Data management method, apparatus and device

Abstract

The application relates to the technical field of Internet of Things, and discloses a data management method, device and equipment. The method comprises the following steps: acquiring original multi-source data of each service, obtaining an encrypted data packet based on an encrypted transmission channel and the original multi-source data, writing the encrypted data packet into a distributed ledger, obtaining a target storage record, and managing the original multi-source data based on the target storage record and a digital twin algorithm. The application combines the writing of data into the distributed ledger with the digital twin algorithm to form a "storage record-simulation-management and control" closed-loop management system, meets the demand of the power industry for the whole life cycle supervision of data, and realizes the multidimensional protection of data in the collection, storage and management links by fusing the characteristics of the encrypted transmission channel and the distributed ledger.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things, and in particular to a data management method, device and equipment. BACKGROUND

[0002] In the field of electric power industry, power plants as the core hub of energy systems involve the coordinated operation of multiple subsystems such as boilers, steam turbines, generator units, environmental protection equipment and power grid dispatching in the operation process, and need to collect massive multi-source heterogeneous data such as temperature, pressure, vibration and energy consumption in real time.

[0003] However, the prior art has significant defects: firstly, there are problems such as great difference in equipment brands, non-uniform communication protocols, sensor network coverage blind area in the data collection layer, resulting in widespread data island phenomenon and difficulty in integrating cross-system data; secondly, the data management lacks a trusted security mechanism, the traditional centralized storage architecture is vulnerable to single point failure, and the encryption strength is insufficient in the data transmission process, which has the risk of tampering or leakage. In addition, the operation and maintenance decision of the power plant highly depends on the historical data analysis, but the existing system lacks the whole life cycle record of data source, processing process and operation trace, and it is difficult to support fault backtracking and intelligent diagnosis. SUMMARY

[0004] The main purpose of the present application is to provide a data management method, device and equipment, aiming at solving the technical problems of multi-source data island and non-traceability caused by scattered data collection and lack of trusted management mechanism in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides a data management method, which comprises:

[0006] Obtaining original multi-source data of each business, and obtaining encrypted data packets based on an encrypted transmission channel and the original multi-source data;

[0007] Writing the encrypted data packets into a distributed ledger to obtain a target record;

[0008] Based on the target record and a digital twin algorithm, the original multi-source data is managed.

[0009] In an embodiment, before the step of writing the encrypted data packets into the distributed ledger to generate a record, the method further comprises:

[0010] Deploying a consortium chain node through a blockchain algorithm, and dividing the consortium chain node into a core coordination node, a regional processing node and an edge storage node;

[0011] In the core coordination node, the encrypted data packets are consensus processed through a consensus algorithm and quantum secure communication to obtain a consensus processing result;

[0012] verifying the encrypted data packet based on the regional processing node, to obtain a verification result;

[0013] deploying a distributed storage cluster in combination with a static encryption algorithm on the edge storage node;

[0014] generating a distributed ledger based on the consensus processing result, the verification result, and the distributed storage cluster.

[0015] In an embodiment, the step of generating a distributed ledger based on the consensus processing result, the verification result, and the distributed storage cluster comprises:

[0016] nonlinearly combining a consensus element of the consensus processing result and a verification element of the verification result based on a weighted fusion algorithm, to obtain a composite verification mark, the composite verification mark comprising a device unique code and a time window;

[0017] establishing a multi-level mapping relationship between the encrypted data packet and a corresponding sharded storage unit in the distributed storage cluster based on the composite verification mark;

[0018] generating a distributed ledger based on the consensus element, the composite verification mark, and the multi-level mapping relationship.

[0019] In an embodiment, the step of establishing a multi-level mapping relationship between the encrypted data packet and a corresponding sharded storage unit in the distributed storage cluster based on the composite verification mark comprises:

[0020] generating a spatiotemporal feature vector based on the device unique code and the time window;

[0021] extracting key frequency domain features of the encrypted data packet through a wavelet transform function, and mapping the key frequency domain features into a dynamic perturbation factor using a chaotic mapping algorithm;

[0022] obtaining a multi-level mapping relationship based on the spatiotemporal feature vector and the dynamic perturbation factor.

