Data security management and analysis system and method based on block chain technology

Abstract

The invention discloses a data security management and analysis system and method based on a block chain technology, and relates to the technical field of data security management, and the method comprises the following steps: S100, obtaining advertisement putting, e-commerce supply chain and e-commerce customer extension data, and carrying out the preprocessing; s200, performing hash processing on the collected data to generate a unique identifier, performing encryption processing on the sensitive data, executing a hierarchical storage strategy by using an intelligent contract, and allocating a timestamp to generate a traceability chain; s300, performing statistics on the current business demand data volume and the distribution priority, calculating the available capacity and the lowest security capacity of the storage environment, judging the storage capacity, allocating a newly added storage task, and generating a storage scheme evaluation cost to select an optimal scheme to execute storage; and S400, evaluating and analyzing task priorities, distributing calculation and storage resources in combination with an intelligent contract, performing hierarchical storage on optimization results, and generating strategy suggestions, thereby realizing data security management and analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of data security management, and specifically to a data security management and analysis system and method based on blockchain technology. Background Art

[0002] As enterprises' demands for data security and intelligent management continue to grow, traditional data management and analysis systems have shown many shortcomings when dealing with complex and diverse data types, high-frequency access, and storage requirements.

[0003] The existing system lacks a refined hierarchical processing mechanism and dynamic adaptability in the process of data collection, storage and analysis. Especially when facing the multi-dimensional processing of data in multiple fields such as advertising, e-commerce supply chain and e-commerce customer development, it often relies on decentralized collection and processing mechanisms and lacks a unified data integration method, resulting in low data flow efficiency and poor timeliness. It is difficult to meet business needs, especially when dealing with large-scale real-time data. In addition, the existing technology lacks systematic encryption protection and access control when processing sensitive data such as order information, logistics tracking and user privacy, which can easily lead to data leakage or unauthorized access. Then, the traditional storage system fails to fully consider the data traceability requirements and it is difficult to achieve effective traceability of the data source and change process. Finally, when storing high-frequency access and low-frequency access data, the traditional system lacks a hierarchical storage strategy, resulting in low efficiency and large resource occupation of high-frequency data storage, and lack of flexibility in low-frequency data storage. The overall storage resource utilization is not high and it is difficult to adapt to dynamically changing business needs. Therefore, there is an urgent need for a data security management and analysis system and method based on blockchain technology. Through the integrated design of data collection, storage and analysis, combined with smart contracts, encryption technology and hierarchical storage strategies, it can meet the needs of multi-field data management and analysis. Summary of the invention

[0004] The purpose of the present invention is to provide a data security management and analysis system and method based on blockchain technology to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: a data security management and analysis method based on blockchain technology, the method comprising the following steps:

[0006] S100, obtain data on advertising, e-commerce supply chain and e-commerce customer development, perform pre-processing after data collection, classify data by combining rules and clustering algorithms, mark timestamps and perform hierarchical storage;

[0007] S200, hash the collected data to generate a unique identifier, encrypt the sensitive data, use smart contracts to implement a hierarchical storage strategy for high-frequency and low-frequency data, and assign timestamps to generate a traceability chain;

[0008] S300, counting the current business demand data volume and distribution priority, calculating the available capacity and minimum safe capacity of the storage environment, determining the storage capacity to allocate new storage tasks, and generating storage solution evaluation costs to select the optimal solution for storage execution;

[0009] S400, evaluate the priority of analysis tasks, allocate computing and storage resources in combination with smart contracts, optimize the hierarchical storage of results, store analysis results by node priority, and generate policy recommendations.

[0010] In step S100, the specific steps include:

[0011] S101. Obtaining business data related to advertising, e-commerce supply chain, and e-commerce customer acquisition, wherein the relevant business data includes click-through rate, display rate, advertising budget, and user feedback in advertising, order information, logistics tracking, product source and quality information in the e-commerce supply chain, and visit volume, conversion rate, and user retention rate in e-commerce customer acquisition;

[0012] S102. Verify data anomalies through blockchain smart contracts, eliminate duplicate, lost or tampered data, and classify the verified data according to the business types of advertising and e-commerce based on rules and clustering algorithms. At the same time, mark the data with timestamps and store the data in a hierarchical manner in the blockchain according to the classification results.

