CN112435105A

CN112435105A - Rental risk assessment method, device, equipment and system based on block chain

Info

Publication number: CN112435105A
Application number: CN202110110606.0A
Authority: CN
Inventors: 张宇豪; 齐翔; 章鹏
Original assignee: Alipay Hangzhou Information Technology Co Ltd; Ant Blockchain Technology Shanghai Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd; Ant Blockchain Technology Shanghai Co Ltd
Priority date: 2021-01-27
Filing date: 2021-01-27
Publication date: 2021-03-02

Abstract

The embodiment of the specification provides a block chain-based lease risk assessment method, device, equipment and system, wherein the method comprises the following steps: acquiring running data of target rental equipment rented by a target renter from a block chain system; acquiring a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental equipment; performing risk evaluation processing on the lease service of the target lease party in the future preset time period relative to the target lease equipment by using the obtained evaluation model based on the operation data to obtain risk evaluation result information; the operation data are collected by an internet of things module arranged in the target leasing equipment and uploaded to the block chain system.

Description

Rental risk assessment method, device, equipment and system based on block chain

Technical Field

The present invention relates to the field of data processing technologies, and in particular, to a block chain-based rental risk assessment method, apparatus, device, and system.

Background

With the continuous development of social lifestyles, more and more products start a financing rental model. In order to ensure the effective operation of the financing lease service, the lessor usually sends out a plurality of dispatching personnel before the lease, explores the qualification of the lessee in the modes of field visit, data lookup and the like, and determines whether to lease the relevant products to the lessee according to the qualification. Meanwhile, in the post-renting stage, the renter intermittently monitors the repayment behavior of the renter and the like to determine whether the renter has credit risk. However, the current post-rental risk monitoring is difficult, and since expensive products are managed and used by the renters during the renting stage, once the renters have the conditions of maliciously overdue return of the products, rent arrearage of the rent, and the like, the renters face huge risks of product and fund loss and the like.

Disclosure of Invention

One or more embodiments of the present specification provide a block chain-based rental risk assessment method. The method comprises the step of obtaining running data of target leasing equipment leased by a target lessee from a blockchain system. The operation data are collected by an internet of things module arranged in the target leasing equipment and uploaded to the block chain system. And acquiring a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental equipment. And performing risk assessment processing on the lease service of the target lease equipment in a future preset time period by using the assessment model based on the operation data to obtain risk assessment result information.

One or more embodiments of the present specification provide a block chain-based rental risk assessment apparatus. The device comprises a first acquisition module, and the first acquisition module is used for acquiring the running data of the target rental equipment rented by the target renter from the blockchain system. The operation data are collected by an internet of things module arranged in the target leasing equipment and uploaded to the block chain system. The device also comprises a second acquisition module for acquiring a pre-trained evaluation model for risk evaluation of the rental business of the target rental equipment. The device further comprises an evaluation module, and the evaluation module is used for carrying out risk evaluation processing on the lease service of the target lease equipment in a future preset time period by using the evaluation model based on the operation data to obtain risk evaluation result information.

One or more embodiments of the present specification provide a block chain-based rental risk assessment system. The system comprises target rental equipment, a block chain system and a wind control system. The target leasing equipment acquires the operation data of the target leasing equipment through an internet of things module arranged on the target leasing equipment and uploads the operation data to the block chain system. And the block chain system stores the operation data uploaded by the Internet of things module. And the wind control system acquires the operation data of the target rental equipment from the block chain system. And acquiring a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental equipment. And performing risk evaluation processing on the lease service of the target lease equipment by the target lessee of the target lease equipment in a future preset time period by using the evaluation model based on the operation data to obtain risk evaluation result information.

One or more embodiments of the present specification provide a block chain-based rental risk assessment apparatus. The apparatus includes a processor. The apparatus also comprises a memory arranged to store computer executable instructions. The computer-executable instructions, when executed, cause the processor to obtain operational data for a target rental device rented by a target tenant from a blockchain system. The operation data are collected by an internet of things module arranged in the target leasing equipment and uploaded to the block chain system. And acquiring a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental equipment. And performing risk assessment processing on the lease service of the target lease equipment in a future preset time period by using the assessment model based on the operation data to obtain risk assessment result information.

One or more embodiments of the present specification provide a storage medium. The storage medium is used to store computer-executable instructions. The computer-executable instructions, when executed by the processor, obtain operational data for a target rental device rented by a target tenant from the blockchain system. The operation data are collected by an internet of things module arranged in the target leasing equipment and uploaded to the block chain system. And acquiring a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental equipment. And performing risk assessment processing on the lease service of the target lease equipment in a future preset time period by using the assessment model based on the operation data to obtain risk assessment result information.

Drawings

In order to more clearly illustrate one or more embodiments or technical solutions in the prior art in the present specification, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive exercise;

fig. 1 is a schematic view of a scenario of a block chain-based rental risk assessment method according to one or more embodiments of the present disclosure;

fig. 2 is a first flowchart of a block chain-based rental risk assessment method according to one or more embodiments of the present disclosure;

fig. 3 is a second flowchart of a block chain-based rental risk assessment method according to one or more embodiments of the present disclosure;

fig. 4 is a third flowchart of a block chain-based rental risk assessment method according to one or more embodiments of the present disclosure;

fig. 5 is a schematic block diagram illustrating a block chain-based rental risk assessment apparatus according to one or more embodiments of the present disclosure;

fig. 6 is a first schematic composition diagram of a block chain-based rental risk assessment system according to one or more embodiments of the present disclosure;

fig. 7 is a schematic diagram illustrating a second component of a block chain-based rental risk assessment system according to one or more embodiments of the present disclosure;

fig. 8 is a schematic structural diagram of a block chain-based rental risk assessment apparatus according to one or more embodiments of the present disclosure.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.