[0023] In an embodiment, the step of generating a distributed ledger based on the consensus element, the composite verification mark, and the multi-level mapping relationship comprises:

[0024] packing the consensus element and the composite verification mark into a transaction unit, and verifying the transaction unit through a signature algorithm to obtain a verified transaction unit;

[0025] generating a transaction hash based on the verified transaction unit, and updating an initial surface ledger based on the transaction hash to obtain an updated surface ledger;

[0026] A hash pointer chain is created through the transaction hash and the device unique code, and an initial intermediate layer ledger is updated based on the hash pointer chain to obtain an updated intermediate layer ledger;

[0027] The encrypted data packet is encrypted according to the time window and the device unique code to obtain a double-layer encrypted data packet, and an initial bottom layer ledger is updated based on the double-layer encrypted data packet to obtain an updated bottom layer ledger;

[0028] The distributed ledger is generated according to the updated surface layer ledger, the updated intermediate layer ledger, the updated bottom layer ledger, and the multi-level mapping relationship.

[0029] In an embodiment, the step of obtaining the original multi-source data of each service and obtaining an encrypted data packet based on an encrypted transmission channel and the original multi-source data comprises:

[0030] Based on the standardized interface of the blockchain light node, the original multi-source data of each service is collected according to a preset mapping rule;

[0031] The original multi-source data is preprocessed by hashing and encrypted by segmentation to obtain processed data;

[0032] Based on the encrypted transmission channel, the processed data is encrypted by a national encryption algorithm and a zero-knowledge proof algorithm to obtain an encrypted data packet.

[0033] In an embodiment, the step of writing the encrypted data packet into a distributed ledger to obtain a target evidence record comprises:

[0034] The encrypted data packet is written into a distributed ledger through a consistent hashing algorithm to generate a first evidence record;

[0035] According to a multi-level mapping relationship, the first evidence record is associated with the encrypted data packet to obtain a structured associated evidence chain;

[0036] Based on the structured associated evidence chain, an intermediate layer verification record of the distributed ledger is generated by combining a signature algorithm and a hash pointer chain, and a second evidence record is obtained based on the intermediate layer verification record;

[0037] The first evidence record and the second evidence record are aggregated through a zero-knowledge proof algorithm to generate a target evidence record.

[0038] In an embodiment, the step of managing the original multi-source data based on the target evidence record and a digital twin algorithm comprises:

[0039] The device unique code in the target evidence record is mapped to the original multi-source data through a digital twin algorithm;

[0040] construct a simulation scene based on the mapping relationship, and obtain a digital twin simulation result according to the simulation scene;

[0041] If the digital twin simulation result deviates from an expected threshold, a preset label and a history operation log in a target record are called, and the original multi-source data are managed in combination with an intelligent contract protocol.

[0042] In addition, to achieve the above object, the present application further provides a data management device, which comprises:

[0043] a data acquisition module, configured to acquire original multi-source data of each service, and obtain an encrypted data packet based on an encrypted transmission channel and the original multi-source data;

[0044] a record acquisition module, configured to write the encrypted data packet into a distributed ledger, and obtain a target record;

[0045] a data management module, configured to manage the original multi-source data based on the target record and a digital twin algorithm.

[0046] In addition, to achieve the above object, the present application further provides a data management device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the data management method as described above.

[0047] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, the computer program being executable by a processor to implement the steps of the data management method as described above.

[0048] In addition, to achieve the above object, the present application further provides a computer program product, which comprises a computer program, the computer program being executable by a processor to implement the steps of the data management method as described above.

