Block chain smart contract-based big data security reward and punishment automatic decision-making system

Abstract

The invention discloses a block chain smart contract-based big data security reward and punishment automatic decision-making system, and belongs to the technical field of block chains, the system comprises a data collection unit, an examination and approval unit and an evidence storage unit, reward and punishment record evidence storage is performed by using a block chain, and the examination and approval process is automatically executed through a smart contract. The smart contract is configured to be automatically triggered and executed when a preset condition is met. The reward and punishment approval process is automatically executed through the intelligent contract, manual intervention is not needed, and the approval speed and the system response capability are remarkably improved. And meanwhile, the deviation of manual judgment is reduced, and the consistency and objectivity of reward and punishment decisions are ensured. The block chain technology is adopted to store the reward and punishment records, it is ensured that the data cannot be tampered, and the security and integrity of the data are provided through encryption processing and a signature mechanism. Therefore, the tamper-resistant capability of the system is enhanced, and the transparency and credibility of reward and punishment management are also improved.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, and more specifically, to an automated decision-making system for big data security rewards and punishments based on blockchain smart contracts. Background Technology

[0002] With the continuous development of information technology, enterprises and institutions often need to handle large amounts of data and complex approval processes when making security management and reward / punishment decisions. However, traditional reward / punishment management systems usually rely on manual approval, which not only leads to low decision-making efficiency but may also introduce biases due to human intervention, thus affecting the fairness and consistency of decisions. In addition, the storage and management of reward / punishment records usually rely on centralized databases, which are susceptible to the risk of data tampering or loss. Therefore, ensuring data security and immutability is a major challenge in enterprise management.

[0003] Due to its decentralized and immutable characteristics, blockchain technology is gradually being applied across various industries, particularly offering unique advantages in data security, information traceability, and transparency. Through encryption technology and consensus mechanisms, blockchain can effectively solve the data security problems inherent in traditional storage methods. Furthermore, with the rise of smart contracts, enterprises can automatically execute pre-set decision-making rules without human intervention, providing a new technological means to improve management efficiency and reduce human intervention.

[0004] However, despite the excellent performance of blockchain technology in data storage and management, effectively integrating it with traditional enterprise management processes, particularly automating and intelligentizing reward and punishment decisions in dynamic environments, remains a pressing challenge. Most existing blockchain-based management systems are limited to data storage and transaction transparency, failing to fully utilize the automation and conditional triggering mechanisms of smart contracts to optimize decision-making processes. Therefore, designing a system capable of automatically executing reward and punishment decisions on the blockchain while ensuring data security, transparency, and efficiency has become a key research and development direction.

[0005] To address this, this invention proposes an automated decision-making system for big data security rewards and punishments based on blockchain smart contracts. By combining blockchain technology, smart contracts, and multidimensional data analysis, this invention aims to automate the execution of reward and punishment decisions, securely store and manage data, and ensure the fairness, transparency, and efficiency of the decisions. This technology not only effectively avoids misjudgments caused by human intervention but also dynamically adjusts management strategies based on multidimensional data, thereby improving the level of intelligence in security management. Summary of the Invention

[0006] 1. Technical problems to be solved

[0007] In view of the problems existing in the prior art, the purpose of this invention is to provide a big data security reward and punishment automated decision-making system based on blockchain smart contracts. It realizes the automated execution of reward and punishment decisions, secure data storage and management by combining blockchain technology, smart contracts and multi-dimensional data analysis.

[0008] 2. Technical Solution

[0009] To solve the above problems, the present invention adopts the following technical solution.

[0010] The big data security reward and punishment automated decision-making system based on blockchain smart contracts includes a data collection unit, an approval unit, and an evidence storage unit. It uses blockchain to record and store reward and punishment information, and automatically executes the approval process through smart contracts. The smart contracts are configured to be automatically triggered when preset conditions are met.

[0011] Based on the above characteristics, the evidence storage unit is configured with an intelligent hierarchical storage strategy based on data access frequency, timeliness requirements and security level, and manages the data lifecycle through a metadata-driven mechanism of timestamps and business tags.

