CN113487402A - Supply chain financial platform based on credit granting model - Google Patents
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Abstract
The invention relates to the technical field of supply chain finance, in particular to a supply chain finance platform based on a credit granting model, which comprises a plurality of supply chain data nodes and an interactive server, wherein the supply chain data nodes are connected with a plurality of supply chain enterprises, the supply chain data nodes are operated with a data storage module, a data verification module and a credit granting model execution module, the data storage module stores qualification data, mortgage asset data and service data of the supply chain enterprises, the credit granting model execution module inputs the service data of the supply chain enterprises into the credit granting model, the interactive server performs data interaction with a credit institution, receives the credit granting model submitted by the credit institution, sends the credit granting model to the supply chain data nodes connected with a loan target supply chain enterprise for execution, obtains credit granting amount, and develops credit business according to the credit granting amount. The substantial effects of the invention are as follows: the privacy of supply chain enterprises is effectively protected, the authenticity of credit data is guaranteed, the fund circulation is promoted, and the bad account rate is reduced.
Description
Technical Field
The invention relates to the technical field of supply chain finance, in particular to a supply chain financial platform based on a credit granting model.
Background
The supply chain refers to a system which surrounds a core enterprise, starts with kit parts, produces intermediate products and final products, and finally delivers the products to consumers through a distribution network, and comprises a plurality of bodies such as material suppliers, manufacturers, warehousers, carriers, distributors, retailers, and end customers. There are many businesses involved in the supply chain that involve upstream and downstream industries. The method is suitable for medium and small enterprises, even micro enterprises and initial enterprises. These businesses participate in supply chain activities, provide products or services to their downstream businesses, and purchase products or services from their upstream businesses. These micro-enterprises and initial enterprises are often small in scale, imperfect in management, low in qualification, and difficult to obtain credit support for credit organizations. But participate in the supply chain and depend on other enterprises in the supply chain, so that the supply chain is 'honor and loss'. Thus, it is now emerging that core enterprises or enterprises of a certain size in the supply chain guarantee these small micro-enterprises for credit support. However, such a guarantee brings risks and costs to the enterprises providing the guarantee, and it is difficult to obtain a sufficient credit line even with the guarantee since the bank cannot acquire complete business management data and qualification data.
The block chain technology is a decentralized distributed account book system, records the characteristic that data in the system can be traced, is not easy to lose and cannot be tampered, can provide credible proofs for data of enterprises and organizations, and efficiently constructs credibility between the enterprises and the organizations with low cost.
However, although the block chain technology can provide reliable business data of the supply chain enterprise, the block chain technology also causes the leakage of the business data of the supply chain enterprise. Bringing disadvantages to the development of supply chain enterprises. Therefore, a credit scheme which can provide credible business and qualification data, does not cause leakage of core data of supply chain enterprises and has both credibility and confidentiality needs to be provided.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the existing supply chain financial platform cannot take credibility and confidentiality into account. The technical scheme is that by means of the credit granting model, a credit institution can obtain a credible credit limit evaluation result under the condition that original data of a supply chain enterprise applying for loan is not obtained, credit business is carried out, and credibility and confidentiality are considered.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a supply chain financial platform based on a credit granting model comprises a plurality of supply chain data nodes and an interaction server, wherein the supply chain data nodes are connected with a plurality of supply chain enterprises, the supply chain data nodes are operated with a data storage module, a data verification module and a credit granting model execution module, the data storage module stores qualification data, mortgage asset data and service data of the supply chain enterprises, the data verification module verifies the data of the data storage module, the credit granting model execution module inputs the service data of the supply chain enterprises into the credit granting model, the credit granting model outputs credit granting limits corresponding to the supply chain enterprises, the interaction server is connected with a credit institution and performs data interaction with the credit institution, the interaction server receives the credit granting model submitted by the credit institution and sends the credit granting model to the supply chain data nodes connected with a loan target supply chain enterprise for execution, and obtaining the credit line, and developing credit business according to the credit line. The qualification data of the supply chain enterprise, such as passing the quality system certification, the registered fund meeting the preset conditions, and the obtained certificate approved by other banks, etc. Mortgage asset data includes real estate, such as real estate, as well as mobile assets, such as cars, held investments, and the like. The business data comprises historical turnover, historical profit, historical supply contract and the like. The credit model obtains credit line according to various data of the enterprise applying for loan. The specific credit granting model is specifically formulated by a bank according to the self needs, belongs to the known technology in the field, and does not need or is not convenient for clearly defining the specific credit granting rules of the credit granting model. When the credit model is sent to the supply chain data node, the supply chain data node extracts the data of the supply chain enterprise to be applied for loan, such as the registered fund of the enterprise, whether the data passes the quality system authentication, whether mortgage house property/vehicle property exists, whether mortgage investment exists, historical annual business volume and annual profit, and inputs the data into the credit model, and the credit model specifically obtains whether loan is given and the highest credit amount given. And (4) specifically adjusting data according to the credit granting model by the bank, wherein if the registered fund of the enterprise is not considered, the corresponding supply chain data node does not need to extract the registered fund of the enterprise. For historical annual turnover and annual profit, the supply chain data node needs to collect, summarize and calculate all stored business data according to the years. And the business turnover and the profit of the supply chain enterprises in the previous financial year can be calculated and stored for later use.