Fig. 1 is a schematic view of an application scenario of a block chain-based rental risk assessment method according to one or more embodiments of the present specification, as shown in fig. 1, the scenario includes: the system comprises target leasing equipment, a target lessee of the target leasing equipment, a target lessor of the target leasing equipment, a wind control system and a block chain system. The target leasing equipment is provided with an Internet of things module, the Internet of things module has a data acquisition function and a data communication function, and can acquire operation data of the target leasing equipment and upload the operation data to the block chain system. The target rental device may be any electronic device (e.g., a mobile phone), mechanical device (e.g., an excavator), electrical device (e.g., a generator), travel device (e.g., a vehicle), etc. (the mechanical device is taken as an example in fig. 1). Both the target lessor and the target lessor may be individuals, businesses, organizations, etc. (only individuals are shown in fig. 1); the target renter and the target renter are both connected to the block chain system, and participate in consensus processing in the process of uploading the running data of the target renter to the block chain system; the target lessee and the target lessor may deploy a node device (not shown in fig. 1) accessing the block chain system to access the block chain system, or the target lessee and the target lessor may access another node device (not shown in the figure) through which the block chain system is accessed. The wind control system is connected to the block chain system, and can provide rental risk assessment service based on the operation data in the block chain system. The blockchain system includes a plurality of node devices (not shown in fig. 1) accessing the blockchain, and the blockchain stores data, such as operation data of the rental device.

Specifically, the wind control system acquires the running data of the target leasing equipment rented by the target lessee from the block chain system; and acquiring a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental equipment, and performing risk evaluation processing on the rental business of the target rental party in a future preset time period on the basis of the acquired operation data by using the acquired evaluation model to obtain risk evaluation result information. Therefore, after-renting risk assessment processing is carried out on the basis of the pre-trained assessment model and the operation data in the block chain system, the risk assessment efficiency is greatly improved, early risk early warning of the renting business of the target renter is realized, the target renter can carry out risk control in advance, and accordingly loss is reduced; and the operation data in the block chain system is used as an evaluation basis, and the validity of the operation data is ensured based on the non-tamper property of the block chain, so that the accuracy of a risk evaluation result is ensured.

Based on the application scenario architecture, one or more embodiments of the present specification provide a lease risk assessment method based on a block chain. Fig. 2 is a schematic flowchart of a block chain-based rental risk assessment method according to one or more embodiments of the present disclosure, where the method in fig. 2 can be executed by the wind control system in fig. 1, as shown in fig. 2, and the method includes the following steps:

step S102, acquiring running data of target rental equipment rented by a target renter from a block chain system; the operation data is collected by an Internet of things module arranged in the target leasing equipment and uploaded to the block chain system;

and uploading the running data to the block chain system after passing the consensus verification processing participated by the target lessee and the target lessee. Specifically, as shown in fig. 3, when the target lender receives a lease request sent by the target lender, if it is determined according to the lease request that the preset lease condition is met, the target lender is provided with the target lease equipment; in the process that a target renter uses target renting equipment, when the target renting equipment is started to enter a working state, an internet of things module in the target renting equipment starts to acquire running data of the target renting equipment, and the running data acquired in a corresponding time period is uploaded to a block chain system according to a preset time interval (for example, 24 hours). And performing consensus verification on the operation data uploaded by the Internet of things module by block chain link points such as a target lessee, a target lessee and the like in the block chain system, and uploading the operation data to the block chain system by the block chain link points with data storage authority when the consensus verification is passed. And the wind control system acquires the operation data of the target rental equipment from the block chain system so as to perform risk evaluation processing. The operation data comprises the operation position, the operation time, the engine rotating speed, the oil consumption and the like of the target rental equipment; the Internet of things module comprises a GPS sensor, a rotating speed sensor, an oil consumption sensor and the like.

It should be noted that, in practical applications, whether to allocate the consensus verification authority to the target lessee and the target lessee may be determined according to the requirement of whether the target lessee and the target lessee participate in the consensus verification. When the target lessee or the target lessor does not have a need to participate in the consensus verification, the consensus verification process may not be involved.

Step S104, acquiring a pre-trained evaluation model for risk evaluation of the rental business of the target rental equipment;

in the embodiment of the specification, an evaluation model can be trained in advance, and the evaluation model can be used for carrying out risk evaluation on rental services of different types of rental equipment; corresponding evaluation models can be trained respectively aiming at different types of rental equipment, the association relation between the evaluation models and the equipment types is established, and correspondingly, the associated evaluation models can be obtained according to the equipment types of the target rental equipment based on the association system.

And S106, performing risk evaluation processing on the lease service of the target lease party on the target lease equipment in a future preset time period by using the obtained evaluation model based on the operation data to obtain risk evaluation result information.