[0012] In some embodiments, the smart contract includes a multi-level condition nesting module, which sequentially includes a security event type classification identifier, a quantitative score of event severity, and a score of the historical security behavior record of the employee involved, and performs logical operations in the form of a directed acyclic graph through an on-chain rule engine.

[0013] Based on the above characteristics, the smart contract and the process engine module are coupled through an event-driven interface, and the process instance ID, the current approval node status, and the operator information are used as the sharding routing index key when writing data.

[0014] In some embodiments, sensitive fields in the approval comments are encrypted using attribute-based encryption combined with a key rotation mechanism, and event messages are protected for integrity through a Merkle tree structure and a batch signature mechanism.

[0015] Based on the above characteristics, when executing the approval process, the smart contract calls the multi-dimensional reward and punishment quota pool data in the distributed cache to verify the quota, and suspends execution and triggers a quota warning event when the quota is insufficient.

[0016] In some embodiments, the execution results of smart contracts are synchronized to the employee digital profile system through a cross-chain relay mechanism, and the boundary nodes use lightweight state proof technology to verify the authenticity of reward and punishment records.

[0017] Based on the above characteristics, the evidence storage unit provides employee digital file writing services to the outside world through a standardized REST interface, and supports multi-dimensional aggregation queries and vectorized index acceleration based on business dimensions.

[0018] In some embodiments, the priority weight of employees in the safety training push strategy is dynamically adjusted based on the weighted fusion result of the points corresponding to the current reward or punishment and the historical points.

[0019] Based on the above characteristics, the smart contract is configured with a time window-based delayed execution mechanism and is connected to the time lock module and the appeal processing subsystem.

[0020] 3. Beneficial effects

[0021] Compared with the prior art, the advantages of this invention are:

[0022] 1) This invention automatically executes the reward and punishment approval process through smart contracts, eliminating the need for manual intervention and significantly improving approval speed and system responsiveness. Simultaneously, it reduces the bias of human judgment, ensuring the consistency and objectivity of reward and punishment decisions.

[0023] 2) Blockchain technology is used to store reward and punishment records, ensuring data immutability, and encryption and signature mechanisms provide data security and integrity. This not only enhances the system's resistance to tampering but also increases the transparency and credibility of reward and punishment management.

[0024] 3) By introducing a multi-level condition nesting module and combining multi-dimensional data such as employee historical behavior records and event severity, the system can execute reward and punishment decisions more accurately, avoiding misjudgments or unfairness that may be caused by overly simplistic approval rules.

[0025] 4) Based on the weighted fusion of reward and punishment points and historical points, the system can dynamically adjust the priority of employees in the safety training push strategy, ensuring the reasonable allocation of safety training resources, thereby improving the level of intelligence in safety management.

[0026] 5) Introducing a time-window-based delayed execution mechanism and an appeal handling subsystem into smart contracts allows for a buffer period before reward and punishment decisions are implemented, improving the system's fault tolerance and fairness, and ensuring that employees have an appeal channel when dissatisfied with decisions. Detailed Implementation

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0028] Example 1:

[0029] The big data security reward and punishment automated decision-making system based on blockchain smart contracts mainly consists of a data collection unit, an approval unit, and an evidence storage unit. The data collection unit includes an event entry module and a data encryption module. The event entry module receives reward and punishment event information input by security management personnel, and the data encryption module uses the AES-256 algorithm to encrypt the reward and punishment event information. The approval unit includes a smart contract execution module, which automatically executes the reward and punishment approval process according to preset approval rules. The evidence storage unit includes a blockchain node module, which writes the encrypted reward and punishment records to the Ethereum blockchain for evidence storage. The data collection unit is connected to the approval unit via gigabit Ethernet, and the approval unit is connected to the evidence storage unit via the internet.

