Data processing method, electronic device and program product

Abstract

The invention provides a data processing method, electronic equipment and a program product. The method comprises the steps of outputting a target processing rule by using a first agent in response to a data processing rule adjustment request; deploying the target processing rule to a block chain network through a first smart contract to obtain rule version hash; performing data processing on the target data by using the target processing rule to generate a data processing record; deploying the data processing record and the rule version hash to a block chain network through a second smart contract, and generating a processing record hash; in response to the data review request, based on a processing record hash included in the data review request, acquiring a data processing record and a target processing rule corresponding to the processing record hash from the block chain network; inputting the data processing record and the target processing rule into a second intelligent agent, and outputting a rechecking result by using the second intelligent agent; and sending a re-checking result to the client.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more particularly to data processing methods, electronic devices, and program products. Background Technology

[0002] With the development of computer technology, it has been widely used in data management applications. For example, user points management, coupon management, credit score management, and so on.

[0003] In existing technologies, data management and processing rules are relatively fixed and are typically not modified once established. However, the external market environment is constantly changing, and over time, previously defined data processing rules become inadequate for the latest demands. During data processing, errors are inevitable due to various factors, leading to disputes over the results. Resolving these disputes requires a tedious process of gathering extensive evidence, consuming significant manpower and time. Therefore, a solution is needed that can improve the dynamic processing capabilities of data. Summary of the Invention

[0004] This disclosure provides data processing methods, electronic devices, and program products.

[0005] According to a first aspect of this disclosure, a data processing method is provided. The method specifically includes: responding to a data processing rule adjustment request, using a first intelligent agent to output a target processing rule; deploying the target processing rule to a blockchain network via a first smart contract to obtain a rule version hash; processing target data using the target processing rule to generate a data processing record; deploying the data processing record and the rule version hash to the blockchain network via a second smart contract to generate a processing record hash; responding to a data verification request, based on the processing record hash included in the data verification request, obtaining the data processing record and the target processing rule corresponding to the processing record hash from the blockchain network; inputting the data processing record and the target processing rule into a second intelligent agent, using the second intelligent agent to output a verification result; and sending the verification result to a client.

[0006] Based on the above scheme, a complete chain of evidence is constructed, from rules to calculations, to dispute review, and then to adjudication. Through a dual-hash association mechanism of rule version hash and processing record hash, the transparency and traceability of the dynamic rule system are achieved.

[0007] According to at least one embodiment of this disclosure, in response to a data processing rule adjustment request, a target processing rule is output using a first intelligent agent, including: parsing the data processing rule adjustment request to obtain historical data records and current market data; inputting the historical data records and current market data into the first intelligent agent for analysis to determine rule adjustment parameters; and generating the target processing rule using the rule adjustment parameters.

[0008] According to the above scheme, the first intelligent agent performs in-depth analysis of historical and market data to dynamically optimize rule parameters, so that the incentive strategy matches the market dynamics in real time.

[0009] According to at least one embodiment of this disclosure, the target processing rule is deployed to the blockchain network through a first smart contract to obtain a rule version hash, including: digitally signing the target processing rule to generate a rule update transaction; broadcasting the rule update transaction to the blockchain network; and obtaining the rule version hash generated by the blockchain network after the rule update transaction is confirmed by the blockchain network and added to a block.

[0010] According to the above scheme, the characteristic of the blockchain network to automatically generate transaction hashes is used to make the rule version hashes the key link in building a complete chain of evidence, ensuring that rule changes are transparent and verifiable.

[0011] According to at least one embodiment of this disclosure, target data is processed using target processing rules to generate a data processing record, including: querying a first smart contract to obtain a target processing rule matching the current time and the corresponding rule version hash; extracting corresponding rule parameters from the target processing rule based on the business type of the target data; calculating the data processing result based on the rule parameters and the target data; and generating a data processing record, which includes a data processing timestamp, a data processing result, a business identifier, an original voucher hash, and a rule version hash.

[0012] According to the above scheme, by explicitly including the rule version hash in the data processing record, the precise correlation between the rule and the calculation result is achieved, ensuring that each integral calculation can be traced back to the specific rule version used, and providing an objective and reliable basis for subsequent review.

[0013] According to at least one embodiment of this disclosure, data processing records and rule version hashes are deployed to a blockchain network via a second smart contract, and a processing record hash is generated. This includes: encapsulating the data processing record into a structured record object, the structured record object including a data processing subject identifier, data processing type, data processing result, timestamp, business identifier, rule version hash, and the processing record hash and original voucher hash of the previous data processing record; digitally signing the data processing record to generate a data processing record transaction; broadcasting the data processing record transaction to the blockchain network; and obtaining the processing record hash generated by the blockchain network after the data processing record transaction is confirmed by the blockchain network and added to a block; updating the data processing record index in the second smart contract based on the processing record hash, and establishing a mapping relationship between the data processing record and the processing record hash.

[0014] Based on the above scheme, a chain-like traceability structure for data processing records is constructed. An immutable record chain is formed by hashing the previous record. At the same time, an efficient data processing record index is established, enabling the system to quickly locate and verify historical points records, thereby improving query efficiency and data credibility.

[0015] According to at least one embodiment of this disclosure, in response to a data review request, based on the processing record hash contained in the data review request, the data processing record and target processing rule corresponding to the processing record hash are obtained from the blockchain network, including: using a first smart agent to parse the data review request and extract the processing record hash; calling the query interface of a second smart contract to obtain the corresponding data processing record according to the processing record hash; extracting the rule version hash from the data processing record; calling the query interface of the first smart contract to obtain the target processing rule according to the rule version hash; verifying whether the target processing rule is in a valid state at the time point corresponding to the data processing record, wherein a valid state includes the target processing rule's effective timestamp being earlier than or equal to the data processing record's timestamp and its expiration timestamp being later than the data processing record's timestamp.

[0016] According to the above scheme, the validity of the rules at a specific point in time can be accurately verified. Through dual verification (time range and active status), it is ensured that the review is based on the actual version of the rules applied when the dispute occurred, so as to obtain a more comprehensive and accurate review result.

[0017] According to at least one embodiment of this disclosure, the method of parsing a data review request using a first intelligent agent and extracting a processing record hash includes: inputting a natural language description of the data review request into the first intelligent agent; the first intelligent agent analyzing the semantic features of the natural language description according to a preset intent recognition rule; and obtaining a structured parsing result output by the first intelligent agent, wherein the structured parsing result includes a review intent identifier, a parsing confidence level, a processing record hash, and a description of the reason for the dispute.

[0018] According to the above solution, the automatic conversion of natural language review requests to structured queries and the verification of confidence through parsing ensure the validity of the requests, enabling brokers to express disputes in a natural way and improving the efficiency of review request submission.

[0019] According to at least one embodiment of this disclosure, data processing records and target processing rules are input into a second intelligent agent, and the second intelligent agent outputs a review result, including: inputting data processing records and target processing rules into the second intelligent agent; verifying whether the data processing records meet the rule requirements using the target processing rules; and determining whether the data processing process is compliant using the original voucher content corresponding to the original voucher hash in the data processing records; and generating a review result based on the rule verification result and the compliance judgment result.

[0020] According to the above scheme, a dual mechanism of rule verification and compliance judgment is used to achieve comprehensive verification of "whether the calculation is correct" and "whether the business is real", and the multimodal analysis capability of the second intelligent agent is used to improve the accuracy of the review.

[0021] According to at least one embodiment of this disclosure, a review result is generated based on the rule verification result and the compliance judgment result, including: if the rule verification result is verification failure, and / or the compliance judgment result is non-compliant, a data correction instruction is generated; and the data correction result is deployed to the blockchain network; if the rule verification result is verification success and the compliance judgment result is compliant, the review result is sent to the user's client.