Specifically, the operation data is input into the obtained evaluation model, and risk evaluation processing is performed on the lease service of the target lease party on the target lease equipment in a future preset time period, so that risk evaluation result information is obtained. The risk assessment processing can comprise assessing whether a target lessee has default risk, assessing whether target lease equipment has abnormity risk and the like; the default risk comprises overdue return risk of the target rental equipment, arrearage rent risk and the like. The target rental equipment is taken as an excavator for explanation, and when the operation position in the operation data deviates from the normal station at high frequency, the instability of the operation project is reflected to a certain extent; when the running time, the engine rotating speed, the oil consumption and the like are low, the idle time of the target leasing equipment is represented, and benefits cannot be created for the target lessee, so that the target lessee possibly has the risk of defaulting the rent; furthermore, when the rotating speed or the oil consumption of the engine is abnormal, the performance of the target rental equipment can be represented to be abnormal. Therefore, the risk assessment processing can be carried out on the leasing business of the target leasing equipment by utilizing the assessment model based on the acquired operation data.

In one or more embodiments of the present description, risk assessment processing after renting is performed based on a pre-trained assessment model and operation data of renting equipment stored in a blockchain system, so that risk assessment efficiency is greatly improved, advanced risk early warning for renting services of a target renter is realized, and the target renter is facilitated to perform risk control in advance, so that loss is reduced; and the operation data in the block chain system is used as an evaluation basis, and the validity of the operation data is ensured based on the non-tamper property of the block chain, so that the accuracy of a risk evaluation result is ensured.

In order to meet different risk assessment requirements, the wind control system acquires the operation data of the target rental equipment from the block chain system when determining that the risk assessment conditions are met. Specifically, step S102 may include:

and if the preset risk evaluation condition is met, acquiring the running data of the target rental equipment rented by the target renter from the block chain system.

Optionally, the wind control system performs risk assessment processing according to a preset frequency, and correspondingly, as shown in fig. 3, if the wind control system determines that a preset time point is reached, it determines that a preset risk assessment condition is met, and acquires operation data of a target rental device rented by a target tenant from the blockchain system; after the risk assessment result information is obtained, saving the risk assessment result information to a specified storage position; and if receiving an evaluation result acquisition request sent by the target leasing party, sending the risk evaluation result information in the storage position to the target leasing party. The risk assessment result information in the storage location is sent to the target lender, the latest risk assessment result information can be sent to the target lender, the latest preset number of risk assessment result information can also be sent to the target lender, the risk assessment result information in the corresponding time period can also be sent to the target lender according to the time period (such as 8-month 1-2020-8-month 31) specified by the target lender, the obtaining time of the target lender obtaining the assessment result every time can also be recorded, and the risk assessment result information of the last obtaining time is sent to the target lender. The designated storage position and the preset frequency can be set automatically in practical application according to needs, for example, the designated storage position is a cloud end, and the preset frequency is risk assessment processing performed every 48 hours.

Or, as shown in fig. 4, if the wind control system receives a risk assessment request sent by a target renter, determining that a preset risk assessment condition is met, and acquiring operation data of target renting equipment rented by the target renter from the block chain system; correspondingly, after the risk assessment result information is obtained, the risk assessment result information is sent to the target renter.

In consideration of the fact that a plurality of lessees and a plurality of lessees access the block chain system, in order to ensure privacy of the operation data, the internet of things module of each lease device can encrypt the collected operation data according to a preset encryption mode to obtain a ciphertext of the operation data, and upload the ciphertext of the operation data to the block chain system. Accordingly, step S102 may include:

and acquiring the ciphertext of the operation data of the target leasing equipment rented by the target tenant from the block chain system, and decrypting the ciphertext of the operation data according to a preset decryption mode to obtain the operation data.

Specifically, association management can be established between the device identifier of each rental device and the corresponding decryption algorithm and decryption key, and the association relationship is stored in the wind control system; correspondingly, the Internet of things module uploads the ciphertext of the operation data and the equipment identifier of the target rental equipment to the block chain; the wind control system obtains the ciphertext of the operation data and the corresponding equipment identification from the block chain, obtains the associated decryption algorithm and the decryption key from the stored association relation according to the equipment identification, and decrypts the ciphertext of the operation data according to the obtained decryption algorithm and the decryption key to obtain the operation data.

Further, in order to improve the accuracy of the evaluation, in one or more embodiments of the present application, the risk evaluation processing is performed based on the operation data within a preset historical time. Specifically, step S102 may include: and acquiring the running data of the target leasing equipment rented by the target lessee in the preset historical duration from the block chain system by taking the current time as the deadline. The preset historical time length can be set automatically in practical application according to needs, such as 30 days with the current time as the deadline.

In order to facilitate management of the evaluation model, in one or more embodiments of the present application, the evaluation model may be deployed in a cloud; accordingly, step S104 may include: and acquiring a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental equipment from the cloud. The cloud end can be a private cloud or a public cloud.

Further, in order to ensure the non-tamper-ability of the evaluation model, and thus ensure the accuracy of the evaluation result, in one or more embodiments of the present application, the evaluation model may also be saved into the block chain; accordingly, step S104 may include: and calling a first intelligent contract in the blockchain system, and acquiring the evaluation model from the blockchain system based on the first intelligent contract. Specifically, the storage address of the evaluation model in the blockchain system may be preset in a first intelligent contract, the wind control system invokes the first intelligent contract, and the evaluation model is obtained from the blockchain system according to the preset storage address based on the first intelligent contract.

