Data processing method and device based on block chain, storage medium and electronic equipment

Abstract

The invention discloses a data processing method and device based on a block chain, a storage medium and electronic equipment. Relates to the technical field of block chains, and the method comprises the steps that a value evaluation request for a target financial institution is initiated to a target block chain through a client, and the value evaluation request at least comprises historical business data of the target financial institution and a historical evaluation value corresponding to the historical business data; calling an intelligent contract through the target block chain to process the historical business data and the historical evaluation value to obtain a target evaluation value; and based on the target evaluation value, carrying out optimization processing on the financial service of the target financial institution. Through the business optimization method and device, the problem that the business optimization effect of the financial institution is poor due to the fact that the business of the financial institution is adjusted and optimized based on expert experience in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the field of blockchain technology, and more specifically, to a blockchain-based data processing method and device, storage medium, and electronic device. Background Art

[0002] In order to promote the development of financial institutions, it is necessary to regularly optimize and adjust the business of financial institutions. However, the existing technology generally evaluates the value of financial institutions and the operation of their businesses based on expert experience, and then adjusts and optimizes the businesses based on the evaluation results. However, this method is overly dependent on manual labor and has the problem of poor results in optimizing the business of financial institutions.

[0003] Currently, no effective solution has been proposed to address the problem that related technologies use expert experience to adjust and optimize the business of financial institutions, resulting in poor results in optimizing the business of financial institutions. Summary of the invention

[0004] The main purpose of this application is to provide a data processing method and device, a storage medium and an electronic device based on blockchain, so as to solve the problem in related technologies that the business of financial institutions is adjusted and optimized based on expert experience, resulting in poor results in optimizing the business of financial institutions.

[0005] In order to achieve the above purpose, according to one aspect of the present application, a data processing method based on blockchain is provided. The method comprises: initiating a value assessment request for a target financial institution to a target blockchain through a client, wherein the value assessment request at least includes: historical business data of the target financial institution and the historical assessment value corresponding to the historical business data; processing the historical business data and the historical assessment value through the target blockchain calling a smart contract to obtain a target assessment value; and optimizing the financial business of the target financial institution based on the target assessment value.

[0006] Furthermore, the target blockchain calls a smart contract to process the historical business data and the historical evaluation value to obtain a target evaluation value, including: calling a target smart contract through multiple endorsement nodes in the target blockchain to process the historical business data and the historical evaluation value to obtain multiple initial evaluation values; verifying the multiple initial evaluation values ​​through the client, and if the verification passes, packaging the multiple initial evaluation values ​​to obtain a target transaction; consensus processing is performed on the target transaction through the sorting node and the submission node in the target blockchain, and after the consensus is passed, the target evaluation value is obtained based on the multiple initial evaluation values.

[0007] Furthermore, the historical business data and the historical evaluation values ​​are processed by calling a target smart contract through multiple endorsement nodes in the target blockchain to obtain multiple initial evaluation values, including: calling a first smart contract in the target smart contract through the multiple endorsement nodes to perform standardized processing on the historical business data and the historical evaluation values ​​to obtain processed historical business data and processed historical evaluation values; calling a second smart contract in the target smart contract through the multiple endorsement nodes to fit the processed historical business data and the processed historical evaluation values ​​to obtain a target linear regression model; calling a third smart contract in the target smart contract through the multiple endorsement nodes to predict the evaluation value of the target financial institution based on the target linear regression model to obtain the multiple initial evaluation values.

[0008] Furthermore, the processed historical business data and the processed historical evaluation value are fitted by calling the second smart contract in the target smart contract through the multiple endorsement nodes to obtain the target linear regression model, including: judging whether the correlation coefficient between the historical evaluation value and the historical business data is greater than a preset threshold; when the correlation coefficient between the historical evaluation value and the historical business data is greater than the preset threshold, fitting the processed historical business data and the processed historical evaluation value based on the weighted least squares method to obtain the target linear regression model.

[0009] Furthermore, fitting the processed historical business data and the processed historical assessment value based on the weighted least squares method to obtain the target linear regression model includes: determining a first weight value, and obtaining a weight matrix based on the first weight value; estimating the model parameters of the linear regression model based on the weight matrix, the processed historical business data and the processed historical assessment value through the weighted least squares method to obtain a first linear regression model; calculating the T value and P value of the first linear regression model to obtain a target T value and a target P value; when the target T value and the target P value meet the preset requirements, determining the target linear regression model based on the first linear regression model.

[0010] Furthermore, after calculating the T value and P value of the first linear regression model to obtain the target T value and the target P value, the method also includes: if the target T value and the target P value do not meet the preset requirements, updating the first weight value to obtain a second weight value; repeating the step of obtaining a weight matrix based on the second weight value until the target T value and the target P value of the current linear regression model meet the preset requirements, and determining the target linear regression model based on the current linear regression model.

[0011] Furthermore, the multiple endorsement nodes call a third smart contract in the target smart contract to predict the assessment value of the target financial institution based on the target linear regression model to obtain the multiple initial assessment values, including: determining the prediction time dimension in the value assessment request; obtaining the business data of the target financial institution in the target time period based on the prediction time dimension; and calculating the business data of the target time period through the target linear regression model to obtain the multiple initial assessment values.

[0012] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a data processing device based on blockchain is provided. The device includes: an initiating unit, which is used to initiate a value assessment request for a target financial institution to a target blockchain through a client, wherein the value assessment request at least includes: historical business data of the target financial institution and the historical assessment value corresponding to the historical business data; a processing unit, which is used to process the historical business data and the historical assessment value through the target blockchain calling a smart contract to obtain a target assessment value; an optimization unit, which is used to optimize the financial business of the target financial institution based on the target assessment value.

[0013] Furthermore, the processing unit includes: a first processing sub-unit, used to call the target smart contract through multiple endorsement nodes in the target blockchain to process the historical business data and the historical evaluation value to obtain multiple initial evaluation values; a second processing sub-unit, used to verify the multiple initial evaluation values ​​through the client, and if the verification passes, package the multiple initial evaluation values ​​to obtain the target transaction; a third processing sub-unit, used to perform consensus processing on the target transaction through the sorting node and the submission node in the target blockchain, and after the consensus passes, obtain the target evaluation value based on the multiple initial evaluation values.

[0014] Furthermore, the processing unit includes: a fourth processing sub-unit, used to call the first smart contract in the target smart contract through the multiple endorsement nodes to perform standardized processing on the historical business data and the historical evaluation value, so as to obtain the processed historical business data and the processed historical evaluation value; a fitting sub-unit, used to call the second smart contract in the target smart contract through the multiple endorsement nodes to fit the processed historical business data and the processed historical evaluation value, so as to obtain a target linear regression model; a prediction sub-unit, used to call the third smart contract in the target smart contract through the multiple endorsement nodes to predict the evaluation value of the target financial institution based on the target linear regression model, so as to obtain the multiple initial evaluation values.

[0015] Furthermore, the fitting subunit includes: a judgment module, used to judge whether the correlation coefficient between the historical evaluation value and the historical business data is greater than a preset threshold; a fitting module, used to fit the processed historical business data and the processed historical evaluation value based on the weighted least squares method to obtain the target linear regression model when the correlation coefficient between the historical evaluation value and the historical business data is greater than the preset threshold.

[0016] Furthermore, the fitting module includes: a first determination submodule, used to determine a first weight value, and obtain a weight matrix based on the first weight value; an estimation submodule, used to estimate the model parameters of the linear regression model based on the weight matrix, the processed historical business data and the processed historical evaluation value by weighted least squares method, and obtain a first linear regression model; a calculation submodule, used to calculate the T value and P value of the first linear regression model to obtain a target T value and a target P value; a second determination submodule, used to determine the target linear regression model based on the first linear regression model when the target T value and the target P value meet preset requirements.

[0017] Furthermore, the device also includes: an updating unit, which is used to calculate the T value and P value of the first linear regression model to obtain the target T value and the target P value. If the target T value and the target P value do not meet the preset requirements, the first weight value is updated to obtain a second weight value; an execution unit, which is used to repeatedly execute the step of obtaining a weight matrix based on the second weight value until the target T value and the target P value of the current linear regression model meet the preset requirements, and determine the target linear regression model based on the current linear regression model.

[0018] Furthermore, the prediction subunit includes: a determination module, used to determine the prediction time dimension in the value assessment request; an acquisition module, used to acquire the business data of the target financial institution in the target time period based on the prediction time dimension; and a calculation module, used to calculate the business data of the target time period through the target linear regression model to obtain the multiple initial assessment values.

[0019] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute any one of the above-mentioned blockchain-based data processing methods.

[0020] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, including a memory storing an executable program; and a processor for running the program, wherein the program executes any one of the above-mentioned blockchain-based data processing methods when running.

[0021] In an embodiment of the present application, a value assessment request for a target financial institution is initiated to a target blockchain through a client, wherein the value assessment request includes at least: historical business data of the target financial institution and historical assessment value corresponding to the historical business data; the historical business data and the historical assessment value are processed by the target blockchain through a smart contract to obtain a target assessment value; based on the target assessment value, the financial business of the target financial institution is optimized, and the historical business data and the historical assessment value are evaluated by calling a smart contract through the target blockchain, and the financial business of the target financial institution is optimized according to the target assessment value, thereby avoiding the problem of excessive reliance on manual labor caused by the prior art relying on expert experience to adjust and optimize the business of the financial institution, thereby achieving the technical effect of improving the effect of business optimization on the financial institution, and further solving the technical problem of adjusting and optimizing the business of the financial institution based on expert experience in the related technology, resulting in poor effect of business optimization on the financial institution. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 It is a hardware structure block diagram of a computer terminal for implementing a blockchain-based data processing method provided in an embodiment of the present application;

[0024] Figure 2 is a flowchart of a blockchain-based data processing method provided in an embodiment of the present application;

[0025] Figure 3 This is a schematic diagram of a processing architecture for executing a blockchain-based data processing method provided in an embodiment of the present application. Figure 1 ;

[0026] Figure 4 This is a schematic diagram of a processing architecture for executing a blockchain-based data processing method provided in an embodiment of the present application. Figure 2 ;

[0027] Figure 5 This is a schematic diagram of a processing architecture for executing a blockchain-based data processing method provided in an embodiment of the present application. Figure 3 ;

[0028] Figure 6 is a schematic diagram of a blockchain-based data processing device provided according to an embodiment of the present application;

[0029] Figure 7 It is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] It should be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with relevant laws, regulations and standards, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation entrances are provided for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions to provide users with corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making; if the user chooses to refuse, the expert decision-making process will be entered.

[0033] Example 1

[0034] According to an embodiment of the present application, a method embodiment of blockchain-based data processing is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0035] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing a data processing method based on blockchain is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0036] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0037] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the blockchain-based data processing method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned blockchain-based data processing method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0038] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0039] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0040] Under the above operating environment, this application provides Figure 2 The blockchain-based data processing method shown. Figure 2 This is a flowchart of a blockchain-based data processing method according to Embodiment 1 of the present application.

[0041] Step S201, initiating a value assessment request for a target financial institution to a target blockchain through a client, wherein the value assessment request includes at least: historical business data of the target financial institution and historical assessment value corresponding to the historical business data.

[0042] Optionally, in order to ensure data security and immutability, a distributed ledger blockchain system (i.e., the target blockchain mentioned above) is introduced to ensure that information is not maliciously tampered with through the blockchain. Blockchain technology is a new technical architecture established by various technologies to ensure the consistency of data in the distributed database blockchain to achieve decentralization and immutability. The core technologies of blockchain include point-to-point transmission, encryption algorithms, and consensus mechanisms.

[0043] Point-to-point transmission refers to the use of P2P network protocols to achieve end-to-end data transmission. Exchanges in the blockchain (such as transfers and transactions) do not require confirmation and authorization from a third-party guarantee agency. The encryption algorithm used in this application is an asymmetric encryption algorithm. Asymmetric encryption technology refers to encryption technology integrated into the blockchain to meet security requirements and ownership verification requirements. Asymmetric encryption uses two asymmetric keys in the encryption and decryption process, namely public keys and private keys. Asymmetric keys have the following two characteristics: After using one key (public key or private key) to encrypt information, it can only be decrypted with another corresponding key. The public key is open to everyone, and the private key is kept confidential. No one can deduce the corresponding private key through the public key.

[0044] The consensus mechanism refers to an algorithm that reaches a consensus on the order of things within a period of time. It is the foundation and soul of the blockchain. On the blockchain, each node will have a copy of all transaction ledgers recorded on the chain. Every time a new transaction is generated on the chain, each node receives the information at a different time. In order to prevent the information from being corrupted and written into the block, all nodes need to confirm the received messages.

[0045] The current consensus mechanisms include Proof of Work (POW), Proof of Stake (POS), Delegated Proof of Stake (DPOS) and Practical Byzantine Fault Tolerance (PBFT).

[0046] In this solution, the user initiates a value assessment request for the target financial institution to the target blockchain through the client. It should be noted that before the user initiates a request to the target blockchain through the client, it is necessary to call the member service of the target blockchain, register and obtain an identity certificate to ensure the security of the data.

[0047] In an optional embodiment, the value assessment request initiated by the user includes at least variables that affect the value of the target financial institution, for example, the historical business data of the target financial institution. It should be noted that the historical business data may include human cost data of financial business, and may also include risk data of financial business, operational stability data, etc. The value assessment request also includes the historical assessment value corresponding to the historical business data. The impact relationship between the historical business data and the historical assessment value can be fitted.

[0048] In an optional embodiment, the value assessment request may also include the smart contract identifier to be called for this transaction and the client signature. The smart contract identifier instructs the endorsement node to call the corresponding smart contract to process the historical business data and historical assessment value. The client signature is used to ensure the legitimacy of the user's identity.

[0049] Step S202, calling the smart contract through the target blockchain to process the historical business data and the historical evaluation value to obtain the target evaluation value.

[0050] Optionally, the above-mentioned value assessment request is sent to multiple endorsement nodes of the target blockchain, and the endorsement nodes process the historical business data and the historical assessment value to obtain the target assessment value. That is, the endorsement nodes predict the value of the current financial institution based on the historical business data and the historical assessment value to obtain the above-mentioned target assessment value.

[0051] Step S203: Optimizing the financial services of the target financial institution based on the target evaluation value.

[0052] Optionally, the financial business of the target financial institution is optimized according to the target valuation value. For example, the target valuation value is compared with the historical valuation value. If the target valuation value is lower than the historical valuation value, the handling strategy of the financial business and other aspects can be optimized.

[0053] To summarize, a value assessment request for a target financial institution is initiated to a target blockchain through a client, wherein the value assessment request includes at least: historical business data of the target financial institution and historical assessment value corresponding to the historical business data; the historical business data and historical assessment value are processed by the target blockchain through a smart contract to obtain a target assessment value; based on the target assessment value, the financial business of the target financial institution is optimized, and the historical business data and historical assessment value are evaluated by calling a smart contract through the target blockchain, and the financial business of the target financial institution is optimized according to the target assessment value, thereby avoiding the problem of excessive reliance on manual labor caused by the prior art relying on expert experience to adjust and optimize the business of financial institutions, thereby achieving the technical effect of improving the effect of business optimization of financial institutions, and further solving the technical problem of poor business optimization effect caused by adjusting and optimizing the business of financial institutions based on expert experience in related technologies.

[0054] Optionally, in the blockchain-based data processing method provided in the embodiment of the present application, the historical business data and the historical evaluation value are processed by calling the smart contract through the target blockchain to obtain the target evaluation value, which includes: calling the target smart contract through multiple endorsement nodes in the target blockchain to process the historical business data and the historical evaluation value to obtain multiple initial evaluation values; verifying the multiple initial evaluation values ​​through the client, and if the verification passes, packaging the multiple initial evaluation values ​​to obtain the target transaction; consensus processing is performed on the target transaction through the sorting node and the submission node in the target blockchain, and after the consensus is passed, the target evaluation value is obtained based on the multiple initial evaluation values.

[0055] In an optional embodiment, the target evaluation value can be obtained by the following steps: using multiple endorsement nodes in the target blockchain to call the target smart contract to process historical business data and historical evaluation values ​​to obtain multiple initial evaluation values. It should be noted that the target smart contract can be an evaluation value result prediction smart contract.

[0056] After obtaining the above-mentioned multiple initial evaluation values, the endorsement node will return the initial evaluation result and its own CA certificate signature to the client. After receiving the information returned by the endorsement node, the client will determine whether the proposal result is consistent and whether it is executed in accordance with the specified endorsement policy. If there is not enough endorsement, the processing is terminated; otherwise, the application client packages the data together to form a transaction (i.e., the target transaction mentioned above) and signs it, and sends it to the sorting node (i.e., the Order node). The target transaction is processed by consensus through the sorting node and the submission node, that is, the sorting node sorts the received transactions by consensus, and then packages a batch of transactions together to generate a new block according to the block generation strategy and sends it to the submission node (referring to the Peer node). (7) After receiving the block, the submission node will perform consensus processing on each transaction in the block, and after completion, it will append the block to the local blockchain and modify the world state (the latest value of all keys). After the consensus is passed, the target evaluation value is obtained based on multiple initial evaluation values.

[0057] In an optional embodiment, the consensus algorithm is a solution to ensure the consistency of the distributed system. The consensus algorithm is a process in computer science used to achieve consistency between distributed processes or systems for a single data value. The consensus algorithm aims to achieve the reliability of multiple unreliable nodes in the network. Solving the consensus problem is very important in distributed computing and multi-agent systems.

[0058] The consensus algorithm must satisfy two properties to ensure consistency between nodes, namely safety and liveness. Safety means that each node guarantees the same input sequence and produces the same output results on each node. When the nodes receive the same series of transactions, the same state changes will occur on each node, and the algorithm must be executed the same way on each node system. Liveness means that when communication is normal, each non-faulty node will eventually receive each submitted transaction.

[0059] The POA consensus algorithm is a consensus algorithm based on credibility (or reputation). In this algorithm, miners are selected as block validators based on their personal credibility rather than the pledged cryptocurrency. Block validators have the authority to generate blocks, sign and upload to the chain.

[0060] In this scheme, the evaluation value of financial institutions is predicted. Therefore, in order to ensure efficient operation, two points must be guaranteed. The first is that the nodes in the designated area are active, and it is best to select the leader nodes from the nodes in the current area according to personal reputation. The evaluation criteria for personal reputation include two points. The first point is that there is no record of malicious tampering with blockchain information in the past six months, and it has been running safely and stably; the second point is that it is an active node in the blockchain system and has maintained a good record of synchronizing data to the chain safely, stably and accurately. At the same time, select nodes with higher block efficiency from the nodes that stand out. The biggest advantage of selecting such a leader node is that it can ensure that the verifier is more efficient in generating blocks and uploading to the chain.

[0061] At the same time, the node that becomes the final leader node must still meet the following conditions to become the authority that issues orders in the blockchain system. First, the identity must be formally verified on the network, and the identity information of the node must be found in the public domain to ensure that malicious nodes have no chance to pass the identity authentication and ensure the security of the node. Second, the participants must have certain qualifications (see the evaluation criteria for personal reputation in this plan, which is also the key improvement part of the consensus algorithm in this plan). Third, it is necessary to ensure that the verification process is normal and consistent with other authorities.

[0062] Specifically, after business personnel in different regions trigger the return to the financial institution to assess the value, they will trigger the smart contract to execute related operations respectively. Multiple ordinary nodes will generate n transaction data. After the consensus of the authoritative nodes selected by the above scheme, the consensus data will be stored on the chain.

[0063] By calling smart contracts on the target blockchain to evaluate historical business data and historical assessment values, the problem of over-reliance on manual labor caused by existing technologies relying on expert experience to adjust and optimize the business of financial institutions is avoided, thereby achieving the technical effect of improving the effectiveness of business optimization for financial institutions.

[0064] Optionally, in the blockchain-based data processing method provided in the embodiment of the present application, the target smart contract is called through multiple endorsement nodes in the target blockchain to process historical business data and historical evaluation values ​​to obtain multiple initial evaluation values, including: calling the first smart contract in the target smart contract through multiple endorsement nodes to standardize the historical business data and historical evaluation values ​​to obtain processed historical business data and processed historical evaluation values; calling the second smart contract in the target smart contract through multiple endorsement nodes to fit the processed historical business data and the processed historical evaluation values ​​to obtain a target linear regression model; calling the third smart contract in the target smart contract through multiple endorsement nodes to predict the evaluation value of the target financial institution based on the target linear regression model to obtain multiple initial evaluation values.

[0065] In an optional embodiment, the following steps are used to obtain multiple initial evaluation values: multiple endorsement nodes call the first smart contract in the target smart contract to standardize the historical business data and the historical evaluation value. For example, the first smart contract is a data standardization smart contract, and the first smart contract is used to convert the input variables that affect the initial evaluation value, which are data of different dimensions and orders of magnitude, into the same standard. The accuracy and reliability of data analysis are improved through data standardization. It is difficult to directly compare and analyze data of different dimensions and orders of magnitude when they are mixed together, but through standardization, they can be converted into data of the same scale for comparison and analysis, thereby improving the accuracy and reliability of data analysis. For example, the data after data standardization shown in Table 1.

[0066] Table 1

[0067]

[0068]

[0069] Then, multiple endorsement nodes call the second smart contract in the target smart contract to fit the processed historical business data and the processed historical evaluation value to obtain the target linear regression model. For example, the target linear regression model is Y=-0.6184+0.7147*X1+0.8324*X2+εt, where εt~(0,σ 2 ), εt is the error term, that is, the error term has a mean of 0 and a variance of σ 2Normal distribution. Y is the estimated value of the target financial institution to be predicted, X1 is the labor cost in the business data, and X2 is other data in the business data. Finally, the third smart contract in the target smart contract is called by multiple endorsement nodes to predict the estimated value of the target financial institution based on the target linear regression model to obtain multiple initial estimated values. It should be noted that different time dimensions can be selected for prediction when predicting the initial estimated value, such as monthly dimension, quarterly dimension, and annual dimension.

[0070] Smart contracts can effectively reduce the involvement of intermediaries or human factors, and improve the accuracy and security of data processing.

[0071] Optionally, in the blockchain-based data processing method provided in an embodiment of the present application, a second smart contract in the target smart contract is called through multiple endorsement nodes to fit the processed historical business data and the processed historical evaluation value to obtain a target linear regression model, including: judging whether the correlation coefficient between the historical evaluation value and the historical business data is greater than a preset threshold; when the correlation coefficient between the historical evaluation value and the historical business data is greater than the preset threshold, fitting the processed historical business data and the processed historical evaluation value based on the weighted least squares method to obtain the target linear regression model.

[0072] In an optional embodiment, the target linear regression model described above may be obtained by the following steps: the correlation coefficient between the historical assessment value and the historical business data is calculated, for example, the Pearson correlation coefficient between the historical assessment value and the historical business data is calculated, and then it is determined whether the correlation coefficient between the historical assessment value and the historical business data is greater than a preset threshold, for example, 0.5. If the correlation coefficient between the historical assessment value and the historical business data is greater than the preset threshold, the target linear regression model is obtained by fitting the processed historical business data and the processed historical assessment value by weighted least squares method.

[0073] By calculating the Pearson correlation coefficient between the historical assessment value and the historical business data, we can accurately determine whether there is a heteroscedasticity problem at present, and then improve the accuracy of the subsequent fitting linear regression model.

[0074] Optionally, in the blockchain-based data processing method provided in an embodiment of the present application, the processed historical business data and the processed historical assessment value are fitted based on the weighted least squares method to obtain a target linear regression model, including: determining a first weight value, and obtaining a weight matrix based on the first weight value; estimating the model parameters of the linear regression model based on the weight matrix, the processed historical business data, and the processed historical assessment value by the weighted least squares method to obtain a first linear regression model; calculating the T value and P value of the first linear regression model to obtain a target T value and a target P value; when the target T value and the target P value meet the preset requirements, determining the target linear regression model based on the first linear regression model.

[0075] In an optional embodiment, after calculating the T value and P value of the first linear regression model to obtain the target T value and target P value, the method also includes: if the target T value and target P value do not meet the preset requirements, updating the first weight value to obtain a second weight value; repeating the step of obtaining a weight matrix based on the second weight value until the target T value and target P value of the current linear regression model meet the preset requirements, and determining the target linear regression model based on the current linear regression model.

[0076] In an optional embodiment, the following steps may be used to fit the target linear regression model: First, determine a first weight value m, for example, m=1.5. Then obtain a weight matrix W based on the first weight value. The first linear regression model is obtained by estimating the model parameters of the linear regression model based on the weight matrix, the processed historical business data, and the processed historical evaluation value using the weighted least squares method. The weighted least squares parameter estimation formula is: The initial form of the target linear regression model is Y = β0 + β1*X1 + β2*X2 + εt. Among them, are the model parameters of the linear regression model, namely β0, β1 and β2. X is the processed historical business data.

[0077] The model parameters in the linear regression model are obtained by weighted least squares method, and then the first linear regression model mentioned above is obtained. Then the T value (the abbreviation of t statistic, used to test whether the coefficient of each independent variable in the model is significantly not zero) and P value (the probability value corresponding to the t statistic) of the first linear regression model are calculated, and it is judged whether the target T value and the target P value meet the preset requirements, that is, whether the two variables X1 and X2 are significant enough according to the target T value and the target P value. If it is significant enough, the first linear regression model is used to determine the target linear regression model.

[0078] If it is not significant enough, the first weight value is updated to obtain the second weight value, and the step of obtaining the weight matrix according to the second weight value is repeated until the target T value and target P value of the current linear regression model meet the preset requirements, and the target linear regression model is determined based on the current linear regression model.

[0079] For example, the correlation coefficient between historical appraisal value and historical business data is shown in Table 2.

[0080] Table 2

[0081]

[0082] From Table 2, we can find that there is an obvious correlation between the historical assessment value and the historical business data, and it is necessary to fit it according to the weighted least squares method to obtain the final target linear regression model.

[0083] First, assuming that the weight value is m=1.5, the rating results after obtaining the first display regression model are shown in Table 3.

[0084] Table 3

[0085] Estimated value Standard Deviation T-value P-value Significance intercept -0.5811 0.2145 -2.709 0.0732 X1 0.6715 0.2349 2.859 0.0646 X2 0.8760 0.3361 2.606 0.0799

[0086] The final modeling results show that both variables X1 and X2 are not significant enough, and R 2 =0.7417, P = 0.061. Obviously, it is inappropriate to select m = 1.5 to adjust the correlation between heteroscedasticity and X2. 2 It is the abbreviation of coefficient of determination, which is used to measure the degree to which the model explains the variation of the dependent variable.

[0087] Assuming m=2, a weighted regression analysis model is established and the modeling results are shown in Table 4.

[0088] Table 4

[0089] Estimated value Standard Deviation T-value P-value Significance intercept -0.2909 0.1232 -2.362 0.0359 * X1 0.3582 0.1440 2.488 0.0285 * X2 0.4388 0.2897 1.515 0.1558

[0090] In the final modeling results, the two variables X1 are significant, while X2 is not significant and the gap from 0.05 is large, indicating that the solution to the heteroscedasticity problem is not ideal, and R 2 =0.6913, P=0.0003433. Obviously, the method of selecting m=2.5 to adjust the heteroscedasticity and the correlation with X2 solves the problem that the variable X1 and the evaluation value should maintain a significant relationship between the variables, but R 2 The fact that the value decreased instead of increased indicates that the heteroscedasticity problem has not been effectively solved.

[0091] Finally, it was assumed that m=2.5 and a weighted regression analysis model was established. The modeling results are shown in Table 5.

[0092] Table 5

[0093] Estimated value Standard Deviation T-value P-value Significance intercept -0.6184 0.1301 -4.753 0.0177 * X1 0.7147 0.1502 4.758 0.0176 * X2 0.8324 0.3267 2.548 0.0841

[0094] The final modeling results show that when m = 2.5, the two variables X1 are significant, and the difference between X2 and 0.05 is small, indicating that the solution to the heteroscedasticity problem has been improved, and R 2 =0.9061, P=0.01338. Obviously, the method of selecting m=2.5 to adjust the heteroscedasticity and the correlation with X2 ensures that the variable X1 and the annual bank regional output value should still be significant. 2 The significant improvement in the value indicates that the heteroscedasticity problem has been effectively solved. From the above processing flow, the final model is determined to use Y = -0.6184 + 0.7147 * X1 + 0.8324 * X2 + εt, where εt ~ (0, σ 2 ).

[0095] By fitting the processed historical business data and the processed historical assessment value through weighted least squares method in the presence of heteroscedasticity problem to obtain the target linear regression model, the impact of heteroscedasticity can be effectively reduced and the accuracy of the target linear regression model can be improved.

[0096] Optionally, in the blockchain-based data processing method provided in the embodiment of the present application, a third smart contract in the target smart contract is called through multiple endorsement nodes to predict the assessment value of the target financial institution based on the target linear regression model to obtain multiple initial assessment values, including: determining the prediction time dimension in the value assessment request; based on the prediction time dimension, obtaining the business data of the target financial institution in the target time period; calculating the business data of the target time period through the target linear regression model to obtain multiple initial assessment values.

[0097] In an optional embodiment, when predicting the evaluation value of the target financial institution, the following steps are also included: clarifying the prediction time dimension in the value evaluation request, for example, the monthly dimension, the quarterly dimension or the annual dimension, and then obtaining the business data of the target financial institution in the target time period, for example, if the prediction dimension is the monthly dimension, then obtain the business data of one month, if the prediction dimension is the quarterly dimension, then obtain the business data of one quarter. Finally, bring the business data into the target linear regression model to obtain the final multiple initial evaluation values.

[0098] The flexibility of value assessment of financial institutions is improved through dimension selection.

[0099] In an optional embodiment, the structural diagram of the target blockchain is as follows: Figure 3As shown in the figure, it includes the view layer, control layer, business layer and chain code layer. The view layer provides the user with a visual interface and interaction; the control layer sends requests at regular intervals, accesses the business layer, and then calls the user chain code to implement data standardization, model heteroscedasticity test, use weighted least squares method to solve heteroscedasticity risks, and predict bank regional output value results and other smart contracts.

[0100] In an optional embodiment, the following may be used: Figure 4 The schematic diagram shown implements the value assessment of the financial institution: the application client calls the member service through the SDK to register and enroll, and the user obtains an identity certificate that allows the user to use the device for predicting the regional output value of the bank.

[0101] The application client initiates a transaction proposal to the blockchain network through the SDK. The transaction proposal sends the smart contract identifier to be called for this transaction (data standardization, model heteroscedasticity test, use of weighted least squares method to solve heteroscedasticity risks and value prediction of financial institutions), contract method (specific implementation logic of smart contract method) and parameter information (mandatory items required to call the corresponding smart contract and the input flag that can uniquely identify the smart contract) and client signature (an identifier that can identify which transaction channel the customer is in and a label to verify identity) to the endorsement node.

[0102] After receiving the transaction signature, the endorsing node verifies the signature and confirms whether the submitter has the authority to perform the operation. It also simulates the execution of the smart contract according to the endorsement policy and returns the result and its respective CA signature to the application client.

[0103] After receiving the information returned by the endorsement node, the application client will display the model to the client to determine the acquiring transaction scenario to which the transaction belongs, determine whether the proposal results are consistent, and whether they are executed in accordance with the specified endorsement policy. The application client packages the data together to form a transaction, signs it, and sends it to the sorting role.

[0104] The sorting node performs consensus sorting on the received transactions, and finally packages a batch of transactions together according to the block generation strategy, generates a new block, and sends it to the committing node.

[0105] After receiving the block, the commit node will verify each transaction in the block to check whether the input and output that the transaction depends on are consistent with the current state of the blockchain. After completion, it will append the block to the local blockchain and modify the world state.

[0106] In an optional embodiment, the following may be used: Figure 5 The schematic diagram of the data processing architecture shown in FIG. 1 realizes the value assessment of financial institutions, such as Figure 5 As shown, the data processing architecture includes: a smart contract design module, a heteroscedasticity identification module and a consensus algorithm module.

[0107] The blockchain-based data processing method provided in the embodiment of the present application initiates a value assessment request for a target financial institution to the target blockchain through a client, wherein the value assessment request includes at least: historical business data of the target financial institution and the historical assessment value corresponding to the historical business data; the historical business data and the historical assessment value are processed by the target blockchain invoking a smart contract to obtain a target assessment value; based on the target assessment value, the financial business of the target financial institution is optimized, and the historical business data and the historical assessment value are evaluated by the target blockchain invoking a smart contract, and the financial business of the target financial institution is optimized according to the target assessment value, thereby avoiding the problem of excessive reliance on manual labor caused by the prior art relying on expert experience to adjust and optimize the business of the financial institution, thereby achieving the technical effect of improving the effect of business optimization on the financial institution, and further solving the technical problem in the related technology of adjusting and optimizing the business of the financial institution based on expert experience, resulting in poor effect of business optimization on the financial institution.

[0108] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0109] Example 2

[0110] The embodiment of the present application also provides a data processing device based on blockchain. It should be noted that the data processing device based on blockchain in the embodiment of the present application can be used to execute the data processing method based on blockchain provided in the embodiment of the present application. The following introduces the data processing device based on blockchain provided in the embodiment of the present application.

[0111] According to an embodiment of the present application, a device for implementing the above-mentioned blockchain-based data processing method is also provided, such as Figure 6 As shown, the device includes: an initiating unit 601, a processing unit 602 and an optimizing unit 603.

[0112] The initiating unit 601 is used to initiate a value assessment request for a target financial institution to the target blockchain through a client, wherein the value assessment request at least includes: historical business data of the target financial institution and historical assessment values ​​corresponding to the historical business data;

[0113] Processing unit 602, used to process historical business data and historical evaluation values ​​by calling smart contracts through the target blockchain to obtain a target evaluation value;

[0114] The optimization unit 603 is used to optimize the financial business of the target financial institution based on the target evaluation value.

[0115] The blockchain-based data processing device provided in the embodiment of the present application initiates a value assessment request for a target financial institution to the target blockchain through the initiating unit 601 through the client, wherein the value assessment request at least includes: historical business data of the target financial institution and the historical assessment value corresponding to the historical business data; the processing unit 602 processes the historical business data and the historical assessment value by calling the smart contract through the target blockchain to obtain the target assessment value; the optimization unit 603 optimizes the financial business of the target financial institution based on the target assessment value, thereby avoiding the problem of excessive reliance on manual labor caused by the prior art relying on expert experience to adjust and optimize the business of the financial institution, thereby achieving the technical effect of improving the effect of optimizing the business of the financial institution, and further solving the technical problem of adjusting and optimizing the business of the financial institution based on expert experience in the related technology, resulting in poor optimization effect of the business of the financial institution.

[0116] Optionally, in the blockchain-based data processing device provided in the embodiment of the present application, the processing unit includes: a first processing sub-unit, used to call the target smart contract through multiple endorsement nodes in the target blockchain to process historical business data and historical evaluation values, and obtain multiple initial evaluation values; a second processing sub-unit, used to verify the multiple initial evaluation values ​​through the client, and if the verification passes, package the multiple initial evaluation values ​​to obtain the target transaction; a third processing sub-unit, used to perform consensus processing on the target transaction through the sorting node and the submission node in the target blockchain, and after the consensus is passed, obtain the target evaluation value based on the multiple initial evaluation values.

[0117] Optionally, in the blockchain-based data processing device provided in the embodiment of the present application, the processing unit includes: a fourth processing sub-unit, used to call the first smart contract in the target smart contract through multiple endorsement nodes to perform standardized processing on historical business data and historical evaluation values, and obtain processed historical business data and processed historical evaluation values; a fitting sub-unit, used to call the second smart contract in the target smart contract through multiple endorsement nodes to fit the processed historical business data and the processed historical evaluation values, and obtain a target linear regression model; a prediction sub-unit, used to call the third smart contract in the target smart contract through multiple endorsement nodes to predict the evaluation value of the target financial institution based on the target linear regression model, and obtain multiple initial evaluation values.

[0118] Optionally, in the blockchain-based data processing device provided in the embodiment of the present application, the fitting subunit includes: a judgment module, used to judge whether the correlation coefficient between the historical evaluation value and the historical business data is greater than a preset threshold; a fitting module, used to fit the processed historical business data and the processed historical evaluation value based on the weighted least squares method when the correlation coefficient between the historical evaluation value and the historical business data is greater than the preset threshold, to obtain a target linear regression model.

[0119] Optionally, in the blockchain-based data processing device provided in an embodiment of the present application, the fitting module includes: a first determination submodule, used to determine a first weight value, and obtain a weight matrix based on the first weight value; an estimation submodule, used to estimate the model parameters of the linear regression model based on the weight matrix, processed historical business data and processed historical evaluation value by weighted least squares method, to obtain a first linear regression model; a calculation submodule, used to calculate the T value and P value of the first linear regression model to obtain a target T value and a target P value; a second determination submodule, used to determine the target linear regression model based on the first linear regression model when the target T value and the target P value meet preset requirements.

[0120] Optionally, in the blockchain-based data processing device provided in an embodiment of the present application, the device also includes: an updating unit, for calculating the T value and P value of the first linear regression model to obtain the target T value and the target P value, and if the target T value and the target P value do not meet the preset requirements, then updating the first weight value to obtain a second weight value; an execution unit, for repeatedly executing the step of obtaining a weight matrix based on the second weight value until the target T value and the target P value of the current linear regression model meet the preset requirements, and determining the target linear regression model based on the current linear regression model.

[0121] Optionally, in the blockchain-based data processing device provided in the embodiment of the present application, the prediction subunit includes: a determination module, used to determine the prediction time dimension in the value assessment request; an acquisition module, used to obtain the business data of the target financial institution in the target time period based on the prediction time dimension; a calculation module, used to calculate the business data of the target time period through a target linear regression model to obtain multiple initial assessment values.

[0122] It should be noted that the initiating unit 601, the processing unit 602 and the optimizing unit 603 correspond to steps S201 to S203 in the first embodiment, and the three units and the corresponding steps implement the same examples and application scenarios, but are not limited to the contents disclosed in the first embodiment. It should be noted that the modules or units may be hardware components or software components stored in a memory (e.g., the memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n), and the modules may also be part of the device and run in the computer terminal 10 provided in the first embodiment.

[0123] Example 3

[0124] An embodiment of the present application may provide an electronic device, Figure 7 is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 7 As shown, the electronic device may include: one or more ( Figure 7 (only one is shown) processor 702, memory 704, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0125] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0126] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: initiate a value assessment request for the target financial institution to the target blockchain through the client, wherein the value assessment request at least includes: the historical business data of the target financial institution and the historical assessment value corresponding to the historical business data; process the historical business data and the historical assessment value through the target blockchain calling the smart contract to obtain the target assessment value; and optimize the financial business of the target financial institution based on the target assessment value.

[0127] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: calling the smart contract through the target blockchain to process the historical business data and the historical evaluation value to obtain the target evaluation value, including: calling the target smart contract through multiple endorsement nodes in the target blockchain to process the historical business data and the historical evaluation value to obtain multiple initial evaluation values; verifying the multiple initial evaluation values ​​through the client, and if the verification passes, packaging the multiple initial evaluation values ​​to obtain the target transaction; consensus processing of the target transaction through the sorting node and the submission node in the target blockchain, and after the consensus passes, obtaining the target evaluation value based on the multiple initial evaluation values.

[0128] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: calling the target smart contract through multiple endorsement nodes in the target blockchain to process the historical business data and the historical evaluation value to obtain multiple initial evaluation values, including: calling the first smart contract in the target smart contract through multiple endorsement nodes to standardize the historical business data and the historical evaluation value to obtain the processed historical business data and the processed historical evaluation value; calling the second smart contract in the target smart contract through multiple endorsement nodes to fit the processed historical business data and the processed historical evaluation value to obtain the target linear regression model; calling the third smart contract in the target smart contract through multiple endorsement nodes to predict the evaluation value of the target financial institution based on the target linear regression model to obtain multiple initial evaluation values.

[0129] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: calling the second smart contract in the target smart contract through multiple endorsement nodes to fit the processed historical business data and the processed historical evaluation value to obtain the target linear regression model, including: judging whether the correlation coefficient between the historical evaluation value and the historical business data is greater than a preset threshold; when the correlation coefficient between the historical evaluation value and the historical business data is greater than the preset threshold, fitting the processed historical business data and the processed historical evaluation value based on the weighted least squares method to obtain the target linear regression model.

[0130] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: fitting the processed historical business data and the processed historical evaluation value based on the weighted least squares method to obtain the target linear regression model, including: determining a first weight value, and obtaining a weight matrix based on the first weight value; estimating the model parameters of the linear regression model based on the weight matrix, the processed historical business data and the processed historical evaluation value through the weighted least squares method to obtain a first linear regression model; calculating the T value and P value of the first linear regression model to obtain a target T value and a target P value; when the target T value and the target P value meet the preset requirements, determining the target linear regression model based on the first linear regression model.

[0131] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: after calculating the T value and P value of the first linear regression model to obtain the target T value and target P value, the method also includes: if the target T value and target P value do not meet the preset requirements, the first weight value is updated to obtain the second weight value; the step of obtaining the weight matrix based on the second weight value is repeated until the target T value and target P value of the current linear regression model meet the preset requirements, and the target linear regression model is determined based on the current linear regression model.

[0132] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: calling the third smart contract in the target smart contract through multiple endorsement nodes to predict the assessment value of the target financial institution based on the target linear regression model, and obtaining multiple initial assessment values, including: determining the prediction time dimension in the value assessment request; based on the prediction time dimension, obtaining the business data of the target financial institution in the target time period; calculating the business data of the target time period through the target linear regression model to obtain multiple initial assessment values.

[0133] It can be understood by those skilled in the art that Figure 7 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 7 The structure of the electronic device is not limited. Figure 7 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 7 Different configurations shown.

[0134] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0135] Example 4

[0136] The embodiment of the present application also provides a computer-readable storage medium. Optionally, in this embodiment, the above storage medium can be used to store the program code executed by the blockchain-based data processing method provided in the above embodiment 1.

[0137] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0138] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the steps of a blockchain-based data processing method.

[0139] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0140] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0141] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0144] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0145] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A data processing method based on blockchain, characterized in that: include: Initiating a value assessment request for a target financial institution to a target blockchain through a client, wherein the value assessment request at least includes: historical business data of the target financial institution and a historical assessment value corresponding to the historical business data; The target blockchain is used to call a smart contract to process the historical business data and the historical evaluation value to obtain a target evaluation value; Based on the target assessment value, the financial business of the target financial institution is optimized.

2. The method according to claim 1, characterized in that The target blockchain calls a smart contract to process the historical business data and the historical evaluation value, and obtains the target evaluation value including: Invoking a target smart contract through multiple endorsement nodes in the target blockchain to process the historical business data and the historical evaluation values ​​to obtain multiple initial evaluation values; The client verifies the multiple initial evaluation values, and if the verification passes, packages the multiple initial evaluation values ​​to obtain a target transaction; The target transaction is processed by consensus through the sorting nodes and the committing nodes in the target blockchain, and after the consensus is passed, the target evaluation value is obtained according to the multiple initial evaluation values.

3. The method according to claim 2, characterized in that The target smart contract is called by multiple endorsement nodes in the target blockchain to process the historical business data and the historical evaluation value, and multiple initial evaluation values ​​are obtained, including: Calling the first smart contract in the target smart contract through the multiple endorsement nodes to perform standardized processing on the historical business data and the historical evaluation value to obtain processed historical business data and processed historical evaluation value; Calling a second smart contract in the target smart contract through the multiple endorsement nodes to fit the processed historical business data and the processed historical evaluation value to obtain a target linear regression model; The plurality of endorsement nodes call a third smart contract in the target smart contract to predict the assessed value of the target financial institution based on the target linear regression model to obtain the plurality of initial assessed values.

4. The method according to claim 3, characterized in that Calling the second smart contract in the target smart contract through the multiple endorsement nodes to fit the processed historical business data and the processed historical evaluation value to obtain a target linear regression model includes: Determining whether the correlation coefficient between the historical evaluation value and the historical business data is greater than a preset threshold; When the correlation coefficient between the historical evaluation value and the historical business data is greater than a preset threshold, the processed historical business data and the processed historical evaluation value are fitted based on the weighted least squares method to obtain the target linear regression model.

5. The method according to claim 4, characterized in that Fitting the processed historical business data and the processed historical evaluation value based on the weighted least squares method to obtain the target linear regression model includes: Determine a first weight value, and obtain a weight matrix according to the first weight value; estimating the model parameters of the linear regression model based on the weight matrix, the processed historical business data and the processed historical evaluation value by weighted least squares method to obtain a first linear regression model; Calculating the T value and P value of the first linear regression model to obtain a target T value and a target P value; When the target T value and the target P value meet preset requirements, the target linear regression model is determined according to the first linear regression model.

6. The method according to claim 5, characterized in that After calculating the T value and the P value of the first linear regression model to obtain a target T value and a target P value, the method further includes: If the target T value and the target P value do not meet the preset requirement, updating the first weight value to obtain a second weight value; Repeat the step of obtaining a weight matrix according to the second weight value until the target T value and the target P value of the current linear regression model meet the preset requirements, and determine the target linear regression model according to the current linear regression model.

7. The method according to claim 3, characterized in that The multiple initial evaluation values ​​obtained by calling the third smart contract in the target smart contract through the multiple endorsement nodes to predict the evaluation value of the target financial institution based on the target linear regression model include: Determining a forecast time dimension in the value assessment request; According to the forecast time dimension, obtaining the business data of the target financial institution in the target time period; The business data of the target time period is calculated using the target linear regression model to obtain the multiple initial evaluation values.

8. A data processing device based on blockchain, characterized in that: include: An initiating unit, configured to initiate a value assessment request for a target financial institution to a target blockchain through a client, wherein the value assessment request at least includes: historical business data of the target financial institution and a historical assessment value corresponding to the historical business data; A processing unit, configured to process the historical business data and the historical evaluation value by invoking a smart contract through the target blockchain to obtain a target evaluation value; An optimization unit is used to optimize the financial business of the target financial institution based on the target evaluation value.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the blockchain-based data processing method described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory storing an executable program; A processor, configured to run the program, wherein the program, when running, executes the blockchain-based data processing method described in any one of claims 1 to 7.

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