[0022] According to the above scheme, a precise condition judgment and data correction closed loop can be achieved, ensuring that erroneous data is corrected in a timely manner and that the operation is transparent and traceable. At the same time, detailed verification basis is provided to improve user acceptance.

[0023] According to at least one embodiment of this disclosure, in response to a data processing rule adjustment request, before outputting a target processing rule using the first intelligent agent, the method further includes: monitoring market data, and generating a data processing rule adjustment request when the monitored market data change exceeds a change threshold; or, receiving rule adjustment instruction information and generating a data processing rule adjustment request.

[0024] According to the above plan, the rule adjustment will shift from a passive response to a proactive adaptation. Through a dual trigger mechanism of automatic market data monitoring and manual instructions, the incentive strategy will be matched with market dynamics in real time.

[0025] According to at least one embodiment of this disclosure, the method further includes: responding to a query request for target data, using a first intelligent agent to parse it into an executable structured query instruction; wherein the structured query instruction includes: a query method name and query parameters; based on the structured query instruction, obtaining the corresponding data processing record from the blockchain network; converting the obtained data processing record into a visualized data report, the visualized data report including a detailed list of data processing results, trend charts, and traceability information of the calculation logic; and returning the visualized data report to the client in natural language form, the natural language form including an explanatory description of the data processing results and a highlighting of key indicators.

[0026] According to the above solution, end-to-end automation from natural language queries to visual insights is achieved, and the system transparency is enhanced by tracing information through computational logic.

[0027] According to at least one embodiment of this disclosure, before generating the data processing record, the method further includes: uploading the original voucher to a distributed storage system and obtaining the original voucher hash corresponding to the original voucher, so as to include the original voucher hash as a key field in the data processing record.

[0028] According to the above scheme, an efficient model of on-chain evidence storage and off-chain storage is achieved, and the integrity and verifiability of the evidence are ensured by the hash of the original certificate.

[0029] According to a second aspect of this disclosure, an electronic device is provided, comprising: a memory storing execution instructions; and a processor executing the execution instructions stored in the memory, such that the processor performs a first aspect of any embodiment of this disclosure.

[0030] According to a third aspect of this disclosure, a readable storage medium is provided, wherein executable instructions are stored therein, which, when executed by a processor, are used to implement a first aspect of any embodiment of this disclosure.

[0031] According to a fourth aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements a first aspect of any embodiment of this disclosure. Attached Figure Description

[0032] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0033] Figure 1 This is a schematic flowchart of a data processing method provided in an embodiment of the present disclosure.

[0034] Figure 2 A flowchart illustrating the target processing rule generation method provided in this embodiment of the disclosure.

[0035] Figure 3 This is a flowchart illustrating a blockchain data processing method provided in an embodiment of the present disclosure.

[0036] Figure 4 This is a schematic flowchart of a data processing record generation method provided in an embodiment of the present disclosure.

[0037] Figure 5 This is a schematic flowchart of another blockchain processing method provided in an embodiment of this disclosure.

[0038] Figure 6 This is a flowchart illustrating the review process provided in an embodiment of the present disclosure.

[0039] Figure 7 This is a flowchart illustrating the method for extracting and processing record hashes during the review process, as provided in an embodiment of this disclosure.

[0040] Figure 8 This is a flowchart illustrating the verification result output method provided in the embodiments of this disclosure.

[0041] Figure 9 This is a flowchart illustrating the method for generating review results provided in an embodiment of this disclosure.

[0042] Figure 10 This is a schematic diagram illustrating the structure of a data processing system as an example of this disclosure.

[0043] Figure 11 This is a schematic block diagram of a data processing apparatus according to one embodiment of the present disclosure.

[0044] Figure 12 This is a schematic block diagram of an electronic device according to one embodiment of the present disclosure. Detailed Implementation

[0045] The present disclosure will now be described in further detail with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0046] It should be noted that, where there is no conflict, the embodiments and features described in this disclosure can be combined with each other. The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0047] In existing scenarios such as points management and transaction data management, different data processing rules are set, such as defining the circumstances under which points will be awarded to users and different reward rules. These rules, once set, generally remain unchanged. However, as the market environment changes over time, the old rules become increasingly inadequate. Abruptly changing the data processing rules can lead to chaotic data processing results (e.g., incorrect points). When there are disputes over the data (e.g., users questioning the accuracy of their points), significant manpower and time are required to search for historical records and relevant evidence. Clearly, the existing rules update and data management capabilities are relatively weak. Therefore, a comprehensive technical solution is needed to improve rule updates, data processing, and data anomaly resolution capabilities.

[0048] Figure 1 This is a schematic flowchart illustrating the data processing method provided in an embodiment of this disclosure. Figure 1 The method shown includes steps 101 to 107. This method can be executed by a server and can also be combined with blockchain and distributed storage.

[0049] Specifically, Figure 1 The method shown includes step 101: in response to a data processing rule adjustment request, outputting a target processing rule using a first agent.

[0050] When the server receives a request to adjust data processing rules, it first parses and processes the request through a first intelligent agent. This first intelligent agent, acting as an intent recognition module, is responsible for parsing user input (e.g., in natural language) and identifying the intent to adjust the rules. For example, when a business department issues a natural language instruction to "increase the holiday discount ratio for various businesses," the first intelligent agent can accurately identify that this is a rule adjustment request and extract key parameters (such as "various businesses," "discount," and "ratio"). Subsequently, the first intelligent agent distributes the task to the corresponding processing module, which generates specific target processing rules based on market data and historical records. These target processing rules include, but are not limited to, optimized values ​​for parameters such as discount weight values ​​and credit score thresholds. It should be noted that target processing rules differ across business scenarios, and users can define their own target processing rules according to their actual application needs. This is merely an example and does not constitute a limitation on the technical solution of this application.

[0051] Step 102: Deploy the target processing rules to the blockchain network through the first smart contract to obtain the rule version hash.

[0052] In practical applications, target processing rules can be encapsulated as structured data objects, digitally signed by a management account, used to generate rule update transactions, and then broadcast to the blockchain network. This method ensures that all versions of the data processing rules (including the latest generated target processing rules) are effectively preserved, providing reliable evidence for subsequent reviews.

[0053] For example, when an administrator issues an instruction to "increase the discount rate for various services during holidays," the `updateRule` method of the first smart contract is invoked, storing the rule parameters of the target processing rule on the blockchain network. After processing the transaction, the blockchain network returns a rule version hash (e.g., 0xabc123…), which will serve as an important basis for subsequent associations.

[0054] Step 103: Use the target processing rules to process the target data and generate data processing records.

[0055] After the target processing rules are stored on the blockchain, the server uses these rules to process the target data. This target data could be, for example, sales performance of various businesses or agent points. Based on the parameters in the target processing rules, the corresponding data processing results are calculated, and a data processing record is generated. This record includes not only the calculation results but also a data processing timestamp, business identifier, original voucher hash, and rule version hash. The various data contained in this record can serve as a basis for subsequent verification.

[0056] Step 104: Deploy the data processing record and rule version hash to the blockchain network via a second smart contract and generate the processing record hash.

[0057] In practical applications, data processing records can be encapsulated into structured record objects, including the data processing subject identifier, data processing type, data processing result, timestamp, business identifier, rule version hash, and the processing record hash and original voucher hash of the previous data processing record. This data processing record, after being digitally signed, forms a data processing record transaction and is broadcast to the blockchain network.

[0058] For example, when a broker completes a transaction, they calculate the broker's points according to the currently valid data processing rules, generate a points record, and write the points record to the blockchain. After the blockchain network confirms the transaction, it returns a processing record hash (e.g., 0xdef456…), which will be used in any subsequent possible review process.