[0013] In step S200, the specific steps include:

[0014] S201. Hash all collected data to generate a unique identifier, and encrypt sensitive data such as order information and logistics tracking using homomorphic encryption or zero-knowledge proof technology;

[0015] S202. Based on business needs, the storage strategy is executed by using blockchain smart contracts. High-frequency access data is stored in the main chain, and low-frequency access data is transferred to the side chain according to business priority to release main chain resources. At the same time, a unique timestamp is assigned to each stored data to mark the generation or processing time point, and a traceability chain is generated based on blockchain technology to achieve full-link recording from data collection to storage.

[0016] In step S300, the specific steps include:

[0017] S301. Count the data volume of current business demand, including the total amount of new and updated data, and count the distribution priority of the demand data, including high-frequency access and low-frequency access data, and calculate the available storage capacity in the current distributed storage environment, and set the minimum safe data storage capacity for resource allocation;

[0018] S302, determine demand and storage capacity, when D T ≤DV -D0, no need to add new storage tasks, directly call the existing on-chain data to meet business needs, when D T >D V -D0, calculate the total amount of data that needs to be stored as D T -D V +D0, and set the priority of the newly added storage task according to the urgency of the task, where D T Indicates the data demand, D V Indicates the available storage capacity, D0 indicates the minimum safe data storage capacity;

[0019] S303, adding storage task D T -D V +D0 is allocated according to the time period T, generating multiple storage solutions S1, S2, ..., S N , calculate the comprehensive cost for each storage solution, the formula is as follows:

[0020]

[0021] in, represents the encryption energy consumption of the i-th data block, E comm Represents the energy consumption of on-chain communication, T store Indicates the time window in which the storage task is completed. represents the operation and maintenance cost of the jth node, U eff Indicates the effective utilization rate of current resources and reflects the actual load of resource changes. access Indicates the total number of access requests on the chain, F response Represents the response efficiency, and selects the optimal cost solution J according to the comprehensive cost evaluation result min Perform storage task allocation.

[0022] In step S400, the specific steps include:

[0023] S401, based on the data collected and classified in step S100, calculate and analyze the task priority, and the task priority evaluation formula is as follows: i =W i *F i / T i +1 / (C i +R i ), where P i represents the priority score of the i-th analysis task, W i represents the importance weight of the task, F i represents the access frequency of the task, T i represents the time window for task completion, C i represents the computing resource consumption required for the task, R iIndicates the remaining resource ratio of the storage node. The execution order of the analysis tasks is determined based on the calculation results, and corresponding resources are allocated to each task in combination with the computing resource limit.

[0024] S402, control the storage task priority and data access rights generated in step S300 through smart contracts, assign access rights according to user roles to ensure that high-authority users have priority access to sensitive data, adjust permission rules and update configurations according to role or task changes, and record access logs to mark time, role, and operation content for auditing. When abnormal behaviors such as frequent queries or unauthorized access are detected, limit user permissions and suspend task execution, and notify administrators to adjust permission policies to ensure safe operation. Combine tasks and storage priorities to assign analysis tasks to distributed computing nodes, and optimize task load distribution based on node resource utilization, task urgency, and allocation policies;

[0025] S403, optimize tiered storage, the calculation formula is as follows: S j =V j *U j / F j +1 / B j ; Among them, S j represents the storage priority score of the jth storage node, V j Indicates the access frequency of the analysis results, U j Indicates the current utilization of the node, F j represents the response time of the node, B j Indicates the remaining bandwidth of the node, uses the hierarchical storage optimization formula to determine the node priority for storing analysis results, stores high-frequency access results in high-efficiency nodes, and transfers low-frequency access results to secondary nodes;

[0026] S404. Based on the analysis results, machine learning algorithms are used to generate intelligent strategy recommendations, including advertising optimization strategies to adjust budget allocation and target population selection, supply chain path optimization solutions to improve logistics node selection and predict bottleneck problems, and customer relationship optimization strategies to provide personalized promotions or recommendations.

[0027] The data security management and analysis system based on blockchain technology includes: a data acquisition module, a storage management module, a storage capacity matching module and a data analysis module. The data acquisition module is used to obtain data on advertising, e-commerce supply chain and e-commerce customer development, and pre-process the data after completing data collection. The data is classified by combining rules and clustering algorithms, and the timestamp is marked and stored in a hierarchical manner. The storage management module is used to perform hash processing on the collected data to generate a unique identifier, and encrypt sensitive data. The hierarchical storage strategy of high-frequency and low-frequency data is executed by using smart contracts, and timestamps are assigned to generate a traceability chain. The storage capacity matching module is used for statistical The current business demand data volume and distribution priority, calculate the available capacity and minimum safe capacity of the storage environment, determine the storage capacity to allocate new storage tasks, and generate the storage plan evaluation cost to select the optimal plan to execute storage. The data analysis module is used to evaluate the analysis task priority, allocate computing and storage resources in combination with smart contracts, optimize the result tiered storage, store the analysis results according to node priority, and generate policy recommendations. The output end of the data acquisition module is connected to the input end of the storage management module, the output end of the storage management module is connected to the input end of the storage capacity matching module, and the output end of the storage capacity matching module is connected to the input end of the data analysis module.

