Jewelry transaction data secure storage method and system based on block chain

Abstract

The invention belongs to the technical field of data storage, and discloses a jewelry transaction data secure storage method and system based on a block chain. The method comprises the following steps: a cloud data center deploys a block chain consensus and storage network and constructs a data security storage engine; the data server carries out encryption and uploads the encrypted data to the cloud data center; the cloud data center carries out decryption; the cloud data center is used for generating a real-time retrieval tag, a real-time data analysis result, a real-time storage resource scheduling scheme and a real-time data storage strategy of the decrypted real-time jewelry transaction data by using a data security storage engine; and the cloud data center is used for safely storing the encrypted real-time jewelry transaction data, the corresponding real-time retrieval tag and the real-time data analysis result by using a block chain consensus and storage network. According to the invention, the problems of insufficient security, poor traceability, low storage efficiency and waste of storage resources in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data storage, and in particular relates to a method and system for securely storing jewelry transaction data based on blockchain. Background Art

[0002] Jewelry transaction data covers information on the 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 the performance of listed companies, which helps to gain a deeper understanding of the current status and trends of the industry. With the increasing frequency of jewelry transactions, the secure 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 exposed many defects in practical applications, including:

[0004] 1) Insufficient security: Traditional databases use centralized storage, which makes them easy targets for hacker attacks. Once hacked, a large amount of data may be leaked, which poses a risk of centralized storage. Conventional encryption algorithms may face the risk of being cracked, especially as computing power increases, some encryption methods become less secure and have limited encryption strength.

[0005] 2) Poor traceability: In a centralized system, data may be tampered with by insiders or external attackers, which is difficult to detect and trace in time. The complete historical records of jewelry transactions are not public, making it difficult for consumers and regulators to verify the authenticity and legality of transactions, and transaction records are not transparent.

[0006] 3) Low storage efficiency: The existing technology lacks intelligent data processing and automatic storage technology, and requires a lot of manual intervention, resulting in low storage efficiency of jewelry transaction data and unable to meet the application scenarios of large-scale data;

[0007] 4) Waste of storage resources: Most existing technologies store data based on preset static storage strategies, and are unable to dynamically adjust storage strategies based on the real-time situation of jewelry transaction data, resulting in a large amount of storage resource waste. Summary of the invention

[0008] In order to solve the problems of insufficient security, poor traceability, low storage efficiency and waste of storage resources in the prior art, the present invention aims to provide a blockchain-based jewelry transaction data secure storage method and system.

[0009] The technical solution adopted by the present invention is:

[0010] A method for securely storing jewelry transaction data based on blockchain, comprising the following steps:

[0011] Cloud data centers use blockchain technology to deploy blockchain consensus and storage networks, and use artificial intelligence algorithms to build data security storage engines;

[0012] The data server encrypts the real-time jewelry transaction data using a random encryption algorithm, and uploads the 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 decrypted real-time jewelry transaction data into the data security storage engine;

[0014] 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 solutions, and real-time data storage strategies for decrypted real-time jewelry transaction data;

[0015] The cloud data center uses blockchain consensus and storage network to securely store encrypted real-time jewelry transaction data, corresponding real-time retrieval tags, and real-time data analysis results based on real-time storage resource scheduling plans and real-time data storage strategies.

[0016] Further, 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;

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

[0018] Furthermore, the cloud data center uses blockchain technology to deploy blockchain consensus and storage networks, and uses artificial intelligence algorithms to build a data security storage engine, including the following steps:

[0019] Cloud data centers use blockchain technology to connect all data servers as data nodes in a distributed manner to obtain blockchain consensus and storage networks;

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

[0021] Collecting some historical jewelry transaction data, and preprocessing some historical jewelry transaction data to obtain some preprocessed historical jewelry transaction data;

[0022] Based on several pre-processed historical jewelry transaction data, an artificial intelligence algorithm is used to build a data security storage engine.