[0059] Step 105: In response to the data verification request, based on the processing record hash contained in the data verification request, obtain the data processing record and target processing rule corresponding to the processing record hash from the blockchain network.

[0060] Upon receiving a data review request, the system first parses the processing record hash contained within the request. Then, using this hash value as a query key, it calls the query interface of the second smart contract to retrieve the corresponding data processing record. From this data processing record, it extracts the rule version hash and further calls the query interface of the first smart contract to obtain the corresponding target processing rule. The processing record hash and the rule version hash together constitute a complete chain of evidence, enabling the system to accurately trace the rule version upon which a specific data processing was based.

[0061] For example, when the system lags and a user's workload data reporting fails, and the user has questions about their workload, they can initiate a "workload review" request. By using the processing record hash in the review request, the specific point deduction record can be quickly located, and the rule version on which the record was generated can be obtained, providing objective evidence for subsequent reviews.

[0062] Step 106: Input the data processing records and target processing rules into the second intelligent agent, and use the second intelligent agent to output the verification results.

[0063] The second agent, acting as a review auxiliary module, is responsible for in-depth data analysis. Specifically, it first verifies whether the data processing records comply with the target processing rules; for example, it can recalculate the data processing results and compare them with the results in the records. Simultaneously, the second agent utilizes other data from the data processing records, such as transaction vouchers, and uses multimodal analysis to determine whether the data processing process is compliant. Based on these analyses, the second agent generates structured review results.

[0064] For example, when processing workload verification, if there are no errors, the user will be given a reason; if errors are found, the workload will be restored in a timely manner.

[0065] Step 107: Send the verification results to the client.

[0066] It should be noted that the review results may include a review analysis report, the ruling, recommendations for points adjustments, and detailed reasons. These results are presented in a user-friendly format, enabling brokers to clearly understand the basis and conclusions of the review.

[0067] Based on the aforementioned publicly available solutions, this method cleverly utilizes the rule version hash and processing record hash automatically generated by the blockchain network during the transaction confirmation process. This constructs a complete chain of evidence, from rule changes to data calculation, dispute requests, and finally, the output of the review results. In the event of a dispute, the relevant data processing record and the underlying rule version can be quickly located based on the processing record hash, automatically constructing a complete chain of evidence without manual intervention. Furthermore, leveraging the blockchain network's ability to automatically generate transaction hashes, these hash values ​​serve as key identifiers linking different stages. The rule version hash associates dynamic rules with the score calculation results, while the processing record hash serves as the starting point for the review process, forming a closed-loop chain of evidence. This leverages the immutability and traceability advantages of the blockchain while avoiding over-reliance on large amounts of data stored on-chain. Additionally, the dual smart contract design (the first smart contract manages the rule version, and the second smart contract records the data processing results) achieves separation of responsibilities, maintaining the flexibility of dynamically adjustable rules while ensuring the reliability of data processing. When the market environment changes, the system can quickly adjust the rules; when a dispute arises, it can conduct objective reviews based on historical rules, achieving a balance between flexibility and reliability.

[0068] In one or more embodiments of this disclosure, such as Figure 2 This is a flowchart illustrating the target processing rule generation method provided in this embodiment of the disclosure. Figure 2 As shown, in response to a data processing rule adjustment request, the first intelligent agent outputs the target processing rule, including: Step 201: Parsing the data processing rule adjustment request to obtain historical data records and current market data. Step 202: Inputting the historical data records and current market data into the first intelligent agent for analysis, determining the rule adjustment parameters, and generating the target processing rule using the rule adjustment parameters.

[0069] It should be noted that the current market data mentioned here can be data that reflects the current market situation, including but not limited to key indicators such as the rate of change in average city prices, changes in transaction volume, and inventory days. It can also be data related to a user's workload completion status.

[0070] When the system receives a data processing rule adjustment request, it first parses the request content. This data processing rule adjustment request can originate from two sources: one is initiated by the user, and the other is triggered and generated by monitoring the market environment and detecting changes in market data exceeding a threshold.

[0071] The system parses data processing rule adjustment requests, extracts key information, and retrieves relevant historical data records and current market data. Specifically, it obtains historical data records related to the request from the internal database, including historical points records and historical performance data; simultaneously, it obtains current market data from the market data interface, including key indicators such as the city average price change rate, transaction volume changes, and inventory days.

[0072] The first agent mentioned here can be based on Large Language Model (LLM) technology to perform deep analysis of input data and determine the optimal rule adjustment parameters. For example, the first agent performs the following analysis process: assessing the comparison between the current market situation and historical data to identify market trends; analyzing the performance differences of different business types to determine the business areas that need to be prioritized for incentives; and calculating the optimized values ​​of each rule parameter based on the company's strategic goals. The input received by the first agent includes: the original text of the user query, the agent role definition, the session context or relevant background information, and the mandatory JSON format response.

[0073] Furthermore, complete target processing rules can be generated using rule adjustment parameters. These rules not only include the rule adjustment parameters but can also include complete information such as rule identifier, rule proposer address, effective date, expiration date, and rule description. To facilitate data processing, the rule adjustment parameters can be encapsulated into structured rule objects, with necessary metadata added, and output conforming to a predefined JSON format can be generated. This output includes an analysis report, proposed rule changes, and executable instructions.

[0074] For example, the adjustment scheme for the points rule parameters in the target processing rules may include a credit score threshold, as well as the corresponding effective date and expiration date.

[0075] Based on the publicly available solutions described above, by using a first intelligent agent to analyze market data and historical records in real time and dynamically optimize rule parameters, incentive strategies can be synchronized with market dynamics in real time, thereby enabling data processing rules to better adapt to the current market environment. Furthermore, the first intelligent agent can accurately interpret intent and generate structured rule adjustment schemes, significantly reducing the operational threshold for rule adjustments.

[0076] In one or more embodiments of this disclosure, such as Figure 3 This is a flowchart illustrating a blockchain data processing method provided in an embodiment of this disclosure. Figure 3As shown, the target processing rule is deployed to the blockchain network through a first smart contract to obtain a rule version hash, including: Step 301: Digitally sign the target processing rule to generate a rule update transaction. Step 302: Broadcast the rule update transaction to the blockchain network. Step 303: After the rule update transaction is confirmed by the blockchain network and added to a block, obtain the rule version hash generated by the blockchain network. Furthermore, all historical rules under the same rule identifier with an effective timestamp earlier than the effective timestamp of the target processing rule can be marked as inactive, while the target processing rule can be marked as the currently active rule.

[0077] It should be noted that the first smart contract mentioned here is a blockchain contract used to manage rule versions. It is responsible for storing the rule identifier, rule parameters, rule proposer address, effective timestamp, expiration timestamp, and rule description information of each version of the rule (including the target processing rule), and maintaining the rule version history.

[0078] In practical applications, the structured data object of the target processing rules is encrypted using the private key of a pre-configured account to generate a unique digital signature.

[0079] After generating the digital signature, the system encapsulates the target processing rule, the digital signature, and transaction metadata (such as the transaction counter nonce, gas price, etc.) into a rule update transaction. This rule update transaction specifies the address of the first smart contract to be called, the rule update (updateRule) method, and the parameters required for the method call.

[0080] Transaction updates are broadcast to the blockchain. Nodes in the blockchain network that have won the right to record transactions receive the transaction and verify its signature validity, nonce order, and gas fees. Verified transactions are placed in a transaction pool to await packaging. In a permissioned blockchain environment, transactions are sent to predefined verification nodes; in a public blockchain environment, transactions are broadcast to the entire P2P network.

[0081] After a blockchain network packages a transaction into a block and reaches consensus, it generates a unique hash value (i.e., a rule version hash) for that transaction. This hash value is calculated from the transaction content using a cryptographic hash function and is unique and immutable. The rule version hash can be obtained by monitoring transaction confirmation events on the blockchain network or by actively querying the transaction status. Once obtained, the system stores the rule version hash in a local database for subsequent data processing and association.