[0030] Example 2

[0031] The big data security reward and punishment automated decision-making system based on blockchain smart contracts mainly consists of a data collection unit, an approval unit, and an evidence storage unit. The data collection unit includes an event entry module and a data encryption module. The event entry module receives reward and punishment event information input by security management personnel, and the data encryption module uses the AES-256 algorithm to encrypt the reward and punishment event information. The approval unit includes a smart contract execution module, which is configured to automatically trigger execution when preset conditions are met and automatically execute the reward and punishment approval process according to preset approval rules. The evidence storage unit includes a blockchain node module, which writes the encrypted reward and punishment records into the Ethereum blockchain for evidence storage. The data collection unit is connected to the approval unit via gigabit Ethernet, and the approval unit is connected to the evidence storage unit via the Internet. Compared with the original embodiment, this embodiment achieves complete automation of the reward and punishment approval process by configuring the smart contract to automatically trigger execution when preset conditions are met. Approval can be initiated without manual intervention, improving system response speed and execution efficiency, while enhancing the objectivity and consistency of reward and punishment management.

[0032] Example 3

[0033] The big data security reward and punishment automated decision-making system based on blockchain smart contracts mainly consists of a data collection unit, an approval unit, and an evidence storage unit. The data collection unit includes an event entry module and a data encryption module. The event entry module receives reward and punishment event information input by security management personnel, and the data encryption module uses the AES-256 algorithm to encrypt the reward and punishment event information. The approval unit includes a smart contract execution module, which automatically executes the reward and punishment approval process according to preset approval rules. The evidence storage unit includes a blockchain node module and a smart hierarchical storage module. The blockchain node module writes the encrypted reward and punishment records to the Ethereum blockchain for evidence storage. The smart hierarchical storage module is configured with a smart hierarchical storage strategy based on data access frequency, timeliness requirements, and security levels, and manages the data lifecycle through a metadata-driven mechanism using timestamps and business tags. The data collection unit is connected to the approval unit via gigabit Ethernet, and the approval unit is connected to the evidence storage unit via the Internet.

[0034] Compared with Example 1, this example adds an intelligent hierarchical storage module to the evidence storage unit and introduces an intelligent hierarchical storage strategy based on data access frequency, timeliness requirements and security level. Combined with a metadata-driven mechanism of timestamps and business tags, it realizes refined management of the lifecycle of reward and punishment record data, and improves the storage efficiency, access performance and security compliance of the system in long-term operation.

[0035] Example 4

[0036] The big data security reward and punishment automated decision-making system based on blockchain smart contracts mainly consists of a data collection unit, an approval unit, and an evidence storage unit. The data collection unit includes an event entry module and a data encryption module. The event entry module receives reward and punishment event information input by security management personnel, and the data encryption module uses the AES-256 algorithm to encrypt the reward and punishment event information. The approval unit includes a smart contract execution module. This module embeds a multi-level condition nesting module, which sequentially includes a security event type classification identifier, a quantitative score for event severity, and a score for the historical security behavior record of the involved employee. It performs logical operations using an on-chain rule engine in the form of a directed acyclic graph, automatically executing the reward and punishment approval process according to preset approval rules. The evidence storage unit includes a blockchain node module. This module writes the encrypted reward and punishment records to the Ethereum blockchain for evidence storage. The data collection unit is connected to the approval unit via gigabit Ethernet, and the approval unit is connected to the evidence storage unit via the internet.

[0037] Compared with the original embodiment 1, this embodiment introduces a multi-level condition nesting module and its included security event type classification identifier, event severity quantitative score and historical security behavior record score of the involved employee, and adopts a directed acyclic graph structure to perform logical operations in the on-chain rule engine, which significantly improves the refinement of the approval logic and the accuracy of decision-making, and effectively avoids misjudgment or unfairness caused by single rules or manual intervention.

[0038] Example 5

[0039] Example 2: A big data security reward and punishment automated decision-making system based on blockchain smart contracts mainly consists of a data collection unit, an approval unit, and an evidence storage unit. The data collection unit includes an event entry module and a data encryption module. The event entry module receives reward and punishment event information input by security management personnel, and the data encryption module uses the AES-256 algorithm to encrypt the reward and punishment event information. The approval unit includes a smart contract execution module and a process engine module. The smart contract execution module and the process engine module are coupled through an event-driven interface. The smart contract execution module automatically executes the reward and punishment approval process according to preset approval rules, and uses the process instance ID, the current approval node status, and the operator information as the sharding routing index key when writing data. The evidence storage unit includes a blockchain node module. The blockchain node module writes the encrypted reward and punishment records into the Ethereum blockchain for evidence storage. The data collection unit is connected to the approval unit via gigabit Ethernet, and the approval unit is connected to the evidence storage unit via the Internet.