[0023] Furthermore, based on some pre-processed historical jewelry transaction data, an artificial intelligence algorithm is used to build a data security storage engine, including the following steps:

[0024] Based on some pre-processed historical jewelry transaction data, a deep learning algorithm is used to build a data analysis model and generate some historical data analysis results;

[0025] Based on some pre-processed historical jewelry transaction data, a natural language processing algorithm is used to build a retrieval tag generation model and generate some historical real-time retrieval tags;

[0026] Based on the analysis results of several historical data, a data storage strategy generation model is constructed using a reinforcement learning algorithm, and several historical data storage strategies and corresponding historical data storage strategy generation experiences are generated;

[0027] According to several historical data storage strategies, a storage resource scheduling model is constructed using swarm intelligence optimization algorithm.

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

[0029] The retrieval tag generation model is built based on the BERT-CRF algorithm;

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

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

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

[0033] The data server confirms the user's real-time jewelry transaction data, collects the corresponding user's biometric features, and generates a random key based on the biometric features;

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

[0035] Using QKA technology, random keys are sent to the cloud data center through quantum communication channels, and encrypted real-time jewelry transaction data is uploaded to the cloud data center through public communication channels.

[0036] Furthermore, a data security storage engine is used to generate real-time retrieval tags, real-time data analysis results, real-time storage resource scheduling solutions and real-time data storage strategies for the decrypted real-time jewelry transaction data, including the following steps:

[0037] According to the decrypted real-time jewelry transaction data, a retrieval tag generation model of the data security storage engine is used to generate retrieval tags to obtain real-time retrieval tags;

[0038] According to the decrypted real-time jewelry transaction data, the data analysis model of the data security storage engine is used to perform data analysis to obtain the corresponding real-time data analysis results;

[0039] According to the real-time data analysis results, a data storage strategy generation model of the data security storage engine is used to generate a data storage strategy to obtain a real-time data storage strategy;

[0040] The cloud data center, according to the real-time data storage strategy, uses the storage resource scheduling model of the data security storage engine to schedule storage resources and obtain a real-time storage resource scheduling solution.

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

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

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

[0044] After the consensus is successful, the encrypted real-time jewelry transaction data, the corresponding real-time retrieval tags and the real-time data analysis results are securely stored using data storage partitions according to the real-time data storage strategy.

[0045] Furthermore, after consensus is successful, the encrypted real-time jewelry transaction data, the corresponding real-time retrieval tags, and the real-time data analysis results are securely stored using data storage partitions according to the real-time data storage strategy, including the following steps:

[0046] After the consensus is successful, the encrypted real-time jewelry transaction data is sharded according to the real-time data sharding decision of the real-time data storage strategy to obtain several encrypted real-time data shards;

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

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

[0049] Write the real-time local storage address of the encrypted real-time data shard, the corresponding real-time retrieval tag, and the real-time data analysis result into the data storage account book of the data storage network, and generate a real-time data storage record;

[0050] Use the blockchain consensus network to reach consensus on real-time data storage records and obtain real-time distributed storage addresses;

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

[0052] If the storage node receives a number of real-time storage success messages exceeding a preset number threshold, the secure storage step is terminated; otherwise, the secure storage continues.

[0053] A blockchain-based jewelry transaction data security storage system is used to implement a jewelry transaction data security storage method. The system is set 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 connected to a number of data servers for communication, 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 invention are:

[0055] The present invention discloses a blockchain-based jewelry transaction data security storage method and system. Through decentralized blockchain technology, a blockchain consensus and storage network is constructed to disperse storage risks, prevent single attack points, greatly enhance data protection capabilities, and utilize random encryption algorithms to ensure high security of data during transmission and storage, effectively resist cracking attempts, and improve data security. The tamper-proof characteristics of the blockchain consensus and storage network ensure the integrity and authenticity of transaction data, facilitate real-time traceability and verification, and make the distributed ledger public and transparent, thereby enhancing the trust of consumers and regulators in transaction records. Artificial intelligence algorithms are introduced to construct a data security storage engine to achieve intelligent data processing and automatic storage, reduce manual intervention, improve storage efficiency, and adapt to large-scale data application scenarios. The storage strategy is dynamically adjusted to flexibly allocate storage resources according to real-time jewelry transaction data to avoid resource waste. The storage resource scheduling scheme generated by the data security storage engine ensures efficient use of resources.

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

[0057] Figure 1 It is a flowchart of the method for securely storing jewelry transaction data based on blockchain in the present invention.