[0082] It's important to note that the rule version hash is not pre-generated or manually specified. Instead, it's a unique transaction identifier automatically generated by the blockchain network after the rule update transaction is confirmed and added to a block. This leverages the inherent characteristics of the blockchain to ensure the uniqueness and immutability of the rule version, laying the foundation for building a complete chain of evidence.

[0083] After successfully deploying the target processing rule to the blockchain, all historical rules with an effective timestamp earlier than the target processing rule can be marked as inactive, while the target processing rule is marked as the currently active rule. This ensures the atomicity and consistency of rule state changes. Historical rules marked as inactive remain on the blockchain but are no longer used for new data processing; they are only used for historical tracing.

[0084] Based on the publicly available solutions described above, by using the first smart contract to manage rule versions, the rules themselves are stored on the blockchain as mutable but traceable objects. Each rule has a unique rule version hash, forming a complete history of rule evolution, thereby effectively solving the problem of inaccurate correspondence between data processing results and data processing rule versions.

[0085] In one or more embodiments of this disclosure, such as Figure 4 This is a schematic flowchart illustrating the data processing record generation method provided in an embodiment of this disclosure. Figure 4 As shown, target data is processed using target processing rules to generate a data processing record, including: Step 401: Query the first smart contract to obtain the target processing rule matching the current time and the corresponding rule version hash. Step 402: Extract the corresponding rule parameters from the target processing rule based on the business type of the target data. Step 403: Calculate the data processing result based on the rule parameters and the target data. Step 404: Generate a data processing record, which includes a data processing timestamp, data processing result, business identifier, original voucher hash, and rule version hash.

[0086] It should be noted that the original credential hash refers to the hash value of the original business credential stored in a distributed file system (such as IPFS), which is used to verify the integrity and authenticity of the original credential.

[0087] In practical applications, the first smart contract is queried to obtain the target processing rule that matches the current time. Specifically, the query interface of the first smart contract is called, passing in the current timestamp to retrieve the rule version valid at that time. The first smart contract, based on the passed timestamp, queries all historical rules under the same rule identifier, filters out rules that meet the condition "effective timestamp ≤ current timestamp < expiration timestamp," and returns the rule and its version hash. It's important to note that what needs to be obtained is the target processing rule that precisely matches the current time, not the latest rule. This is because rules may be adjusted multiple times within a day, and it's crucial to ensure that the rule version that perfectly matches the data processing time is used.

[0088] Furthermore, identify the business type to which the target data belongs (e.g., secondhand sales, new home sales), and then search for rule parameters that match that business type from the target processing rules. Target processing rules can contain rule parameters for multiple business types, and it is necessary to accurately identify and extract parameters that match the current business type.

[0089] Based on the rule parameters and target data, the data processing results are calculated. The specific calculation method varies depending on the business type and rule parameters.

[0090] After calculating the data processing result, a complete data processing record is generated. This record includes not only the calculation result but also contextual information for tracing and verification. The rule version hash, as a key field of the data processing record, is the core of building a complete chain of evidence. Through this field, the system can accurately trace back to the rule version on which the data processing result was based during subsequent review.

[0091] Based on the publicly available solutions described above, by explicitly including rule version hashes in the data processing records, a precise correlation between rules and calculation results is achieved. In the event of a dispute, the system can accurately retrieve the rule version actually applied at the time of the dispute based on the rule version hash in the data processing records, thus verifying the reasonableness of the calculation results. Furthermore, a traceable chain of original vouchers is constructed. When it is necessary to verify the compliance of the data processing process, original business vouchers (such as transaction vouchers) can be obtained through the original voucher hash for multimodal analysis.

[0092] In one or more embodiments of this disclosure, such as Figure 5 This is a flowchart illustrating another blockchain processing method provided in an embodiment of this disclosure. Figure 5As shown, the data processing record and rule version hash are deployed to the blockchain network through a second smart contract, and a processing record hash is generated, including: Step 501: Encapsulating the data processing record into a structured record object, which includes the data processing subject identifier, data processing type, data processing result, timestamp, business identifier, rule version hash, and the processing record hash and original voucher hash of the previous data processing record. Step 502: Digitally signing the data processing record to generate a data processing record transaction. Step 503: Broadcasting the data processing record transaction to the blockchain network. After the data processing record transaction is confirmed by the blockchain network and added to a block, the processing record hash generated by the blockchain network is obtained. Step 504: Updating the data processing record index in the second smart contract based on the processing record hash to establish a mapping relationship between the data processing record and the processing record hash.

[0093] It should be noted that the second smart contract mentioned here is a blockchain contract used to record data processing results. It is responsible for storing data processing records, establishing a data processing record index, and maintaining the chain relationship between data processing records. The processing record hash mentioned here refers to a unique transaction identifier automatically generated by the blockchain network after confirming the data processing record transaction. It is used to identify a specific data processing record and is also the key basis for querying data processing records during subsequent review.

[0094] In practical applications, the generated data processing records are encapsulated as structured record objects. These structured record objects are organized according to a predefined data structure, ensuring that all necessary fields are complete and formatted uniformly. Specifically, the data processing subject identifier identifies the subject requesting the data processing, such as a broker ID. The data processing type identifies the category of data processing, such as performance points, workload, or credit score. The timestamp indicates the time when the data processing occurred. The business identifier is the associated business ID, such as an order number.

[0095] Using the private key of the account corresponding to the data processing entity, the structured record object is encrypted to generate a unique digital signature. After generating the digital signature, the structured record object, the digital signature, and transaction metadata are encapsulated into a data processing record transaction. This transaction specifies the address of the second smart contract to be invoked, the recordTransaction method, and the parameters required for the method call.

[0096] It's important to note that digital signatures verify the identity of the transaction initiator and the integrity of the transaction data. When verifying a transaction, nodes in the blockchain network use the corresponding public key to verify the validity of the signature, ensuring that the transaction has not been tampered with and originates from a legitimate entity.

[0097] Furthermore, libraries such as Web3.js or Ethers.js can be used to connect to blockchain node providers, sending data processing records and transactions to multiple nodes in the network. Upon receiving a transaction, the nodes in the blockchain network verify its signature validity, nonce order, and gas fees. Transactions that pass verification are placed in a transaction pool to await packaging. Once a transaction is selected by a node and packaged into a block, and the block receives sufficient confirmations, the transaction is considered confirmed.

[0098] It's important to note that the processing record hash is not pre-generated or manually specified. Instead, it's a unique transaction identifier automatically generated by the blockchain network after the data processing record transaction is confirmed and added to a block. This ensures that each data processing record has a unique and tamper-proof identifier.

[0099] Furthermore, the internal methods of the second smart contract are invoked to establish a mapping relationship between the data processing subject identifier and the processing record hash, supporting fast queries based on the subject identifier.

[0100] The data structure maintained by the second smart contract may include: Subject-Record Mapping: `mapping(address => bytes32[]) public subjectRecords`, storing the hashes of all records for each subject. Record Details: `mapping(bytes32 => Record) public records`, storing detailed information for each record. This data structure allows for efficient querying of all data processing records for a specific subject, or obtaining record details by processing record hashes.

[0101] Based on the publicly available solutions described above, a chain-like traceability structure for data processing records is constructed. Each record points to the previous record, forming an immutable record chain, which greatly enhances the reliability and traceability of the data. It not only verifies the correctness of calculations but also the correctness of rule application, solving the problem that traditional systems cannot verify rule application.