[0049] The technical solution provided by the present application acquires original multi-source data of each service, obtains an encrypted data packet based on an encrypted transmission channel and the original multi-source data, writes the encrypted data packet into a distributed ledger, obtains a target record, and manages the original multi-source data based on the target record and a digital twin algorithm. The present application combines data writing into a distributed ledger with a digital twin algorithm to form a "record-simulation-management" closed-loop management system, meets the demand of the power industry for data full life cycle supervision, and realizes multi-dimensional protection of data in the links of collection, storage and management by combining the characteristics of an encrypted transmission channel and a distributed ledger. BRIEF DESCRIPTION OF DRAWINGS

[0050] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application.

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings required by the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings can also provide other drawings for those skilled in the art based on these drawings without any creative effort.

[0052] Figure 1 A flowchart provided for the data management method embodiment one of the present application;

[0053] Figure 2 A flowchart provided for the data management method embodiment two of the present application;

[0054] Figure 3 A module structure diagram of the data management device embodiment of the present application;

[0055] Figure 4 A device structure diagram of the hardware running environment involved in the data management method in the embodiment of the present application.

[0056] The purpose implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings in combination with the embodiments. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application, and are not used to limit the present application.

[0058] In order to better understand the technical solutions of the present application, the following will be described in detail in combination with the drawings in the specification and specific embodiments.

[0059] The prior art has significant defects: first, the data acquisition layer has great differences in equipment brands, non-uniform communication protocols, sensor network coverage blind area and other problems, resulting in the phenomenon of data island being common, and it is difficult to integrate cross-system data; second, the data management lacks a credible security mechanism, the traditional centralized storage architecture is vulnerable to single point failure, and the encryption strength is insufficient in the data transmission process, which has the risk of tampering or leakage. In addition, the operation and maintenance decision of power plant highly depends on historical data analysis, but the existing system lacks the whole life cycle record of data source, processing process and operation trace, and it is difficult to support fault backtracking and intelligent diagnosis.

[0060] Therefore, in order to overcome the above-mentioned defects, the present application provides a solution, which combines data writing into a distributed ledger with a digital twin algorithm to form a "evidence storage-simulation-control" closed-loop management system, meets the demand of the power industry for data full life cycle supervision, and realizes multi-dimensional protection of data in the collection, storage and management links by combining the characteristics of encrypted transmission channel and distributed ledger.

[0061] It should be noted that the execution subject of each embodiment of the present application can be a computing service system with data processing, network communication and program running functions, such as an electronic system, a data management system, etc. capable of realizing the above functions. Hereinafter, the data management system (hereinafter referred to as "system") is taken as an example to describe each embodiment.

[0062] Based on this, the data management method provided in the embodiments of the present application is described below with reference to Figure 1 , Figure 1 The flowchart of the first embodiment of the data management method of the present application is shown in FIG. 1.

[0063] In this embodiment, the data management method includes steps S10-S30:

[0064] In step S10, the original multi-source data of each business is obtained, and encrypted data packets are obtained based on the encrypted transmission channel and the original multi-source data.

[0065] In the industrial scene, especially in the power generation field, multi-source data (such as equipment operation parameters, energy scheduling instructions, environmental monitoring data, etc.) is usually scattered in heterogeneous systems, and is easily affected by network delay, protocol difference and security risk in the collection process. In order to solve the problem of data island and meet the demand of trusted management, the present application proposes a data processing mechanism combining standardized access and end-to-end encryption, which aims to realize cross-system data integration through a unified interface, and to guarantee the confidentiality and integrity of data in the collection stage by combining encryption transmission technology.

[0066] Specifically, the original multi-source data can be collected from each business system, uniformly accessed and preprocessed through a standardized interface. The original data is hashed to fix the integrity, and then encrypted by segmenting as needed. The processed data is encrypted at the bottom layer using a national encryption algorithm, and an unreadable encryption proof is generated by combining a zero-knowledge proof algorithm. Finally, the encrypted data packets are encapsulated into independent encrypted data packets through an encrypted transmission channel (such as a national encryption SSL / TLS protocol), to ensure the confidentiality, integrity and non-repudiation of data in the transmission process.