[0028] The data collection module includes a business data collection unit and a data preprocessing unit. The business data collection unit is used to collect business data related to advertising, e-commerce supply chain and e-commerce customer development, including click-through rate, display rate, delivery budget and user feedback in advertising, order information, logistics tracking, product source and quality information in the e-commerce supply chain, and visit volume, conversion rate and user retention rate in e-commerce customer development. The data preprocessing unit verifies the collected data anomalies through blockchain smart contracts, eliminates duplicate, lost or tampered data, and classifies the verified data according to the business type of advertising and e-commerce based on rules and clustering algorithms, and marks the data with timestamps and performs preliminary hierarchical storage based on the classification results.

[0029] The storage management module includes a data encryption unit, a hierarchical storage unit and a traceability management unit. The data encryption unit is used to perform hash processing on all collected data to generate a unique identifier, and use homomorphic encryption or zero-knowledge proof technology to encrypt sensitive data of order information and logistics tracking. The hierarchical storage unit is used to execute a hierarchical storage strategy through smart contracts according to business needs. High-frequency access data is stored in the main chain, and low-frequency access data is transferred to the side chain according to priority to release main chain resources. The traceability management unit is used to assign a unique timestamp to each stored data to mark the generation or processing time point, and generate a traceability chain based on blockchain technology to achieve full-link recording from data collection to storage.

[0030] The storage capacity matching module includes a demand assessment unit, a capacity calculation unit and a storage optimization unit. The demand assessment unit is used to count the data volume of the current business demand, including the total amount of new and updated data, and evaluate the distribution priority of the demand data. The capacity calculation unit is used to calculate the available capacity and the minimum safe storage capacity of the current storage environment, and evaluate whether new storage tasks are needed. The storage optimization unit is used to generate a storage plan for the new storage task, and use a comprehensive cost calculation formula to evaluate the storage plan, and select the cost-optimal plan to perform storage task allocation.

[0031] The data analysis module includes a task priority evaluation unit, a permission management unit, a hierarchical storage optimization unit and a strategy generation unit. The task priority evaluation unit is used to calculate the priority of the analysis task and determine the execution order of the task according to the task importance, access frequency, computing resources and storage resources. The permission management unit is used to control the task and data access rights based on the smart contract, adjust the permission allocation rules and record the access log. The hierarchical storage optimization unit is used to calculate the storage priority of the analysis results, optimize the result storage, store the high-frequency access results in the efficient nodes, and store the low-frequency access results in the secondary nodes. The strategy generation unit is used to generate intelligent strategy recommendations using machine learning algorithms, including advertising optimization strategies to adjust budget allocation and target population selection, supply chain path optimization solutions to improve logistics node selection and predict bottleneck problems, and customer relationship optimization strategies to provide personalized promotion or recommendation solutions.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The present invention generates a traceability chain and access log through blockchain technology to record the entire process of data collection, storage and access. The present invention can improve the compliance and transparency of data management, respond to abnormal behaviors in a timely manner, and ensure the stable operation of the system.

[0034] 2. The present invention combines blockchain technology with smart contracts to perform hierarchical management of data storage and access, achieve optimized storage of high-frequency and low-frequency data, and ensure that the storage needs of multiple business scenarios are met under limited resources. Compared with the single storage mode in the prior art, the present invention improves the utilization rate of storage resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of the data security management and analysis method based on blockchain technology of the present invention;

[0036] Figure 2 This is a structural diagram of the data security management and analysis system based on blockchain technology of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] like Figure 1-Figure 2 As shown, the present invention provides a technical solution, a data security management and analysis method based on blockchain technology, which includes the following steps:

[0039] S100, obtain data on advertising, e-commerce supply chain and e-commerce customer development, perform pre-processing after data collection, classify data by combining rules and clustering algorithms, mark timestamps and perform hierarchical storage;

[0040] S200, hash the collected data to generate a unique identifier, encrypt the sensitive data, use smart contracts to implement a hierarchical storage strategy for high-frequency and low-frequency data, and assign timestamps to generate a traceability chain;

[0041] S300, counting the current business demand data volume and distribution priority, calculating the available capacity and minimum safe capacity of the storage environment, determining the storage capacity to allocate new storage tasks, and generating storage solution evaluation costs to select the optimal solution for storage execution;

[0042] S400, evaluate the priority of analysis tasks, allocate computing and storage resources in combination with smart contracts, optimize the hierarchical storage of results, store analysis results by node priority, and generate policy recommendations.