[0102] In one or more embodiments of this disclosure, such as Figure 6 This is a flowchart illustrating the review process provided in an embodiment of this disclosure. Figure 6As shown, in response to a data review request, based on the processing record hash contained in the data review request, the data processing record and target processing rule corresponding to the processing record hash are obtained from the blockchain network, including: Step 601: Using a first smart agent to parse the data review request and extract the processing record hash. Step 602: Calling the query interface of the second smart contract to obtain the corresponding data processing record based on the processing record hash. Step 603: Extracting the rule version hash from the data processing record. Step 604: Calling the query interface of the first smart contract to obtain the target processing rule based on the rule version hash. Step 605: Verifying whether the target processing rule is in a valid state at the time point corresponding to the data processing record. A valid state includes the target processing rule's effective timestamp being earlier than or equal to the data processing record's timestamp and its expiration timestamp being later than the data processing record's timestamp.

[0103] It should be noted that a data review request refers to a user's request to review the data processing results if they disagree with them, such as a natural language description like "Please review the workload".

[0104] In practical applications, when the system receives a data review request, it first uses a first agent to parse the request. Specifically, the natural language description of the data review request is input into the first agent, which analyzes the semantic features of the natural language description according to preset intent recognition rules and outputs a structured parsing result. This structured parsing result includes a review intent identifier, parsing confidence level, processing record hash, and a description of the reason for the dispute. The parsing confidence level is verified to be greater than a preset threshold (e.g., 0.8). If it is, the validity of the data review request is confirmed, and the processing record hash is extracted as a key basis for subsequent queries. It should be noted that this processing record hash can accurately locate the specific data processing record in dispute, avoiding the difficulty in locating the record due to unclear descriptions in traditional reviews, or the inability to accurately find the data processing record.

[0105] When searching for the corresponding data processing record using the processing record hash, a query request is sent to the second smart contract, passing in the processing record hash. The second smart contract returns the corresponding data processing record based on its internal storage structure. The second smart contract uses the `mapping(bytes32 => Record) public records` data structure to quickly locate and return the data processing record corresponding to a specified hash value. This record contains complete information such as the data processing subject identifier, data processing type, data processing result, timestamp, business identifier, rule version hash, the processing record hash of the previous data processing record, and the original voucher hash.

[0106] After obtaining the data processing records, their structure is further analyzed to extract the rule version hash field. This rule version hash is the key link between the data processing records and the corresponding data processing rules. Through this hash value, the rule version on which the data processing result was generated can be precisely traced. This is the core step in building a complete chain of evidence, thereby verifying whether the correct rules were used during the data computation process.

[0107] When searching for data processing rules, a query request is sent to the first smart contract, passing in the rule version hash. The first smart contract returns the corresponding rule details based on its internal storage structure. The first smart contract uses the `mapping(bytes32 => Rule) public ruleByHash` data structure to quickly locate and return the rule details corresponding to a specified hash value.

[0108] The system compares the effective and ineffective timestamps of the target processing rule with the timestamp of the data processing record: if the effective timestamp of the target processing rule is less than or equal to the timestamp of the data processing record but less than the ineffective timestamp, then the target processing rule is valid; otherwise, it is invalid. Simultaneously, it verifies whether the target processing rule is marked as active. When a new rule is added to the blockchain, all historical rules under the same rule identifier with effective timestamps earlier than the new rule's effective timestamp are marked as inactive, and the new rule is marked as the currently active rule. Therefore, within the valid timeframe, only the latest rule should be marked as active.

[0109] If the target processing rule is valid and marked as active at the time corresponding to the data processing record, then the rule is confirmed to be the correct rule for generating the data processing record; otherwise, there may be an error in rule application.

[0110] Based on the aforementioned publicly available scheme, data processing records are automatically obtained through record hashing, and target processing rules are automatically obtained through rule version hashing, thus constructing a complete chain of evidence from dispute to calculation to rule. By verifying the validity of the target processing rule at the corresponding time point in the data processing record, the correctness of rule application can be accurately determined.

[0111] In one or more embodiments of this disclosure, such as Figure 7This is a flowchart illustrating a method for extracting processing record hashes during the review process provided in this embodiment of the disclosure. The method involves using a first intelligent agent to parse the data review request and extract the processing record hash, including: Step 701: Inputting a natural language description of the data review request into the first intelligent agent. Step 702: The first intelligent agent analyzes the semantic features of the natural language description according to preset intent recognition rules. Step 703: Obtaining the structured parsing result output by the first intelligent agent, the structured parsing result including a review intent identifier, parsing confidence level, processing record hash, and a description of the reason for the dispute.

[0112] In practical applications, the system receives text or speech-to-text review requests submitted by users through the client interface, such as "My workload was short-recorded by 50 items on 2024-03-15". The system passes the complete natural language description as input to the first agent, including all contextual information. The first agent, acting as the intent recognition module, has the core function of understanding user intent and extracting key information to provide a foundation for subsequent processing. It should be noted that the input to the first agent is the raw natural language description, without preprocessing or simplification, ensuring a complete understanding of the user's intent.

[0113] The specific analysis process of the first intelligent agent is as follows: First, basic processing such as word segmentation and stop word removal is performed on the input text. Key entities in the text are identified, such as time (2024-07-15), business type (e.g., second-hand sales), and numerical values ​​(e.g., underestimation). Further, the intent of the user request is determined (rule adjustment, dispute appeal, or consultation / answer). Key information such as processing record hash and dispute reason is extracted from the text. Pre-defined intent recognition rules define the behavior of the first intelligent agent. For example, this rule can be preset (e.g., set via a prompt word) such as "You are a workload system customer service assistant. Your task is to accurately identify user requests for workload review and extract key information." These rules ensure that the behavior of the first intelligent agent conforms to business needs and avoids excessive divergence. It should be noted that the analysis process of the first intelligent agent follows strict input / output specifications, mandating that the first intelligent agent return results in a specified JSON format.

[0114] Furthermore, the first agent returns results in a predefined JSON format. These results include: a review intent identifier (a code identifying the type of review request, such as "performance_dispute" indicating a workload review request), a parsing confidence score (a numerical value representing the reliability of the parsing result, ranging from 0.0 to 1.0), a hash of the processed record, and a description of the reason for the dispute. It should be noted that when the parsing confidence score is greater than a preset threshold (e.g., 0.8), the validity of the data review request is confirmed, and the structured parsing result is used as input for the review process. If the parsing confidence score is lower than the threshold, the user is asked to clarify or provide more information.

[0115] Based on the publicly available solutions described above, the deep integration of the large language model (i.e., the first intelligent agent) with the review request parsing enables automatic conversion from natural language to precise queries. Through the first intelligent agent's deep understanding of the natural language description, key information can be automatically extracted, and a precise representation of the review request can be constructed, effectively improving review processing efficiency.

[0116] In one or more embodiments of this disclosure, such as Figure 8 This is a flowchart illustrating the verification result output method provided in an embodiment of this disclosure. Figure 8 As shown, the data processing records and target processing rules are input into the second intelligent agent, and the second intelligent agent outputs the verification result, including: Step 801: Input the data processing records and target processing rules into the second intelligent agent. Step 802: Verify whether the data processing records meet the rule requirements using the target processing rules; and determine whether the data processing process is compliant using the original voucher content corresponding to the original voucher hash in the data processing records. Step 803: Generate the verification result based on the rule verification result and the compliance judgment result.

[0117] In practical applications, data processing records and target processing rules are input into a second intelligent agent for in-depth analysis. Specifically, complete data processing records (including data processing timestamps, data processing results, business identifiers, original voucher hashes, and rule version hashes) and target processing rules are passed as input to the second intelligent agent. This second intelligent agent undertakes the core task of review and analysis. It receives structured data as input and outputs structured review results, ensuring the automation and operability of the entire process.