[0067] It should be noted that the data management method of the present application introduces dynamic rotation key technology in the data encryption link, and realizes periodic or event-triggered key update through the built-in key management module. Specifically, the system generates a new temporary key according to the preset strategy (such as automatic rotation at dawn every day or when the single data encryption volume reaches the threshold) to replace the original key to encrypt sensitive fields (such as device unique code, time window). The key generation process combines quantum random number source and national secret algorithm (such as SM4) to ensure the unpredictability of the key. The new and old keys are synchronized to the authorized node through a bidirectional encryption channel, and the old key is invalidated and physically erased immediately. This mechanism effectively resists the risk of long-term key leakage, meets the dynamic encryption requirements of relevant policies for critical information infrastructure, and is compatible with lightweight verification technologies such as zero-knowledge proof, balancing security and performance overhead.

[0068] As an embodiment, the above step S10 in the present embodiment can include: based on the standardized interface of the blockchain light node, collecting original multi-source data of each business according to a preset mapping rule; performing hash preprocessing and segmented encryption on the original multi-source data to obtain processed data; based on an encrypted transmission channel, encrypting the processed data through a national secret algorithm and a zero-knowledge proof algorithm to obtain an encrypted data packet.

[0069] Specifically, based on the standardized interface of the blockchain light node (such as complying with the remote procedure call transmission protocol), the original multi-source data is collected from each business system according to a preset mapping rule. The preset mapping rule defines the conversion logic, format standardization and cross-system data association method of the data field, ensuring that data from different sources can be structured and integrated. For example, the RESTful API or database query statement of each business system is called through the standardized interface of the light node, the required data field is filtered and format cleaned as needed, and finally the original data set conforming to the unified data model is generated.

[0070] The integrated original multi-source data is subjected to hash preprocessing, and the integrity of the data is fixed by calculating a fixed-length hash value (such as SHA-256), and a corresponding hash digest is generated. Subsequently, according to the data sensitivity level and transmission efficiency requirement, a segmented encryption technology (such as AES-256 block encryption) is used to divide the original data into multiple encrypted segments, and each segment is independently encrypted to improve parallel processing capability and fault tolerance. The processed data includes original data segments, hash digest and segmented key meta information, forming a structured data unit.

[0071] The processed data is submitted to the target node through a pre-established encrypted transmission channel. During transmission, the entire data unit is first encrypted at the bottom layer using a national encryption algorithm (such as the SM4 symmetric encryption algorithm) to ensure the confidentiality and anti-interception capability of the data on the transmission link; secondly, a zero-knowledge proof algorithm (such as zk-SNARKs) is used to generate a verifiable proof of the encrypted data, which can verify the legitimacy of the data without revealing the specific content. Finally, the national encryption data packet and the zero-knowledge proof are combined and packaged as a complete encrypted data packet to complete the end-to-end secure transmission and non-repudiation guarantee.

[0072] In step S20, the encrypted data packet is written into the distributed ledger to obtain the target evidence record.

[0073] It should be understood that the encrypted data packet can be written into the distributed ledger to generate an initial evidence record. The storage location is determined by a consistent hashing algorithm, and the association between the evidence record and the data packet is established according to a multi-level mapping relationship (such as a device unique code and a time window) to form a structured evidence chain. The intermediate layer verification record is generated using a signature algorithm and a hash pointer chain, and the initial evidence record and the intermediate layer record are aggregated using a zero-knowledge proof algorithm to generate the final target evidence record.

[0074] Therefore, as an embodiment, the above step S20 in the present embodiment can include: generating a first evidence record by writing the encrypted data packet into the distributed ledger through a consistent hashing algorithm; associating the first evidence record with the encrypted data packet according to a multi-level mapping relationship to obtain a structured associated evidence chain; generating an intermediate layer verification record of the distributed ledger based on the structured associated evidence chain, combining a signature algorithm and a hash pointer chain, and obtaining a second evidence record based on the intermediate layer verification record; and generating a target evidence record by aggregating the first evidence record and the second evidence record through a zero-knowledge proof algorithm.