Considering whether a lessee has default risks, the default risks are related to operation data of the leasing equipment, and are also related to operation data of the lessee, such as quality data (such as industrial and commercial information, enterprise scale, industrial position, financial condition and the like) and financial data (such as initial payment proportion, total repayment amount, repayment interest rate, repayment amount of target leasing equipment and the like), such as gradually reduced enterprise scale, unstable financial state, huge total repayment amount and the like, and the default risks that the lessee possibly faces enterprise loss, fund pressure and the like can be represented, so that default risks that the lessee cannot repay according to term exist. Based on this, in one or more embodiments of the application, when a target lessee sends a lease request to a target lessee, the lease request carries current operation data of the target lessee, after the target lessee determines to lease target lease equipment to the target lessee, the operation data in the lease request and identification information of the target lessee are sent to a wind control system in a correlation manner, and the wind control system manages and stores the received operation data and the identification information of the target lessee to a specified database. Correspondingly, the wind control system shown in fig. 3 further obtains the operation data of the target tenant from the specified database according to the identification information of the target tenant, and performs risk assessment processing based on the operation data of the target rental device and the operation data of the target tenant. Specifically, step S104 may include: acquiring operation data of a target lessee of target lease equipment from a specified database; and performing risk assessment processing on the lease service of the target lease party on the target lease equipment in a future preset time period by using the obtained assessment model based on the operation data and the operation data.

It should be noted that for short term leases (e.g., half a year), no update process may be performed on the operating data for which the target tenant in the database is designated; for long-term lease (for example, more than one year), qualification data of the target lessee, such as enterprise conditions, may change, so the wind control system may further obtain the qualification data of the target lessee from the related website according to a preset frequency according to a preset mode, and update the operation data in the designated database according to the obtained data.

In order to realize the assessment of the lease breach risk, in one or more embodiments of the present application, step S102 further includes the following steps S100-2 and S100-4:

s100-2, obtaining related rental data in a preset historical time period, and determining the obtained related rental data as data to be trained;

specifically, lease related data in a preset historical time period is obtained from the block chain. In consideration of the fact that the data volume of the data required by model training is usually large, and the data volume stored in the blockchain is limited at the early stage and may not be enough to meet the training requirement, based on this, in one or more real-time applications, the wind control system can also obtain the leasing related data in the preset historical time period maintained by the target leasing party from the target leasing party. The lease related data comprise historical operation data and lease default record data of each historical lessee in a preset historical period, historical operation data of lease equipment leased by each historical lessee and the like.

And S100-4, performing training processing based on data to be trained according to a preset training mode to obtain an evaluation model.

Since it is necessary to predict whether there is a risk in the future based on historical data, in one or more embodiments of the present application, a training process is performed based on a preset sliding time window. Specifically, step S100-4 may include the following steps S100-42 to S100-48:

s100-42, generating a plurality of samples based on data to be trained according to a preset sliding time window;

specifically, according to a preset sliding time window, an observation period and a performance period in a preset historical time period are determined; determining characteristic data according to the data to be trained in the observation period; determining label data of the characteristic data according to data to be trained in a presentation period; the feature data and the tag data are associated and determined as a sample. The width of the sliding time window can be set in practical application according to the requirement.

Wherein determining the feature data according to the data to be trained in the observation period comprises: determining characteristic data according to historical operation data of historical lessee users in an observation period and historical operation data of leased equipment leased by the historical lessee users; the historical operational data includes historical qualification data and historical financial data. Determining label data of the feature data according to the data to be trained in the presentation period, wherein the label data comprises: and according to a preset marking rule, determining label data of the characteristic data corresponding to the historical lessee party based on the lease default record data of the historical lessee users in the presentation period. The characteristic data can be different according to different renting equipment, and the specific type and marking rule of the characteristic data can be set automatically in practical application according to requirements; in the process of determining the characteristic data, the historical operating data of the rental equipment can be processed and cleaned according to the specific type of the characteristic data.

As an example, the lessee is an enterprise, and the characteristic data comprises the financial condition, the enterprise size, the total payment amount and the payment period number of the lessee; the average daily operating time of the rental equipment and the times that the geographical offset between each week and the ordinary station exceeds the preset offset; the sliding time window is 3 months; the marking rule comprises that the number of days of continuous defaulting of rent is zero, and the label data 0 of the white sample is marked; the number of continuous defaulting days of rent is 1-10 days, and label data 1 which is characterized as a gray sample is marked; the rental is held for more than 10 consecutive days of delinquent, and label data 2, characterized as a black sample, is marked. The current time is 9/1/2020, and the rental related data between 3/1/2020 and 8/31/2020 is acquired and determined as the data to be trained; taking the day of 5/31/2020 as an observation point, the observation period is defined as 3/1/2020 to 5/31/2020, and the presentation period is defined as 6/1/2020 to 8/31/2020. For a historical lessee user A, according to operation data of the historical lessee user A between 3/1/2020 and 5/31/2020, determining that the financial condition of characteristic data is good, the enterprise scale is that employees are between 200 and 300 persons, the total repayment amount is 30 thousands, and the repayment period number is 24 periods; for leased equipment (such as an excavator) which is leased by a historical leased user A and is identified as 12345, data cleaning is carried out on the daily start-up time and the daily geographic offset from a common station in the operation data between 3/1/2020 and 5/2020/31 to obtain the number 0 that the average daily start-up time is 8 hours and the geographic offset from the common station per week exceeds the preset offset. Determining continuous default days of rent to be zero according to the lease default record data of the historical lease user A from 6/1/2020 to 31/8/2020, and determining the corresponding marking data to be 0 representing the white sample; a sample with the characteristic data of good financial status, the enterprise size of 200-300 employees, 30 ten thousand total payment sum, 24 payment period and 0 marking data is obtained.