[0043] In step S100, the specific steps include:

[0044] S101. Obtaining business data related to advertising, e-commerce supply chain, and e-commerce customer acquisition, wherein the relevant business data includes click-through rate, display rate, advertising budget, and user feedback in advertising, order information, logistics tracking, product source and quality information in the e-commerce supply chain, and visit volume, conversion rate, and user retention rate in e-commerce customer acquisition;

[0045] S102. Verify data anomalies through blockchain smart contracts, eliminate duplicate, lost or tampered data, and classify the verified data according to the business types of advertising and e-commerce based on rules and clustering algorithms. At the same time, mark the data with timestamps and store the data in a hierarchical manner in the blockchain according to the classification results.

[0046] In step S200, the specific steps include:

[0047] S201. Hash all collected data to generate a unique identifier, and encrypt sensitive data such as order information and logistics tracking using homomorphic encryption or zero-knowledge proof technology;

[0048] S202. Based on business needs, the storage strategy is executed by using blockchain smart contracts. High-frequency access data is stored in the main chain, and low-frequency access data is transferred to the side chain according to business priority to release main chain resources. At the same time, a unique timestamp is assigned to each stored data to mark the generation or processing time point, and a traceability chain is generated based on blockchain technology to achieve full-link recording from data collection to storage.

[0049] In step S300, the specific steps include:

[0050] S301. Count the data volume of current business demand, including the total amount of new and updated data, and count the distribution priority of the demand data, including high-frequency access and low-frequency access data, and calculate the available storage capacity in the current distributed storage environment, and set the minimum safe data storage capacity for resource allocation;

[0051] S302, determine demand and storage capacity, when D T ≤D V -D0, no need to add new storage tasks, directly call the existing on-chain data to meet business needs, when D T >D V -D0, calculate the total amount of data that needs to be stored as D T -D V +D0, and set the priority of the newly added storage task according to the urgency of the task, where D T Indicates the data demand, D V Indicates the available storage capacity, D0 indicates the minimum safe data storage capacity;

[0052] S303, adding storage task D T -D V +D0 is allocated according to the time period T, generating multiple storage solutions S1, S2, ..., S N , calculate the comprehensive cost for each storage solution, the formula is as follows:

[0053]

[0054] in, represents the encryption energy consumption of the i-th data block, E comm Represents the energy consumption of on-chain communication, T store Indicates the time window in which the storage task is completed. represents the operation and maintenance cost of the jth node, U eff Indicates the effective utilization rate of current resources and reflects the actual load of resource changes. accessIndicates the total number of access requests on the chain, F response Represents the response efficiency, and selects the optimal cost solution J according to the comprehensive cost evaluation result min Perform storage task allocation.

[0055] In step S400, the specific steps include:

[0056] S401, based on the data collected and classified in step S100, calculate and analyze the task priority, and the task priority evaluation formula is as follows: i =W i *F i / T i +1 / (C i +R i ), where P i represents the priority score of the i-th analysis task, W i represents the importance weight of the task, F i represents the access frequency of the task, T i represents the time window for task completion, C i represents the computing resource consumption required for the task, R i Indicates the remaining resource ratio of the storage node. The execution order of the analysis tasks is determined based on the calculation results, and corresponding resources are allocated to each task in combination with the computing resource limit.

