU (Central Processing Unit) usage rate of the off-chain system increases during format conversion processing, which may not be able to respond to the reception and sending of consortium chain network data packets in time, resulting in an increase in the data packet transmission delay fluctuation value. The increase in the data packet transmission delay fluctuation value may cause the time of data arriving at the off-chain system to be uneven. When data is concentrated, the system needs to process each data packet in turn, and the conversion of subsequent data packets may need to wait for the completion of the previous processing, thereby increasing the data format conversion time. If the weighted score calculation time increases, it may occupy more system resources, resulting in a decrease in the resources available for data format conversion, thereby prolonging the data format conversion time.

[0055] This example accurately locates the source of system delay by considering the influence relationship of the three, synchronously collects and integrates the evaluation results of the three parameters of each node, realizes the cross-node coordinated linkage adjustment, and accurately controls the CPU utilization rate through the parameter coordinated adjustment under comprehensive evaluation, avoids the waste of energy caused by excessive resource occupation of a task, dynamically adjusts the resource allocation strategy, for example, large transmission delay fluctuation will cause data retransmission (such as the timeout retransmission mechanism), and the associated optimization can reduce the retransmission rate by adjusting the parameters in advance, reduce data retransmission and redundant processing, and ensure the real-time performance of key data.

[0056] Further, whether there is a data byte number optimization requirement is judged, and the specific process is: judging the numerical change relationship between the obtained off-chain transmission delay evaluation value and the preset off-chain transmission delay evaluation value in the database: if the obtained off-chain transmission delay evaluation value is not greater than the preset off-chain transmission delay evaluation value, it is recorded as off-chain transmission qualified, and the data after off-chain transmission qualified is written into qualified data, recorded as rental off-chain transmission qualified data and the credit risk assessment of the rental user is completed; if the obtained off-chain transmission delay evaluation value is greater than the preset off-chain transmission delay evaluation value, it is recorded as off-chain transmission unqualified and data byte number optimization is performed.

[0057] The specific steps of data byte number optimization are: the sum average result of off-chain transmission delay evaluation value deviation and data byte number score deviation is taken as the byte number adjustment amount of the to-be-transmitted data packet, which is used to prompt the intelligent traffic scheduling router to reduce data transmission queuing delay based on the obtained byte number adjustment amount; the off-chain transmission delay evaluation value deviation is used to measure the difference between the obtained off-chain transmission delay evaluation value and the preset off-chain transmission delay evaluation value, that is, the difference between the obtained off-chain transmission delay evaluation value and the preset off-chain transmission delay evaluation value; the data byte number score deviation represents the absolute value of the difference between the data byte number score corresponding to the rental write qualified data at the end of the off-chain transmission delay evaluation period and the preset data byte number score; the data byte number score represents the ratio of the byte number of the obtained transmission rental write qualified data to the total transmission data byte number; after data byte number optimization, if the re-obtained off-chain transmission delay evaluation value is not greater than the preset off-chain transmission delay evaluation value, the data byte number optimization is completed and the credit risk assessment of the rental user is completed, otherwise it is determined that there is a delay risk in the rental off-chain transmission and off-chain transmission delay risk warning is performed.

[0058] In the embodiment, the PID control algorithm in the scheduling router takes the obtained byte number adjustment amount of the to-be-transmitted data packet as input, and through the synergistic effect of the three links of proportion, integral and differential, reduces the data packet volume to relieve the alliance chain network transmission pressure and dynamically balances the delay and compression efficiency; the preset off-chain transmission delay evaluation value is represented by the sum average result of the corresponding off-chain transmission delay evaluation values in the database at the end of the historical off-chain transmission delay evaluation period; and the preset data byte number score is represented by the sum average result of the corresponding data byte number scores in the database at the end of the historical off-chain transmission delay evaluation period.

[0059] The example converts the two-dimensional index into a single adjustment amount by summing and averaging the off-chain transmission delay bias and the byte number fraction bias, so that the intelligent flow can adjust the data packet sending strategy in real time, reduces transmission errors and retransmission demand through byte number adjustment, ensures the integrity of the rental data on the chain, realizes the double guarantee of data integrity and timeliness, and optimizes the system to trigger a risk warning based on the comprehensive evaluation of the double bias if the delay is still not up to standard. From "bias calculation-adjustment execution-effect verification", a complete closed loop is formed to ensure the effectiveness of the optimization measures.

[0060] As shown in Figure 6 The blockchain-based rental user credit risk assessment system provided by the embodiment of the present application includes a data calling delay evaluation module, a data writing delay evaluation module, and an off-chain transmission delay evaluation module. The data calling delay evaluation module is used to evaluate the data calling delay of the rental user credit level data calling process in the constructed alliance chain network calling stage, and to determine whether there is a block parameter optimization requirement. The data writing delay evaluation module is used to evaluate the data writing delay of the rental user qualified data writing process based on the obtained writing delay data in the data writing stage, and to determine whether there is a block chain data calling optimization requirement. The off-chain transmission delay evaluation module is used to evaluate the off-chain transmission delay of the rental user qualified data off-chain transmission process based on the obtained transmission delay data in the off-chain transmission stage, and to determine whether there is a data byte number optimization requirement.

[0061] In the embodiment, the "calling-writing-transmission" three-stage delay evaluation module is used to realize the delay control of the rental credit data from on-chain calling to off-chain transmission, improve the timeliness of risk assessment, and dynamically schedule resources in each module according to the delay evaluation results. After the single link optimization, the whole link performance is improved, the invalid data is avoided to occupy the on-chain storage, and the evaluation results of each module will trigger the corresponding optimization strategy to form a closed loop of "evaluation-optimization-re-evaluation", support dynamic adaptation to the alliance chain network changes in high-frequency scenarios, and improve the credit evaluation accuracy.

[0062] In summary, the embodiment of the present application evaluates the stability delay by the amount of data transmitted through the link, judges whether to optimize the block parameters according to the stability delay evaluation result, improves the data throughput of the link transmission, reduces the pressure of single data transmission, then evaluates the data write delay based on the write delay data, judges whether to optimize the blockchain data call at the same time, avoids frequent calling of the blockchain in the system high load, reduces the redundant call, reduces the response waiting time, finally evaluates the off-chain transmission delay based on the transmission delay data, judges whether to optimize the data byte number at the same time, reduces the demand for the bandwidth of the blockchain network, transmits more effective data under the same bandwidth cost, reduces the transmission delay, so as to realize the improvement of the reliability of the risk evaluation of the transmission of the credit level data of the leasing user.

[0063] Those skilled in the art will appreciate that embodiments of the application can be supplied as a method, a system, or a computer program product. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage media, etc.) having computer usable program code embodied therein.

[0064] The present application is described in reference to the flowcharts and / or block diagrams of the methods, apparatus (systems) and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the flow Figure 1 The function specified in one flow or multiple flows and / or blocks. Figure 1 The function specified in one flow or multiple flows and / or blocks.

[0065] These computer program instructions can also be stored in a computer readable memory capable of guiding the computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer readable memory produce a product including instruction devices, which implement the flow Figure 1 The function specified in one flow or multiple flows and / or blocks. Figure 1 The function specified in one flow or multiple flows and / or blocks.

[0066] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0067] Although preferred embodiments of the application have been described herein, substitutions and modifications of these preferred embodiments made by those skilled in the art are to be considered within the scope of the application. Therefore, it is intended that the appended claims be construed to include all such substitutions and modifications.

[0068] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A blockchain-based method for evaluating credit risk of a rental user, characterized in that, The method comprises the following steps: Step one, in the constructed alliance chain network calling stage, the data calling delay evaluation of the calling process of the lease user credit level data is carried out, and it is judged whether there is a block parameter optimization demand, wherein the block parameter optimization means reducing the calling delay risk of the block chain by adjusting the block size and the block generation time; Step two, in the data writing stage, the data writing delay evaluation of the writing process of the lease user calling qualified data is carried out based on the obtained writing delay data, and it is judged whether there is a block chain data calling optimization demand, wherein the block chain data calling optimization means reducing the writing delay risk of the block chain by adjusting the number of concurrent calls and the data re-adjusting frequency; Step three, in the off-chain transmission stage, the off-chain transmission delay evaluation of the off-chain transmission process of the lease user writing qualified data is carried out based on the obtained transmission delay data, and it is judged whether there is a data byte number optimization demand, wherein the data byte number optimization means reducing the off-chain transmission delay risk of the block chain by adjusting the data packet byte number. 2.The method of claim 1, wherein, The specific process of judging whether there is a block parameter optimization demand is as follows: The numerical change relationship between the obtained link transmission data amount and the preset link transmission data amount in the database is judged: If the obtained link transmission data amount is not less than the preset link transmission data amount, it is recorded as data calling qualified, and the lease user credit level data after calling is qualified is recorded as lease calling qualified data and enters the data writing stage, wherein the link transmission data amount is used to quantify the alliance chain communication link in the obtained lease user credit level data block chain at the corresponding data transmission rate; If the obtained link transmission data amount is less than the preset link transmission data amount, it is recorded as data calling unqualified and the block parameter optimization is carried out. 3.The method of claim 2, wherein, The specific steps of the block parameter optimization are as follows: The block size adjustment value is obtained in the database based on the obtained link transmission data amount deviation, which is used to prompt the smart contract in the block chain to reduce the data transmission pressure in the block chain network based on the obtained block size adjustment value; At the same time, the block generation time length extension amount is obtained in the database based on the obtained link transmission data amount deviation, which is used to prompt the smart contract in the block chain to improve the transaction processing speed of the block chain network node based on the obtained block generation time length extension amount; After the block parameter optimization, if the re-obtained link transmission data amount is not less than the preset link transmission data amount, the block parameter optimization is completed and enters the data writing stage, otherwise it is judged that there is a transmission delay risk of the alliance chain communication link and a communication link delay risk warning is carried out, wherein the block parameter optimization includes block size optimization and block generation time optimization. 4.The method of claim 1, wherein, The specific process of carrying out data writing delay evaluation of the writing process of the lease user calling qualified data based on the obtained writing delay data is as follows: At the end of the data write delay evaluation period, the obtained write delay data is compared with the preset write delay data in the database, and the write delay data correction value is introduced to correct the comparison results, and the corrected results are coupled to obtain the data write delay evaluation value. The write delay data includes oracle data grabbing frequency, data consensus execution time and consensus verification times, and the data write delay evaluation value represents the quantization data of the write delay data on the data call delay degree. 5.The method of claim 4, wherein, The specific process of judging whether there is a blockchain data call optimization requirement is as follows: Judge the numerical change relationship between the obtained data write delay evaluation value and the preset data write delay evaluation value in the database: If the obtained data write delay evaluation value is not greater than the preset data write delay evaluation value, it is recorded as data write qualified, and the call qualified data after data write qualified is recorded as lease write qualified data and enters the off-chain transmission stage; If the obtained data write delay evaluation value is greater than the preset data write delay evaluation value, it is recorded as data write unqualified and the blockchain data call optimization is performed. 6.The method of claim 5, wherein, The specific steps of the blockchain data call optimization are as follows: Based on the deviation of the obtained data write delay evaluation value, the number of concurrent calls is adjusted in the database to obtain the number of concurrent calls adjustment value, which is used to prompt the resource management and scheduling controller to reduce the contract call resource competition based on the obtained number of concurrent calls adjustment value; After the number of concurrent calls is optimized, if the reobtained data write delay evaluation value is not greater than the preset data write delay evaluation value, the number of concurrent calls is optimized and the off-chain transmission stage is entered, otherwise the data re-adjustment frequency optimization is performed. The specific steps of the data re-adjustment frequency optimization are as follows: Based on the deviation of the obtained data write delay evaluation value, the re-call frequency adjustment value is mapped in the database to reduce the system resource consumption based on the obtained re-call frequency adjustment value. After the data re-adjustment frequency optimization, if the reobtained data write delay evaluation value is not greater than the preset data write delay evaluation value, the data re-adjustment frequency optimization is completed and the off-chain transmission stage is entered, otherwise it is judged that there is a transmission delay risk in the data write stage and the data write delay risk warning is performed. 7.The method of claim 1, wherein, The specific process of evaluating the off-chain transmission delay of the off-chain transmission process of the lease user write qualified data based on the obtained transmission delay data is as follows: At the end of the off-chain transmission delay evaluation period, the obtained transmission delay data is compared with the preset transmission delay data in the database, and the transmission delay data correction value is introduced to correct the comparison results, and the coupling operation is performed on the difference results of each correction processing to obtain the off-chain transmission delay evaluation value. The transmission delay data includes data format conversion time, data packet transmission delay fluctuation value and weighted score calculation time, and the off-chain transmission delay evaluation value represents the quantization data of the transmission delay data on the off-chain transmission delay degree of data. 8.The method of claim 7, wherein, The specific process of judging whether there is a data byte number optimization requirement is as follows: Judge the numerical change relationship between the obtained off-chain transmission delay evaluation value and the preset off-chain transmission delay evaluation value in the database: If the obtained off-chain transmission delay evaluation value is not greater than the preset off-chain transmission delay evaluation value, it is recorded as off-chain transmission qualified, and the data after off-chain transmission qualified is written into qualified data as lease off-chain transmission qualified data and the credit risk assessment of the lease user is completed; If the obtained off-chain transmission delay evaluation value is greater than the preset off-chain transmission delay evaluation value, it is recorded as off-chain transmission unqualified and data byte number optimization is performed. 9.The method of claim 8, wherein, The data byte number optimization has the following specific steps: The sum average result of the off-chain transmission delay evaluation value deviation and the data byte number score deviation is used as the byte number adjustment amount of the to-be-transmitted data packet, which is used to prompt the intelligent traffic scheduling router to reduce data transmission queuing delay based on the obtained byte number adjustment amount; After data byte number optimization, if the re-obtained off-chain transmission delay evaluation value is not greater than the preset off-chain transmission delay evaluation value, the data byte number optimization is completed and the credit risk assessment of the lease user is completed, otherwise it is determined that there is a delay risk in the lease off-chain transmission and off-chain transmission delay risk warning is performed.

10. A system for applying the method for assessing credit risk of a tenant according to any one of claims 1 to 9, characterized in that, It includes: Data call delay evaluation module, data write delay evaluation module and off-chain transmission delay evaluation module; The data call delay evaluation module is used to perform data call delay evaluation on the call process of the lease user credit level data in the constructed alliance chain network call stage, and to judge whether there is a block parameter optimization demand; The data write delay evaluation module is used to perform data write delay evaluation on the write process of the lease user call qualified data based on the obtained write delay data in the data write stage, and to judge whether there is a block chain data call optimization demand; The off-chain transmission delay evaluation module is used to perform off-chain transmission delay evaluation on the off-chain transmission process of the lease user write qualified data based on the obtained transmission delay data in the off-chain transmission stage, and to judge whether there is a data byte number optimization demand.

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