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

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

[0060] Embodiment 1:

[0061] like Figure 1 As shown, this embodiment provides a method for securely storing jewelry transaction data based on blockchain, comprising the following steps:

[0062] S1: Cloud data center, using blockchain technology, deploying blockchain consensus and storage network, and using artificial intelligence algorithms to build a data security storage engine;

[0063] 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;

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

[0065] Cloud data centers use blockchain technology to deploy blockchain consensus and storage networks, and use artificial intelligence algorithms to build a data security storage engine, including the following steps:

[0066] S1-1: Cloud data center, using blockchain technology to connect all data servers as data nodes in a distributed manner to obtain blockchain consensus and storage network;

[0067] S1-2: According to the identity allocation mechanism, several data nodes of the blockchain consensus and storage network are divided into several consensus nodes and several 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 several storage nodes, and the blockchain consensus network is a blockchain area obtained by distributed connection of several consensus nodes;

[0069] S1-3: collecting some historical jewelry transaction data, and preprocessing the some historical jewelry transaction data to obtain some preprocessed historical jewelry transaction data;

[0070] S1-4: Based on some pre-processed historical jewelry transaction data, use artificial intelligence algorithms to build a data security storage engine, including the following steps:

[0071] S1-4-1: Based on some pre-processed historical jewelry transaction data, use deep learning algorithms to build a data analysis model and generate some historical data analysis results;

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

[0073] The key feature extraction module screens several important features of jewelry transaction data through integrated learning of multiple decision trees, and selects key features related to the prediction of key water quality indicators from several important features as the feature basis for subsequent data analysis; the data analysis module is used to analyze jewelry transaction data based on several key features. The data analysis results include abnormal data identification results, market size prediction results, growth analysis results, price trend prediction results, etc.

[0074] S1-4-2: Based on some pre-processed historical jewelry transaction data, a natural language processing algorithm is used to build a retrieval tag generation model and generate some historical real-time retrieval tags;

[0075] The retrieval tag generation model is constructed based on a bidirectional Transformer encoder representation (Bidirectional Encoder Representations from Transformers, BERT)-conditional random field (Conditional Random Field, CRF) algorithm, and the retrieval tag generation model includes a text feature extraction module constructed based on the BERT algorithm and a retrieval tag generation model constructed based on the CRF algorithm, which are sequentially connected;

[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 based on text features;

[0077] S1-4-3: Based on the analysis results of several historical data, a data storage strategy generation model is constructed using a reinforcement learning algorithm, and several historical data storage strategies and corresponding historical data storage strategy generation experiences are generated;

[0078] The data storage strategy generation model is built based on the Multi-Objective Proximal Policy Optimization (MOPPO) algorithm, and the data storage strategy generation model includes an objective function set, an experience replay pool, an Actor network, a Critic network, and an agent. The agent is connected to the objective 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 responsible for outputting the probability distribution of the actions that should be taken in a given state. The goal is to learn an optimal strategy, that is, to maximize the long-term cumulative reward. In the continuous action space, the Actor network usually outputs a mean and an optional variance parameter to describe the probability distribution of the action. The Critic network is responsible for evaluating the value of a given state, that is, predicting the expected return that can be obtained by starting from this state and following the current strategy. It usually outputs a scalar value that represents the value of the state or the state-action value. The experience replay pool is used to store historical experience for reuse during training. The objective function set includes functions of multiple defined data processing objectives, including minimizing distributed storage costs, minimizing distributed storage response time, and maximizing distributed storage efficiency.

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

[0081] S1-4-3-1: Use the MOPPO algorithm to build an initial data storage strategy generation model;

[0082] S1-4-3-2: Set the objective function set, experience replay pool, actor network, critic network and agent for the initial data storage strategy generation model;

[0083] S1-4-3-3: Use the data storage strategy generation problem as the simulation environment for the initial data storage strategy generation model, and set the action space and state space for the agent;

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

[0085] S1-4-3-7: Use the Critic network of the pre-trained data storage strategy generation model to obtain several rewards for generating data storage strategies, and optimize the Actor network of the pre-trained data storage strategy generation model based on the several rewards to obtain an optimized Actor network;

[0086] S1-4-3-8: According to several rewards for generating data storage strategies, the critic network of the pre-trained data storage strategy generation model is optimized to obtain an optimized critic network;