[0118] Furthermore, upon receiving input, the second intelligent agent performs dual verification analysis: rule verification and compliance judgment. Rule verification involves the second intelligent agent recalculating the data processing result based on the rule parameters in the target processing rule and the original business data, and comparing the recalculated result with the data processing result in the data processing record. If they match, the rule verification result is considered successful; otherwise, it is considered unsuccessful.

[0119] Compliance assessment refers to the process where a second intelligent agent retrieves the original credential content from a distributed file system such as IPFS using the original credential hash. Multimodal analysis is then performed on the original credential content, including text semantic understanding, image content recognition, and cross-modal correlation analysis. Based on the analysis results, the agent determines whether the data processing procedure complies with business specifications and company policies. If compliant, the compliance assessment result is deemed compliant; otherwise, it is deemed non-compliant. It should be noted that the second intelligent agent's analysis process adheres to strict input-output specifications to ensure the certainty and operability of the results. The second intelligent agent not only verifies the correctness of the calculations but also verifies the authenticity and compliance of the business operations, implementing a dual verification mechanism.

[0120] Based on the publicly available scheme, the second intelligent agent undertakes the core task of review and analysis, and ensures the accuracy and fairness of the review results through a dual verification mechanism (rule verification + compliance judgment).

[0121] In one or more embodiments of this disclosure, such as Figure 9 This is a flowchart illustrating the method for generating review results provided in this embodiment of the disclosure. Figure 9 As shown, based on the rule verification result and compliance judgment result, a review result is generated, including: Step 901: If the rule verification result is verification failure, and / or the compliance judgment result is non-compliant, a data correction instruction is generated; and the data correction result is deployed to the blockchain network. Step 902: If the rule verification result is verification success, and the compliance judgment result is compliant, the review result is sent to the user's client.

[0122] In practical applications, after completing rule verification and compliance judgment, the second intelligent agent generates structured review results. Specifically, it outputs a review analysis report, which details the verification and judgment process, including recalculation steps and key points of original document analysis. Based on the combination of rule verification and compliance judgment results, the final ruling type is determined; if adjustments are needed, specific adjustment suggestions are provided, including the adjustment type and adjustment value. The reasons and basis for the ruling are explained in detail to ensure transparency and ease of understanding.

[0123] When rule verification fails, compliance judgment results in non-compliance, or both, it indicates an error exists and must be corrected promptly. Specifically, a data correction instruction will be generated, including the specific values ​​to be adjusted and the reasons for the adjustment. The points correction interface of the second smart contract will be called to deploy the data correction result to the blockchain network. The correction transaction hash generated by the blockchain network will be obtained as a unique identifier for the correction operation. The correction operation will be recorded in the data processing log to maintain the integrity and traceability of the records. It should be noted that the data correction operation itself is also recorded on the blockchain, forming a complete operation history.

[0124] When the rule verification result is "verification passed" and the compliance judgment result is "compliant," the current data processing record is confirmed to be correct and error-free. A final review result is generated, including a statement confirming the validity of the current record. The review result is sent to the user's client in a user-friendly format. The review analysis report and the ruling result are displayed on the client to ensure that the user understands the basis for the review. It should be noted that even if the review result is to maintain the status quo, a detailed analysis report will be provided to explain why the current data processing record is correct and compliant. This increases transparency and reduces secondary disputes caused by user misunderstanding.

[0125] Based on the publicly available solutions described above, this approach can accurately identify situations requiring correction and execute corresponding actions, avoiding over-correction or under-correction. When data correction is needed, this method not only generates correction instructions but also deploys the correction results to the blockchain network, constructing a complete data correction closed loop. The correction operation itself is also recorded on the blockchain, ensuring the transparency and traceability of all operations.

[0126] In one or more embodiments of this disclosure, before outputting the target processing rule using the first intelligent agent in response to the data processing rule adjustment request, the method further includes: monitoring market data, and generating a data processing rule adjustment request when the monitored market data change exceeds a change threshold; or, receiving rule adjustment instruction information and generating a data processing rule adjustment request.

[0127] In practical applications, the data acquisition module periodically (e.g., daily) retrieves market data from internal databases and external data sources. This includes data such as the city's average price change rate (the percentage change in the current city's average real estate price relative to the previous period), transaction volume changes, housing inventory days, and the market supply-demand ratio. The acquired market data is then compared and analyzed with historical data to calculate the change rate of each indicator and compare it with preset change thresholds. If a market data change exceeds the preset threshold, a data processing rule adjustment request is automatically generated. It's important to note that the change threshold is set based on historical data analysis and business experience, ensuring that only significant market changes trigger rule adjustments. This avoids frequent rule adjustments due to short-term market fluctuations, maintaining rule stability. Real-time awareness of market environment changes provides a data foundation for dynamic rule adjustments.

[0128] In addition to automatic monitoring and triggering, the system also supports receiving manually submitted rule adjustment instructions. A formal rule adjustment request will only be generated after the manually submitted rule adjustment instructions have been verified by the system.

[0129] Based on the publicly available solutions, it is clear that deeply integrating market data monitoring with rule adjustment processes enables a shift from passive response to proactive adaptation. This allows for the identification of significant market changes, automatically triggering rule adjustment processes and ensuring that incentive strategies remain synchronized with market dynamics.

[0130] In one or more embodiments of this disclosure, the method further includes: responding to a query request for target data, using a first intelligent agent to parse it into an executable structured query instruction; wherein the structured query instruction includes: a query method name and query parameters; based on the structured query instruction, obtaining the corresponding data processing record from the blockchain network; converting the obtained data processing record into a visualized data report, the visualized data report including a detailed list of data processing results, trend charts, and computational logic traceability information; and returning the visualized data report to the client in natural language form, the natural language form including an explanatory description of the data processing results and a highlighting of key indicators.

[0131] In practical applications, when a user submits a query request for target data, the first intelligent agent first parses the request. Specifically, the system inputs the natural language description of the query request into the first intelligent agent. The first intelligent agent analyzes the semantic features of the natural language description according to preset intent recognition rules and outputs a structured query instruction. This structured query instruction includes a query method name and query parameters. The query method name specifies the query function to be called, such as "getAgentScoresByQuarter," which means to obtain broker points by quarter. The query parameters provide the specific values ​​required for function execution, such as broker ID and time. It should be noted that the first intelligent agent achieves automatic conversion from natural language query to blockchain data query through a function call mechanism. The first intelligent agent can identify key entities in the user query (such as time range, business type, broker ID, etc.) and map them to the corresponding query method and parameters. This ensures that the query process requires no manual intervention, and all query operations are logged on the blockchain.

[0132] The corresponding query interface is invoked, and query parameters are passed in to retrieve data processing records that meet the conditions from the blockchain network. For example, through the query interface of the second smart contract, all data processing records of a broker during the specified period can be obtained based on parameters such as broker ID and time. These records include various point types, such as basic points.

[0133] After acquiring the data processing records, they are converted into visual data reports. For example, the data processing records are organized into tables in chronological order, displaying detailed information for each point, including time, business type, point change, rule version, etc. Trend charts, such as quarterly point trend charts and pie charts showing the percentage of various points, can also be generated based on the data processing records to intuitively display point changes. Furthermore, the calculation logic for each point is traceable, including the hash of the rule version used, rule parameters, and links to the original vouchers, supporting click-based tracing. After generating the visual data report, the system converts it into natural language and returns it to the client.

[0134] Based on the publicly available solutions, the first intelligent agent achieves automatic conversion from natural language queries to blockchain data queries through a function call mechanism, ensuring that the query process requires no manual intervention and that all query operations are recorded on the blockchain.

[0135] In one or more embodiments of this disclosure, before generating the data processing record, the method further includes: uploading the original voucher to a distributed storage system and obtaining the original voucher hash corresponding to the original voucher, so as to include the original voucher hash as a key field in the data processing record; wherein the original voucher includes at least one of an authorization contract, an interaction log, and a transaction voucher.