Preferably, the data storage module collects service data of supply chain enterprises, a linear storage area is opened up in a storage space, the service data is stored in a close-proximity mode, the data evidence storage module is inserted into an evidence storage point in a first period after the service data, the evidence storage point occupies a storage space with a preset length, the service data between the two evidence storage points is associated with an enterprise identifier and a standard time stamp and then is subjected to hash value extraction, the hash value is stored in the evidence storage point, the data in the last evidence storage point and the service data hash value of the current evidence storage point are extracted together to serve as an associated hash value to be stored in the evidence storage point, the data evidence storage module sends the latest associated hash value to the interaction server in a second period, the interaction server packs the received associated hash value into a compressed packet, extracts the hash value of the compressed packet and uploads the compressed packet to a block chain for storage, and the data evidence storage module uploads the latest associated hash value to the block chain for storage in a third period. The linear space storage not only accelerates the reading speed, but also brings a large amount of resource consumption if the storage space occupied by the data is changed when the data is maliciously modified afterwards, and the cost and the required time for maliciously modifying the stored data can be effectively improved for the supply chain data node which is accessed with a large enough amount of enterprise data.
Preferably, the interactive server periodically generates 16 pairs of random numbers and exhaustive numbers of power N, the pairs of random numbers and exhaustive numbers meet preset workload certification conditions, the data certification module extracts designated bits of the associated hash value to form a plurality of N-bit numbers, downloads the random numbers and the corresponding exhaustive numbers of which the tail N bits are the same as the N-bit numbers from the interactive server, and stores the random numbers and the corresponding exhaustive numbers in the certification point. The interactive server generates a plurality of pairs of random numbers and exhaustive numbers which meet the workload certification and are used by a plurality of supply chain data nodes, and if the number of the supply chain data nodes is more than the generated pairs of the random numbers and the exhaustive numbers, a large amount of calculation power can be saved after the supply chain data nodes are spread. The supply chain data nodes need to synchronize a large amount of service data of supply chain enterprises in real time, enough residual computing power is difficult to carry out workload certification, and the workload certification can be conveniently and quickly established by associating the existing workload certification provided by the optimal scheme. However, after the period is over, if the supply chain data node maliciously modifies the stored data, the designated bit of the associated hash value is almost inevitably changed, and at this time, the supply chain data node needs to establish a workload proof by itself, which is very labor-consuming and time-consuming, and greatly improves the difficulty and cost of data tampering.
Preferably, the data evidence storage module extracts a plurality of N-bit numbers from the latest associated hash value and the last exhaustive number stored at the last evidence storage point according to a designated position, downloads a random number and a corresponding exhaustive number, of which the last N bits are the same as the N bits, from the interaction server, and stores the random number and the corresponding exhaustive number in the current evidence storage point, wherein the difficulty of the preset workload proving condition is set as the maximum calculation power of the supply chain data node, and the average time for obtaining all required random number and exhaustive number pairs by calculation is longer than a first period. If the data stored in the previous period is tampered with, workload certification almost certainly needs to be carried out again, but the period also needs to obtain the specified bit of the last exhaustive number of the previous period, and if the supply chain data node does not definitely obtain the last exhaustive number before the end of the period to obtain the value of the specified bit, the downloading opportunity of the period is missed. After the time reaches the next period, the supply chain data node not only needs to make up the workload proof of the last period, but also needs to make up the missing workload proof of the last period. The compensation process inevitably misses the workload certification downloading opportunity of the current period, so that the supply chain data node always compensates the workload certification, the loopholes needing to be compensated are larger and larger, the inevitable trace of data tampering is ensured, and the authenticity of the data is identified and stored.
Preferably, the data storage module encrypts the service data between the two evidence points by using a public key of the data storage module, and sends the encrypted service data to a plurality of other supply chain data nodes, records the identifiers of the sent other supply chain data nodes in the evidence points, and acquires the damaged or modified service data from the corresponding other supply chain data nodes when the service data locally stored by the supply chain data nodes is damaged or accidentally modified. The backup of the data is provided, the data can be recovered after being tampered, and the data can be recovered after being lost.
Preferably, the interactive server provides a credit model editing module, the input of the credit model comprises qualification data, mortgage asset data and service data of a supply chain enterprise, the output of the credit model comprises credit line, a credit institution edits the credit model through the credit model editing module, the accepted supply chain enterprise qualification data conditions, mortgage asset data conditions and service data conditions are specified in the credit model, then the credit line which can be correspondingly obtained for each mortgage asset and service scale is edited, the credit model is signed and submitted to the interactive server, the interactive server gives a unique identifier to the credit model after the signature is verified, the credit model is on-line, the summary information of the credit model is disclosed, the summary information comprises credit institution information and credit model on-line time, when the supply chain enterprise wants to apply for loan, selecting a target credit mechanism, downloading a corresponding credit model from an interactive server by a supply chain data node connected with a supply chain enterprise, executing the credit model by a credit model execution module, extracting qualification data, mortgage asset data and service data from the stored data of the corresponding supply chain enterprise by the credit model execution module according to the input required by the credit model, substituting the qualification data, the mortgage asset data and the service data into the credit model to obtain a credit line result, sending the credit line result to the interactive server, transferring the credit line result to the credit mechanism by the interactive server, and performing credit service by the credit mechanism according to the obtained credit line result. The bank edits the credit granting model through the credit granting model editing module, so that a unified credit granting model is formed, and the situation that the credit granting model cannot be executed correctly due to incompatible data formats and the like is avoided.