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

[0088] S1-4-3-10: Store the experience generated by several historical data storage strategies into the experience playback pool;

[0089] S1-4-4: Based on several historical data storage strategies, a storage resource scheduling model is constructed using a swarm intelligence optimization algorithm;

[0090] The storage resource scheduling model is constructed based on the Improved Sparrow Search Algorithm (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 which are connected in sequence;

[0091] The optimization target update module is used to analyze the historical storage resource scheduling decisions in the historical data storage strategy, obtain the historical influencing factors that affect the storage resource scheduling plan, including scheduling response time, storage resource utilization, etc., and define the optimization goals of storage resource scheduling based on the historical influencing factors, such as minimizing response time, maximizing resource utilization, etc.; the initial solution generation module is used to start the optimization process by initializing a set of potential storage resource scheduling plans. Initializing a variety of resource scheduling plans helps to explore the solution space and increase the possibility of finding the global optimal solution, ensuring that all initial solutions meet the constraints in the actual storage resource scheduling and avoiding the generation of invalid solutions; the iterative search module is used to update the sparrow population of the ISSA algorithm in each iteration to find a better storage resource scheduling plan; the vector decoding module is used to convert the optimal solution vector found by the ISSA algorithm into a specific storage resource scheduling plan;

[0092] S2: Data server, using random encryption algorithm to encrypt real-time jewelry transaction data, and upload the encrypted real-time jewelry transaction data to the cloud data center, including the following steps:

[0093] S2-1: Data server, confirms the user's real-time jewelry transaction data, collects the corresponding user's biometric features, and generates a random key based on the biometric features;

[0094] Biometric features (such as fingerprints and iris patterns) are converted into a unique binary representation. This binary value is used as a seed value to generate a random key. The uniqueness of the biometric feature ensures that the generated seed value is unique, making the generated random key highly unique and unpredictable. The generation of random keys ensures that each encryption is independent, increasing the strength and security of encryption.

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

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

[0097] S2-3-1: Build quantum communication lines and public communication channels between data servers and cloud data centers;

[0098] S2-3-2: Use the data server to 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 a cloud data center and a preset error correction code, the key quantum state is error corrected, and the error-corrected key quantum state is measured to obtain a second measurement result;

[0100] S2-3-4: performing a public basis vector comparison and an error rate estimation on the first measurement result and the second measurement result through a data communication line, and generating a homomorphic random key in the cloud data center;

[0101] S3: 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;

[0102] S4: Cloud data center, using data security storage engine, generates real-time retrieval tags, real-time data analysis results, real-time storage resource scheduling scheme and real-time data storage strategy for decrypted real-time jewelry transaction data, including the following steps:

[0103] S4-1: Based on the decrypted real-time jewelry transaction data, a retrieval tag generation model of the data security storage engine is used to generate a retrieval tag to obtain a real-time retrieval tag, including the following steps:

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

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

[0106] S4-1-3: using a retrieval tag generation model of a retrieval tag generation model to generate retrieval tags according to real-time text features to obtain real-time retrieval tags;

[0107] S4-2: Based on the decrypted real-time jewelry transaction data, use the data analysis model of the data security storage engine to perform data analysis to obtain the 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 perform data analysis based on a number of real-time key features to obtain corresponding real-time data analysis results;

[0111] S4-3: Based on the real-time data analysis results, a data storage strategy generation model of the data security storage engine is used to generate a data storage strategy to obtain a real-time data storage strategy, 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: updating the state space of the agent of the data storage strategy generation model according to a number of real-time data analysis states, and obtaining an updated state space;

[0114] S4-3-3: randomly extracting several historical data storage strategies from the experience replay pool to generate experience, and generating several possible data storage actions based on the several historical data storage strategies to generate experience;

[0115] S4-3-4: Update the action space of the agent according to several possible data storage actions to obtain an updated action space;

[0116] S4-3-5: Select a real-time objective function from 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: Based on a number of real-time values, use intelligent agents to control the Actor network and generate a probability distribution of all possible data storage actions corresponding to each real-time data analysis state;

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

[0119] S4-3-8: Integrate the execution data storage actions of all real-time data analysis states in the updated state space 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 schedule storage resources according to the real-time data storage strategy and obtains a real-time storage resource scheduling solution, including the following steps:

[0121] S4-4-1: using the optimization target update module of the storage resource scheduling model, the real-time storage resource scheduling decision in the real-time data storage strategy is analyzed to obtain a real-time impact factor. In this embodiment, the real-time impact factor is the response time;

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

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

[0124] The formula is:

[0125] Fit(x)=A×T β (x)

[0126] Where Fit(x) is the fitness function; T β (x) is the scheduling response time function; A is the scheduling response time weight; x is the ISSA individual reference parameter;

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

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

[0129] The formula is:

[0130]

[0131] Where X' c is the initial ISSA individual of the Circle chaos map; X c * is the randomly generated initial ISSA individual, i.e., the initial solution; mod(*) is the remainder function;

[0132] S4-4-6: Using the iterative optimization module, according to the fitness function, the initial fitness values ​​of all initial ISSA individuals are obtained, and according to the initial fitness values, the initial ISSA individuals are sorted to obtain the initial discoverer, the initial joiner and the initial predator;

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

[0134] The update formula of the discoverer is:

[0135]

[0136] In the formula, are the cth discoverer ISSA individuals of the t+1th and tth iterations respectively; iter max is the maximum number of iterations; ξ is a random number between 0 and 1; Q is a normally distributed random number; L is a 1×D matrix whose elements are all 1; R 2 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 the joiner is:

[0138]

[0139] In the formula, are the cth joiner ISSA individuals in the t+1th and tth iterations respectively; The best position for the exposed person to occupy; is the current worst position; ξ is a random number between 0 and 1; L is a 1×D matrix whose elements are all 1 or -1; A+ is the position update parameter; h is the total number of ISSA individuals;

[0140] The update formula of the predator is:

[0141]

[0142] In the formula, are the cth predator ISSA individuals of the t+1th and tth iterations respectively; δ is the step-size control parameter, and δ=a"·γ", a" is the convergence factor, γ" is a non-zero positive real number for step-size control; 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 the minimum constant to prevent the denominator from being 0;

[0143]

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

[0145] S4-4-8: Use the iterative optimization module and the dynamic reverse learning algorithm to perform dynamic reverse learning on the updated ISSA population to generate a dynamic reverse ISSA population;

[0146] The formula is:

[0147]

[0148] In the formula, is the ISSA individual with dynamic reverse; γ* is the decreasing inertia coefficient; ub is the upper limit of the search space; lb is the lower limit of the search space; For the updated ISSA entity;

[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 number of iterations reaches the maximum number of iterations or the fitness value of the optimal individual meets the requirements, the vector decoding module is used to decode the individual vector of the optimal solution to obtain the optimal real-time storage resource scheduling solution;

[0151] S5: Cloud data center, based on the real-time storage resource scheduling plan and real-time data storage strategy, uses blockchain consensus and storage network to securely store the encrypted real-time jewelry transaction data, the corresponding real-time retrieval tags, and the real-time data analysis results, including the following steps:

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

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

[0154] S5-3: After consensus is successful, the encrypted real-time jewelry transaction data, the corresponding real-time retrieval tags, and the real-time data analysis results are securely stored using data storage partitions according to the real-time data storage strategy, including the following steps:

[0155] S5-3-1: After consensus is successful, the encrypted real-time jewelry transaction data is sharded according to the real-time data sharding decision of the real-time data storage strategy to obtain a number 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, sending a number of encrypted real-time data shards to a number of storage nodes of the data storage partition;

[0157] S5-3-3: Use the storage node of the data storage partition to locally store the received encrypted real-time data fragments, 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 shard, the corresponding real-time retrieval tag, and the real-time data analysis result into the data storage account book of the data storage network, and generate a real-time data storage record;

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

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

[0161] S5-3-7: If the storage node receives a number of real-time storage success messages exceeding a preset number threshold, the secure storage step is terminated; otherwise, the secure storage step continues.

[0162] Embodiment 2:

[0163] like Figure 2 As shown, this embodiment provides a jewelry transaction data security storage system based on blockchain, which is used to implement a jewelry transaction data security storage method. The system is set 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 communicated with a number of data servers, and the cloud data center is provided with a blockchain consensus and storage network and a data security storage engine.