[0075] It can be understood that the first evidence record is generated by writing the encrypted data packet through a consistent hashing algorithm. Based on a consistent hashing algorithm (such as CHASH or Rabin-Karp variant), the hash value of the encrypted data packet is mapped to a set of storage nodes of the distributed ledger. The algorithm distributes data through a ring-shaped hash space and dynamically adjusts data distribution according to node weight to ensure data redundancy and load balancing. When writing, each node checks whether it is a responsible node according to the hash value, and if so, stores the data packet locally and generates a digital fingerprint containing a timestamp, node identifier and hash value to form the first evidence record. This record fixes the storage location and writing time of the data packet, serving as a basic anchor point for subsequent verification.

[0076] A multi-level index (such as a B+ tree or a hash table) is constructed based on the device unique code and the time window, and the hash fingerprint in the first evidence record is associated with the storage path of the encrypted data packet. For example, the device unique code is used as a primary key, and the time window is used as a secondary key, and a composite index key is generated through a nonlinear combination. During the mapping process, the system checks the consistency of the spatiotemporal characteristics (such as the generation time range) of the data packet with the evidence record, and writes the association relationship into the distributed index layer to form a structured associated evidence chain. The chain connects the evidence record and the original data packet through a traceable pointer mechanism (such as a double-linked list), ensuring that the entire link is auditable.

[0077] The structured associated evidence chain is subjected to signature processing, that is, first, a digital signature is generated for the evidence chain content (including the evidence record hash, mapping relationship, and device code) through a private key, and the validity of the signature is verified using a public key to ensure the identity of the signer; second, the hash values of each node of the evidence chain are extracted, and an intermediate layer verification record is constructed through a chain hash pointer (such as SHA-256 concatenation). The record contains the signature result, hash pointer tree, and timestamp, forming an unalterable logical chain to prove the legal association of the evidence record and the data packet.

[0078] The first evidence record (containing data packet storage information) and the second evidence record (verification result of the associated evidence chain) are input into a zero-knowledge proof generator. An algorithm such as zk-SNARKs is used to construct a circuit, and the authenticity condition of the evidence record is encoded as an arithmetic constraint to generate a proof and a verification key that satisfy the condition. The final target evidence record is composed of the original evidence data, the proof, and the verification key, which can verify the logical consistency of the data packet and the evidence record without exposing the underlying business data details, achieving high-order privacy protection and verifiability.

[0079] In step S30, the original multi-source data is managed based on the target evidence record and a digital twin algorithm.

[0080] It should be understood that the device unique code is extracted from the target evidence record and dynamically bound to the original multi-source data through a digital twin algorithm. Based on the mapping relationship, a simulation scenario is constructed to simulate the business running state and generate results for data management.

[0081] As an implementation, step S30 in the embodiment can include: establishing a mapping relationship between the device unique code in the target evidence record and the original multi-source data through a digital twin algorithm; constructing a simulation scenario based on the mapping relationship, and obtaining a digital twin simulation result according to the simulation scenario; if the digital twin simulation result deviates from an expected threshold, invoking a preset label and a historical operation log in the target evidence record, and managing the original multi-source data in combination with a smart contract protocol.

[0082] Specifically, the device unique code (such as IMEI, serial number, etc. Identifier) is extracted from the target record, and the metadata (such as device type, attribute definition) corresponding to the code is called through the standardized interface of the digital twin engine. Based on the field mapping rules in the metadata (such as the timestamp field corresponding to the time window of the data packet), the physical parameters (such as temperature, pressure value) in the original multi-source data are logically bound to the virtual parameters of the digital twin model. For example, if the device code is associated with sensor data, the real-time readings in the original data are mapped to the analog variables in the twin model, forming a dynamic data flow channel.