[0136] In practical applications, after a business operation (such as completing a real estate transaction) occurs, the relevant original documents need to be uploaded to a distributed storage system. These original documents include, but are not limited to, interaction logs (e.g., email correspondence) and transaction vouchers. These original documents are uploaded to the distributed storage system (such as IPFS) via an API interface. After the original documents are successfully uploaded to the distributed storage system, the system obtains the original document hash corresponding to that original document. Specifically, the distributed storage system (such as IPFS) generates a unique CID (Content Identifier) ​​for the uploaded file; this CID is essentially the cryptographic hash value of the original document. The CID is extracted from the response of the distributed storage system and stored locally as the original document hash.

[0137] Furthermore, after obtaining the original voucher hash, it is included as a key field in the subsequently generated data processing records.

[0138] Based on the publicly available solutions mentioned above, storing large amounts of original credentials through a distributed storage system and only uploading the hash values ​​to the blockchain satisfies the storage requirements of a complete evidence chain without excessively occupying the blockchain's storage space.

[0139] To facilitate understanding, the data processing system will be described in detail below with reference to specific embodiments. For example... Figure 10 This is a schematic diagram illustrating the structure of a data processing system as exemplified in this disclosure. For example... Figure 10 As shown, the system includes: a user layer, a large model-driven intelligent agent cluster layer, a blockchain interaction module, and a data and storage layer.

[0140] Among them, there are multiple agents in the large model-driven agent cluster layer, namely Agent1 for intent recognition, Agent2 for dynamic rule engine, Agent3 for review assistance module, Agent4 for dialogue management module, and Agent5 for blockchain interaction module.

[0141] The three different data processing procedures are explained through specific examples.

[0142] The first scenario involves adjusting data processing rules. For example, an administrator might issue an instruction to "increase the discount rate for various services during holidays."

[0143] Agent1 (equivalent to the first intelligent agent mentioned above) recognizes the rule_adjustment intent and routes the task to Agent2.

[0144] Agent2 reads the latest market data from its internal database, and after analysis by the LLM, generates a structured rule change proposal (proposed_rule_change).

[0145] The proposal was sent to Agent5. Agent5 constructed a transaction, calling the updateRule method of the ScoreRuleRegistry smart contract on the blockchain to permanently record the new rule on the chain.

[0146] The second scenario is when there is a dispute that requires review. For example, a user submits a review request saying, "My workload needs to be reviewed."

[0147] Agent1 recognizes the dispute_arbitration intent and routes the task to Agent3.

[0148] Agent3 (equivalent to the second intelligent agent mentioned above) first retrieves the relevant workload transaction records and the IPFS hash of the original certificate from the blockchain through Agent5.

[0149] Based on this hash, Agent3 retrieves the actual evidence files (such as interaction logs) from the IPFS distributed storage network.

[0150] LLM analyzes multimodal evidence and generates an arbitration result.

[0151] Agent5 calls the executeArbitration method of ArbitrationContract on the blockchain to execute a ruling (such as correcting workload).

[0152] The third scenario is when information needs to be retrieved. For example, a broker might ask, "What was my total workload for Q2?"

[0153] Agent1 identifies the query intent and routes the task to Agent4.

[0154] Agent4's LLM parses natural language into structured queries (function_call), specifying the function to be called and the parameters (e.g., getAgentScoresByQuarter(agent_id, quarter)).

[0155] Agent5 executes the query function to read the broker's workload record from the blockchain.

[0156] The query results are returned to Agent4, and LLM integrates the data into natural language and visual chart suggestions, which are then returned to the user.

[0157] It should be noted that each artificial intelligence model in the above embodiments is trained or fine-tuned as needed before use, so that it can achieve the functions and effects required by the embodiments.

[0158] Based on any of the above embodiments, this disclosure also provides a data processing apparatus. Figure 11 This is a schematic block diagram of a data processing apparatus according to one embodiment of the present disclosure. Figure 11 As shown, the data processing device includes: an output module 111, used to output a target processing rule using a first smart agent in response to a data processing rule adjustment request; an on-chain module 112, used to deploy the target processing rule to a blockchain network via a first smart contract to obtain a rule version hash; a generation module 113, used to process target data using the target processing rule to generate a data processing record; an on-chain module 112, used to deploy the data processing record and rule version hash to a blockchain network via a second smart contract and generate a processing record hash; a verification module 114, used to retrieve the data processing record and target processing rule corresponding to the processing record hash from the blockchain network based on the processing record hash included in the data verification request in response to a data verification request; an output module 111, used to input the data processing record and target processing rule into a second smart agent and output the verification result using the second smart agent; and a sending module 115, used to send the verification result to a client.

[0159] Output module 111 is used to parse the data processing rule adjustment request to obtain historical data records and current market data; input the historical data records and current market data into the first intelligent agent for analysis to determine the rule adjustment parameters; and generate the target processing rule using the rule adjustment parameters.

[0160] The on-chain module 112 is used to digitally sign the target processing rules and generate rule update transactions; broadcast the rule update transactions to the blockchain network; and obtain the rule version hash generated by the blockchain network after the rule update transactions are confirmed by the blockchain network and added to the block.

[0161] The generation module 113 is used to query the first smart contract to obtain the target processing rule and the corresponding rule version hash that matches the current time; extract the corresponding rule parameters from the target processing rule based on the business type of the target data; calculate the data processing result according to the rule parameters and the target data; and generate a data processing record, which includes a data processing timestamp, data processing result, business identifier, original voucher hash and rule version hash.

[0162] The on-chain module 112 is used to encapsulate data processing records into structured record objects. The structured record objects include data processing subject identifier, data processing type, data processing result, timestamp, business identifier, rule version hash, and the processing record hash and original voucher hash of the previous data processing record. It digitally signs the data processing records to generate data processing record transactions. It broadcasts the data processing record transactions to the blockchain network. After the data processing record transactions are confirmed by the blockchain network and added to the block, it obtains the processing record hash generated by the blockchain network. It updates the data processing record index in the second smart contract based on the processing record hash and establishes a mapping relationship between data processing records and processing record hashes.

[0163] The review module 114 is used to parse the data review request using the first smart agent and extract the processing record hash; call the query interface of the second smart contract to obtain the corresponding data processing record based on the processing record hash; extract the rule version hash from the data processing record; call the query interface of the first smart contract to obtain the target processing rule based on the rule version hash; and verify whether the target processing rule is in a valid state at the time point corresponding to the data processing record. The valid state includes the target processing rule's effective timestamp being earlier than or equal to the data processing record's timestamp and its expiration timestamp being later than the data processing record's timestamp.

[0164] The review module 114 is used to input the natural language description of the data review request into the first intelligent agent; the first intelligent agent analyzes the semantic features of the natural language description according to the preset intent recognition rules; and obtains the structured parsing result output by the first intelligent agent, which includes the review intent identifier, parsing confidence, processing record hash and description of the reason for the dispute.

[0165] The output module 111 is used to input data processing records and target processing rules into the second intelligent agent; to verify whether the data processing records meet the rule requirements using the target processing rules; and to determine whether the data processing process is compliant using the original voucher content corresponding to the original voucher hash in the data processing records; and to generate a review result based on the rule verification result and the compliance judgment result.

[0166] Output module 111 is used to generate a data correction instruction if the rule verification result is verification failure and / or the compliance judgment result is non-compliant; and deploy the data correction result to the blockchain network; if the rule verification result is verification success and the compliance judgment result is compliance, the review result is sent to the user's client.

[0167] The generation module 113 is used to monitor market data. When the monitored market data changes exceed the change threshold, it generates a data processing rule adjustment request; or, upon receiving a rule adjustment instruction, it generates a data processing rule adjustment request.

[0168] Optionally, it also includes a query module 116, used to respond to a query request for target data by parsing it into an executable structured query instruction using a first intelligent agent; wherein the structured query instruction includes: query method name and query parameters; based on the structured query instruction, obtain the corresponding data processing record from the blockchain network; convert the obtained data processing record into a visualized data report, the visualized data report including a detailed list of data processing results, trend charts, and traceability information of the calculation logic; and return the visualized data report to the client in natural language form, the natural language form including an explanatory description of the data processing results and a highlighting of key indicators.

[0169] Optionally, it also includes a storage module 117 for uploading the original voucher to a distributed storage system and obtaining the original voucher hash corresponding to the original voucher, so as to include the original voucher hash as a key field in the data processing record.

[0170] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0171] The data processing method implemented in the specific embodiments of this disclosure can be executed by electronic devices such as servers (including local servers or cloud computing platforms).

[0172] Therefore, based on any of the above embodiments, this disclosure also provides an electronic device that can execute the data processing method of any of the embodiments described above.

[0173] Figure 12 This is a schematic block diagram of an electronic device according to one embodiment of the present disclosure.

[0174] The hardware architecture of the electronic device 1000 can be implemented using a bus architecture. The bus architecture can include any number of interconnect buses and bridges, depending on the specific application of the hardware and overall design constraints. Bus 1100 connects various circuits, including one or more processors 1200, memory 1300, and / or hardware modules. Bus 1100 can also connect various other circuits 1400, such as peripheral devices, voltage regulators, power management circuits, external antennas, etc.

[0175] Bus 1100 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one connection line is used in this diagram, but this does not imply that there is only one bus or only one type of bus.

[0176] This disclosure also provides a readable storage medium storing a computer program that, when executed by a processor, is used to implement the methods described above. A "readable storage medium" can be any means capable of containing, storing, communicating, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples of a readable storage medium include: an electrical connection with one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable read-only memory (CDROM), etc.

[0177] This disclosure also provides a computer program product, the methods of which can be implemented wholly or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented wholly or partially as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed, all or part of the processes or functions of this disclosure are performed.

[0178] Computer programs or instructions can be stored in a readable storage medium or transferred from one readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The readable storage medium can be any available medium capable of access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video optical disc; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or it can include both volatile and non-volatile types of storage media.

[0179] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0180] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0181] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0182] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0183] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., refer to specific features, structures, or characteristics described in connection with that embodiment / mode or example, which are included in at least one embodiment / mode or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0184] It is understood that before using the technical solutions disclosed in the embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained. Simultaneously, sensitive information should be de-identified (e.g., de-identification, anonymization, etc.). For example, in response to receiving a user's active request, a prompt message can be sent to the user to clearly inform them that the requested operation will require the acquisition and use of their personal information. This allows the user to choose whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media that perform the operations of this disclosure's technical solutions, based on the prompt message. As an optional but non-limiting implementation, the method of sending a prompt message to the user in response to receiving a user's active request can be, for example, a pop-up window, where the prompt message can be presented in text form. Furthermore, the pop-up window can also include a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0185] It is understood that the above notification and user authorization acquisition process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure. The data involved in the technical solution of this disclosure (including but not limited to the data itself, the acquisition or use of data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0186] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0187] Those skilled in the art should understand that the above embodiments are merely for illustrating the present disclosure and are not intended to limit the scope of the disclosure. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present disclosure.

Claims

1. A data processing method, characterized in that, The method includes: In response to a data processing rule adjustment request, the first intelligent agent outputs the target processing rule; The target processing rules are deployed to the blockchain network through a first smart contract to obtain the rule version hash; The target data is processed using the aforementioned target processing rules to generate a data processing record; The data processing record and the rule version hash are deployed to the blockchain network through a second smart contract, and a processing record hash is generated; In response to a data verification request, based on the processing record hash contained in the data verification request, the data processing record corresponding to the processing record hash and the target processing rule are obtained from the blockchain network; The data processing records and the target processing rules are input into the second intelligent agent, and the second intelligent agent outputs the verification results. The verification results are sent to the client.

2. The data processing method according to claim 1, characterized in that, The step of deploying the target processing rule to the blockchain network through the first smart contract to obtain the rule version hash includes: The target processing rule is digitally signed to generate a rule update transaction; Broadcast the rule update transaction to the blockchain network; Once the rule update transaction is confirmed by the blockchain network and added to a block, the rule version hash generated by the blockchain network is obtained.

3. The data processing method according to claim 2, characterized in that, The step of processing the target data using the target processing rules to generate data processing records includes: Query the first smart contract to obtain the target processing rule that matches the current time and the corresponding rule version hash; Based on the business type of the target data, extract the corresponding rule parameters from the target processing rules; The data processing result is calculated based on the rule parameters and the target data; Generate the data processing record containing the results of the data processing.

4. The data processing method of claim 1, wherein, The step of deploying the data processing record and the rule version hash to the blockchain network via a second smart contract and generating a processing record hash includes: The data processing record and the rule version hash are encapsulated into a structured record object, which includes a data processing subject identifier, a data processing type, a data processing result, a timestamp, a business identifier, the rule version hash, and the processing record hash and the original voucher hash of the previous data processing record. The data processing record is digitally signed to generate a data processing record transaction; The data processing record is broadcast to the blockchain network. After the data processing record transaction is confirmed by the blockchain network and added to a block, the processing record hash generated by the blockchain network is obtained. Update the data processing record index in the second smart contract based on the processing record hash, and establish a mapping relationship between the data processing record and the processing record hash.

5. The data processing method of claim 1, wherein, In response to a data verification request, based on the processing record hash included in the data verification request, the process retrieves the data processing record corresponding to the processing record hash and the target processing rule from the blockchain network, including: The first intelligent agent is used to parse the data verification request and extract the hash of the processing record. Call the query interface of the second smart contract to obtain the corresponding data processing record based on the processing record hash; Extract the rule version hash from the data processing record; Call the query interface of the first smart contract to obtain the target processing rule according to the rule version hash; Verify whether the target processing rule is in a valid state at the time point corresponding to the data processing record. The valid state includes the target processing rule having an effective timestamp earlier than or equal to the timestamp of the data processing record and an invalid timestamp later than the timestamp of the data processing record.

6. The data processing method according to claim 5, characterized in that, The step of using the first intelligent agent to parse the data verification request and extract the processing record hash includes: The natural language description of the data verification request is input into the first intelligent agent; The first intelligent agent analyzes the semantic features of the natural language description according to preset intent recognition rules; Obtain the structured parsing result output by the first intelligent agent. The structured parsing result includes the review intent identifier, parsing confidence, the processing record hash, and the description of the reason for the dispute.

7. The data processing method of claim 1, wherein, The step of inputting the data processing record and the target processing rule into the second intelligent agent, and using the second intelligent agent to output the verification result, includes: The data processing records and the target processing rules are input into the second intelligent agent; The target processing rules are used to verify whether the data processing record meets the rule requirements; and the original voucher content corresponding to the original voucher hash in the data processing record is used to determine whether the data processing process is compliant. The review result is generated based on the rule verification result and the compliance judgment result.

8. The data processing method according to claim 7, characterized in that, The review result is generated based on the rule verification result and the compliance judgment result, including: If the rule verification result is verification failure, and / or the compliance judgment result is non-compliance, then a data correction instruction is generated; and the data correction result is deployed to the blockchain network. If the rule verification result is "verification passed" and the compliance judgment result is "compliance judged", then the review result is sent to the user's client.

9. An electronic device, comprising: include: The memory stores execution instructions; as well as A processor that executes execution instructions stored in the memory, causing the processor to perform the method of any one of claims 1 to 8.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 8.

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