For another example, for the historical lessee user B, according to the operation data of the historical lessee user B between 3/1/2020 and 5/31/2020, the financial condition of the characteristic data is determined to be general, the enterprise size is that employees are between 50 and 100 persons, the total repayment amount is 25 ten thousand, and the repayment period number is 24 periods; and (3) performing data cleaning on the rented equipment (such as a generator) of which the equipment identifier is 12678 rented by the historical renter B, wherein the operation data comprises the daily start-up time and the daily geographic offset from the ordinary station in the range from 3/1/2020 to 5/2020/31 to obtain the number 2 that the average daily start-up time is 4 hours and the geographic offset from the ordinary station per week exceeds the preset offset. Determining continuous default days of rent to be 5 days according to lease default record data of the historical lessee user B from 6/month and 1/day in 2020 to 8/month and 31/day in 2020, wherein the corresponding marking data is 1 for representing the gray sample; a sample is obtained with the characteristic data of general financial status, 50-100 staff for enterprise scale, 25 ten thousand total payment sum, 24 payment period and 1 marking data.

In order to improve the performance of the evaluation model, in one or more embodiments of the present application, the determining the observation period and the performance period within the preset historical period according to the preset sliding time window may further include:

dividing a preset historical time period into a plurality of sub-historical time periods according to a preset time period division rule; an observation period and a presentation period within each sub-history period are determined.

As an example, the current time is 9/1/2020, rental-related data between 3/1/2020 and 8/31/2020 is acquired and determined as data to be trained, and the time interval division rule is to divide bimonthly into one sub-history time interval; the sliding time window is 1 month; dividing 3/month 1 of 2020 into 4/month 30 of 2020 into a sub-history period, and determining 3/month 1 of 2020 to 3/month 31 of 2020 within the sub-history period as an observation period and 4/month 1 of 2020 to 4/month 30 of 2020 as a presentation period; dividing 5/month 1 in 2020 to 6/month 30 in 2020 into a sub-history period, and determining 5/month 1 in 2020 to 5/month 31 in 2020 within the sub-history period as an observation period and 6/month 1 in 2020 to 6/month 30 in 2020 as a presentation period; dividing 1/7/2020 to 31/8/2020 into a sub-history period, determining 1/7/2020 to 31/7/2020 within the sub-history period as an observation period, and determining 1/8/2020 to 31/8/2020 as a presentation period.

S100-44, dividing a sample into a training set, a first verification set and a second verification set;

the second validation set may also be referred to as an OOT (out Of time) set.

When the preset history period is divided into a plurality of sub-history periods, samples corresponding to different sub-history periods can be determined to be a set, so that the time corresponding to the training set, the first verification set and the second verification set is different, the problem of overfitting in the training process is avoided, and the stability of the evaluation model obtained by training is improved. For example, samples generated based on data to be trained in a sub-history period of 3/month 1/2020 to 4/month 30/2020 are divided into a training set, and samples generated based on data to be trained in a sub-history period of 5/month 1/2020 to 6/month 30/2020 are divided into a first verification set; samples generated based on data to be trained in a sub-historical period of from 1/7/2020 to 31/8/2020 are divided into a second verification set.

S100-46, training based on a training set according to a preset training mode to obtain an initial evaluation model;

the training mode can be set in practical application according to needs, such as logistic regression, decision tree, machine learning based on tree model, and the like.

And S100-48, if the initial evaluation model passes the verification based on the first verification set and the second verification set, determining the initial evaluation model as a final evaluation model.

Specifically, when an initial evaluation model is obtained, the initial evaluation model is verified based on a first verification set to obtain a first verification result; if the first verification result represents that the verification fails, continuing training based on the training set and a preset tuning strategy to obtain a current updated initial evaluation model, and verifying the current updated initial evaluation model based on the first verification set; if the first verification result represents that the verification is passed, verifying the corresponding initial evaluation model based on a second verification set to obtain a second verification result, if the second verification result represents that the verification is not passed, continuing to perform training on the currently updated initial evaluation model based on the training set and a preset tuning strategy, and verifying the currently updated initial evaluation model based on the first verification set; and if the second verification result represents that the verification is passed, determining the corresponding initial evaluation model as a final evaluation model. The tuning strategy can be set automatically according to needs in practical application, such as KS index utilization, AUC head accuracy and the like. Therefore, verification processing is carried out on the basis of the first verification set and the second verification set to obtain a final evaluation model, and the stability of the evaluation model and the accuracy of an evaluation result can be improved.

Corresponding to the above-described block chain-based rental risk assessment method, based on the same technical concept, one or more embodiments of the present specification further provide a block chain-based rental risk assessment apparatus. Fig. 8 is a schematic block diagram illustrating a block chain-based rental risk assessment apparatus according to one or more embodiments of the present disclosure, as shown in fig. 5, the apparatus includes:

a first obtaining module 201, configured to obtain, from a blockchain system, operation data of a target rental device rented by a target tenant; the operating data is collected by an Internet of things module arranged in the target leasing equipment and uploaded to the block chain system;

the second obtaining module 202 is configured to obtain a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental device;

and the evaluation module 203 is used for performing risk evaluation processing on the lease service of the target lease equipment in a future preset time period by using the evaluation model based on the operation data to obtain risk evaluation result information.