[0164] Initialization unit, used to deploy blockchain consensus and storage network using blockchain technology, and build a data security storage engine using artificial intelligence algorithms;

[0165] A data decryption unit, used to decrypt the encrypted real-time jewelry transaction data and input the obtained decrypted real-time jewelry transaction data into a data security storage engine;

[0166] A data processing unit, used to generate a real-time retrieval tag, real-time data analysis results, a real-time storage resource scheduling plan and a real-time data storage strategy for the decrypted real-time jewelry transaction data using a data security storage engine;

[0167] A secure storage unit is used to securely store encrypted real-time jewelry transaction data, corresponding real-time retrieval tags, and real-time data analysis results using blockchain consensus and storage networks according to real-time storage resource scheduling plans and real-time data storage strategies;

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

[0169] The present invention discloses a blockchain-based jewelry transaction data security storage method and system. Through decentralized blockchain technology, a blockchain consensus and storage network is constructed to disperse storage risks, prevent single attack points, greatly enhance data protection capabilities, and utilize random encryption algorithms to ensure high security of data during transmission and storage, effectively resist cracking attempts, and improve data security. The tamper-proof characteristics of the blockchain consensus and storage network ensure the integrity and authenticity of transaction data, facilitate real-time traceability and verification, and make the distributed ledger public and transparent, thereby enhancing the trust of consumers and regulators in transaction records. Artificial intelligence algorithms are introduced to construct a data security storage engine to achieve intelligent data processing and automatic storage, reduce manual intervention, improve storage efficiency, and adapt to large-scale data application scenarios. The storage strategy is dynamically adjusted to flexibly allocate storage resources according to real-time jewelry transaction data to avoid resource waste. The storage resource scheduling scheme generated by the data security storage engine ensures efficient use of resources.

[0170] The present invention is not limited to the above optional implementations, and anyone can derive other various forms of products under the enlightenment of the present invention. The above specific implementations should not be understood as limiting the scope of protection of the present invention. The scope of protection of the present invention should be based on the definition in the claims, and the description can be used to interpret the claims.

Claims

1. A blockchain-based jewelry transaction data security storage method, characterized by: The steps include: Cloud data centers use blockchain technology to deploy blockchain consensus and storage networks, and use artificial intelligence algorithms to build data security storage engines; The data server encrypts the real-time jewelry transaction data using a random encryption algorithm, and uploads the encrypted real-time jewelry transaction data to the cloud data center; 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 solutions, and real-time data storage strategies for decrypted real-time jewelry transaction data; The cloud data center uses blockchain consensus and storage network to securely store encrypted real-time jewelry transaction data, corresponding real-time retrieval tags, and real-time data analysis results based on real-time storage resource scheduling plans and real-time data storage strategies.

2. According to claim 1, a method for securely storing jewelry transaction data based on blockchain, characterized in that: 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 blockchain consensus and storage network includes a data storage network and a blockchain consensus network.

3. A blockchain-based jewelry transaction data security storage method according to claim 2, characterized in that: Cloud data centers use blockchain technology to deploy blockchain consensus and storage networks, and use artificial intelligence algorithms to build a data security storage engine, including the following steps: Cloud data centers use blockchain technology to connect all data servers as data nodes in a distributed manner to obtain blockchain consensus and storage networks; According to the identity allocation mechanism, several data nodes of the blockchain consensus and storage network are divided into several consensus nodes and several storage nodes to obtain a data storage network and a blockchain consensus network; Collecting some historical jewelry transaction data, and preprocessing some historical jewelry transaction data to obtain some 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.

4. A blockchain-based jewelry transaction data security storage method according to claim 3, characterized in that: Based on some pre-processed historical jewelry transaction data, an artificial intelligence algorithm is used to build a data security storage engine, including the following steps: Based on some pre-processed historical jewelry transaction data, a deep learning algorithm is used to build a data analysis model and generate some historical data analysis results; Based on some pre-processed historical jewelry transaction data, a natural language processing algorithm is used to build a retrieval tag generation model and generate some historical real-time retrieval tags; Based on the analysis results of several historical data, a data storage strategy generation model is constructed using a reinforcement learning algorithm, and several historical data storage strategies and corresponding historical data storage strategy generation experiences are generated; According to several historical data storage strategies, a storage resource scheduling model is constructed using swarm intelligence optimization algorithm.