[0057] S402, control the storage task priority and data access rights generated in step S300 through smart contracts, assign access rights according to user roles to ensure that high-authority users have priority access to sensitive data, adjust permission rules and update configurations according to role or task changes, and record access logs to mark time, role, and operation content for auditing. When abnormal behaviors such as frequent queries or unauthorized access are detected, limit user permissions and suspend task execution, and notify administrators to adjust permission policies to ensure safe operation. Combine tasks and storage priorities to assign analysis tasks to distributed computing nodes, and optimize task load distribution based on node resource utilization, task urgency, and allocation policies;

[0058] S403, optimize tiered storage, the calculation formula is as follows: S j =V j *U j / F j +1 / B j ; Among them, S j represents the storage priority score of the jth storage node, V j Indicates the access frequency of the analysis results, U j Indicates the current utilization of the node, F j represents the response time of the node, B jIndicates the remaining bandwidth of the node, uses the hierarchical storage optimization formula to determine the node priority for storing analysis results, stores high-frequency access results in high-efficiency nodes, and transfers low-frequency access results to secondary nodes;

[0059] S404. Based on the analysis results, machine learning algorithms are used to generate intelligent strategy recommendations, including advertising optimization strategies to adjust budget allocation and target population selection, supply chain path optimization solutions to improve logistics node selection and predict bottleneck problems, and customer relationship optimization strategies to provide personalized promotions or recommendations.

[0060] The data security management and analysis system based on blockchain technology includes: a data acquisition module, a storage management module, a storage capacity matching module and a data analysis module. The data acquisition module is used to obtain data on advertising, e-commerce supply chain and e-commerce customer development, and pre-process the data after completing data collection. The data is classified by combining rules and clustering algorithms, and the timestamp is marked and stored in a hierarchical manner. The storage management module is used to perform hash processing on the collected data to generate a unique identifier, and encrypt sensitive data. The hierarchical storage strategy of high-frequency and low-frequency data is executed by using smart contracts, and timestamps are assigned to generate a traceability chain. The storage capacity matching module is used for statistical The current business demand data volume and distribution priority, calculate the available capacity and minimum safe capacity of the storage environment, determine the storage capacity to allocate new storage tasks, and generate the storage plan evaluation cost to select the optimal plan to execute storage. The data analysis module is used to evaluate the analysis task priority, allocate computing and storage resources in combination with smart contracts, optimize the result tiered storage, store the analysis results according to node priority, and generate policy recommendations. The output end of the data acquisition module is connected to the input end of the storage management module, the output end of the storage management module is connected to the input end of the storage capacity matching module, and the output end of the storage capacity matching module is connected to the input end of the data analysis module.

[0061] The data collection module includes a business data collection unit and a data preprocessing unit. The business data collection unit is used to collect business data related to advertising, e-commerce supply chain and e-commerce customer development, including click-through rate, display rate, delivery budget and user feedback in advertising, order information, logistics tracking, product source and quality information in the e-commerce supply chain, and visit volume, conversion rate and user retention rate in e-commerce customer development. The data preprocessing unit verifies the collected data anomalies through blockchain smart contracts, eliminates duplicate, lost or tampered data, and classifies the verified data according to the business type of advertising and e-commerce based on rules and clustering algorithms, while marking timestamps for the data and performing preliminary hierarchical storage based on the classification results.

[0062] The storage management module includes a data encryption unit, a hierarchical storage unit and a traceability management unit. The data encryption unit is used to perform hash processing on all collected data to generate a unique identifier, and to encrypt sensitive data such as order information and logistics tracking using homomorphic encryption or zero-knowledge proof technology. The hierarchical storage unit is used to execute a hierarchical storage strategy through smart contracts according to business needs. High-frequency access data is stored in the main chain, and low-frequency access data is transferred to the side chain according to priority to release main chain resources. The traceability management unit is used to assign a unique timestamp to each piece of stored data to mark the generation or processing time point, and to generate a traceability chain based on blockchain technology to achieve full-link recording from data collection to storage.

[0063] The storage capacity matching module includes a demand assessment unit, a capacity calculation unit and a storage optimization unit. The demand assessment unit is used to count the data volume of the current business demand, including the total amount of new and updated data, and evaluate the distribution priority of the demand data. The capacity calculation unit is used to calculate the available capacity and minimum safe storage capacity of the current storage environment, and evaluate whether new storage tasks are needed. The storage optimization unit is used to generate storage plans for new storage tasks, and use a comprehensive cost calculation formula to evaluate the storage plans, and select the cost-optimal plan to execute storage task allocation.

[0064] The data analysis module includes a task priority evaluation unit, a permission management unit, a hierarchical storage optimization unit and a strategy generation unit. The task priority evaluation unit is used to calculate the priority of the analysis task and determine the execution order of the task according to the task importance, access frequency, computing resources and storage resources. The permission management unit is used to control the task and data access rights based on the smart contract, adjust the permission allocation rules and record the access log. The hierarchical storage optimization unit is used to calculate the storage priority of the analysis results, optimize the result storage, store the high-frequency access results in the efficient nodes, and store the low-frequency access results in the secondary nodes. The strategy generation unit is used to generate intelligent strategy recommendations using machine learning algorithms, including advertising optimization strategies to adjust budget allocation and target population selection, supply chain path optimization solutions to improve logistics node selection and predict bottleneck problems, and customer relationship optimization strategies to provide personalized promotion or recommendation solutions.

[0065] In the embodiment: the advertising, e-commerce supply chain and customer development data are safely managed and analyzed, resource allocation is optimized and intelligent strategies are generated. The platform processes 50GB of data every day, including advertising, e-commerce supply chain and e-commerce customer development related data. The present invention is used to achieve safe management and analysis of data, and obtains a click-through rate of 4.5%, a display rate of 70%, a delivery budget of 50,000 yuan and 58 user feedbacks in advertising, 10,000 orders per day in the e-commerce supply chain, real-time updates of logistics tracking, product source and quality information involving 20 suppliers and 100 products, and an average daily visit volume of 50,000 times in e-commerce customer development, a conversion rate of 2.5%, a user retention rate of 60%, and a profit. Use blockchain smart contracts to detect the integrity of collected data, remove 5% of duplicate or abnormal data, and classify data by business type and sensitivity level: advertising data is marked as level 1 sensitive, e-commerce data is marked as level 2 sensitive, and user data is marked as level 3 sensitive. At the same time, timestamp the data, store level 1 sensitive data in the main chain, and level 2 and level 3 data are stored in the side chain in layers. Generate a unique identifier for advertising data through a hash algorithm, use homomorphic encryption for order data and logistics tracking data, use zero-knowledge proof encryption for user privacy data, store high-frequency access data in the main chain, store low-frequency access data in the side chain, and attach timestamps to generate a traceability chain to record the full-link information of data collection, storage and processing, and obtain data demand D T The current storage capacity is 50GB. V The minimum safe capacity D0 is 10GB. T Greater than D V -D0, calculate the total amount of data that needs to be stored as D T -D V +D0=10GB, storage plan: S1, main chain adds 5GB of storage, side chain adds 5GB of storage, S2, main chain adds 3GB of storage, side chain adds 7GB of storage, according to is 10 units, E comm is 20 units, T store For 4 hours, is 50 units, U eff 0.85, L access 600, F response is 0.9, the comprehensive cost J1 = 732.99, the comprehensive cost J2 = 734.24, so S1 is selected as the cost-optimal storage solution. According to the weight of 4, F i =1500, T i = 5 hours, C i =3, R i =0.6 Advertising click-through rate analysis task P1 = 1200.278, according to the weight of 3, F i =800, Ti = 6 hours, C i =2.5, R i =0.7 User retention rate analysis task P2 = 400.278, assign permissions according to task priority, prioritize computing resources for high-priority tasks, record access logs, mark time, user role, and operation content, according to node 1: V j =1000 times / day, U j =0.9, F j = 0.5 seconds, B j =10Mbps, according to the optimized tiered storage calculation formula, S1 = 1800.1, according to node 2: V j =500 times / day, U j =0.7, F j = 1.0 sec, B j =5Mbps. According to the optimized tiered storage calculation formula, S2=350.2 is obtained. High-frequency access results are stored in node 1, and low-frequency results are stored in node 2.

[0066] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A data security management and analysis method based on blockchain technology, characterized by: The method comprises the following steps: S100, obtain data on advertising, e-commerce supply chain and e-commerce customer development, perform pre-processing after data collection, classify data by combining rules and clustering algorithms, mark timestamps and perform hierarchical storage; S200, hash the collected data to generate a unique identifier, encrypt the sensitive data, use smart contracts to implement a hierarchical storage strategy for high-frequency and low-frequency data, and assign timestamps to generate a traceability chain; S300, counting the current business demand data volume and distribution priority, calculating the available capacity and minimum safe capacity of the storage environment, determining the storage capacity to allocate new storage tasks, and generating storage solution evaluation costs to select the optimal solution for storage execution; S400, evaluate the priority of analysis tasks, allocate computing and storage resources in combination with smart contracts, optimize the hierarchical storage of results, store analysis results by node priority, and generate policy recommendations.

2. The data security management and analysis method based on blockchain technology according to claim 1 is characterized in that: In step S100, the specific steps include: S101. Obtaining business data related to advertising, e-commerce supply chain, and e-commerce customer acquisition, wherein the relevant business data includes click-through rate, display rate, advertising budget, and user feedback in advertising, order information, logistics tracking, product source and quality information in the e-commerce supply chain, and visit volume, conversion rate, and user retention rate in e-commerce customer acquisition; S102. Verify data anomalies through blockchain smart contracts, eliminate duplicate, lost or tampered data, and classify the verified data according to the business types of advertising and e-commerce based on rules and clustering algorithms. At the same time, mark the data with timestamps and store the data in a hierarchical manner in the blockchain according to the classification results.

3. The data security management and analysis method based on blockchain technology according to claim 1 is characterized in that: In step S200, the specific steps include: S201. Hash all collected data to generate a unique identifier, and encrypt sensitive data such as order information and logistics tracking using homomorphic encryption or zero-knowledge proof technology; S202. Based on business needs, the storage strategy is executed by using blockchain smart contracts. High-frequency access data is stored in the main chain, and low-frequency access data is transferred to the side chain according to business priority to release main chain resources. At the same time, a unique timestamp is assigned to each stored data to mark the generation or processing time point, and a traceability chain is generated based on blockchain technology to achieve full-link recording from data collection to storage.

4. The data security management and analysis method based on blockchain technology according to claim 1 is characterized in that: In step S300, the specific steps include: S301. Count the data volume of current business demand, including the total amount of new and updated data, and count the distribution priority of the demand data, including high-frequency access and low-frequency access data, and calculate the available storage capacity in the current distributed storage environment, and set the minimum safe data storage capacity for resource allocation; S302, determine demand and storage capacity, when D T ≤D V -D0, no need to add new storage tasks, directly call the existing on-chain data to meet business needs, when D T >D V -D0, calculate the total amount of data that needs to be stored as D T -D V +D0, and set the priority of the newly added storage task according to the urgency of the task, where D T Indicates the data demand, D V Indicates the available storage capacity, D0 indicates the minimum safe data storage capacity; S303, adding storage task D T -D V +D0 is allocated according to the time period T, generating multiple storage solutions S1, S2, ..., S N , calculate the comprehensive cost for each storage solution, the formula is as follows: in, represents the encryption energy consumption of the i-th data block, E comm Represents the energy consumption of on-chain communication, T store Indicates the time window in which the storage task is completed. represents the operation and maintenance cost of the jth node, U eff Indicates the effective utilization rate of current resources and reflects the actual load of resource changes. access Indicates the total number of access requests on the chain, F response Represents the response efficiency, and selects the optimal cost solution J according to the comprehensive cost evaluation result min Perform storage task allocation.

5. The data security management and analysis method based on blockchain technology according to claim 1 is characterized in that: In step S400, the specific steps include: S401, based on the data collected and classified in step S100, calculate and analyze the task priority, and the task priority evaluation formula is as follows: i =W i *F i / T i +1 / (C i +R i ), where P i represents the priority score of the i-th analysis task, W i represents the importance weight of the task, F i represents the access frequency of the task, T i represents the time window for task completion, C i represents the computing resource consumption required for the task, R i Indicates the remaining resource ratio of the storage node. The execution order of the analysis tasks is determined based on the calculation results, and corresponding resources are allocated to each task in combination with the computing resource limit. S402, control the storage task priority and data access rights generated in step S300 through smart contracts, assign access rights according to user roles to ensure that high-authority users have priority access to sensitive data, adjust permission rules and update configurations according to role or task changes, and record access logs to mark time, role, and operation content for auditing. When abnormal behaviors such as frequent queries or unauthorized access are detected, limit user permissions and suspend task execution, and notify administrators to adjust permission policies to ensure safe operation. Combine tasks and storage priorities to assign analysis tasks to distributed computing nodes, and optimize task load distribution based on node resource utilization, task urgency, and allocation policies; S403, optimize tiered storage, the calculation formula is as follows: S j =V j *U j / F j +1 / B j ; Among them, S j represents the storage priority score of the jth storage node, V j Indicates the access frequency of the analysis results, U j Indicates the current utilization of the node, F j represents the response time of the node, B j Indicates the remaining bandwidth of the node, uses the hierarchical storage optimization formula to determine the node priority for storing analysis results, stores high-frequency access results in high-efficiency nodes, and transfers low-frequency access results to secondary nodes; S404. Based on the analysis results, machine learning algorithms are used to generate intelligent strategy recommendations, including advertising optimization strategies to adjust budget allocation and target population selection, supply chain path optimization solutions to improve logistics node selection and predict bottleneck problems, and customer relationship optimization strategies to provide personalized promotions or recommendations.