Preferably, when the trust model execution module executes the trust model, a separate storage area is opened up, the extracted qualification data, mortgage asset data and service data are packaged and extracted to obtain a hash value as a data hash value, the trust model extracted hash value is used as a model hash value, and the data hash value and the model hash value are uploaded to a block chain for storage. Other supply chain data nodes can read qualification data, mortgage asset data and business data containing supply chain enterprise sensitive data extracted by the supply chain data node in an external consensus contract mode, verify whether a credit model is correctly executed, and verify whether the business data extracted by the supply chain data node is true and correct. And signing the verification result after the verification is passed. And if the verification fails, carrying out corresponding report, and punishing by an interactive server or a supply chain data node autonomous mechanism.
Preferably, when the credit agency signs the credit model and submits the signed credit model to the interactive server, the credit agency submits example data at the same time, the example data comprises supply chain enterprise qualification data, mortgage asset data, service data and corresponding credit line, the interactive server substitutes the example data into the received credit model after verifying the signature, and after the result output by the verification credit model is matched with the credit line in the example data, the credit model is on-line and the summary information of the credit model is disclosed. The use of example data ensures that the trust model can be executed correctly.
Preferably, the supply chain data nodes invite a plurality of other supply chain data nodes to carry out verification executed by the credit granting model, the supply chain data nodes package and encrypt the data used by the credit granting model and send the data to the verified other supply chain data nodes, after receiving the data, the other supply chain data nodes extract qualification data, mortgage asset data and service data from the data, substitute the qualification data, obtain a credit granting amount result, compare the credit granting amount result with a credit amount obtained by the supply chain data nodes, if the credit granting amount result is consistent with the credit amount obtained by the supply chain data nodes, the verification is passed, the verification result is signed and sent to the supply chain data nodes for storage, if the credit granting amount result is inconsistent with the credit amount obtained by the supply chain data nodes, the verification is failed, and the verification result is associated with relevant information and sent to the interaction server after the signature. The related information comprises the verified supply chain data node and the credit model identification.
The substantial effects of the invention are as follows: 1) the credit line is evaluated by using qualification data and service data of a supply chain enterprise through a supply chain data node by means of a credit model, the service data of the supply chain enterprise does not need to leave the supply chain data node, so that the leakage situation is avoided, the privacy of the supply chain enterprise is effectively protected, the credible credit line obtained by evaluating the supply chain data node is guaranteed through block chain verification and is submitted to a credit institution for credit service, and the authenticity of the credit data is guaranteed; 2) by collecting qualification data and service data of a supply chain enterprise, comprehensive data reference can be provided for credit line assessment, a credit institution is facilitated to promote a credit line, fund circulation is promoted, authenticity of enterprise data is guaranteed by means of block chain evidence, and bad account rate is reduced; 3) the reliability of the business data is improved through workload certification, the cost of data tampering is further improved, and the business data of supply chain enterprises is guaranteed to be real and credible.
Drawings
Fig. 1 is a schematic diagram of a supply chain financial platform according to an embodiment.
FIG. 2 is a diagram illustrating a structure of a data storage module according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a supply chain data node structure according to an embodiment.
FIG. 4 is a schematic diagram of an on-line structure of a signaling model according to an embodiment.
FIG. 5 is a diagram illustrating a structure of a trust model according to an embodiment.
Fig. 6 is a schematic diagram illustrating a trust model verification method according to an embodiment.
Fig. 7 is a schematic diagram illustrating a working process of a data certification module according to an embodiment.
Wherein: 10. credit agency, 20, interactive server, 30, supply chain data node, 40, enterprise, 50, blockchain, 31, data storage module, 32, data storage module, 33, credit model execution module, 51, unique identifier, 52, summary information, 53, qualification data, 54, mortgage asset data, 55, credit line, 311, business data, 312, hash value, 313, associated hash value, 521, credit agency information, 522, online time.
Detailed Description
The following provides a more detailed description of the present invention, with reference to the accompanying drawings.
The first embodiment is as follows:
a supply chain financial platform based on a credit model, please refer to FIG. 1, the embodiment includes a plurality of supply chain data nodes 30 and an interaction server 20, the supply chain data nodes 30 are connected to a plurality of supply chain enterprises 40, the supply chain data nodes 30 operate a data storage module 31, a data verification module 32 and a credit model execution module 33, the data storage module 31 stores qualification data 53, mortgage asset data 54 and business data 311 of the supply chain enterprises 40, the data verification module 32 verifies the data of the data storage module 31, the credit model execution module 33 inputs the business data 311 of the supply chain enterprises 40 into the credit model, the credit model outputs a credit limit 55 corresponding to the supply chain enterprises 40, the interaction server 20 is connected to the credit institution 10 and performs data interaction with the credit institution 10, the interaction server 20 receives the credit model submitted by the credit institution 10, and sends the credit model to the supply chain data node 30 connected with the loan target supply chain enterprise 40 for execution, obtains the credit line 55, and develops credit business according to the credit line 55.