[0083] Based on the mapping relationship, the digital twin simulation environment is initialized, the historical operation data is loaded as the initial state, and the simulation parameters (such as time span, physical law constraints) are set. The model is driven to run through discrete event simulation or real-time stream processing engine, simulating the actual operation process of the device or business. During the simulation process, the twin model continuously receives incremental updates of the original multi-source data, and outputs simulation results (such as predicted failure probability, energy consumption curve) containing key performance indicators. The simulation results are stored in a structured data form, with a timestamp and a confidence label.

[0084] When the specified indicators (such as temperature exceeding the safety threshold) in the simulation results deviate from the preset threshold, the system automatically queries the preset labels (such as device risk level, emergency strategy ID) and historical operation logs (such as recent maintenance records, fault work orders) in the target record. Through the intelligent contract protocol (such as Ethereum Solidity contract), the predefined management rules are called: if the label is marked as a high-risk device, perform data isolation operation; if the historical log shows that similar faults have been repaired, trigger the automatic verification process. After the management action is completed, the operation record is fed back to the digital twin model to update the simulation parameters, forming a closed-loop control.

[0085] The embodiment obtains the original multi-source data of each business, and obtains encrypted data packets based on the encrypted transmission channel and the original multi-source data, writes the encrypted data packets into the distributed ledger, obtains the target record, and manages the original multi-source data based on the target record and the digital twin algorithm. By combining writing data into the distributed ledger with the digital twin algorithm, a "record-simulation-management" closed-loop management system is formed, meeting the needs of the power industry for data lifecycle management, and combining the characteristics of encrypted transmission channels and distributed ledgers to realize multi-dimensional protection of data in the collection, storage, and management links.

[0086] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as the above-mentioned embodiment one can refer to the above introduction, and will not be repeated hereinafter. On this basis, please refer to Figure 2 , before the step S10, it can also include steps S01-S05:

[0087] Step S01, deploying a consortium chain node through a blockchain algorithm, and dividing the consortium chain node into a core coordination node, a regional processing node, and an edge storage node.

[0088] The consortium chain network architecture is constructed through a blockchain algorithm, and the nodes are divided into three categories according to functions: a core coordination node, a regional processing node, and an edge storage node. The core coordination node serves as a network control center, responsible for global consensus scheduling and cross-regional communication; the regional processing node is based on geographical or business partitioning, and undertakes local data verification and transaction processing responsibilities; the edge storage node deploys a distributed storage cluster through a static encryption algorithm, stores encrypted data packets in place and guarantees offline availability. Nodes establish encrypted channels through quantum secure communication protocols (such as quantum-resistant key exchange PQKE) to ensure the confidentiality and attack resistance of node interactions.

[0089] Step S02, in the core coordination node, the encrypted data packet is processed by a consensus algorithm and quantum secure communication to obtain a consensus processing result.

[0090] A consensus algorithm (such as Byzantine Fault Tolerance (BFT) or improved PBFT) is run on the core coordination node, and a quantum secure communication module is combined to process the encrypted data packet. The specific process includes: the node collects the data packet to be consensus, generates dynamic weights through a quantum random number generator, determines the consensus result based on the weight voting mechanism; quantum secure communication ensures the non-tamperability of the voting process, preventing malicious nodes from forging voting information. The final consensus processing result contains data packet validity markers, participating consensus node signatures, and timestamps, forming a collective authentication certificate with legal effect.

[0091] Step S03, verifying the encrypted data packet based on the regional processing node to obtain a verification result.

[0092] The regional processing node receives the encrypted data packet broadcast by the core coordination node and performs double verification: first, it verifies the legality of the data packet source through a device fingerprint library (such as whether the device unique code is registered in the whitelist); second, it checks the hash consistency of the data packet with the existing evidence record using a lightweight blockchain verification engine (such as the SPV protocol). The verification result contains device state identification (such as "valid" or "suspicious"), a verification time window, and a regional node signature, forming a traceable distributed verification evidence chain.

[0093] Step S04, in the edge storage node, a distributed storage cluster is deployed in combination with a static encryption algorithm.

