A blockchain-based method and system for secure storage of jewelry transaction data

Abstract

The application belongs to the technical field of data storage, and discloses a kind of jewelry transaction data security storage method and system based on block chain.The method comprises the following steps: cloud data center, deploy blockchain consensus and storage network, and build data security storage engine;Data server, encryption, and upload to cloud data center;Cloud data center, decryption;Cloud data center, using data security storage engine, generate the real-time search tag of real-time jewelry transaction data after decryption, real-time data analysis result, real-time storage resource scheduling scheme and real-time data storage strategy;Cloud data center, using blockchain consensus and storage network, the security storage of encrypted real-time jewelry transaction data, corresponding real-time search tag and real-time data analysis result is carried out.The application solves the problems of insufficient security, poor traceability, low storage efficiency and waste of storage resources in the prior art.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data storage, and particularly relates to a jewelry transaction data security storage method and system based on a block chain. BACKGROUND

[0002] Jewelry transaction data covers information on market size, growth, category structure, retail channels, import and export of the jewelry industry. These data show the details of the jewelry industry in terms of market size, category structure, retail channels, import and export and listed company performance, which helps to understand the current situation and trends of the industry. With the increasing frequency of jewelry transactions, the security storage of jewelry transaction data has become an important part of the digital transformation of the jewelry industry.

[0003] In the field of jewelry transaction data storage, existing technologies mainly rely on traditional centralized databases and conventional encryption methods. However, these technologies have many defects in practical application, including:

[0004] 1) Insufficient security: Traditional databases use centralized storage, which is easy to become a single target for hacker attacks. Once broken, it may lead to massive data leakage, and there is a risk of centralized storage. Conventional encryption algorithms may be at risk of being cracked, especially as computing power improves, some encryption methods become no longer secure, and encryption strength is limited;

[0005] 2) Poor traceability: In a centralized system, data can be tampered with by internal personnel or external attackers, and it is difficult to be discovered and traced in time, and data tampering is difficult to detect; the complete history of jewelry transactions is not public, and consumers and regulatory agencies cannot verify the authenticity and legality of transactions, and transaction records are not transparent;

[0006] 3) Low storage efficiency: In existing technologies, there is a lack of intelligent data processing and automatic storage technology, which requires a lot of manual intervention, resulting in low storage efficiency of jewelry transaction data, which cannot meet the application scenarios of large-scale data;

[0007] 4) Storage resource waste: Most existing technologies store data according to pre-set static storage strategies, and cannot dynamically adjust storage strategies according to real-time jewelry transaction data, resulting in a lot of storage resource waste. SUMMARY

[0008] In order to solve the problems of insufficient security, poor traceability, low storage efficiency and storage resource waste in existing technologies, the application aims to provide a jewelry transaction data security storage method and system based on a block chain.

[0009] The technical solution adopted by the application is:

[0010] A blockchain-based jewelry transaction data security storage method, comprising the following steps:

[0011] A cloud data center uses blockchain technology to deploy a blockchain consensus and storage network and uses an artificial intelligence algorithm to build a data security storage engine.

[0012] A data server uses a random encryption algorithm to encrypt real-time jewelry transaction data and uploads the obtained encrypted real-time jewelry transaction data to the cloud data center.

[0013] The cloud data center decrypts the encrypted real-time jewelry transaction data and inputs the obtained decrypted real-time jewelry transaction data into the data security storage engine.

[0014] The cloud data center uses the data security storage engine to generate real-time search tags for the decrypted real-time jewelry transaction data, real-time data analysis results, real-time storage resource scheduling schemes, and real-time data storage strategies.

[0015] The cloud data center uses the blockchain consensus and storage network to securely store the encrypted real-time jewelry transaction data, corresponding real-time search tags, and real-time data analysis results according to the real-time storage resource scheduling schemes and real-time data storage strategies.

[0016] Further, the data security storage engine includes a search tag generation model, a data analysis model, a data storage strategy generation model, and a storage resource scheduling model.

[0017] The blockchain consensus and storage network includes a data storage network and a blockchain consensus network.

[0018] Further, the cloud data center uses blockchain technology to deploy a blockchain consensus and storage network and uses an artificial intelligence algorithm to build a data security storage engine, comprising the following steps:

[0019] The cloud data center uses blockchain technology to distribute all data servers as data nodes to obtain a blockchain consensus and storage network.

[0020] According to an identity allocation mechanism, a number of data nodes of the blockchain consensus and storage network are divided into a number of consensus nodes and a number of storage nodes to obtain a data storage network and a blockchain consensus network.

[0021] A number of historical jewelry transaction data are collected and preprocessed to obtain a number of preprocessed historical jewelry transaction data.

[0022] According to the number of preprocessed historical jewelry transaction data, an artificial intelligence algorithm is used to build a data security storage engine.

[0023] Further, according to a number of pre-processed historical jewelry transaction data, using artificial intelligence algorithm, building a data security storage engine, including the following steps:

[0024] According to a number of pre-processed historical jewelry transaction data, using deep learning algorithm, building a data analysis model, and generating a number of historical data analysis results;

[0025] According to a number of pre-processed historical jewelry transaction data, using natural language processing algorithm, building a retrieval label generation model, and generating a number of historical real-time retrieval labels;

[0026] According to a number of historical data analysis results, using reinforcement learning algorithm, building a data storage strategy generation model, and generating a number of historical data storage strategies and corresponding historical data storage strategy generation experience;

[0027] According to a number of historical data storage strategies, using swarm intelligence optimization algorithm, building a storage resource scheduling model.

[0028] Further, the data analysis model is based on RF-MLP algorithm;

[0029] The retrieval label generation model is based on BERT-CRF algorithm;

[0030] The data storage strategy generation model is based on MOPPO algorithm;

[0031] The storage resource scheduling model is based on ISSA algorithm.

[0032] Further, the data server uses a random encryption algorithm to encrypt real-time jewelry transaction data, and uploads the obtained encrypted real-time jewelry transaction data to a cloud data center, including the following steps:

[0033] The data server confirms the real-time jewelry transaction data of the user, collects the corresponding biological characteristics of the user, and generates a random key according to the biological characteristics;

[0034] According to the random key, using a random encryption algorithm, encrypting the real-time jewelry transaction data to obtain encrypted real-time jewelry transaction data;

[0035] Using QKA technology, through a quantum communication channel, sending the random key to the cloud data center, and through a public communication channel, uploading the encrypted real-time jewelry transaction data to the cloud data center.

[0036] Further, using the data security storage engine, generating real-time retrieval labels, real-time data analysis results, real-time storage resource scheduling schemes, and real-time data storage strategies of the decrypted real-time jewelry transaction data, including the following steps:

[0037] According to the decrypted real-time jewelry transaction data, using the data security storage engine's search tag generation model, search tag generation is performed to obtain real-time search tags;

[0038] According to the decrypted real-time jewelry transaction data, using the data security storage engine's data analysis model, data analysis is performed to obtain corresponding real-time data analysis results;

[0039] According to the real-time data analysis results, using the data security storage engine's data storage strategy generation model, data storage strategy generation is performed to obtain real-time data storage strategies;

[0040] The cloud data center, according to the real-time data storage strategy, uses the data security storage engine's storage resource scheduling model to perform storage resource scheduling to obtain a real-time storage resource scheduling scheme.

[0041] Further, the cloud data center, according to the real-time storage resource scheduling scheme and the real-time data storage strategy, uses the blockchain consensus and storage network to securely store the encrypted real-time jewelry transaction data, the corresponding real-time search tags and the real-time data analysis results, including the following steps:

[0042] The cloud data center, according to the real-time storage resource scheduling scheme, schedules the corresponding data storage partition in the data storage network of the blockchain consensus and storage network;

[0043] According to the decrypted real-time jewelry transaction data, generate a real-time storage request, and use the blockchain consensus network of the blockchain consensus and storage network to perform consensus on the real-time storage request;

[0044] After consensus, according to the real-time data storage strategy, using the data storage partition, securely storing the encrypted real-time jewelry transaction data, the corresponding real-time search tags and the real-time data analysis results.

[0045] Further, after consensus, according to the real-time data storage strategy, using the data storage partition, securely storing the encrypted real-time jewelry transaction data, the corresponding real-time search tags and the real-time data analysis results, including the following steps:

[0046] After consensus, according to the real-time data storage strategy, the real-time data sharding decision is made to perform data sharding on the encrypted real-time jewelry transaction data to obtain a plurality of encrypted real-time data shards;

[0047] According to the real-time distributed storage decision of the real-time data storage strategy, the plurality of encrypted real-time data shards are sent to the plurality of storage nodes of the data storage partition;

[0048] The storage node using the data storage partition locally stores the received encrypted real-time data shard, returns a real-time local storage address, and sends real-time storage success information to other storage nodes;

[0049] The real-time local storage address of the encrypted real-time data shard, the corresponding real-time search tag, and the real-time data analysis result are written into a data storage ledger of the data storage network, and a real-time data storage record is generated;

[0050] The real-time data storage record is consensused using a blockchain consensus network, and a real-time distributed storage address is obtained;

[0051] After the consensus is successful, the real-time distributed storage address, the corresponding real-time search tag, and the real-time data analysis result are written into a distributed ledger of the blockchain consensus network;

[0052] If the storage node receives a number of real-time storage success information exceeding a preset number threshold, the secure storage step is ended, otherwise, the secure storage is continued.

[0053] A jewelry transaction data security storage system based on a blockchain is used to implement a jewelry transaction data security storage method, the system is arranged in a cloud data center, and the system includes an initialization unit, a data decryption unit, a data processing unit, and a secure storage unit, the cloud data center is in communication connection with a plurality of data servers, and the cloud data center is provided with a blockchain consensus and storage network and a data security storage engine.

[0054] The beneficial effects of the present application are:

[0055] The present application discloses a jewelry transaction data security storage method and system based on a blockchain, which uses decentralized blockchain technology to build a blockchain consensus and storage network, disperses storage risks, prevents single attack points, greatly enhances data protection capabilities, uses a random encryption algorithm to ensure the high security of data in the transmission and storage process, effectively resists cracking attempts, and improves data security; the tamper-proof nature of the blockchain consensus and storage network ensures the integrity and authenticity of transaction data, facilitates real-time tracing and verification, the distributed ledger is open and transparent, and the trust of consumers and regulatory agencies on transaction records is improved; an artificial intelligence algorithm is introduced to build a data security storage engine, realizing intelligent data processing and automatic storage, reducing manual intervention, improving storage efficiency, and adapting to large-scale data application scenarios; dynamically adjusting the storage strategy, flexibly allocating storage resources according to real-time jewelry transaction data, avoiding resource waste, and the storage resource scheduling scheme generated by the data security storage engine ensures efficient use of resources.

[0056] Other beneficial effects of the present application will be further described in the specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 is a flow chart of the blockchain-based jewelry transaction data security storage method in the present application.

[0058] Figure 2 is a structural diagram of the blockchain-based jewelry transaction data security storage system in the present application. DETAILED DESCRIPTION

[0059] The present application will be further explained in conjunction with the accompanying drawings and specific embodiments.

[0060] Embodiment 1:

[0061] As shown in the Figure 1 , the present embodiment provides a blockchain-based jewelry transaction data security storage method, comprising the following steps:

[0062] S1: The cloud data center uses blockchain technology to deploy a blockchain consensus and storage network, and uses an artificial intelligence algorithm to build a data security storage engine;

[0063] The data security storage engine includes a retrieval label generation model, a data analysis model, a data storage strategy generation model, and a storage resource scheduling model;

[0064] The blockchain consensus and storage network includes a data storage network and a blockchain consensus network;

[0065] The cloud data center uses blockchain technology to deploy a blockchain consensus and storage network, and uses an artificial intelligence algorithm to build a data security storage engine, comprising the following steps:

[0066] S1-1: The cloud data center uses blockchain technology to distribute all data servers as data nodes for distributed connection to obtain a blockchain consensus and storage network;

[0067] S1-2: According to an identity allocation mechanism, a plurality of data nodes of the blockchain consensus and storage network are divided into a plurality of consensus nodes and a plurality of storage nodes to obtain a data storage network and a blockchain consensus network;

[0068] The data storage network is a blockchain area obtained by distributed connection of a plurality of storage nodes, and the blockchain consensus network is a blockchain area obtained by distributed connection of a plurality of consensus nodes;

[0069] S1-3: Collect a plurality of historical jewelry transaction data and pre-process the plurality of historical jewelry transaction data to obtain a plurality of pre-processed historical jewelry transaction data;

[0070] S1-4: According to a plurality of pre-processed historical jewelry transaction data, using artificial intelligence algorithm, constructing data security storage engine, including the following steps:

[0071] S1-4-1: According to a plurality of pre-processed historical jewelry transaction data, using deep learning algorithm, constructing data analysis model, and generating a plurality of historical data analysis results;

[0072] The data analysis model is constructed based on Random Forest (RF)-Multilayer Perceptron (MLP) algorithm, and the data analysis model includes a key feature extraction module based on RF algorithm and a data analysis module based on MLP algorithm connected in sequence;

[0073] The key feature extraction module filters a plurality of important features of the jewelry transaction data through ensemble learning of multiple decision trees, selects key features related to the prediction of key water quality indicators from the plurality of important features as the feature basis for subsequent data analysis; The data analysis module is used for jewelry transaction data analysis according to a plurality of key features, and the data analysis result includes abnormal data identification result, market size prediction result, growth analysis result, price trend prediction result, etc.;

[0074] S1-4-2: According to a plurality of pre-processed historical jewelry transaction data, using natural language processing algorithm, constructing retrieval label generation model, and generating a plurality of historical real-time retrieval labels;

[0075] The retrieval label generation model is constructed based on Bidirectional Encoder Representations from Transformers (BERT)-Conditional Random Field (CRF) algorithm, and the retrieval label generation model includes a text feature extraction module based on BERT algorithm and a retrieval label generation model based on CRF algorithm connected in sequence;

[0076] The text feature extraction module is pre-trained with a large amount of text data, so that the module can accurately identify text features for subsequent label prediction; The retrieval label generation model generates retrieval labels according to the text features;

[0077] S1-4-3: According to a plurality of historical data analysis results, using reinforcement learning algorithm, constructing data storage strategy generation model, and generating a plurality of historical data storage strategies and corresponding historical data storage strategy generation experience;

[0078] The data storage strategy generation model is constructed based on a multi-objective proximal policy optimization (MOPPO) algorithm, and includes a target function set, an experience replay pool, an actor network, a critic network, and an agent, wherein the agent is connected to the target function set, the experience replay pool, the actor network, and the critic network respectively.

[0079] The actor network of the data storage strategy generation model is configured to output a probability distribution of actions that should be taken in a given state, and the target is to learn an optimal policy that maximizes long-term cumulative rewards. In a continuous action space, the actor network usually outputs a mean value and an optional variance parameter to describe the probability distribution of actions. The critic network is configured to evaluate the value of a given state, i.e., to predict the expected return that can be obtained by following the current policy from the state. It usually outputs a scalar value representing the value of the state or the state-action value. The experience replay pool is configured to store historical experiences for reuse in the training process. The target function set includes functions defining multiple data processing targets, including minimizing distributed storage cost, minimizing distributed storage response time, and maximizing distributed storage efficiency.

[0080] According to the results of analysis of a plurality of historical data, a data storage strategy generation model is constructed using a reinforcement learning algorithm, and a plurality of historical data storage strategies and corresponding historical data storage strategy generation experiences are generated, including the following steps:

[0081] S1-4-3-1: An initial data storage strategy generation model is constructed using the MOPPO algorithm.

[0082] S1-4-3-2: The initial data storage strategy generation model is set with a target function set, an experience replay pool, an actor network, a critic network, and an agent.

[0083] S1-4-3-3: The data storage strategy generation problem is used as a simulation environment for the initial data storage strategy generation model, and the agent is set with an action space and a state space.

[0084] S1-4-3-4: Based on any target function in the target function set, the initial data storage strategy generation model is pre-trained according to the results of analysis of a plurality of historical data, to obtain a pre-trained data storage strategy generation model, and a plurality of historical data storage strategies and corresponding historical data storage strategy generation experiences are generated.

[0085] S1-4-3-7: using the pre-trained data storage strategy generation model Critic network, obtaining the reward of several generated data storage strategies, and optimizing the pre-trained data storage strategy generation model Actor network according to the reward, obtaining the optimized Actor network;

[0086] S1-4-3-8: according to the reward of several generated data storage strategies, optimizing the Critic network of the pre-trained data storage strategy generation model, obtaining the optimized Critic network;

[0087] S1-4-3-9: traversing all objective functions in the objective function set, repeating the above steps of adversarial training, obtaining the final data storage strategy generation model with the optimized Actor network and the optimized Critic network;

[0088] S1-4-3-10: store several historical data storage strategy generation experiences in the experience replay pool;

[0089] S1-4-4: according to several historical data storage strategies, using swarm intelligence optimization algorithm to construct storage resource scheduling model;

[0090] The storage resource scheduling model is constructed based on the improved sparrow optimization (ISSA) algorithm, and the storage resource scheduling model comprises an optimization target updating module, an initialization module, an iterative optimization module and a vector decoding module connected in sequence.

[0091] The optimization target updating module is used to analyze the historical storage resource scheduling decisions in the historical data storage strategy, obtain the historical influence factors affecting the storage resource scheduling scheme, including scheduling response time, storage resource utilization rate, etc., and define the optimization target of the storage resource scheduling according to the historical influence factors, such as minimizing the response time, maximizing the resource utilization rate, etc. The initial solution generation module is used to start the optimization process by initializing a set of potential storage resource scheduling schemes. The diversified resource scheduling schemes help to explore the solution space and improve the possibility of finding the global optimal solution, ensuring that all initial solutions meet the constraint conditions in actual storage resource scheduling and avoiding the generation of invalid solutions. The iterative search module is used to find better storage resource scheduling schemes by updating the sparrow population of the ISSA algorithm in each iteration. The vector decoding module is used to convert the optimal solution vector found by the ISSA algorithm into a specific storage resource scheduling scheme.

[0092] S2: data server, using a random encryption algorithm to encrypt real-time jewelry transaction data, and uploading the obtained encrypted real-time jewelry transaction data to a cloud data center, comprising the following steps:

[0093] S2-1: a data server, confirming real-time jewelry transaction data of a user, collecting a corresponding biological feature of the user, and generating a random key according to the biological feature;

[0094] The biological feature (such as a fingerprint, an iris pattern) is converted into a unique binary representation, and the binary value is used as a seed value to generate a random key. The uniqueness of the biological feature ensures that the generated seed value is unique, so that the generated random key has high uniqueness and unpredictability. The generation of the random key ensures that each encryption is independent, increasing the strength and security of the encryption;

[0095] S2-2: According to the random key, using a random encryption algorithm, the real-time jewelry transaction data is encrypted to obtain encrypted real-time jewelry transaction data;

[0096] S2-3: Using quantum key distribution (QKD) technology, through a quantum communication channel, the random key is sent to the cloud data center, and through a public communication channel, the encrypted real-time jewelry transaction data is uploaded to the cloud data center, including the following steps:

[0097] S2-3-1: Build a quantum communication line and a public communication channel between the data server and the cloud data center;

[0098] S2-3-2: Using the data server, convert the random key into a key quantum state, send the key quantum state to the cloud data center through the quantum communication line, and measure the key quantum state to obtain a first measurement result;

[0099] S2-3-3: Using the cloud data center, using a pre-set error correction code, correcting the key quantum state, and measuring the error-corrected key quantum state to obtain a second measurement result;

[0100] S2-3-4: Through the data communication line, the first measurement result and the second measurement result are compared and error rate estimated, and the homomorphic random key is generated in the cloud data center;

[0101] S3: The cloud data center decrypts the encrypted real-time jewelry transaction data, and inputs the obtained decrypted real-time jewelry transaction data into the data security storage engine;

[0102] S4: The cloud data center uses the data security storage engine to generate real-time search tags, real-time data analysis results, real-time storage resource scheduling schemes, and real-time data storage strategies of the decrypted real-time jewelry transaction data, including the following steps:

[0103] S4-1: According to the decrypted real-time jewelry transaction data, using the search tag generation model of the data security storage engine, search tag generation is performed to obtain real-time search tags, including the following steps:

[0104] S4-1-1: Input the decrypted real-time jewelry transaction data into the search tag generation model of the data security storage engine;

[0105] S4-1-2: Use the text feature extraction module of the search tag generation model to extract real-time text features of the decrypted real-time jewelry transaction data;

[0106] S4-1-3: Use the search tag generation model of the search tag generation model to generate search tags according to the real-time text features, and obtain real-time search tags;

[0107] S4-2: According to the decrypted real-time jewelry transaction data, using the data analysis model of the data security storage engine, data analysis is performed to obtain corresponding real-time data analysis results, including the following steps:

[0108] S4-2-1: Input the decrypted real-time jewelry transaction data into the data analysis model of the data security storage engine;

[0109] S4-2-2: Use the key feature extraction module of the data analysis model to extract several real-time key features of the decrypted real-time jewelry transaction data;

[0110] S4-2-3: Use the data analysis module of the data analysis model to analyze the data according to the several real-time key features, and obtain the corresponding real-time data analysis results;

[0111] S4-3: According to the real-time data analysis results, using the data storage strategy generation model of the data security storage engine, data storage strategy generation is performed to obtain real-time data storage strategies, including the following steps:

[0112] S4-3-1: Analyze the real-time data analysis results to obtain several real-time data analysis states;

[0113] S4-3-2: According to the several real-time data analysis states, update the state space of the agent of the data storage strategy generation model to obtain the updated state space;

[0114] S4-3-3: Randomly extract several historical data storage strategy generation experiences from the experience replay pool, and generate several possible data storage actions according to the several historical data storage strategy generation experiences;

[0115] S4-3-4: According to the several possible data storage actions, update the action space of the agent to obtain the updated action space;

[0116] S4-3-5: Select a real-time objective function in the objective function set of the data storage strategy generation model, and based on the real-time objective function, use the agent to control the Critic network to generate the real-time value of all possible data storage actions in the updated action space for each real-time data analysis state in the updated state space;

[0117] S4-3-6: According to the real-time value, use the agent to control the Actor network to generate the probability distribution of all possible data storage actions corresponding to each real-time data analysis state;

[0118] S4-3-7: The possible data storage action with the highest probability distribution in the updated action space is taken as the execution data storage action for the real-time data analysis state;

[0119] S4-3-8: The execution data storage actions of all real-time data analysis states in the updated state space are integrated to obtain a real-time data storage strategy;

[0120] S4-4: The cloud data center uses the storage resource scheduling model of the data security storage engine to perform storage resource scheduling according to the real-time data storage strategy, and obtains a real-time storage resource scheduling scheme, including the following steps:

[0121] S4-4-1: The optimization objective update module of the storage resource scheduling model is used to analyze the real-time storage resource scheduling decision in the real-time data storage strategy to obtain a real-time influence factor, and in this embodiment, the real-time influence factor is the response time;

[0122] S4-4-2: According to the real-time influence factor, the optimization objective update module of the storage resource scheduling model is used to update the optimization objective to obtain an updated optimization objective, and the updated optimization objective is to minimize the scheduling response time;

[0123] S4-4-3: According to the updated optimization objective, the fitness function of the iterative optimization module of the storage resource scheduling model is updated to obtain an updated fitness function;

[0124] The formula is:

[0125] Fit(x) = A x T β (x)

[0126] In the formula, Fit(x) is the fitness function; T β (x) is the scheduling response time function; A is the scheduling response time weight; and x is the ISSA individual parameter;

[0127] S4-4-4: The real-time storage resource scheduling scheme is encoded into the individual vector of the initialization module, and the algorithm parameters and the maximum number of iterations of the ISSA algorithm are set according to the algorithm parameters and the maximum number of iterations;

[0128] S4-4-5: Based on the algorithm parameters and individual vectors of the ISSA algorithm, the initialization module is used to initialize the population based on the Circle chaotic mapping sequence, resulting in an initial ISSA population including several initial ISSA individuals.

[0129] The formula is:

[0130]

[0131] In the formula, X' c For the initial ISSA individuals of the Circle chaotic map; X c * The initial ISSA individuals are randomly generated, i.e., the initial solution; mod(*) is the modulo function;

[0132] S4-4-6: Using the iterative optimization module, the initial fitness values ​​of all initial ISSA individuals are obtained according to the fitness function. Based on the initial fitness values, the initial ISSA individuals are sorted to obtain the initial discoverers, initial joiners, and initial predators.

[0133] S4-4-7: Use the iterative optimization module to update the initial ISSA population to obtain an updated ISSA population; the updated ISSA population includes updated discoverers, updated joiners, and updated predators.

[0134] The update formula for the discoverer is:

[0135]

[0136] In the formula, They are the c-th discoverer ISSA individuals in the (t+1)th and tth iterations, respectively; iter max ξ is the maximum number of iterations; Q is a normally distributed random number; L is a 1×D matrix with all elements being 1; R² is the warning value; ST is the safety threshold; c is the ISSA individual indicator; i is the update parameter.

[0137] The update formula for new members is:

[0138]

[0139] In the formula, These are the c-th ISSA individuals that joined at the (t+1)th and tth iterations, respectively. The best position for those whose identities will be exposed; The worst position is ξ; ξ is a random number between 0 and 1; L is a 1×D matrix whose elements are all 1s or -1s; A +is the position update parameter; h is the total number of ISSA individuals;

[0140] The update formula of the predator is:

[0141]

[0142] wherein, are the cth predator ISSA individual in the (t+1)th and tth iteration respectively; δ is a step control parameter, and δ=a"·γ", a" is a convergence factor, and γ" is a step control positive real number not equal to 0; is the current best position; f c , f g , f w are the current, best and worst fitness of the ISSA individual respectively; γ is a minimum constant to prevent the denominator from being 0;

[0143]

[0144] wherein, a" is a convergence factor; tanh(.) is a hyperbolic tangent function; t is an iteration indicator; t max is the maximum iteration number; a max , a min are the maximum and minimum values of the convergence factor respectively; λ is a decreasing rate parameter, and k" is a decreasing period parameter, λ=-2π, k"=π;

[0145] S4-4-8: using the iterative optimization module, using a dynamic reverse learning algorithm, performing dynamic reverse learning on the updated ISSA population to generate a dynamically reversed ISSA population;

[0146] The formula is:

[0147]

[0148] wherein, is a dynamically reversed ISSA individual; γ* is a decreasing inertia coefficient; ub is the upper limit of the search space; lb is the lower limit of the search space; is the updated ISSA individual;

[0149] S4-4-9: using the iterative optimization module, according to the fitness function, obtaining the updated fitness values of all ISSA individuals in the updated ISSA population and the dynamically reversed ISSA population, and obtaining the optimal solution according to the updated fitness values;

[0150] S4-4-10: if the iteration number reaches the maximum iteration number or the fitness value of the optimal individual meets the requirement, using the vector decoding module to decode the individual vector of the optimal solution to obtain the optimal real-time storage resource scheduling scheme;

[0151] S5: The cloud data center, according to the real-time storage resource scheduling scheme and the real-time data storage strategy, uses the blockchain consensus and the storage network to securely store the encrypted real-time jewelry transaction data, the corresponding real-time search tags and the real-time data analysis results, including the following steps:

[0152] S5-1: The cloud data center, according to the real-time storage resource scheduling scheme, schedules the corresponding data storage partition in the data storage network of the blockchain consensus and the storage network;

[0153] S5-2: According to the decrypted real-time jewelry transaction data, generate a real-time storage request, and use the blockchain consensus network of the blockchain consensus and the storage network to reach a consensus on the real-time storage request;

[0154] S5-3: After the consensus is successful, according to the real-time data storage strategy, use the data storage partition to securely store the encrypted real-time jewelry transaction data, the corresponding real-time search tags and the real-time data analysis results, including the following steps:

[0155] S5-3-1: After the consensus is successful, according to the real-time data sharding decision of the real-time data storage strategy, perform data sharding on the encrypted real-time jewelry transaction data to obtain a plurality of encrypted real-time data shards;

[0156] S5-3-2: According to the real-time distributed storage decision of the real-time data storage strategy, send the plurality of encrypted real-time data shards to a plurality of storage nodes of the data storage partition;

[0157] S5-3-3: Use the storage nodes of the data storage partition to locally store the received encrypted real-time data shards, return the real-time local storage address, and send real-time storage success information to other storage nodes;

[0158] S5-3-4: Write the real-time local storage address of the encrypted real-time data shards, the corresponding real-time search tags and the real-time data analysis results into the data storage ledger of the data storage network, and generate a real-time data storage record;

[0159] S5-3-5: Use the blockchain consensus network to reach a consensus on the real-time data storage record, and obtain a real-time distributed storage address;

[0160] S5-3-6: After the consensus is successful, write the real-time distributed storage address, the corresponding real-time search tags and the real-time data analysis results into the distributed ledger of the blockchain consensus network;

[0161] S5-3-7: If the storage node receives more than a preset number of real-time storage success information, end the secure storage step, otherwise, continue to securely store.

[0162] Embodiment 2:

[0163] As Figure 2 shown, the embodiment provides a blockchain-based jewelry transaction data security storage system for implementing a jewelry transaction data security storage method, the system is arranged in a cloud data center, and the system includes an initialization unit, a data decryption unit, a data processing unit, and a security storage unit, the cloud data center is respectively in communication connection with a plurality of data servers, and the cloud data center is provided with a blockchain consensus and storage network and a data security storage engine.

[0164] The initialization unit is used for deploying a blockchain consensus and storage network using blockchain technology, and constructing a data security storage engine using an artificial intelligence algorithm;

[0165] The data decryption unit is used for decrypting the encrypted real-time jewelry transaction data, and inputting the obtained decrypted real-time jewelry transaction data into the data security storage engine;

[0166] The data processing unit is used for generating real-time search tags, real-time data analysis results, real-time storage resource scheduling schemes, and real-time data storage strategies of the decrypted real-time jewelry transaction data using the data security storage engine;

[0167] The security storage unit is used for storing the encrypted real-time jewelry transaction data, the corresponding real-time search tags, and the real-time data analysis results using the blockchain consensus and storage network according to the real-time storage resource scheduling schemes and the real-time data storage strategies;

[0168] The data server is used for encrypting the real-time jewelry transaction data using a random encryption algorithm, and uploading the obtained encrypted real-time jewelry transaction data to the cloud data center.

[0169] The application discloses a blockchain-based jewelry transaction data security storage method and system, which constructs a blockchain consensus and storage network through a decentralized blockchain technology, disperses storage risks, prevents single attack points, greatly enhances data protection capabilities, uses a random encryption algorithm to ensure the high security of data in the transmission and storage process, effectively resists cracking attempts, and improves data security; the non-tamperable characteristics of the blockchain consensus and storage network guarantee the integrity and authenticity of transaction data, facilitate real-time tracing and verification, the distributed ledger is open and transparent, improves the trust of consumers and regulatory agencies on transaction records; an artificial intelligence algorithm is introduced to construct a data security storage engine, realizes intelligent data processing and automatic storage, reduces manual intervention, improves storage efficiency, and adapts to large-scale data application scenarios; dynamically adjust the storage strategy, flexibly allocate storage resources according to the real-time jewelry transaction data, avoid resource waste, and ensure the efficient use of resources through the storage resource scheduling scheme generated by the data security storage engine.

[0170] The application is not limited to the above-mentioned optional embodiments, and anyone can derive other various forms of products under the inspiration of the application. The above-mentioned specific embodiments should not be understood as limiting the protection scope of the application, and the protection scope of the application should be defined by the claims, and the specification can be used to interpret the claims.

Claims

1. A method for securely storing jewelry transaction data based on blockchain, characterized in that: Includes the following steps: Cloud data centers utilize blockchain technology to deploy blockchain consensus and storage networks, and employ artificial intelligence algorithms to build a secure data storage engine. The data security storage engine includes a retrieval tag generation model, a data analysis model, a data storage strategy generation model, and a storage resource scheduling model; The aforementioned blockchain consensus and storage network includes a data storage network and a blockchain consensus network; The data analysis model described above is built based on the RF-MLP algorithm; The aforementioned retrieval tag generation model is built based on the BERT-CRF algorithm; The data storage strategy generation model is constructed based on the MOPPO algorithm; The storage resource scheduling model is constructed based on the ISSA algorithm, and the storage resource scheduling model includes an optimization target update module, an initialization module, an iterative optimization module, and a vector decoding module connected in sequence. The data server uses a random encryption algorithm to encrypt real-time jewelry transaction data and then uploads the encrypted real-time jewelry transaction data to the cloud data center, including the following steps: The data server confirms the user's real-time jewelry transaction data, collects the corresponding user's biometric characteristics, and generates a random key based on the biometric characteristics; Based on a random key, a random encryption algorithm is used to encrypt real-time jewelry transaction data, resulting in encrypted real-time jewelry transaction data. Using QKA technology, random keys are sent to the cloud data center through a quantum communication channel, and encrypted real-time jewelry transaction data is uploaded to the cloud data center through a public communication channel. The cloud data center decrypts the encrypted real-time jewelry transaction data and inputs the decrypted real-time jewelry transaction data into the data security storage engine. The cloud data center uses a data security storage engine to generate real-time retrieval tags, real-time data analysis results, real-time storage resource scheduling schemes, and real-time data storage strategies for decrypted real-time jewelry transaction data, including the following steps: Based on the decrypted real-time jewelry transaction data, the search tag generation model of the data security storage engine is used to generate search tags and obtain real-time search tags. Based on the decrypted real-time jewelry transaction data, the data analysis model of the data security storage engine is used to perform data analysis and obtain the corresponding real-time data analysis results. Based on the real-time data analysis results, the data storage strategy generation model of the data security storage engine is used to generate the data storage strategy and obtain the real-time data storage strategy. In a cloud data center, based on the real-time data storage strategy, the storage resource scheduling model of the data security storage engine is used to perform storage resource scheduling, resulting in a real-time storage resource scheduling scheme, including the following steps: The optimization objective update module of the storage resource scheduling model is used to analyze the real-time storage resource scheduling decision in the real-time data storage strategy to obtain the real-time impact factor, which is the response time. Based on the real-time impact factor, the optimization target update module of the storage resource scheduling model is used to update the optimization target, and the updated optimization target is to minimize the scheduling response time. Based on the updated optimization objective, the fitness function of the iterative optimization module of the storage resource scheduling model is updated to obtain the updated fitness function; The formula is: In the formula, The fitness function; This is the scheduling response time function; Weights for scheduling response time; For ISSA individual reference parameters; The real-time storage resource scheduling scheme is encoded as an individual vector of the initialization module, and the algorithm parameters and maximum number of iterations of the ISSA algorithm are set accordingly. Based on the algorithm parameters and individual vectors of the ISSA algorithm, the initialization module is used to initialize the population based on the Circle chaotic mapping sequence, resulting in an initial ISSA population including several initial ISSA individuals. The formula is: In the formula, The initial ISSA individuals for the Circle chaotic mapping; The initial ISSA individuals are randomly generated, i.e., the initial solution; mod(*) is the modulo function; Using the iterative optimization module, the initial fitness values ​​of all initial ISSA individuals are obtained according to the fitness function. Based on the initial fitness values, the initial ISSA individuals are sorted to obtain the initial discoverers, initial joiners, and initial predators. The initial ISSA population is updated using the iterative optimization module to obtain the updated ISSA population. The updated ISSA population includes newer discoverers, newer incorporators, and newer predators; The update formula for the discoverer is: In the formula, The first t +1、 t The iteration of the ... c One discoverer, an ISSA individual; This represents the maximum number of iterations. A random number between 0 and 1; These are normally distributed random numbers. for A matrix whose elements are all 1s; This is the warning value; This is a safety threshold; c For ISSA individual indicators; To update parameters; The update formula for new members is: In the formula, The first t +1、 t The iteration of the ... c One ISSA individual member; The best position for those whose identities will be exposed; This is the worst position at present; A random number between 0 and 1; for A matrix whose elements are all 1s or -1s; Update parameters for location; The total number of ISSA individuals; The predator's update formula is: In the formula, The first t +1、 t The iteration of the ... c One predator ISSA individual; This is the step size control parameter, and , The convergence factor is The step size is a non-zero positive real number; This is the current optimal position; These represent the current, best, and worst fitness of the ISSA individual, respectively. To minimize the constant value, preventing the denominator from being zero; In the formula, is the convergence factor; tanh(.) is the hyperbolic tangent function; For iteration indication; This represents the maximum number of iterations. a max , a min These are the maximum and minimum values ​​of the convergence factor, respectively; λ For the deceleration rate parameter, For decreasing period parameters, λ =-2 π , = π ; Using the iterative optimization module and the dynamic back-learning algorithm, the updated ISSA population is dynamically back-learned to generate a dynamically back-learned ISSA population. The formula is: In the formula, For dynamically reversed ISSA individuals; The coefficient of inertia is decreasing; This represents the upper limit of the search space. This is the lower bound of the search space; For updated ISSA individuals; Using the iterative optimization module, based on the fitness function, the updated fitness values ​​of all ISSA individuals in the updated ISSA population and the dynamically reversed ISSA population are obtained, and the optimal solution is obtained based on the updated fitness values. If the number of iterations reaches the maximum number of iterations or the fitness value of the best individual meets the requirements, then the vector decoding module is used to decode the individual vector of the best solution to obtain the optimal real-time storage resource scheduling scheme. The cloud data center, based on the real-time storage resource scheduling scheme and real-time data storage strategy, uses blockchain consensus and storage network to securely store encrypted real-time jewelry transaction data, corresponding real-time search tags, and real-time data analysis results.