According to the block chain-based lease risk assessment device provided by one or more embodiments of the specification, the lease-behind-lease risk assessment processing is performed based on the pre-trained assessment model and the operation data of the lease equipment stored in the block chain, so that the risk assessment efficiency is greatly improved, advance early warning of lease-behind risks of tenants is realized, the target leaser can perform risk control in advance, and losses are reduced; and the operation data in the block chain is used as an evaluation basis, and the validity of the operation data is ensured based on the non-tamper property and the consensus mechanism of the block chain, so that the accuracy of a risk evaluation result is ensured.

Optionally, the first obtaining module 201 obtains the operation data of the target rental device rented by the target tenant from the blockchain system if it is determined that the preset risk assessment condition is met.

Optionally, the evaluation module 203 acquires operation data of the target tenant from a designated database; and the number of the first and second groups,

and performing risk assessment processing on the renting business of the target renter about the target renting equipment in a future preset time period by using the assessment model based on the operation data and the operation data.

Optionally, the apparatus further comprises: a third acquisition module and a training module;

the third acquisition module acquires the lease related data in a preset historical time period and determines the lease related data as data to be trained;

and the acquisition training module is used for carrying out training processing based on the data to be trained according to a preset training mode to obtain the evaluation model.

Optionally, the training module divides the data to be trained into a plurality of samples according to a preset sliding time window; and the number of the first and second groups,

dividing the sample into a training set, a first validation set and a second validation set;

training based on the training set to obtain an initial evaluation model;

and if the initial evaluation model passes the verification of the first verification set and the second verification set, determining the initial evaluation model as a final evaluation model.

Optionally, the training module determines an observation period and a performance period within the preset historical time period according to a preset sliding time window; and the number of the first and second groups,

determining characteristic data according to the data to be trained in the observation period;

determining label data of the feature data according to the data to be trained in the presentation period;

and associating the characteristic data and the label data and determining the characteristic data and the label data as a sample.

According to the block chain-based lease risk assessment device provided by one or more embodiments of the specification, risk assessment processing after lease is performed based on the pre-trained assessment model and the operation data of the lease equipment stored in the block chain system, so that the risk assessment efficiency is greatly improved, early risk early warning of lease service of a target lessee is realized, the risk control of the target lessee is facilitated in advance, and the loss is reduced; and the operation data in the block chain system is used as an evaluation basis, and the validity of the operation data is ensured based on the non-tamper property of the block chain, so that the accuracy of a risk evaluation result is ensured.

It should be noted that, the embodiment of the device for assessing rental risk based on a block chain in this specification and the embodiment of the method for assessing rental risk based on a block chain in this specification are based on the same inventive concept, and therefore, for specific implementation of this embodiment, reference may be made to the implementation of the method for assessing rental risk based on a block chain in the foregoing description, and repeated details are not described again.

Further, on the basis of the same technical concept, one or more embodiments of the present specification further provide a lease risk assessment system based on a block chain corresponding to the above-described lease risk assessment method based on a block chain. Fig. 6 is a schematic composition diagram of a block chain-based rental risk assessment system according to one or more embodiments of the present disclosure, as shown in fig. 6, the system includes: target rental equipment 301, a block chain system 302 and a wind control system 303;

the target rental equipment 301 acquires the operation data of the target rental equipment 301 through an internet of things module arranged on the target rental equipment 301 and uploads the operation data to the blockchain system 302;

the block chain system 302 stores the operation data uploaded by the internet of things module;

the wind control system 303 acquires the operation data of the target rental equipment from the blockchain system, and acquires a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental equipment; and performing risk evaluation processing on the lease service of the target lease equipment by the target lessee of the target lease equipment in a future preset time period by using the evaluation model based on the operation data to obtain risk evaluation result information.

Optionally, as shown in fig. 7, the system further includes:

target lessees

304 and 305 of the target rental device;

the target lessee 304 accesses the blockchain system 302 and participates in consensus processing of the running data;

the target lender 305 accesses the blockchain system 302 and participates in the consensus process of the operational data.

According to the block chain-based lease risk assessment system provided by one or more embodiments of the specification, risk assessment processing after lease is performed based on the pre-trained assessment model and the operation data of lease equipment stored in the block chain system, so that the risk assessment efficiency is greatly improved, early risk early warning of lease service of a target lessee is realized, the risk control of the target lessee is facilitated, and the loss is reduced; and the operation data in the block chain system is used as an evaluation basis, and the validity of the operation data is ensured based on the non-tamper property of the block chain, so that the accuracy of a risk evaluation result is ensured.

It should be noted that, the embodiment of the rental risk assessment system based on the block chain in this specification and the embodiment of the rental risk assessment method based on the block chain in this specification are based on the same inventive concept, and therefore, specific implementation of this embodiment may refer to implementation of the aforementioned corresponding rental risk assessment method based on the block chain, and repeated details are not repeated.

Further, corresponding to the above-described block chain-based rental risk assessment method, based on the same technical concept, one or more embodiments of the present specification further provide a block chain-based rental risk assessment apparatus for executing the above-described block chain-based rental risk assessment method, and fig. 8 is a schematic structural diagram of the block chain-based rental risk assessment apparatus provided in one or more embodiments of the present specification.