[0094] A static encryption algorithm (such as AES-256-CBC) is deployed on the edge storage node to build a distributed storage cluster, and the encrypted data packet is split into multiple shards. Each shard is redundantly stored in different nodes using erasure coding technology (such as Reed-Solomon), and the shard key is bound to the device unique code. The storage cluster achieves load balancing through a consistent hash ring, and the storage state hash value is periodically synchronized between nodes to detect data anomalies. When receiving a storage evidence request, the cluster automatically triggers the data retrieval process, reassembles the original data packet based on the shard key, and returns the integrity check value to the verification layer.

[0095] Step S05, generating a distributed ledger based on the consensus processing result, the verification result, and the distributed storage cluster.

[0096] The embodiment adapts to the performance requirements of power plant large-scale data processing by constructing a three-level node hierarchical architecture, that is, proposing to perform consensus processing through a core coordination node, verification through a regional processing node, and distributed storage through an edge storage node, forming a consortium chain deployment mode.

[0097] As an implementation manner, the above step S05 in the embodiment can include:

[0098] The consensus elements of the consensus processing result and the verification elements of the verification result are nonlinearly combined based on a weighted fusion algorithm to obtain a composite verification mark, and the composite verification mark includes a device unique code and a time window;

[0099] Based on the composite verification mark, a multi-level mapping relationship is established between the encrypted data packet and the corresponding shard storage unit in the distributed storage cluster;

[0100] A distributed ledger is generated based on the consensus elements, the composite verification mark, and the multi-level mapping relationship.

[0101] It can be understood that based on the summary information (such as data packet hash value, consensus node signature) of the consensus processing result and the parameters (such as device fingerprint, regional verification timestamp) of the verification result, a sensitivity coefficient (reflecting the importance of data type, such as key business data with a weight of 0.7), a node credibility (based on historical behavior statistics, a trusted node weight of 0.8-0.9, and an untrusted node of 0.5-0.6), and a time decay factor (weight exponential decay over time, formula e^(-λt), λ is the decay rate) are introduced. The composite weight is calculated through a nonlinear combination formula (such as W = a x S + b x C + g x D, where S is the sensitivity coefficient, C is the node credibility, and D is the time decay factor), and the composite verification mark containing the device unique code and the time window is generated. The mark solidifies the weight allocation logic through a hash function (such as SM3), ensuring the traceability of the verification process.

[0102] The device unique code in the composite verification mark is used as a primary index to call the sharding strategy of the distributed storage cluster (such as a consistent hash ring), and the encrypted data packet is mapped to the corresponding shard storage unit; a time window field is used to calculate a time hash value (such as HMAC-SHA256) to generate a secondary index and bind it to the life cycle of the storage unit. A nonlinear function (such as polynomial interpolation) is used to distribute a composite weight to the multi-level index to establish a multi-level mapping relationship from the device code to the storage unit. For example, high-sensitivity data (weight ≥ 0.8) is mapped to a core storage node with high redundancy, low-sensitivity data (weight < 0.5) is stored in an edge node, and data with a short time window (such as TTL ≤ 1 hour) is preferentially allocated to a low-latency partition.

[0103] The consensus elements (abstract information, parameters), the composite verification mark (containing dynamic weight), and the multi-level mapping relationship are input into the distributed ledger generation engine. The transaction unit is encapsulated through a chain structure: the first layer writes the hash values of the original consensus elements and the composite verification mark, the second layer adds the logical expression of the multi-level mapping relationship (such as a mapping tree structure), and the bottom layer stores the binding of the device unique code and the physical address of the storage unit. The smart contract is used to automatically execute the weight verification rule (such as a total weight threshold ≥ 0.9 to be effective), and a zero-knowledge proof algorithm is used to generate a proof that satisfies the weight constraint. The final ledger records all the logical links in a nested structure (such as JSON-LD), ensuring that each layer of verification can be independently traced and cannot be tampered with.