2. The method for securely storing jewelry transaction data based on blockchain according to claim 1, characterized in that: Cloud data centers utilize blockchain technology, deploying a blockchain consensus and storage network, and employing artificial intelligence algorithms to build a secure data storage engine, including the following steps: Cloud data centers use blockchain technology to connect all data servers as data nodes in a distributed manner, thereby achieving blockchain consensus and a storage network. Based on the identity allocation mechanism, several data nodes in the blockchain consensus and storage network are divided into several consensus nodes and several storage nodes, resulting in a data storage network and a blockchain consensus network. Collect a number of historical jewelry transaction data and preprocess the historical jewelry transaction data to obtain a number of preprocessed historical jewelry transaction data. Based on several pre-processed historical jewelry transaction data, an artificial intelligence algorithm is used to build a data security storage engine.

3. The method for securely storing jewelry transaction data based on blockchain according to claim 2, characterized in that: Based on several preprocessed historical jewelry transaction data, an artificial intelligence algorithm is used to build a secure data storage engine, including the following steps: Based on several preprocessed historical jewelry transaction data, a data analysis model is constructed using deep learning algorithms, and several historical data analysis results are generated. Based on several preprocessed historical jewelry transaction data, a retrieval tag generation model is constructed using natural language processing algorithms, and several historical real-time retrieval tags are generated. Based on the analysis results of several historical data, a data storage strategy generation model is constructed using reinforcement learning algorithms, and several historical data storage strategies and corresponding historical data storage strategy generation experiences are generated. Based on several historical data storage strategies, a storage resource scheduling model is constructed using a swarm intelligence optimization algorithm.

4. The method for securely storing jewelry transaction data based on blockchain according to claim 3, characterized in that: The cloud data center, based on a real-time storage resource scheduling scheme and real-time data storage strategy, uses blockchain consensus and a storage network to securely store encrypted real-time jewelry transaction data, corresponding real-time search tags, and real-time data analysis results, including the following steps: In cloud data centers, the corresponding data storage partitions are scheduled within the data storage network of the blockchain consensus and storage network, based on the real-time storage resource scheduling scheme. Based on the decrypted real-time jewelry transaction data, a real-time storage request is generated, and consensus is reached on the real-time storage request using a blockchain consensus network and a storage network. Once consensus is reached, the encrypted real-time jewelry transaction data, corresponding real-time search tags, and real-time data analysis results are securely stored using data storage partitions, according to the real-time data storage strategy.

5. A method for securely storing jewelry transaction data based on blockchain according to claim 4, characterized in that: After consensus is reached, according to the real-time data storage strategy, data storage partitions are used to securely store the encrypted real-time jewelry transaction data, corresponding real-time search tags, and real-time data analysis results, including the following steps: After the consensus is reached, the encrypted real-time jewelry transaction data is sharded according to the real-time data storage strategy and the real-time data sharding decision, resulting in several encrypted real-time data shards. Based on the real-time distributed storage decision of the real-time data storage strategy, several encrypted real-time data fragments are sent to several storage nodes of the data storage partition; The storage node using the data storage partition stores the received encrypted real-time data fragments locally, returns the real-time local storage address, and sends real-time storage success information to other storage nodes. The encrypted real-time data fragments are written to the real-time local storage address, the corresponding real-time retrieval tag, and the real-time data analysis results in the data storage network's data storage ledger, and a real-time data storage record is generated. Using a blockchain consensus network, consensus is reached on real-time data storage records, and real-time distributed storage addresses are obtained; Once consensus is reached, the real-time distributed storage address, the corresponding real-time retrieval tag, and the real-time data analysis results will be written into the distributed ledger of the blockchain consensus network. If the storage node receives more than a preset threshold number of real-time storage success messages, the secure storage step ends; otherwise, secure storage continues.

6. A blockchain-based secure storage system for jewelry transaction data, used to implement the secure storage method for jewelry transaction data as described in any one of claims 1-5, characterized in that: The system is located in a cloud data center and includes an initialization unit, a data decryption unit, a data processing unit, and a secure storage unit. The cloud data center is connected to several data servers and is equipped with a blockchain consensus and storage network and a data security storage engine.

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