The qualification data 53 of the supply chain enterprise 40 is, for example, certified by the quality system, the registered funds meet the predetermined conditions, and other bank approved certificates are obtained.
Mortgage asset data 54 includes real estate, such as real estate, as well as mobile assets, such as cars, held investments, and the like. The business data 311 includes historical turnover, historical profit, historical supply contract, and the like. The credit model obtains a credit line 55 according to various data of the enterprise 40 applying for loan. The specific credit granting model is specifically formulated by a bank according to the self needs, belongs to the known technology in the field, and does not need or is not convenient for clearly defining the specific credit granting rules of the credit granting model. When the credit model is sent to the supply chain data node 30, the supply chain data node 30 extracts the data of the supply chain enterprise 40 to which the credit is to be applied, such as the registered funds of the enterprise 40, whether the data passes the quality system certification, whether the data is mortgage house/vehicle property, whether the data is mortgage investment, historical annual business volume and annual profit, and inputs the data into the credit model, and the credit model specifically obtains whether the credit is given and the highest credit amount given. The data upon which the bank specifically coordinates the trust model, such as by not considering the registered funds for enterprise 40, then the corresponding supply chain data node 30 need not withdraw the registered funds for enterprise 40. For historical annual turnover and annual profit, the supply chain data node 30 needs to collect, summarize, and calculate all the stored business data 311 according to the years. Or the business and profit of the supply chain enterprise 40 in the previous financial year can be calculated and stored for later use.
The data storage module 31 collects the service data 311 of the supply chain enterprise 40, a linear storage area is created in a storage space, the service data 311 is stored in an adjacent manner, please refer to fig. 2, the data evidence storage module 32 inserts evidence storage points after the service data 311 in a first period, the evidence storage points occupy a storage space with a preset length, the service data 311 between two evidence storage points is associated with the enterprise 40 identifier and the standard time stamp and then extracts the hash value 312, the hash value 312 is stored in the evidence storage points, the data in the last evidence storage point and the service data 311 of the current evidence storage point are extracted together to obtain the hash value 312 which is stored in the evidence storage points as the associated hash value 313, and the first evidence storage point extracts the hash value 312 of the service data 311 and a random number which is randomly generated together to obtain the hash value 312 as the associated hash value 313. The qualification data 53 for the supply chain enterprise 40 is publicly approved by the relevant department, so that the certification is not required, and the certification can be checked with the relevant department only after the qualification data 53 is obtained. And qualification data 53 for supply chain enterprise 40 may change as enterprise 40 evolves and is not fixed in a credentialing manner.
The data evidence storing module 32 sends the latest associated hash value 313 to the interaction server 20 in the second period, the interaction server 20 packs the received associated hash value 313 into a compressed packet, extracts the hash value 312 of the compressed packet and uploads the compressed packet to the block chain 50 for storage, and the data evidence storing module 32 uploads the latest associated hash value 313 to the block chain 50 for storage in the third period. The linear space storage not only accelerates the reading speed, but also brings a large amount of resource consumption if the storage space occupied by the data is changed when the data is maliciously modified afterwards, and the cost and the required time for maliciously modifying the stored data can be effectively increased for the supply chain data node 30 which is accessed with a large enough amount of enterprise 40 data.
Referring to fig. 3, the interactive server 20 provides a trust model editing module, the input of the trust model includes qualification data 53, mortgage asset data 54 and business data 311 of the supply chain enterprise 40, the output of the trust model includes a trust amount 55, the credit agency 10 edits the trust model through the trust model editing module, the condition of the qualification data 53, mortgage asset data 54 and business data 311 of the supply chain enterprise 40 accepted by the trust model are specified in the trust model, then the credit limit that can be obtained correspondingly to each mortgage asset and business scale is edited, the trust model is signed and then submitted to the interactive server 20, please refer to fig. 4, the interactive server 20 gives a unique identifier 51 to the trust model after verifying the signature and puts the trust model on line, the summary information 52 of the trust model is disclosed, the summary information 52 includes credit agency information 521 and the time-line 522 of the trust model, when the supply chain enterprise 40 wants to apply for loan, a target credit agency 10 is selected, a corresponding credit model is downloaded from the interactive server 20 by a supply chain data node 30 connected with the supply chain enterprise 40, the credit model executed by a credit model execution module 33 refers to fig. 5, the credit model execution module 33 extracts qualification data 53, mortgage-able asset data 54 and business data 311 from the stored data of the corresponding supply chain enterprise 40 according to the input required by the credit model, substitutes the data into the credit model to obtain a credit limit 55 result, sends the credit limit 55 result to the interactive server 20, and transfers the credit limit 55 result to the credit agency 10 by the interactive server 20, and the credit agency 10 performs credit business according to the obtained credit limit 55 result. The bank edits the credit granting model through the credit granting model editing module, so that a unified credit granting model is formed, and the situation that the credit granting model cannot be executed correctly due to incompatible data formats and the like is avoided.