As shown in fig. 8, the block chain based rental risk assessment apparatus may have a relatively large difference due to different configurations or performances, and may include one or more processors 401 and a memory 402, where one or more stored applications or data may be stored in the memory 402. Wherein memory 402 may be transient or persistent. The application program stored in memory 402 may include one or more modules (not shown), each of which may include a series of computer-executable instructions in a block chain-based rental risk assessment facility. Still further, processor 401 may be configured to communicate with memory 402 to execute a series of computer-executable instructions in memory 402 on a blockchain-based rental risk assessment device. The block chain-based rental risk assessment apparatus may also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input-output interfaces 405, one or more keyboards 406, and the like.

In a particular embodiment, a blockchain-based rental risk assessment apparatus includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the blockchain-based rental risk assessment apparatus, and execution of the one or more programs by one or more processors includes computer-executable instructions for:

acquiring running data of target rental equipment rented by a target renter from a block chain system; the operating data is collected by an Internet of things module arranged in the target leasing equipment and uploaded to the block chain system;

acquiring a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental equipment;

and performing risk assessment processing on the lease service of the target lease equipment in a future preset time period by using the assessment model based on the operation data to obtain risk assessment result information.

Optionally, the computer executable instructions, when executed, obtain operating data of a target rental device rented by a target tenant from the blockchain system, comprising:

Optionally, when executed, the computer-executable instructions, using the evaluation model, perform risk evaluation processing on the rental business of the target rental party with respect to the target rental device in a preset period in the future based on the operating data, including:

acquiring operation data of the target lessee from a specified database;

Optionally, the computer executable instructions, when executed, further comprise:

obtaining related lease data in a preset historical time period, and determining the related lease data as data to be trained;

and training based on the data to be trained according to a preset training mode to obtain the evaluation model.

Optionally, when executed, the computer-executable instructions perform training processing based on the data to be trained according to a preset training mode, including:

generating a plurality of samples based on the data to be trained according to a preset sliding time window;

training based on the training set according to a preset training mode to obtain an initial evaluation model;

According to the block chain-based lease risk assessment equipment provided by one or more embodiments of the specification, risk assessment processing after lease is performed on the basis of the pre-trained assessment model and the operation data of the lease equipment stored in the block chain system, so that the risk assessment efficiency is greatly improved, early risk warning of lease service of a target lessee is realized, the risk control of the target lessee is facilitated in advance, and the loss is reduced; and the operation data in the block chain system is used as an evaluation basis, and the validity of the operation data is ensured based on the non-tamper property of the block chain, so that the accuracy of a risk evaluation result is ensured.

It should be noted that, the embodiment of the device for assessing rental risk based on a block chain in this specification and the embodiment of the method for assessing rental risk based on a block chain in this specification are based on the same inventive concept, and therefore, for specific implementation of this embodiment, reference may be made to the implementation of the method for assessing rental risk based on a block chain described above, and repeated details are not described again.

Further, corresponding to the above-described rental risk assessment method based on a block chain, based on the same technical concept, one or more embodiments of the present specification further provide a storage medium for storing computer executable instructions, where in a specific embodiment, the storage medium may be a usb disk, an optical disk, a hard disk, and the like, and when the storage medium stores the computer executable instructions, the following process can be implemented:

Optionally, the computer-executable instructions stored in the storage medium, when executed by the processor, obtain operating data of a target rental device rented by a target tenant from the blockchain system, including:

Optionally, the computer-executable instructions stored in the storage medium, when executed by the processor, perform risk assessment processing on the rental business of the target rental party with respect to the target rental device in a preset period of time in the future by using the assessment model based on the running data, including:

acquiring operation data of the target lessee from a specified database;

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, further comprise:

Optionally, when executed by a processor, the computer-executable instructions stored in the storage medium perform training processing based on the data to be trained according to a preset training mode, including:

When the computer executable instructions stored in the storage medium provided by one or more embodiments of the present specification are executed by the processor, the post-rental risk assessment processing is performed based on the pre-trained assessment model and the running data of the rental device stored in the block chain system, so that the risk assessment efficiency is greatly improved, the advanced risk early warning of the rental service of the target renter is realized, and the target renter is facilitated to perform risk control in advance, so that the loss is reduced; and the operation data in the block chain system is used as an evaluation basis, and the validity of the operation data is ensured based on the non-tamper property of the block chain, so that the accuracy of a risk evaluation result is ensured.

It should be noted that the embodiment of the storage medium in this specification and the embodiment of the block chain-based rental risk assessment method in this specification are based on the same inventive concept, and therefore, for specific implementation of this embodiment, reference may be made to implementation of the aforementioned corresponding block chain-based rental risk assessment method, and repeated details are not repeated.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the units may be implemented in the same software and/or hardware or in multiple software and/or hardware when implementing the embodiments of the present description.

One skilled in the art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto 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 flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of this document and is not intended to limit this document. Various modifications and changes may occur to those skilled in the art from this document. Any modifications, equivalents, improvements, etc. which come within the spirit and principle of the disclosure are intended to be included within the scope of the claims of this document.

Claims

1. A lease risk assessment method based on a block chain comprises the following steps:

2. The method of claim 1, the target lessee and target lessor of the target rental device accessing the blockchain system;

and uploading the running data to the block chain system after passing the consensus verification processing participated by the target lessee and the target lessee.