[0040] Compared with Example 1, this example adds a process engine module to the approval unit and couples the smart contract execution module with the process engine module through an event-driven interface. At the same time, when writing data, the process instance ID, the current approval node status, and the operator information are introduced as sharding routing index keys. This achieves accurate mapping and efficient routing between the reward and punishment approval process status and the blockchain evidence data, improving the system's data writing performance and process traceability in high-concurrency scenarios.

[0041] Example 6

[0042] Example 2: A blockchain-based smart contract-based automated decision-making system for big data security rewards and punishments mainly consists of a data collection unit, an approval unit, and a notarization unit. The data collection unit includes an event entry module and a data encryption module. The event entry module receives reward and punishment event information input by security management personnel, and the data encryption module uses the AES-256 algorithm to encrypt the reward and punishment event information. The approval unit includes a smart contract execution module. The smart contract execution module automatically executes the reward and punishment approval process according to preset approval rules, and uses an attribute-based encryption mechanism to perform secondary encryption on sensitive fields when generating approval opinions. This attribute-based encryption mechanism, combined with a key rotation mechanism, updates the encryption key according to a preset time period. The notarization unit includes a blockchain node module. The blockchain node module organizes the encrypted reward and punishment records into a Merkle tree structure and uses a batch signature mechanism to digitally sign the Merkle tree root value to ensure the integrity of the event message. Then, the signed record is written to the Ethereum blockchain for notarization. The data collection unit is connected to the approval unit via gigabit Ethernet, and the approval unit is connected to the notarization unit via the Internet.

[0043] Compared with Example 1, this example improves the fine-grained access control and long-term security of sensitive data in the approval process by introducing attribute-based encryption and key rotation mechanisms for sensitive fields in the approval comments. At the same time, the Merkle tree structure and batch signature mechanism are used to protect the integrity of event messages, effectively preventing the reward and punishment records from being tampered with during transmission and evidence storage, and enhancing the non-repudiation and audit reliability of the system.

[0044] Example 7

[0045] The big data security reward and punishment automated decision-making system based on blockchain smart contracts mainly consists of a data collection unit, an approval unit, and an evidence storage unit. The data collection unit 1 includes an event entry module and a data encryption module. The event entry module receives reward and punishment event information input by security management personnel, and the data encryption module uses the AES-256 algorithm to encrypt the reward and punishment event information. The approval unit includes a smart contract execution module and a distributed caching module. The smart contract execution module automatically executes the reward and punishment approval process according to preset approval rules, and during execution, it calls the multi-dimensional reward and punishment quota pool data stored in the distributed caching module for quota verification. When the quota is insufficient, the smart contract execution module suspends the approval process and triggers a quota warning event. The evidence storage unit includes a blockchain node module. The blockchain node module writes the encrypted reward and punishment records to the Ethereum blockchain for evidence storage. The data collection unit is connected to the approval unit via gigabit Ethernet, and the approval unit is connected to the evidence storage unit via the Internet.

[0046] Compared with Example 1, this example introduces a distributed caching module and a quota verification and early warning mechanism to achieve dynamic control of reward and punishment resources, effectively prevent over-quota approval, and improve the system's compliance and the accuracy of resource scheduling.

[0047] Example 8

[0048] The big data security reward and punishment automated decision-making system based on blockchain smart contracts mainly consists of a data collection unit, an approval unit, and an evidence storage unit. The data collection unit includes an event entry module and a data encryption module. The event entry module receives reward and punishment event information input by security management personnel, and the data encryption module uses the AES-256 algorithm to encrypt the reward and punishment event information. The approval unit includes a smart contract execution module, which automatically executes the reward and punishment approval process according to preset approval rules. The evidence storage unit includes a blockchain node module, which writes the encrypted reward and punishment records into the Ethereum blockchain for evidence storage. The data collection unit is connected to the approval unit via gigabit Ethernet, and the approval unit is connected to the evidence storage unit via the internet. The approval unit is also connected to a cross-chain relay module, which synchronizes the execution results of the smart contract execution module to the employee digital file system via the cross-chain relay mechanism. The employee digital file system communicates with boundary nodes, which use lightweight state proof technology to verify the authenticity of reward and punishment records from the Ethereum blockchain.