When the trust model is executed by the trust model execution module 33, a separate storage area is opened up, the extracted qualification data 53, the mortgage asset data 54 and the service data 311 are packaged and the hash value 312 is extracted as a data hash value 312, the trust model extracted hash value 312 is used as a model hash value 312, and the data hash value 312 and the model hash value 312 are uploaded to the block chain 50 for storage. Referring to fig. 6, the other supply chain data nodes 30 can read the qualification data 53, mortgage asset data 54, and business data 311 containing sensitive data of the supply chain enterprise 40, which are extracted from the supply chain data node 30, in an external consensus contract manner, verify whether the trust model is correctly executed, and also verify whether the business data 311 extracted from the supply chain data node 30 is true and correct. And signing the verification result after the verification is passed. And if the verification is not passed, corresponding reporting is carried out, and punishment is carried out by the autonomous mechanism of the interaction server 20 or the supply chain data node 30.
When the credit agency 10 submits the signature of the credit model to the interactive server 20, the credit agency submits example data at the same time, the example data comprises the qualification data 53 of the supply chain enterprise 40, the mortgage asset data 54, the business data 311 and the corresponding credit line 55, after the interactive server 20 verifies the signature, the example data is substituted into the received credit model, after the result output by the verification credit model is matched with the credit line 55 in the example data, the credit model is on line and the summary information 52 of the credit model is disclosed. The use of example data ensures that the trust model can be executed correctly.
The supply chain data node 30 invites a plurality of other supply chain data nodes 30 to carry out verification executed by the credit granting model, the supply chain data node 30 packages and encrypts the data used by the credit granting model and sends the data to the other verified supply chain data nodes 30, after the other supply chain data nodes 30 receive the data, the qualification data 53, the mortgage asset data 54 and the service data 311 are extracted from the data, the data are substituted into the credit granting model to obtain the result of credit granting line 55, the result is compared with the credit line obtained by the supply chain data node 30, if the result is consistent, the verification is passed, the verification result is signed and sent to the supply chain data node 30 for storage, if the result is inconsistent, the verification is failed, the verification result is associated with relevant information and signed and sent to the interaction server 20. The relevant information includes the verified supply chain data node 30 and the trust model identification.
Referring to fig. 7, the interactive server 20 periodically generates 16 pairs of N-th power of random numbers and an exhaustive number, the pairs of random numbers and the exhaustive number satisfy a preset workload certification condition, the data storage module 32 extracts designated bits of the associated hash value 313 to form a plurality of N-bit numbers, downloads the random numbers with the same number of N-bit at the tail end and the corresponding exhaustive number from the interactive server 20, and stores the random numbers and the corresponding exhaustive numbers in a storage point. The interaction server 20 generates a plurality of pairs of random numbers and exhaustive numbers satisfying the workload certification for use by the plurality of supply chain data nodes 30, and if the number of the supply chain data nodes 30 is more than the generated pairs of random numbers and exhaustive numbers, the calculation power can be saved. The supply chain data node 30 is difficult to have enough residual computing power for workload certification because a large amount of service data 311 of the supply chain enterprise 40 needs to be synchronized in real time, and the workload certification can be conveniently and quickly established by adopting the related existing workload certification provided by the preferred scheme. However, after the period is over, if the supply chain data node 30 maliciously modifies the stored data, the designated bit of the associated hash value 313 is almost inevitably changed, and at this time, the supply chain data node 30 needs to establish a workload proof by itself, which is very labor-consuming and time-consuming, and greatly increases the difficulty and cost of data tampering.
The data evidence storage module 32 extracts a plurality of N-bit numbers from the latest associated hash value 313 and the last exhaustion number stored in the last evidence storage point according to the designated position, downloads the random number with the same number of the last N bits as the N bits and the corresponding exhaustion number from the interactive server 20, and stores the random number in the current evidence storage point.
In this embodiment, the value of N is 2, the interaction server 20 generates 16 × 16 pairs of random numbers and exhaustive numbers, that is, 256 pairs of random numbers and exhaustive numbers, and the latest associated hash value 313 of the supply chain data node 30 is:
f6aa71ae9975e56a2755debfb543ef0a6f7cbd152340afe319b8b26e94e4f8fethe last exhaustive number stored at the last credit point is: 5c1e2d776，
The first 2, last 2, 5 to 6, 13 to 14 and 19 to 20 bits of which, as well as the last bit of the last exhaustive list and the 4 th last bit of the current associated hash value 313, together constitute 6 groups of designated bits. F6, 71, e5, 55, fe and F6, respectively, 5 random numbers and corresponding exhaustive numbers with the last two digits respectively identical to the 5 groups of designated digits are downloaded from the interaction server 20 and stored in the evidence storage point. If the supply chain data node 30 tampers with the stored data, the associated hash value 313 must change, with the specified 6 sets of specified bits all unchanged with a probability of only 12 times 1 to 16, about two hundred and eighths of a billion. If all 6 designated bits change, the supply chain data node 30 needs to generate 6 sets of workload proofs by itself, which is difficult to accomplish. The probability of each of the 6 groups changing was about 0.92. It can be seen that the supply chain data node 30 necessarily needs to perform one set of workload proofs, and there is a great probability that multiple sets of workload proofs need to be performed. The workload certification refers to that the random number and the exhaustive number together extract the hash value 312, and the first bits of the hash value 312 take values of 0.