3. The method of claim 1, wherein the obtaining operational data of a target rental device rented by a target tenant from a blockchain system comprises:

4. The method of claim 3, the determining that a preset risk assessment condition is met, comprising:

if the preset time point is reached, determining that the preset risk assessment condition is met;

after the risk assessment result information is obtained, the method further comprises the following steps:

storing the risk assessment result information to a designated storage position; and the number of the first and second groups,

and if an evaluation result acquisition request sent by a target leasing party of the target leasing equipment is received, sending the risk evaluation result information in the storage position to the target leasing party.

5. The method of claim 3, the determining that a preset risk assessment condition is met, comprising:

if a risk assessment request sent by a target leasing party of the target leasing equipment is received, determining that a preset risk assessment condition is met;

and sending the risk assessment result information to the target lender.

6. The method of claim 1, wherein the obtaining operational data of a target rental device rented by a target tenant from a blockchain system comprises:

and acquiring the running data of the target leasing equipment rented by the target lessee in a preset historical duration from the blockchain system by taking the current time as the deadline.

7. The method of claim 1, wherein the obtaining operational data of a target rental device rented by a target tenant from a blockchain system comprises:

acquiring the cryptograph of the running data of the target rental equipment rented by the target renter from the block chain system;

and decrypting the ciphertext of the operating data according to a preset decryption mode to obtain the operating data.

8. The method of claim 1, the obtaining a pre-trained assessment model for risk assessment of rental business of the target rental device, comprising:

and acquiring a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental equipment from the cloud.

9. The method of claim 1, the obtaining a pre-trained assessment model for risk assessment of rental business of the target rental device, comprising:

calling a first intelligent contract in the blockchain system, and acquiring the evaluation model from the blockchain system based on the first intelligent contract.

10. The method of claim 1, wherein the performing risk assessment processing on the rental business of the target rental party with respect to the target rental device within a preset period of time in the future by using the assessment model based on the operating data comprises:

acquiring operation data of the target lessee from a specified database;

11. The method of claim 1, further comprising:

12. The method of claim 11, the obtaining rental-related data over a preset historical period, comprising:

and acquiring leasing related data in a preset historical time period from the block chain system.

13. The method according to claim 11, wherein the training process based on the data to be trained according to a preset training mode comprises:

14. The method of claim 13, wherein the dividing the data to be trained into a plurality of samples according to a preset sliding time window comprises:

determining an observation period and a performance period in the preset historical time period according to a preset sliding time window;

15. The method of claim 14, the data to be trained comprising: historical operation data of each historical lessee in the preset historical period, and historical operation data of leased equipment leased by the historical lessee;

the determining feature data according to the data to be trained in the observation period includes:

determining characteristic data according to the historical operation data and the historical operation data in the observation period; wherein the historical operating data comprises historical qualification data and historical financial data.

16. The method of claim 14, the data to be trained comprising: lease default record data of each historical lessee in the preset historical time period;

the determining the label data of the feature data according to the data to be trained in the presentation period includes:

and according to a preset marking rule, determining label data of the feature data corresponding to the historical lessee based on the lease default record data in the presentation period.

17. A block chain-based lease risk assessment device comprises:

the first acquisition module is used for acquiring the running data of the target rental equipment rented by the target renter from the block chain system; the operating data is collected by an Internet of things module arranged in the target leasing equipment and uploaded to the block chain system;

the second acquisition module is used for acquiring a pre-trained evaluation model for performing risk evaluation on the rental business of the target rental equipment;

and the evaluation module is used for carrying out risk evaluation processing on the lease service of the target lease equipment in a future preset time period by using the evaluation model based on the operation data to obtain risk evaluation result information.

18. The apparatus as set forth in claim 17, wherein,

the first obtaining module obtains the running data of the target leasing equipment rented by the target lessee in the preset historical duration from the block chain system by taking the current time as the deadline.

19. The apparatus as set forth in claim 17, wherein,

the evaluation module acquires the operation data of the target lessee from a specified database; and the number of the first and second groups,

20. The apparatus of claim 17, the apparatus further comprising: a third acquisition module and a training module;

21. The apparatus as set forth in claim 20, wherein,

the training module divides the data to be trained into a plurality of samples according to a preset sliding time window; and the number of the first and second groups,

training based on the training set to obtain an initial evaluation model;

22. A block chain based rental risk assessment system, comprising: target rental equipment, a block chain system and a wind control system;

the target leasing equipment acquires the operation data of the target leasing equipment through an internet of things module arranged on the target leasing equipment and uploads the operation data to the block chain system;

the block chain system stores the operation data uploaded by the Internet of things module;

the wind control system acquires the operation data of the target rental equipment from the block chain system, and acquires a pre-trained evaluation model for risk evaluation of the rental business of the target rental equipment; and performing risk evaluation processing on the lease service of the target lease equipment by the target lessee of the target lease equipment in a future preset time period by using the evaluation model based on the operation data to obtain risk evaluation result information.

23. The system of claim 22, further comprising: a target lessee and a target lessor of the target rental equipment;

the target lessee accesses the block chain system and participates in consensus processing of the running data;

and the target leasing party accesses the block chain system and participates in the consensus processing of the running data.

24. A block chain-based rental risk assessment apparatus, comprising:

a processor; and the number of the first and second groups,

a memory arranged to store computer executable instructions that, when executed, cause the processor to:

25. A storage medium storing computer-executable instructions that when executed by a processor implement the following: