An integrated regulatory platform based on distributed blockchain

Abstract

The present invention belongs to the field of data supervision technology. The present invention discloses an integrated supervision platform based on a distributed blockchain, including: authenticating the identities of participants in a target supervision process, obtaining corresponding participation categories, and constructing a corresponding distributed blockchain; the participation categories include regulatory entities, supervised objects, and third-party institutions; collecting data on financial data involved in supervised objects to obtain corresponding financial information; processing the collected financial information to obtain corresponding data to be supervised; and constructing corresponding storage tasks based on the data to be supervised; uploading and storing the obtained data to be supervised in a constructed distributed blockchain based on the constructed storage tasks; consulting the data to be supervised stored by the supervised objects in the distributed blockchain to obtain corresponding consulting results, and generating corresponding supervision results based on the corresponding consulting results. The present invention improves the accuracy and efficiency of supervision.

Description

Technical Field

[0001] The present invention relates to the field of data supervision technology, and more specifically, to an integrated supervision platform based on distributed blockchain. Background Art

[0002] The patent application publication number CN106777923A discloses a hospital information supervision platform and supervision method. The hospital information supervision platform includes a distributed blockchain grid constructed by several data sources and one or more data application parties as supervision nodes, and also includes a blockchain key generation module, a distributed storage module and a perception prompt module. The blockchain key generation module uses blockchain technology to generate corresponding blockchain keys for the medical key information of each supervision node in the distributed blockchain grid through an encryption algorithm; the distributed storage module stores the blockchain key in all supervision nodes of the distributed blockchain grid; each supervision node communicates with each other in the distributed blockchain grid; if the medical key information of any supervision node is modified, the corresponding blockchain key changes, and other supervision nodes will perceive and prompt. The platform realizes multi-point self-supervision and intelligent prompts, reduces the cost of medical key information security management, and can prevent the hospital's medical key information from being improperly modified;

[0003] Compared with existing technologies, traditional financial regulatory approaches face problems such as data silos, information opacity, and inefficient supervision. Financial institutions accumulate vast amounts of customer information, transaction records, and risk assessment data in their daily operations. This data is dispersed across various systems, creating data silos. This not only increases the difficulty of data management but also limits its usefulness. Furthermore, the lack of an effective data sharing mechanism makes it difficult for regulators to obtain comprehensive market information, leading to delayed and inaccurate regulatory decisions. Therefore, breaking down data silos and enabling real-time, accurate, and transparent sharing of financial data has become a pressing issue. In light of this, the present invention proposes an integrated regulatory platform based on a distributed blockchain to address these issues. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solutions: an integrated supervision platform based on a distributed blockchain, comprising:

[0005] A blockchain construction module is used to authenticate the identities of participants in the target regulatory process, obtain corresponding participation categories, and construct corresponding distributed blockchains based on the participation categories; the participation categories include regulatory entities, regulated entities, and third-party institutions;

[0006] The data collection module is used to collect financial data related to the regulated entities and obtain relevant financial information;

[0007] A data processing module is used to process the collected financial information to obtain corresponding data to be regulated; and to construct corresponding storage tasks based on the data to be regulated;

[0008] The data on-chain module is used to upload the acquired data to be regulated to the constructed distributed blockchain based on the constructed storage task;

[0009] The data supervision module is used to review the data to be supervised stored by the supervised objects in the distributed blockchain, obtain corresponding review results, and generate corresponding supervision results based on the corresponding review results and take corresponding supervision measures.

[0010] Furthermore, the process of authenticating the identities of the participants in the target regulatory process, obtaining the corresponding participation categories, and building the corresponding distributed blockchain based on the participation categories includes:

[0011] A user terminal is set up to obtain the participants involved in the corresponding supervision process and mark them as participating users. The participating users log in to the user terminal and upload identity information for identity authentication. If the identity authentication fails, a login failure notification is fed back to the participating user. If the identity authentication is successful, the user terminal obtains the identity information of the corresponding participating user for identity authentication and classifies the corresponding participating users into participation categories based on the identity information. The participation categories include regulatory entities, regulated objects, and third-party institutions. At the same time, a corresponding identity number is generated based on the identity information of the participating users;

[0012] Furthermore, based on the distributed network, a corresponding distributed blockchain is constructed in combination with the participation categories corresponding to the corresponding participating users. The distributed blockchain consists of several supervision nodes, audit nodes and storage nodes.

[0013] Furthermore, the process of collecting financial data related to the regulated entity and obtaining the corresponding financial information includes:

[0014] The data collection module is provided with a plurality of collection units, which collect financial data related to the regulated object from multiple data sources based on the user terminal to obtain corresponding financial information, and the financial information is composed of a plurality of financial data.

[0015] Furthermore, the process of performing data processing on the collected financial information to obtain corresponding data to be regulated and constructing corresponding storage tasks based on the data to be regulated includes:

[0016] Reading the collected financial information and performing data preprocessing on the collected financial information, wherein the preprocessing includes missing value processing and data standardization;

[0017] After data preprocessing is completed, the collection time corresponding to each financial data in the corresponding financial information is obtained respectively, and the timestamp corresponding to the corresponding financial data is obtained based on the collection time, and the timestamp is converted into a binary code to obtain the corresponding time identification code;

[0018] At the same time, the financial data in the corresponding financial information is read and feature segments are extracted to obtain corresponding feature data segments, and corresponding financial data identifiers are constructed based on the feature data segments, and the corresponding financial information is encrypted to obtain corresponding ciphertext data;

[0019] Correlating the financial data identifier corresponding to the corresponding financial data and the corresponding ciphertext data to obtain the corresponding subgroup to be stored;

[0020] Counting the subgroups to be stored and the identity numbers of the regulated objects corresponding to all financial data in the financial information to obtain the corresponding data to be regulated;

[0021] Then, the data capacity corresponding to the corresponding data to be regulated, the financial data identifier corresponding to each financial data, and the identity identifier of the regulated object are obtained, and the corresponding storage task is constructed based on them;

[0022] Furthermore, based on the user terminal, the storage tasks corresponding to the corresponding supervised objects are uploaded to the constructed distributed blockchain.

[0023] Furthermore, the process of constructing a corresponding financial data identifier based on the characteristic data segment includes:

[0024] Read the corresponding feature data segments and assign different levels of weight to each feature data segment, wherein the specific weight depends on the specific content of the feature data segment;

[0025] Read the corresponding characteristic data segments, and randomly generate a set of dynamic data identifiers based on the corresponding characteristic data segments. The dynamic data identifiers can be obtained by the following mathematical formula: Where, The dynamic data representation corresponding to the characteristic data segment is a digital sequence composed of a set of random numbers; Indicates the first The amount of data for each feature data segment; and Respectively represent the maximum and minimum data amounts in the feature data segments corresponding to the corresponding financial data, where Indicates the total number of feature data segments;

[0026] The dynamic data identifiers of all feature data segments are read and dimensionality reduction is performed on them; the dynamic data identifiers after dimensionality reduction are accumulated according to the assigned weights to obtain the corresponding financial character strings; and a unique financial data identifier is constructed based on the financial character strings and the time identifier code.

[0027] Furthermore, the process of encrypting the corresponding financial information to obtain the corresponding ciphertext data includes:

[0028] Acquiring characteristic information corresponding to the corresponding financial data based on the obtained characteristic data fragments;

[0029] Based on the characteristic information, a bifurcated data tree is constructed for the financial information uploaded by the corresponding regulated entity to obtain a corresponding financial data tree;

[0030] Based on the CA-ABE algorithm, the corresponding initial parameters are generated in combination with the identity information of the corresponding supervised object. , initial parameters ; is a prime order The cyclic group of is a generator of the group, is a bilinear map; Model The non-negative minimum simplified residual system of is a prime number; ;

[0031] Then, the corresponding master key is constructed based on the initial parameters and the public key Where, 、 are a random function and a random number respectively, and 、 All∈Zp;

[0032] Pick a random number , and based on random numbers Split the corresponding financial data tree and obtain the secret shards corresponding to the leaf nodes in the corresponding financial data tree ; and perform attribute calculations on the corresponding leaf nodes. The corresponding mathematical formula is: Where, Represents a leaf node The corresponding secret shard; and The first attribute calculation result and the second attribute calculation result of the leaf node are respectively expressed; they can be used as the ciphertext field of the leaf node. The ciphertext data corresponding to the corresponding financial data tree is composed of the first attribute calculation result and the second attribute calculation result of all leaf nodes. Indicates the The random number corresponding to each leaf node is calculated based on the attribute to obtain the corresponding ciphertext data.

[0033] Furthermore, the process of uploading and storing the obtained data to be regulated into the constructed distributed blockchain based on the constructed storage task includes:

[0034] Upon receiving a corresponding storage task, the distributed blockchain will broadcast the corresponding storage task to all storage nodes based on the deployed smart contract, read the financial data identifier in the corresponding storage task, and perform identity verification on it;

[0035] If the verification fails, indicating that the same financial data identifier exists, a storage failure notification and the reason for the failure are fed back to the user terminal, and then the user terminal verifies the corresponding financial data identifier and resubmits it;

[0036] If the verification is successful, indicating that the corresponding financial data identifier does not exist, a storage node is randomly selected as a temporary node, and the other storage nodes are marked as slave nodes;

[0037] The slave node requests a node signature from the corresponding temporary node and attaches its own node signature. After the temporary node obtains the request, it records the node signature of the slave node and feeds back the node signature of the temporary node. Then, the slave node feeds back node registration information to the temporary node. After receiving the corresponding node registration information, the temporary node returns the registered adjacent node information, otherwise it continues to wait. After the slave node receives the registered adjacent node information, it feeds back confirmation information to the temporary node. After the temporary node receives all the confirmation information, it records the corresponding temporary node as the master node and marks the slave node as a child node.

[0038] Repeat the acquisition process of the corresponding master node until all storage nodes are traversed and all marked master nodes are counted; prioritize all master nodes based on the data capacity in the corresponding storage task and the nearest minimum principle, and mark the corresponding master node as the first shared node, the second shared node, ..., the Kth shared node based on the priority ranking, where K represents the total number of master nodes; and feedback the corresponding storage notification to the user terminal;

[0039] After the user terminal receives the corresponding storage notification, the user terminal uploads the obtained data to be regulated to the corresponding first shared node for storage. The first shared node stores its own node signature and the corresponding data to be regulated in the second shared node, and so on, until it is stored in the Kth shared node; at the same time, a corresponding storage log is generated and broadcast to the corresponding distributed blockchain.

[0040] Furthermore, the process of reviewing the data to be supervised stored by the supervised object in the distributed blockchain and obtaining the corresponding review results includes:

[0041] If the participating user is a regulatory entity and the regulatory entity needs to review the historical financial information uploaded by a regulated entity, the regulatory entity may generate a corresponding regulatory request based on the user terminal;

[0042] Feedback the corresponding supervision request to the supervision node corresponding to the corresponding supervision subject; the supervision node searches the storage nodes in the corresponding distributed blockchain based on the supervision request, obtains the corresponding search list, and feeds the storage node back to the corresponding user terminal. The supervision subject can check whether the corresponding search list is the storage node associated with the corresponding regulated object. If not, the supervision subject is reminded to check whether the corresponding supervision request is accurate and conduct a second search; if so, a review notification is fed back to the corresponding supervision node;

[0043] Upon receiving the corresponding query notification, the supervisory node obtains the registration information and node signature of each storage node in the corresponding search list and performs verification. If the verification fails, the supervisory node sends a node information abnormality notification to the corresponding supervised object based on the identity number of the supervised object stored in the corresponding storage node. If the verification passes, the supervisory node sends its own node signature to the storage node. After the storage node receives the corresponding node signature, it sends a confirmation notification to the supervisory node and marks the corresponding storage node as a query node.

[0044] The node to be supervised receives confirmation information from all storage nodes in the corresponding search list and obtains the corresponding reference list. Then, based on the data processing process, the node to be supervised stores the data to be supervised in the corresponding reference node and obtains the corresponding supervision information.

[0045] Furthermore, the supervisory subject may review the corresponding supervisory data and input the corresponding supervisory data as input parameters into a pre-built supervisory model to obtain a corresponding first auxiliary result;

[0046] Furthermore, the regulatory body may generate corresponding monitoring results based on the understanding after reviewing the corresponding regulatory information and the first auxiliary results.

[0047] Furthermore, the process of building a regulatory model includes:

[0048] Obtaining a number of historical financial data and historical monitoring results corresponding to the corresponding financial data, wherein the historical monitoring results are mostly obtained by the monitoring subject through manual evaluation of the corresponding historical financial data;

[0049] Reading the obtained historical financial data and performing feature extraction on the data to obtain financial feature information corresponding to the corresponding historical financial information, wherein the financial feature includes several types of financial data features corresponding to the corresponding historical financial data;

[0050] Based on the historical financial information and the corresponding historical monitoring results, obtaining correlations between different financial data features and the corresponding historical monitoring results, constructing corresponding feature-monitoring graphs based on the correlations, and constructing corresponding training data based on the graphs;

[0051] Constructing a supervision model, wherein the supervision model is based on a convolutional neural network as a skeleton framework; the supervision model includes an input layer, a convolutional layer, a fully connected layer, and an output layer;

[0052] Randomly initialize the weights W and bias terms b in the corresponding supervision model;

[0053] After initialization is completed, a batch of sample data is randomly selected from the training data to iteratively train the corresponding supervision model, and forward propagation is performed on the corresponding sample data participating in the iterative training to obtain the output result corresponding to the output layer. The error value between the output result and the corresponding training sample is recorded based on the supervision loss function, and the loop is iterated until the preset number of iterations is reached or the corresponding error value is less than the preset error threshold, then the training is stopped, otherwise the training continues;

[0054] Backpropagation is performed based on the error value obtained from the supervision loss function, and the weights of the corresponding supervision model are updated; at the same time, the number of corresponding iterative training and the corresponding error value are recorded. If the number of corresponding iterations does not reach the preset number of iterations or the corresponding error value is less than the preset error threshold, a new batch of sample data is selected to continue the iterative cycle until the supervision model meets the requirements;

[0055] The regulatory loss function formula corresponding to the regulatory model is: ]; where represents the function value of the current supervision loss function, 、 Respectively represent The predicted value and true value of the output result corresponding to the sample data; is the total number of sample data participating in iterative training;

[0056] The formula for updating the weight is: Where, is the updated weight, Represents a hypervariable that is set. represents the learning rate of the supervisory model, represents the weight before updating, Initialize the settings.

[0057] The technical effects and advantages of the integrated supervision platform based on distributed blockchain of the present invention are as follows:

[0058] 1. By building a distributed blockchain, the regulatory process is decentralized and data is transparent; all participants can access relevant regulatory data on the blockchain, improving regulatory efficiency and transparency;

[0059] 2. The automated nature of distributed blockchains reduces manual intervention and lowers labor costs in the regulatory process. Furthermore, since data is automatically stored, verified, and transmitted on the blockchain, the use of paper documents is reduced, lowering storage and logistics costs.

[0060] 3. By building a regulatory model and using historical data for training, the platform can achieve intelligent analysis and prediction of financial data, improving the accuracy and timeliness of supervision; it helps to promptly identify potential risks and problems, and take appropriate measures to prevent and deal with them. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a schematic diagram of the system invention of an integrated supervision platform based on distributed blockchain of the present invention;

[0062] Figure 2 This is a flow chart of an integrated supervision platform method based on distributed blockchain of the present invention. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] Example 1

[0065] See also Figure 1 As shown, the integrated supervision platform based on distributed blockchain described in this embodiment includes:

[0066] A blockchain construction module is used to authenticate the identities of participants in the target regulatory process, obtain corresponding participation categories, and construct corresponding distributed blockchains based on the participation categories; the participation categories include regulatory entities, regulated entities, and third-party institutions;

[0067] The data collection module is used to collect financial data related to the regulated entities and obtain relevant financial information;

[0068] A data processing module is used to process the collected financial information to obtain corresponding data to be regulated; and to construct corresponding storage tasks based on the data to be regulated;

[0069] The data on-chain module is used to upload and store the obtained data to be regulated into the constructed distributed blockchain based on the constructed storage task;

[0070] The data supervision module is used to review the data to be supervised stored by the supervised objects in the distributed blockchain, obtain the corresponding review results, and generate corresponding supervision results and take corresponding supervision measures based on the corresponding review results;

[0071] The modules are connected via wired and / or wireless means to achieve data transmission between modules.

[0072] It should be further explained that, in the specific implementation process, the process of authenticating the participants in the target regulatory process, obtaining the corresponding participation category, and building the corresponding distributed blockchain based on the participation category includes:

[0073] A user terminal is set up to obtain the participants involved in the corresponding supervision process and mark them as participating users. The participating users log in to the user terminal and upload identity information for identity authentication. If the identity authentication fails, a login failure notification is fed back to the participating user. If the identity authentication is successful, the user terminal obtains the identity information of the corresponding participating user for identity authentication and classifies the corresponding participating users into participation categories based on the identity information. The participation categories include regulatory entities, regulated objects, and third-party institutions. At the same time, a corresponding identity number is generated based on the identity information of the participating users;

[0074] Furthermore, based on the distributed network, a corresponding distributed blockchain is constructed in combination with the participation categories corresponding to the corresponding participating users. The distributed blockchain is composed of a number of supervision nodes, audit nodes and storage nodes; wherein, the node corresponding to the supervision subject is the supervision node in the distributed blockchain, the third-party organization corresponds to the audit node, and the supervised object corresponds to the storage node.

[0075] It should be further explained that, in the specific implementation process, the process of collecting financial data related to the regulated entities and obtaining the corresponding financial information includes:

[0076] The data collection module is provided with a plurality of collection units, which collect financial data related to the regulated entity from multiple data sources based on the user terminal to obtain corresponding financial information. The financial information is composed of a plurality of financial data, including financial transaction information, financial information, etc. related to the regulated entity;

[0077] It should be further explained that, in the specific implementation process, the process of processing the collected financial information to obtain the corresponding data to be regulated; and constructing the corresponding storage task based on the data to be regulated includes:

[0078] Reading the collected financial information and performing data preprocessing on the collected financial information, wherein the preprocessing includes missing value processing and data standardization;

[0079] The missing value processing is used to fill in the missing data in the collected Internet big data, and the missing value processing adopts a multiple interpolation algorithm; the data standardization refers to the standardization of the corresponding photovoltaic data according to the preset format standard; it is used to make the collected data more complete and in a unified format;

[0080] After data preprocessing is completed, the collection time corresponding to each financial data in the corresponding financial information is obtained respectively, and the timestamp corresponding to the corresponding financial data is obtained based on the collection time, and the timestamp is converted into a binary code to obtain the corresponding time identification code;

[0081] At the same time, the financial data in the corresponding financial information is read and feature segments are extracted to obtain corresponding feature data segments, and each feature data segment is assigned a different level of weight, wherein the specific weight is determined by the specific content of the feature data segment;

[0082] Read the corresponding characteristic data segments, and randomly generate a set of dynamic data identifiers based on the corresponding characteristic data segments. The dynamic data identifiers can be obtained by the following mathematical formula: Where, The dynamic data representation corresponding to the characteristic data segment is a digital sequence composed of a set of random numbers; Indicates the first The amount of data for each feature data segment; and Respectively represent the maximum and minimum data amounts in the feature data segments corresponding to the corresponding financial data, where and is an integer, Indicates the total number of feature data segments;

[0083] Read the dynamic data identifiers of all feature data segments and perform dimensionality reduction processing on them, wherein the dimensionality reduction processing refers to reducing the numbers in the corresponding dynamic data identifiers to 0 or 1;

[0084] Furthermore, the normalized dynamic data identifiers are accumulated according to the assigned weights to obtain the corresponding financial character strings;

[0085] Furthermore, a unique financial data identifier is constructed based on the financial character string and the time identifier code. The financial data identifier between different financial data can not only reflect whether the two are similar, but also reflect the degree of similarity between the two; at the same time, it can also be used as a data identifier for the corresponding financial data;

[0086] Acquire characteristic information corresponding to the corresponding financial data based on the obtained characteristic data segments, wherein the characteristic information includes financial growth rate, transaction status, and other characteristics;

[0087] At the same time, based on the characteristic information, a bifurcated data tree is constructed for the financial information uploaded by the corresponding regulated object to obtain a corresponding financial data tree, which can be expressed as Where, Indicates the identity number of the supervised object; Indicates the first upload time of the supervised object. Financial data, Indicates the The characteristic information corresponding to each financial data;

[0088] Based on the CA-ABE algorithm, the corresponding initial parameters are generated in combination with the identity information of the corresponding supervised object. , initial parameters ; is a prime order The cyclic group of is a generator of the group, is a bilinear map; Model The non-negative minimum simplified residual system of is a prime number; ;

[0089] Then, the corresponding master key is constructed based on the initial parameters and the public key Where, 、 are a random function and a random number respectively, and 、 all ; represents the mapping result of applying a bilinear map to the generators of the corresponding group;

[0090] Pick a random number , and split the corresponding financial data tree based on the random number r to obtain the secret shards corresponding to the leaf nodes in the corresponding financial data tree ; and perform attribute calculations on the corresponding leaf nodes. The corresponding mathematical formula is: Where, Represents a leaf node The corresponding secret shard; and The first attribute calculation result and the second attribute calculation result of the leaf node are respectively expressed; they can be used as the ciphertext field of the leaf node. The ciphertext data corresponding to the corresponding financial data tree is composed of the first attribute calculation result and the second attribute calculation result of all leaf nodes. Indicates the The random number corresponding to each leaf node;

[0091] Then, based on the attribute calculation, corresponding ciphertext data is obtained;

[0092] Correlating the financial data identifier corresponding to the corresponding financial data and the corresponding ciphertext data to obtain the corresponding subgroup to be stored;

[0093] Counting the subgroups to be stored and the identity numbers of the regulated objects corresponding to all financial data in the financial information to obtain the corresponding data to be regulated;

[0094] Then, the data capacity corresponding to the corresponding data to be regulated, the financial data identifier corresponding to each financial data and the identity identifier of the regulated object are obtained, and the corresponding storage task is constructed based on them;

[0095] Furthermore, the storage tasks corresponding to the supervised objects are uploaded to the constructed distributed blockchain based on the user terminal;

[0096] It should be further explained that, in the specific implementation process, the process of uploading and storing the obtained data to be regulated into the constructed distributed blockchain based on the constructed storage task includes:

[0097] Upon receiving a corresponding storage task, the distributed blockchain will broadcast the corresponding storage task to all storage nodes based on the deployed smart contract, read the financial data identifier in the corresponding storage task, and perform identifier verification on it. The identifier verification is used to verify whether the corresponding storage node has the same financial data identifier as that in the corresponding storage task;

[0098] If the verification fails, indicating that the same financial data identifier exists, a storage failure notification and the reason for the failure are fed back to the user terminal, and then the user terminal verifies the corresponding financial data identifier and resubmits it;

[0099] If the verification is successful, indicating that the corresponding financial data identifier does not exist, a storage node is randomly selected as a temporary node, and the other storage nodes are marked as slave nodes;

[0100] Then, the slave node requests a node signature from the corresponding temporary node, and attaches its own node signature. After the temporary node obtains the request, it records the node signature of the slave node and feeds back the node signature of the temporary node. Then, the slave node feeds back node registration information to the temporary node. After receiving the corresponding node registration information, the temporary node returns the registered adjacent node information, otherwise it continues to wait. After the slave node receives the registered adjacent node information, it feeds back confirmation information to the temporary node. After the temporary node receives all the confirmation information, it records the corresponding temporary node as the master node and marks the slave node as a child node.

[0101] Repeat the acquisition process of the corresponding master node until all storage nodes are traversed and all marked master nodes are counted; prioritize all master nodes based on the data capacity in the corresponding storage task and the nearest minimum principle, and mark the corresponding master node as the first shared node, the second shared node, ..., the Kth shared node based on the priority ranking, where K represents the total number of master nodes; and feedback the corresponding storage notification to the user terminal;

[0102] After the user terminal receives the corresponding storage notification, the user terminal uploads the obtained data to be regulated to the corresponding first shared node for storage. The first shared node stores its own node signature and the corresponding data to be regulated in the second shared node, and so on until it is stored in the Kth shared node; at the same time, a corresponding storage log is generated and broadcast to the corresponding distributed blockchain.

[0103] It should be further explained that, in the specific implementation process, the process of reviewing the data to be supervised stored by the supervised object in the distributed blockchain, obtaining the corresponding review results, and generating the corresponding supervision results based on the corresponding review results and taking the corresponding supervision measures includes:

[0104] If the participating user is a regulatory entity and needs to review historical financial information uploaded by a regulated entity, the regulatory entity may generate a corresponding regulatory request based on the user terminal. The regulatory request includes the regulatory entity's identity number, the regulated entity's identity number, and other relevant information.

[0105] Feedback the corresponding supervision request to the supervision node corresponding to the corresponding supervision subject; the supervision node searches the storage nodes in the corresponding distributed blockchain based on the supervision request, obtains a corresponding search list, and includes a number of storage nodes related to the corresponding supervision request. The storage node is fed back to the corresponding user terminal. The supervision subject can check whether the corresponding search list contains a storage node associated with the corresponding supervised object. If not, the supervision subject is reminded to check whether the corresponding supervision request is accurate and conduct a secondary search; if so, a review notification is fed back to the corresponding supervision node;

[0106] Upon receiving the corresponding query notification, the supervisory node obtains the registration information and node signature of each storage node in the corresponding search list and performs verification. If the verification fails, the supervisory node feeds back a node information abnormality notification to the corresponding supervised object based on the identity number of the supervised object stored in the corresponding storage node, so as to remind the corresponding supervised object to conduct information inspection on the uploaded financial information. If the verification passes, the supervisory node sends its own node signature to the storage node. After the storage node receives the corresponding node signature, it feeds back a confirmation notification to the supervisory node and marks the corresponding storage node as a query node.

[0107] The node to be regulated receives confirmation information from all storage nodes in the corresponding search list and obtains the corresponding lookup list. It then performs reverse processing on the data to be regulated stored in the corresponding lookup node based on the data processing process to obtain the corresponding regulatory information. The regulatory information is the original financial information corresponding to the data to be regulated after the reverse processing of the data processing process.

[0108] The supervisory subject may review the corresponding supervisory data and input the corresponding supervisory data as input parameters into a pre-built supervisory model to obtain a corresponding first auxiliary result;

[0109] Furthermore, the supervisory body may generate corresponding supervision results based on the knowledge after reviewing the corresponding supervision information and the first auxiliary result, wherein the supervision results include normal supervision, abnormal supervision and risk supervision;

[0110] Furthermore, the supervisory body takes corresponding supervisory measures based on the supervisory results. If the supervisory result is no abnormal supervision, no other operations are performed. If the supervisory result is abnormal supervision, the supervisory body marks the abnormal part in the corresponding supervisory information and generates a corresponding supervision notice to urge the supervised object to correct the abnormal part. After the correction is completed, a second supervision is carried out. If the supervisory result is risk supervision, the supervisory body dispatches supervisory personnel to conduct on-site supervision of the supervised object.

[0111] It should be further explained that, in the specific implementation process, the process of building a regulatory model includes:

[0112] Obtaining a number of historical financial data and historical monitoring results corresponding to the corresponding financial data, wherein the historical monitoring results are mostly obtained by the monitoring subject through manual evaluation of the corresponding historical financial data;

[0113] Reading the obtained historical financial data and performing feature extraction on the data to obtain financial feature information corresponding to the corresponding historical financial information, wherein the financial feature includes several types of financial data features corresponding to the corresponding historical financial data;

[0114] Based on the historical financial information and the corresponding historical monitoring results, obtaining correlations between different financial data features and the corresponding historical monitoring results, constructing corresponding feature-monitoring graphs based on the correlations, and constructing corresponding training data based on the graphs;

[0115] Constructing a supervision model, wherein the supervision model is based on a convolutional neural network as a skeleton framework; the supervision model includes an input layer, a convolutional layer, a fully connected layer, and an output layer;

[0116] Randomly initialize the weights W and bias terms b in the corresponding supervision model;

[0117] After initialization is completed, a batch of sample data is randomly selected from the training data to iteratively train the corresponding supervision model, and forward propagation is performed on the corresponding sample data participating in the iterative training to obtain the output result corresponding to the output layer. The error value between the output result and the corresponding training sample is recorded based on the supervision loss function, and the loop is iterated until the preset number of iterations is reached or the corresponding error value is less than the preset error threshold, then the training is stopped, otherwise the training continues;

[0118] Backpropagation is performed based on the error value obtained from the supervision loss function, and the weights of the corresponding supervision model are updated; at the same time, the number of corresponding iterative training and the corresponding error value are recorded. If the number of corresponding iterations does not reach the preset number of iterations or the corresponding error value is less than the preset error threshold, a new batch of sample data is selected to continue the iterative cycle until the supervision model meets the requirements;

[0119] The regulatory loss function formula corresponding to the regulatory model is: ]; where represents the function value of the current supervision loss function, 、 Respectively represent The predicted value and true value of the output result corresponding to the sample data; is the total number of sample data participating in iterative training;

[0120] The formula for updating the weight is: Where, is the updated weight, Represents a hypervariable that is set. represents the learning rate of the supervisory model, represents the weight before updating, The initialization parameters to be set.

[0121] The present invention has significant beneficial effects in improving regulatory efficiency, enhancing data security and privacy protection, improving data integrity and traceability, reducing regulatory costs, promoting multi-party collaboration and trust, supporting smart contracts and automated supervision, and improving regulatory accuracy and timeliness.

[0122] Example 2

[0123] See also Figure 2 As shown, for the parts not described in detail in this embodiment, please refer to the description of Example 1. A working method of an integrated supervision platform based on blockchain is provided, including:

[0124] Step 1: Authenticate the identities of the participants in the target regulatory process, obtain the corresponding participation categories, and build the corresponding distributed blockchain based on the participation categories; the participation categories include the regulatory body, the regulated entity, and the third-party organization;

[0125] Step 2: Collect financial data related to the regulated entity to obtain relevant financial information;

[0126] Step 3: Process the collected financial information to obtain corresponding data to be regulated; and construct corresponding storage tasks based on the data to be regulated;

[0127] Step 4: Based on the constructed storage task, the obtained data to be regulated is uploaded and stored in the constructed distributed blockchain;

[0128] Step 5: Review the data to be regulated stored by the regulated object in the distributed blockchain, obtain the corresponding review results, and generate corresponding supervision results based on the corresponding review results and take corresponding supervision measures.

[0129] Example 3

[0130] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the integrated supervision platform based on distributed blockchain provided above is realized.

[0131] Since the electronic device described in this embodiment is an electronic device used to implement an integrated supervision platform based on a distributed blockchain in the embodiment of this application, based on the integrated supervision platform based on a distributed blockchain described in the embodiment of this application, those skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application will not be described in detail here. As long as those skilled in the art implement the electronic device used in the integrated supervision platform based on a distributed blockchain in the embodiment of this application, they are within the scope of protection of this application.

[0132] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0133] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for users of ordinary skill in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. An integrated regulatory platform based on distributed blockchain, characterized by: include: A blockchain building module is used to authenticate the identities of participants in the target regulatory process, obtain corresponding participation categories, and build corresponding distributed blockchains based on the participation categories; The participating categories include regulatory entities, regulated entities and third-party institutions; The data collection module is used to collect financial data related to the regulated entities and obtain relevant financial information; The data processing module is used to process the collected financial information and obtain the corresponding data to be regulated; And construct corresponding storage tasks based on the data to be supervised; including: Reading the collected financial information, performing data preprocessing on the collected financial information, and after the data preprocessing is completed, extracting feature segments from the financial data in the corresponding financial information to obtain corresponding feature data segments, and constructing corresponding financial data identifiers based on the feature data segments, including: Obtaining the collection time corresponding to each financial data in the corresponding financial information respectively, and based on the collection time, obtaining the timestamp corresponding to the corresponding financial data, and constructing a time identification code based on the timestamp; Read the corresponding feature data fragments and assign different levels of weight to each feature data fragment; and generate a set of dynamic data identifiers based on the corresponding feature data fragments ; Read the dynamic data identifiers of all feature data segments and perform dimensionality reduction processing on them; accumulate the dynamic data identifiers after dimensionality reduction processing according to the assigned weights to obtain a financial string; construct a unique financial data identifier based on the financial string and time identifier code; and simultaneously encrypt the corresponding financial information to obtain the corresponding ciphertext data; Correlate the financial data identifiers and ciphertext data to obtain the corresponding subgroups to be stored, count the subgroups to be stored and the identity numbers of the regulated objects corresponding to all financial data, and obtain the corresponding data to be regulated; Obtain the data capacity corresponding to the corresponding data to be regulated, the financial data identifiers corresponding to each financial data, and the identity identifiers of the regulated objects, and build corresponding storage tasks based on them, and upload them to the constructed distributed blockchain; The data on-chain module is used to upload and store the obtained data to be regulated into the constructed distributed blockchain based on the constructed storage task; The data supervision module is used to review the data to be supervised stored by the supervised objects in the distributed blockchain, obtain corresponding review results, and generate corresponding supervision results based on the corresponding review results and take corresponding supervision measures.

2. The integrated supervision platform based on distributed blockchain according to claim 1 is characterized in that: The process of authenticating the participants in the target regulatory process, obtaining the corresponding participation category, and building the corresponding distributed blockchain based on the participation category includes: A user terminal is set up to obtain the participants involved in the corresponding supervision process and mark them as participating users. The participating users log in to the user terminal and upload identity information for identity authentication. If the identity authentication fails, a login failure notification is fed back to the participating user. If the identity authentication is successful, the user terminal obtains the identity information of the corresponding participating user for identity authentication and classifies the corresponding participating users into participation categories based on the identity information. The participation categories include regulatory entities, regulated objects, and third-party institutions. At the same time, a corresponding identity number is generated based on the identity information of the participating users; Furthermore, based on the distributed network, a corresponding distributed blockchain is constructed in combination with the participation categories corresponding to the corresponding participating users. The distributed blockchain consists of several supervision nodes, audit nodes and storage nodes.

3. The integrated supervision platform based on distributed blockchain according to claim 2 is characterized in that: The process of collecting financial data related to the regulated entities and obtaining relevant financial information includes: The data collection module is provided with a plurality of collection units, which collect financial data related to the supervised object based on the user terminal to obtain corresponding financial information, and the financial information is composed of a plurality of financial data.

4. The integrated supervision platform based on distributed blockchain according to claim 1 is characterized in that: The dynamic data identifier The formula for obtaining is: Where, Indicates the dynamic data representation corresponding to the feature data segment; Indicates the first The amount of data for each feature data segment; and Respectively represent the maximum and minimum data amounts in the feature data segments corresponding to the corresponding financial data, where Indicates the total number of feature data segments.

5. The integrated supervision platform based on distributed blockchain according to claim 1 is characterized in that: The process of encrypting the corresponding financial information and obtaining the corresponding ciphertext data includes: Acquiring characteristic information corresponding to the corresponding financial data based on the obtained characteristic data fragments; Based on the characteristic information, a bifurcated data tree is constructed for the financial information uploaded by the corresponding regulated entity to obtain a corresponding financial data tree; Based on the CA-ABE algorithm, the corresponding initial parameters are generated in combination with the identity information of the corresponding supervised object. , initial parameters ; is a prime order The cyclic group of is a generator of the group, is a bilinear map; Model The non-negative minimum simplified residual system of is a prime number; ; Then, the corresponding master key is constructed based on the initial parameters and the public key Where, are a random function and a random number respectively, and 、 all ; represents the mapping result of applying a bilinear map to the generators of the corresponding group; Pick a random number , and based on random numbers Split the corresponding financial data tree and obtain the secret shards corresponding to the leaf nodes in the corresponding financial data tree ; And the mathematical formula for calculating the attributes of the corresponding leaf nodes is: Where, Represents a leaf node The corresponding secret shard; and The first attribute calculation result and the second attribute calculation result of the leaf node are respectively expressed; based on the attribute calculation, corresponding ciphertext data is obtained.

6. The integrated supervision platform based on distributed blockchain according to claim 2 is characterized in that: The process of uploading and storing the acquired data to be regulated into the constructed distributed blockchain based on the constructed storage task includes: Upon receiving a corresponding storage task, the distributed blockchain will broadcast the corresponding storage task to all storage nodes based on the deployed smart contract, read the financial data identifier in the corresponding storage task, and perform identity verification on it; If the verification fails, a storage failure notification and the reason for the failure are fed back to the user terminal, and the user terminal then verifies the corresponding financial data identifier and resubmits the data; If the verification passes, a storage node is randomly selected as a temporary node, and the other storage nodes are marked as slave nodes; The slave node requests a node signature from the corresponding temporary node and attaches its own node signature. After the temporary node obtains the request, it records the node signature of the slave node and feeds back the node signature of the temporary node. Then, the slave node feeds back node registration information to the temporary node. After receiving the corresponding node registration information, the temporary node returns the registered adjacent node information, otherwise it continues to wait. After the slave node receives the registered adjacent node information, it feeds back confirmation information to the temporary node. After the temporary node receives all the confirmation information, it records the corresponding temporary node as the master node and marks the slave node as a child node. Repeat the acquisition process of the corresponding master node until all storage nodes are traversed and all marked master nodes are counted; all master nodes are prioritized based on the data capacity in the corresponding storage task and the nearest minimum principle, and the corresponding master nodes are marked as the first shared node, the second shared node, ..., based on the priority sorting. Shared nodes, Indicates the total number of master nodes; and feeds back corresponding storage notification to the user terminal; After the user terminal receives the corresponding storage notification, the user terminal uploads the obtained data to be supervised to the corresponding first shared node for storage. The first shared node stores its own node signature and the corresponding data to be supervised in the second shared node, and so on until it is stored in the first shared node. Shared nodes; at the same time, corresponding storage logs are generated and broadcast to the corresponding distributed blockchain.

7. The integrated supervision platform based on distributed blockchain according to claim 2 is characterized in that: The process of reviewing the data to be regulated corresponding to the regulated object includes: If the participating user is a regulatory entity and the regulatory entity needs to review the historical financial information uploaded by a regulated entity, the regulatory entity will generate a corresponding regulatory request based on the user terminal; Feedback the corresponding supervision request to the supervision node corresponding to the corresponding supervision subject; the supervision node searches the storage nodes in the corresponding distributed blockchain based on the supervision request, obtains the corresponding search list, and feeds the storage node back to the corresponding user terminal. The supervision subject checks whether the corresponding search list contains the storage node associated with the corresponding supervised object. If not, the supervision subject is reminded to check whether the corresponding supervision request is accurate and conduct a second search; if so, a review notification is fed back to the corresponding supervision node; Upon receiving the corresponding query notification, the supervisory node obtains the registration information and node signature of each storage node in the corresponding search list and performs verification. If the verification fails, the supervisory node sends a node information abnormality notification to the corresponding supervised object based on the identity number of the supervised object stored in the corresponding storage node. If the verification passes, the supervisory node sends its own node signature to the storage node. After the storage node receives the corresponding node signature, it sends a confirmation notification to the supervisory node and marks the corresponding storage node as a query node. The node to be supervised receives confirmation information from all storage nodes in the corresponding search list and obtains the corresponding reference list. Then, based on the data processing process, the node to be supervised stores the data to be supervised in the corresponding reference node and obtains the corresponding supervision information.

8. The integrated supervision platform based on distributed blockchain according to claim 7 is characterized in that: The supervisory subject reviews the corresponding supervisory data and inputs the corresponding supervisory data as input parameters into a pre-built supervisory model to obtain a corresponding first auxiliary result; Furthermore, the regulatory body generates corresponding monitoring results based on the understanding after reviewing the corresponding regulatory information and the first auxiliary results.

9. The integrated supervision platform based on distributed blockchain according to claim 8 is characterized in that: The process of building a governance model involves: Obtaining a number of historical financial data and the historical monitoring results corresponding to the corresponding financial data; Reading the obtained historical financial data and performing feature extraction on the data to obtain financial feature information corresponding to the corresponding historical financial information, wherein the financial feature includes several types of financial data features corresponding to the corresponding historical financial data; Based on the historical financial information and the corresponding historical monitoring results, obtaining correlations between different financial data features and the corresponding historical monitoring results, constructing corresponding feature-monitoring graphs based on the correlations, and constructing corresponding training data based on the graphs; Constructing a supervision model, wherein the supervision model is based on a convolutional neural network as a skeleton framework; the supervision model includes an input layer, a convolutional layer, a fully connected layer, and an output layer; The weights in the corresponding supervision model and the bias term Perform random initialization; After initialization is completed, a batch of sample data is randomly selected from the training data to iteratively train the corresponding supervision model, and forward propagation is performed on the corresponding sample data participating in the iterative training to obtain the output result corresponding to the output layer. The error value between the output result and the corresponding training sample is recorded based on the supervision loss function, and the loop is iterated until the preset number of iterations is reached or the corresponding error value is less than the preset error threshold, then the training is stopped, otherwise the training continues; Backpropagation is performed based on the error value obtained from the supervision loss function, and the weights of the corresponding supervision model are updated; at the same time, the number of corresponding iterative training and the corresponding error value are recorded. If the number of corresponding iterations does not reach the preset number of iterations or the corresponding error value is less than the preset error threshold, a new batch of sample data is selected to continue the iterative cycle until the supervision model meets the requirements; The regulatory loss function formula corresponding to the regulatory model is: ]; where represents the function value of the current supervision loss function, 、 Respectively represent the predicted value and true value of the output result corresponding to the nth sample data; among them, M is the total number of sample data participating in iterative training; The formula for updating the weight is: Where, is the updated weight, Represents a hypervariable that is set. represents the learning rate of the supervisory model, represents the weight before updating, Initialize the settings.

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