5. A blockchain-based jewelry transaction data security storage method according to claim 4, characterized in that: The data analysis model is constructed based on the RF-MLP algorithm; The retrieval tag generation model is constructed 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.

6. A blockchain-based jewelry transaction data security storage method according to claim 5, characterized in that: The data server uses a random encryption algorithm to encrypt the real-time jewelry transaction data, and 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 features, and generates a random key based on the biometric features; According to the random key, the real-time jewelry transaction data is encrypted using a random encryption algorithm to obtain the encrypted real-time jewelry transaction data; Using QKA technology, random keys are sent to the cloud data center through quantum communication channels, and encrypted real-time jewelry transaction data is uploaded to the cloud data center through public communication channels.

7. A blockchain-based jewelry transaction data security storage method according to claim 6, characterized in that: Using the data security storage engine, generating real-time retrieval tags, real-time data analysis results, real-time storage resource scheduling solutions and real-time data storage strategies for decrypted real-time jewelry transaction data includes the following steps: According to the decrypted real-time jewelry transaction data, a retrieval tag generation model of the data security storage engine is used to generate retrieval tags to obtain real-time retrieval tags; According to the decrypted real-time jewelry transaction data, the data analysis model of the data security storage engine is used to perform data analysis to obtain the corresponding real-time data analysis results; According to the real-time data analysis results, a data storage strategy generation model of the data security storage engine is used to generate a data storage strategy to obtain a real-time data storage strategy; The cloud data center, according to the real-time data storage strategy, uses the storage resource scheduling model of the data security storage engine to schedule storage resources and obtain a real-time storage resource scheduling solution.

8. A blockchain-based jewelry transaction data security storage method according to claim 7, characterized in that: The cloud data center uses blockchain consensus and storage network to securely store encrypted real-time jewelry transaction data, corresponding real-time retrieval tags, and real-time data analysis results according to the real-time storage resource scheduling plan and real-time data storage strategy, including the following steps: The cloud data center schedules the corresponding data storage partitions in the data storage network of the blockchain consensus and storage network according to the real-time storage resource scheduling scheme; Generate a real-time storage request based on the decrypted real-time jewelry transaction data, and use the blockchain consensus network of the blockchain consensus and storage network to reach consensus on the real-time storage request; After the consensus is successful, the encrypted real-time jewelry transaction data, the corresponding real-time retrieval tags and the real-time data analysis results are securely stored using data storage partitions according to the real-time data storage strategy.

9. A blockchain-based jewelry transaction data security storage method according to claim 8, characterized in that: After consensus is successful, the encrypted real-time jewelry transaction data, the corresponding real-time retrieval tags, and the real-time data analysis results are securely stored using data storage partitions according to the real-time data storage strategy, including the following steps: After the consensus is successful, the encrypted real-time jewelry transaction data is sharded according to the real-time data sharding decision of the real-time data storage strategy to obtain several encrypted real-time data shards; According to the real-time distributed storage decision of the real-time data storage strategy, a plurality of encrypted real-time data shards are sent to a plurality of storage nodes of the data storage partition; The storage node using the data storage partition stores the received encrypted real-time data shards locally, returns the real-time local storage address, and sends real-time storage success information to other storage nodes; Write the real-time local storage address of the encrypted real-time data shard, the corresponding real-time retrieval tag, and the real-time data analysis result into the data storage account book of the data storage network, and generate a real-time data storage record; Use the blockchain consensus network to reach consensus on real-time data storage records and obtain real-time distributed storage addresses; After the consensus is successful, the real-time distributed storage address, the corresponding real-time retrieval tag, and the real-time data analysis results are written into the distributed ledger of the blockchain consensus network; If the storage node receives a number of real-time storage success messages exceeding a preset number threshold, the secure storage step is terminated; otherwise, the secure storage continues.

10. A jewelry transaction data security storage system based on blockchain, used to implement the jewelry transaction data security storage method as described in any one of claims 1 to 9, characterized in that: 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 respectively communicated with a number of data servers, and the cloud data center is provided with a blockchain consensus and storage network and a data security storage engine.

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