[0049] Compared with Example 1, this example introduces a cross-chain relay module, an employee digital profile system, and boundary nodes, and combines lightweight state proof technology to achieve trusted synchronization and efficient verification of reward and punishment results between heterogeneous systems. This enhances the authority and tamper-proof capability of reward and punishment data in employee profiles, while reducing the computational overhead of cross-system verification.

[0050] Example 9

[0051] The big data security reward and punishment automated decision-making system based on blockchain smart contracts mainly consists of a data collection unit, an approval unit, and an evidence storage unit. The data collection unit includes an event entry module and a data encryption module. The event entry module receives reward and punishment event information input by security management personnel, and the data encryption module uses the AES-256 algorithm to encrypt the reward and punishment event information. The approval unit includes a smart contract execution module, which automatically executes the reward and punishment approval process according to preset approval rules. The evidence storage unit includes a blockchain node module and a REST service module. The blockchain node module writes the encrypted reward and punishment records to the Ethereum blockchain for evidence storage. The REST service module provides employee digital profile writing services through a standardized REST interface and supports multi-dimensional aggregation queries based on business dimensions and vectorized index acceleration. The data collection unit is connected to the approval unit via gigabit Ethernet, and the approval unit is connected to the evidence storage unit via the Internet.

[0052] Compared with Example 1, this example adds a REST service module to the evidence storage unit, enabling the system to not only have blockchain evidence storage capabilities, but also provide employee digital file writing services to the outside world through a standardized interface. Furthermore, through multi-dimensional aggregation query and vectorized index acceleration mechanism, it significantly improves the retrieval efficiency and service capabilities of reward and punishment data in business analysis scenarios.

[0053] Example 10

[0054] The big data security reward and punishment automated decision-making system based on blockchain smart contracts mainly consists of a data collection unit, an approval unit, an evidence storage unit, and a training strategy adjustment unit. The data collection unit includes an event entry module and a data encryption module. The event entry module receives reward and punishment event information input by security management personnel, and the data encryption module uses the AES-256 algorithm to encrypt the reward and punishment event information. The approval unit includes a smart contract execution module. The smart contract execution module automatically executes the reward and punishment approval process according to preset approval rules and outputs the corresponding points value for this reward or punishment. The evidence storage unit includes a blockchain node module. The blockchain node module writes the encrypted reward and punishment records to the Ethereum blockchain for evidence storage. The training strategy adjustment unit includes a points fusion module and a priority weight calculation module. The points fusion module receives the points value corresponding to this reward or punishment and the employee's historical points data, and performs a weighted fusion calculation to obtain a fused point. The priority weight calculation module dynamically adjusts the employee's priority weight in the security training push strategy based on the fused point. The data collection unit is connected to the approval unit via gigabit Ethernet, the approval unit is connected to the evidence storage unit via the Internet, and the approval unit is also connected to the training strategy adjustment unit via an internal data bus.

[0055] Compared with Example 1, this example introduces a training strategy adjustment unit, which realizes dynamic adjustment of the priority weight of employee safety training push based on the weighted fusion result of the current reward and punishment points and historical points. This makes the allocation of safety training resources more accurate and targeted, and improves the closed-loop efficiency of safety management and the level of intelligence in guiding employee behavior.