The difficulty of the preset workload proving condition is set to be that the average time of calculating all pairs of random numbers and exhaustion numbers required for obtaining the maximum calculation power of the supply chain data node 30 is longer than the first period. If the data stored in the previous period is tampered with, workload certification needs to be performed again almost certainly, but the period also needs to obtain the specified bit of the last exhaustive number of the previous period, and if the supply chain data node 30 does not obtain the last exhaustive number explicitly before the end of the period to obtain the value of the specified bit, the downloading opportunity of the period is missed. After the time reaches the next period, the supply chain data node 30 not only needs to make up the workload certification of the previous period, but also needs to make up the missing workload certification of the previous period. The compensation process inevitably misses the workload certification downloading opportunity in the current period, so that the supply chain data node 30 always compensates the workload certification, and the loopholes to be compensated are larger and larger, thereby ensuring that data tampering is inevitably traceable and providing authenticity of data.
The data storage module 31 encrypts the service data 311 between the two evidence points by using its own public key, and sends the encrypted service data 311 to a plurality of other supply chain data nodes 30, records the sent identifiers of the other supply chain data nodes 30 in the evidence points, and when the service data 311 locally stored in the supply chain data nodes 30 is damaged or accidentally modified, acquires the damaged or modified service data 311 from the corresponding other supply chain data nodes 30. The backup of the data is provided, the data can be recovered after being tampered, and the data can be recovered after being lost.
In this embodiment, the service data 311 of the supply chain enterprise 40 does not need to leave the supply chain data node 30, so that leakage does not occur, the privacy of the supply chain enterprise 40 is effectively protected, it is guaranteed through the block chain 50 that the credit line evaluated by the supply chain data node 30 is credible, and the credit line is submitted to the credit agency 10 for credit service, and the authenticity of the credit data is guaranteed.
Example two:
a supply chain financial platform based on a credit model is established in an agricultural product supply chain, and the agricultural product supply chain applied to the embodiment is provided with a core enterprise 40, namely a large agricultural product trading market enterprise 40 which is established near an agricultural product producing area and mainly supplies primary agricultural products or semi-processed agricultural products. Which has a business partnership with a plurality of agricultural product processing plants in local counties. Agricultural products purchased by agricultural product processing factories from farmers are directly or simply processed and packaged and then are sold in grades in agricultural product trading market enterprises 40. Local and foreign food processing plants purchase agricultural products or primary processed products from the agricultural product trading market. The agricultural commodity trading market has good management, and each trade or ordering contract which is committed through the agricultural commodity trading market is recorded.
A supply chain data node 30 is established in each county city, an interaction server 20 is established in one county city, the interaction server 20 and the supply chain data node 30 establish secret communication, and a plurality of agricultural product processing plants in the county cities are all connected to the same supply chain data node 30.
The agricultural product processing plant inputs its qualification data 53 and mortgage asset data 54 into the corresponding supply chain data node 30 for storage. When their qualification data 53 or mortgage asset data 54 changes, they are updated to the supply chain data node 30 in time. The business data 311 of the agricultural product processing plant is synchronized to the supply chain data node 30 in real time, the data synchronization only can increase data and can not delete the transaction data, if the business data 311 needs to be deleted, a corresponding new record for modifying the transaction data or deleting the business data 311 is added, and the business data is synchronized to the supply chain data node 30. The supply chain data node 30 collects and counts the business data 311 of the agricultural product processing plant according to needs or the annual period of the financial resources, and obtains annual sales volume and profits.
In the mature season of agricultural products, a certain agricultural product processing factory is marked as a first processing factory, a large amount of agricultural products need to be purchased in a short period, so that the fund is insufficient, and the borrowing is needed. The processing plant A then submits a loan application to the processing plant B. After receiving the loan application, bank B submits the credit model and the example data to the interactive server 20. The interaction server 20 verifies that the example data passes, associates the credit model with the name of the bank B and the current standard time, and then brings the credit model on line.
TABLE 1 Credit model submitted by Bank B
| Serial number | | Amount unit | |
| 1 | With business license | Must satisfy | |
| 2 | Local property can be mortgage, and the condition of the local property is approximately the same | +20 million | |
| 3 | Can mortgage the foreign property and upload the certified valuation bill | + assessment Single display value 60% | |
| 4 | Can escort the car property, the car property can meet the requirements that the car age is less than 8 years, the purchase price is more than 10 ten thousand and less than 50 ten thousand, and the car invoice needs to be uploaded | +5 million | |
| 5 | Can escort the car products, the car products meet the car age less than 8 years, the purchase price is more than 50 ten thousand, need to upload the car invoice | +15 million | |
| 6 | The turnover of the last year is more than 100 ten thousand | +30 ten thousand |
|
| 7 | The turnover of the last year is more than 20 ten thousand | +10 ten thousand | |
| 8 | The turnover of the last year is more than 3 ten thousand | +1 ten thousand | |
| 9 | Profit of last year | + profit 100% |
The first process plant downloads the second bank's trust model via the supply chain data node 30. The trust model execution module 33 of the supply chain data node 30 calls the qualification data 53 and the mortgage asset data 54 stored in the A factory, and then collects and counts the business data 311 of the A factory.
The first factory has a business license, can mortgage local house property, has no vehicle property, has the turnover of 26 ten thousand in the last year and has the profit of 8 ten thousand. The loan of 30 ten thousand is applied for purchasing a new device and purchasing agricultural products.
TABLE 2 results of substituting the Credit model into the A Process plant
| Serial number | | Amount unit | |
| 1 | With business license | Satisfy the requirement of | |
| 2 | Local property can be mortgage, and the condition of the local property is approximately the same | +20 million | |
| 3 | Can mortgage the foreign property and upload the certified valuation bill | +0 | |
| 4 | Can escort the car property, the car property can meet the requirements that the car age is less than 8 years, the purchase price is more than 10 ten thousand and less than 50 ten thousand, and the car invoice needs to be uploaded | +0 | |
| 5 | Can escort the car products, the car products meet the car age less than 8 years, the purchase price is more than 50 ten thousand, need to upload the car invoice | +0 | |
| 6 | The turnover of the last year is more than 100 ten thousand | +0 | |
| 7 | The turnover of the last year is more than 20 ten thousand | +10 ten thousand | |
| 8 | The turnover of the last year is more than 3 ten thousand | 0 | |
| 9 | Profit of last year | +8 million | |
| Summarizing: 38 ten thousand |
Through the execution result of the credit granting model of the bank B, the bank B knows that 38 ten thousand loans can be made at most according to the qualification, the property and the operation condition of the processing plant A, and the loan is higher than 30 thousand loans applied by the bank B. Thus deciding to loan the first processing plant.
And the bank B feeds back the result of successful loan transaction to the credit model, and the credit model judges that the house property needs to be mortgage according to the final loan amount of 30 thousands. The property data of the process plant a is thus marked as mortgaged and a mortgage record is generated by the supply chain data node 30 for storage and crediting via the data credentialing module 32.
Furthermore, the embodiment can also establish a strong notarization through the notarization at the notarization position by directly using the crediting model to mortgage the events of the house property of the processing plant A in an electronic form.
In the process, the bank B only knows that the processing factory A mortises the house property, and does not know the specific operation data, the turnover and the profit of the processing factory A. But the property does not require confidential privacy data. Although bank b does not know the specific operational data of the process plant a, bank b can still give credit line 55 to the process plant a with confidence. The first factory successfully applies for a loan and the data it needs to keep secret is not revealed to the bank and remains on the supply chain data node 30 to which it is connected. The data of the supply chain data node 30 only needs to be guaranteed to be safe, and the corresponding technology belongs to the technology known in the field and is not the focus of the improvement of the patent, so that the data of the supply chain data node 30 is guaranteed to be safe by the known technology in the implementation.
In the embodiment, a plurality of banks are connected, and if a processing factory A needs to apply for a loan of 40 ten thousand yuan, a bank B does not determine the loan. But bank c pays more attention to profit, so on the basis of table 1, item 9 of the credit model is: + profit 150%. Similarly, the credit model execution module 33 of the supply chain data node 30 substitutes the data of the first process plant into the credit model of the third bank to obtain a credit line amount 55 of 42 ten thousand. Thus, if bank B decides not to make a loan, bank C decides to make a loan.
If there are also a plurality of banks that can give 40 thousands of loans to the processing plant a, the processing plant a may select the bank with the lowest interest rate among the banks to handle the loans.
In the process, the business data 311 of the first processing factory is not disclosed to any bank, and the application materials are not submitted to the counter of any bank independently, and the background data operation is carried out by the financial platform of the supply chain, so that a plurality of banks which can apply for loans can be provided for the first processing factory, and the fund problem is solved for the banks.
Meanwhile, the bank has preference for the business, and can know which enterprises 40 are prone to loan, namely the credit line 55 is high, and which enterprises 40 are prone to conservation, namely the credit line 55 is low without receiving a large amount of trivial business data 311, so that a large amount of workload is saved for the bank. The bank does not need to carry out data investigation and verification, so that the efficiency of loan transaction is accelerated, and the authenticity of the transaction data 311 is ensured. The risk of loan is reduced, namely the bad account rate of the cable is reduced. Meanwhile, the data evidence storage module 32 provided by the financial platform stores the data of the supply chain enterprise 40, and when the enterprise 40 is tied up, the court can perform forced execution according to the profit of the enterprise 40 displayed by the evidence storage data, so that the bad account rate is further reduced, and the difficulty of bank right maintenance is reduced.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.
Claims (9)
1. A supply chain financial platform based on a credit granting model is characterized in that,
the system comprises a plurality of supply chain data nodes and an interactive server, wherein the supply chain data nodes are connected with a plurality of supply chain enterprises, the supply chain data nodes are operated with a data storage module, a data evidence storage module and a credit granting model execution module, the data storage module stores qualification data, mortgage asset data and service data of the supply chain enterprises, the data evidence storage module stores the data of the data storage module, the credit granting model execution module inputs the service data of the supply chain enterprises into a credit granting model, the credit granting model outputs credit granting quota corresponding to the supply chain enterprises, the interactive server is connected with a credit institution and performs data interaction with the credit institution, the interactive server receives the credit granting model submitted by the credit institution and sends the credit granting model to the supply chain data nodes connected with the loan target supply chain enterprises for execution, and obtaining the credit line, and developing credit business according to the credit line.
2. The trust model-based supply chain financial platform of claim 1,
the data storage module collects business data of supply chain enterprises, develops a linear storage area in a storage space, storing the service data in a close-proximity manner, inserting the data evidence storing module into an evidence storing point after the service data in a first period, the certificate storing points occupy a storage space with a preset length, business data between the two certificate storing points are associated with enterprise identification and a standard time stamp, then a hash value is extracted, the hash value is stored in the certificate storing points, the data in the previous certificate storing point and the business data hash value of the current certificate storing point are extracted together to be used as an associated hash value to be stored in the certificate storing points, the data certification module sends the latest associated hash value to the interaction server at a second period, the interactive server packs the received associated hash value into a compressed packet, extracts the hash value of the compressed packet and uploads the hash value to a block chain for storage, and the data evidence storage module uploads the latest associated hash value to the block chain for storage in a third period.
3. The trust model-based supply chain financial platform of claim 2,
the interactive server periodically generates 16N-power random number and exhaustion number pairs, the random number and the exhaustion number pairs meet preset workload proving conditions, the data evidence storing module extracts assigned bits of the associated hash value to form a plurality of N-bit numbers, and the interactive server downloads the random number and the corresponding exhaustion number, wherein the N bits at the tail of the random number are the same as the N bits, and stores the random number and the corresponding exhaustion number into an evidence storing point.
4. The trust model-based supply chain financial platform of claim 2 or 3,
the data evidence storage module extracts a plurality of N-bit numbers from the latest associated hash value and the last exhaustion number stored at the last evidence storage point according to the designated position, downloads the random number and the corresponding exhaustion number with the tail N bits same as the N-bit number from the interactive server and stores the random number and the corresponding exhaustion number into the current evidence storage point, and the difficulty of the preset workload evidence condition is set as the maximum calculation force of the supply chain data node, and the average time of calculating and obtaining all required random numbers and exhaustion number pairs is longer than a first period.
5. The supply chain financial platform based on the credit model as claimed in any one of claims 1 to 3,
the data storage module encrypts the service data between the two evidence storage points by using the public key of the data storage module, and sends the service data to a plurality of other supply chain data nodes, the identification of the other supply chain data nodes sent is recorded in the evidence storage points, and when the service data locally stored by the supply chain data nodes is damaged or accidentally modified, the damaged or modified service data is obtained from the corresponding other supply chain data nodes.
6. The supply chain financial platform based on the credit model as claimed in any one of claims 1 to 3,
the interactive server provides a credit model editing module, the input of the credit model comprises qualification data, mortgage asset data and service data of a supply chain enterprise, the output of the credit model comprises a credit line, a credit institution edits the credit model through the credit model editing module, the qualification data conditions, mortgage asset data conditions and service data conditions of the supply chain enterprise accepted by the credit model are specified in the credit model, then the credit line which can be correspondingly obtained by each mortgage asset and service scale is edited, the credit model is signed and submitted to the interactive server, the interactive server verifies the signature and gives a unique identifier to the credit model and enables the credit model to be on-line, the abstract information of the credit model is disclosed, the abstract information comprises credit institution information and the on-line time of the credit model, when the supply chain enterprise wants to apply for loan, selecting a target credit mechanism, downloading a corresponding credit model from an interactive server by a supply chain data node connected with a supply chain enterprise, executing the credit model by a credit model execution module, extracting qualification data, mortgage asset data and service data from the stored data of the corresponding supply chain enterprise by the credit model execution module according to the input required by the credit model, substituting the qualification data, the mortgage asset data and the service data into the credit model to obtain a credit line result, sending the credit line result to the interactive server, transferring the credit line result to the credit mechanism by the interactive server, and performing credit service by the credit mechanism according to the obtained credit line result.
7. The trust model-based supply chain financial platform of claim 6,
when the credit model execution module executes the credit model, an independent storage area is opened up, the extracted qualification data, the mortgage asset data and the service data are packaged and extracted to obtain a hash value as a data hash value, the credit model extracted hash value is used as a model hash value, and the data hash value and the model hash value are uploaded to a block chain for storage.
8. The trust model-based supply chain financial platform of claim 6,
and when the credit agency signs the trust model and submits the signed trust model to the interactive server, the interactive server simultaneously submits example data, wherein the example data comprises supply chain enterprise qualification data, mortgage asset data, service data and corresponding credit line, the interactive server substitutes the received trust model with the example data after verifying the signature, and after a result output by the verification trust model is matched with the credit line in the example data, the trust model is on line and the summary information of the trust model is disclosed.
9. The trust model-based supply chain financial platform of claim 6,
the supply chain data nodes invite a plurality of other supply chain data nodes to carry out verification executed by a credit granting model, the supply chain data nodes pack and encrypt data used by the credit granting model and send the data to the other verified supply chain data nodes, the other supply chain data nodes extract qualification data, mortgage asset data and service data from the data after receiving the data, substitute the qualification data into the credit granting model and obtain a credit limit result, the credit limit result is compared with a credit limit obtained by the supply chain data nodes, if the qualification data, the mortgage asset data and the service data are consistent, the verification result is signed and sent to the supply chain data nodes for storage, if the qualification data, the mortgage data and the service data are inconsistent, the verification is failed, and the verification result is associated with relevant information and is signed and sent to an interaction server.
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