6. Data security management and analysis system based on blockchain technology, characterized by: The system includes: a data acquisition module, a storage management module, a storage capacity matching module and a data analysis module. The data acquisition module is used to obtain data on advertising, e-commerce supply chain and e-commerce customer development. After completing data collection, preprocessing is performed, and data is classified in combination with rules and clustering algorithms. At the same time, timestamps are marked and hierarchical storage is performed. The storage management module is used to perform hash processing on the collected data to generate a unique identifier, and encrypt sensitive data. The hierarchical storage strategy of high-frequency and low-frequency data is executed by using smart contracts, and timestamps are assigned to generate a traceability chain. The storage capacity matching module is used to count the current business demand data volume and distribution priority, calculate the available capacity and the minimum safe capacity of the storage environment, determine the storage capacity to allocate new storage tasks, and generate a storage solution evaluation cost to select the optimal solution for storage execution. The data analysis module is used to evaluate the priority of analysis tasks, allocate computing and storage resources in combination with smart contracts, optimize the hierarchical storage of results, store analysis results according to node priority, and generate policy recommendations. The output end of the data acquisition module is connected to the input end of the storage management module, the output end of the storage management module is connected to the input end of the storage capacity matching module, and the output end of the storage capacity matching module is connected to the input end of the data analysis module.

7. The data security management and analysis system based on blockchain technology according to claim 6 is characterized in that: The data collection module includes a business data collection unit and a data preprocessing unit. The business data collection unit is used to collect business data related to advertising, e-commerce supply chain and e-commerce customer development, including click-through rate, display rate, delivery budget and user feedback in advertising, order information, logistics tracking, product source and quality information in the e-commerce supply chain, and visit volume, conversion rate and user retention rate in e-commerce customer development. The data preprocessing unit verifies the collected data anomalies through blockchain smart contracts, eliminates duplicate, lost or tampered data, and classifies the verified data according to the business type of advertising and e-commerce based on rules and clustering algorithms, and marks the data with timestamps and performs preliminary hierarchical storage based on the classification results.

8. The data security management and analysis system based on blockchain technology according to claim 6 is characterized by: The storage management module includes a data encryption unit, a hierarchical storage unit and a traceability management unit. The data encryption unit is used to perform hash processing on all collected data to generate a unique identifier, and use homomorphic encryption or zero-knowledge proof technology to encrypt sensitive data of order information and logistics tracking. The hierarchical storage unit is used to execute a hierarchical storage strategy through smart contracts according to business needs. High-frequency access data is stored in the main chain, and low-frequency access data is transferred to the side chain according to priority to release main chain resources. The traceability management unit is used to assign a unique timestamp to each stored data to mark the generation or processing time point, and generate a traceability chain based on blockchain technology to achieve full-link recording from data collection to storage.

9. The data security management and analysis system based on blockchain technology according to claim 6 is characterized in that: The storage capacity matching module includes a demand assessment unit, a capacity calculation unit and a storage optimization unit. The demand assessment unit is used to count the data volume of the current business demand, including the total amount of new and updated data, and evaluate the distribution priority of the demand data. The capacity calculation unit is used to calculate the available capacity and the minimum safe storage capacity of the current storage environment, and evaluate whether new storage tasks are needed. The storage optimization unit is used to generate a storage plan for the new storage task, and use a comprehensive cost calculation formula to evaluate the storage plan, and select the cost-optimal plan to perform storage task allocation.

10. The data security management and analysis system based on blockchain technology according to claim 6 is characterized in that: The data analysis module includes a task priority evaluation unit, a permission management unit, a hierarchical storage optimization unit and a strategy generation unit. The task priority evaluation unit is used to calculate the priority of the analysis task and determine the execution order of the task according to the task importance, access frequency, computing resources and storage resources. The permission management unit is used to control the task and data access rights based on the smart contract, adjust the permission allocation rules and record the access log. The hierarchical storage optimization unit is used to calculate the storage priority of the analysis results, optimize the result storage, store the high-frequency access results in the efficient nodes, and store the low-frequency access results in the secondary nodes. The strategy generation unit is used to generate intelligent strategy recommendations using machine learning algorithms, including advertising optimization strategies to adjust budget allocation and target population selection, supply chain path optimization solutions to improve logistics node selection and predict bottleneck problems, and customer relationship optimization strategies to provide personalized promotion or recommendation solutions.

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