[0056] Example 11

[0057] The blockchain-based smart contract-based big data security reward and punishment automated decision-making system mainly consists of a data collection unit, an approval unit, and an evidence storage unit. The data collection unit includes an event entry module and a data encryption module. The event entry module receives reward and punishment event information input by security management personnel, and the data encryption module uses the AES-256 algorithm to encrypt the reward and punishment event information. The approval unit includes a smart contract execution module, a time lock module, and an appeal handling subsystem. The smart contract execution module automatically executes the reward and punishment approval process according to preset approval rules and is configured with a time window-based delayed execution mechanism. The time lock module communicates with the smart contract execution module to set and control the time window for delayed execution. The appeal handling subsystem communicates with the smart contract execution module to receive and process appeal requests submitted by relevant personnel within the delayed execution window period. The evidence storage unit includes a blockchain node module. The blockchain node module writes the encrypted reward and punishment records into the Ethereum blockchain for evidence storage. The data collection unit connects to the approval unit via gigabit Ethernet, and the approval unit connects to the evidence storage unit via the internet.

[0058] Compared with the original embodiment 1, this embodiment adds a time lock module and an appeal processing subsystem to the approval unit, and enables the smart contract execution module to have a time window-based delayed execution mechanism. This allows the reward and punishment decision to retain a buffer period that can be appealed and revoked before it officially takes effect, thereby improving the fairness and fault tolerance of the system and protecting the legitimate rights and interests of the parties involved.

[0059] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concepts, should be covered within the scope of protection of the present invention.

Claims

1. A big data security reward and punishment automated decision-making system based on blockchain smart contracts, comprising a data collection unit, an approval unit, and a notarization unit, characterized in that: Blockchain is used to record and store rewards and punishments, and the approval process is automatically executed through smart contracts. The smart contracts are configured to be automatically triggered when preset conditions are met.

2. The automated decision-making system for big data security rewards and punishments based on blockchain smart contracts according to claim 1, characterized in that: The evidence storage unit is configured with an intelligent hierarchical storage strategy based on data access frequency, timeliness requirements and security level, and manages the data lifecycle through a metadata-driven mechanism of timestamps and business tags.

3. The automated decision-making system for big data security rewards and punishments based on blockchain smart contracts according to claim 1, characterized in that: The smart contract includes a multi-level nested condition module, which sequentially includes a security event type classification identifier, a quantitative score of event severity, and a score of the historical security behavior record of the employee involved. The module is then used to perform logical operations through an on-chain rule engine in the form of a directed acyclic graph.

4. The automated decision-making system for big data security rewards and punishments based on blockchain smart contracts according to claim 1, characterized in that: The smart contract and the process engine module are coupled through an event-driven interface, and the process instance ID, the current approval node status, and the operator information are used as the sharding routing index key when writing data.

5. The automated decision-making system for big data security rewards and punishments based on blockchain smart contracts according to claim 1, characterized in that: Sensitive fields in the approval comments are encrypted using attribute-based encryption combined with a key rotation mechanism, and event messages are protected for integrity through a Merkle tree structure and a batch signature mechanism.

6. The automated decision-making system for big data security rewards and punishments based on blockchain smart contracts according to claim 1, characterized in that: When executing the approval process, the smart contract calls the multi-dimensional reward and punishment quota pool data in the distributed cache to verify the quota, and suspends execution and triggers a quota warning event when the quota is insufficient.

7. The automated decision-making system for big data security rewards and punishments based on blockchain smart contracts according to claim 1, characterized in that: The execution result of the smart contract is synchronized to the employee digital file system through a cross-chain relay mechanism, and the boundary nodes use lightweight state proof technology to verify the authenticity of the reward and punishment records.

8. The automated decision-making system for big data security rewards and punishments based on blockchain smart contracts according to claim 1, characterized in that: The evidence storage unit provides employee digital file writing services through a standardized REST interface, and supports multi-dimensional aggregation queries based on business dimensions and vectorized index acceleration.

9. The automated decision-making system for big data security rewards and punishments based on blockchain smart contracts according to claim 1, characterized in that: It also includes dynamically adjusting the priority of employees in the safety training push strategy based on the weighted fusion result of the points corresponding to this reward or punishment and the historical points.

10. The automated decision-making system for big data security rewards and punishments based on blockchain smart contracts according to claim 1, characterized in that: The smart contract is configured with a time window-based delayed execution mechanism and is connected to the time lock module and the appeal processing subsystem.