[0104] Further, the step of establishing a multi-level mapping relationship between the encrypted data packet and the corresponding shard storage unit in the distributed storage cluster based on the composite verification mark can include: generating a spatiotemporal feature vector according to the device unique code and the time window; extracting key frequency domain features of the encrypted data packet through a wavelet transform function, and mapping the key frequency domain features into dynamic disturbance factors using a chaotic mapping algorithm; and obtaining the multi-level mapping relationship according to the spatiotemporal feature vector and the dynamic disturbance factors.

[0105] It should be understood that based on the device unique code (such as IMEI, serial number) and the time window (such as the data generation time range), the two are mapped into a multi-dimensional spatiotemporal feature vector through an encoding function. Specifically, the device unique code is converted into a fixed-length unique identifier through a hash function, the time window is decomposed into a start timestamp and a duration, and the two are nonlinearly combined according to weights (such as 70% for the device code and 30% for the time) to generate a continuous vector containing spatial (device identity) and temporal (data timeliness) information. The vector represents the spatiotemporal distribution characteristics of the data, which is used for subsequent dynamic allocation of storage locations.

[0106] The original binary stream of the encrypted data packet is applied with a discrete wavelet transform, which decomposes the signal by selecting a specific wavelet basis function (such as Daubechies-4), extracts high-frequency components (reflecting data mutation characteristics) and low-frequency components (reflecting overall trends). Focus on key frequency domain features (such as energy concentration area or abnormal fluctuation frequency band), calculate the energy value of each frequency band as a feature parameter, and quantify the complexity of the feature through the entropy function. This process converts the encrypted data packet from the time domain to the frequency domain, revealing hidden patterns or abnormal behavior.

[0107] The extracted key frequency domain features are input into a chaotic mapping algorithm (such as the Logistic mapping iteration formula: x_{n+1}=rx_n(1-x_n)), and by adjusting the parameters r and the initial condition x_0, a dynamic disturbance factor sequence with chaotic characteristics is generated. This sequence introduces nonlinear disturbance, breaks the determinism of traditional hash allocation, and enhances the randomness and anti-predictability of the storage path. Finally, the space-time feature vector and the dynamic disturbance factor form a composite index through nonlinear superposition (such as polynomial fusion), which is mapped to the sharding unit of the distributed storage cluster. High-chaos-strength shards are allocated to sensitive data to improve security, and low-strength shards are used for ordinary data to optimize storage efficiency.

[0108] The step of generating a distributed ledger according to the consensus element, the composite verification mark, and the multi-level mapping relationship comprises: packaging the consensus element and the composite verification mark into a transaction unit, and verifying the transaction unit through a signature algorithm to obtain a verified transaction unit; generating a transaction hash according to the verified transaction unit, and updating an initial surface ledger based on the transaction hash to obtain an updated surface ledger; creating a hash pointer chain through the transaction hash and the device unique code, and updating an initial intermediate layer ledger based on the hash pointer chain to obtain an updated intermediate layer ledger; encrypting the encrypted data packet according to the time window and the device unique code to obtain a double-layer encrypted data packet, and updating an initial bottom layer ledger based on the double-layer encrypted data packet to obtain an updated bottom layer ledger; and generating a distributed ledger according to the updated surface ledger, the updated intermediate layer ledger, the updated bottom layer ledger, and the multi-level mapping relationship.

[0109] It can be understood that the consensus elements (such as summary information, parameters) and the composite verification mark (containing device unique code, time window) are packaged as a standardized transaction unit (such as a JSON structure). The content of the transaction unit is digitally signed by a signature algorithm: first, generate a hash value of the transaction content, then sign the hash value with a private key to form a signature field attached to the end of the transaction unit. When verifying, call the public key to verify the validity of the signature, and ensure the credibility of the transaction initiator's identity and the integrity of the data. Then, based on the verified transaction unit, a unique transaction hash value is generated through a hash function (such as RIPEMD-160). The hash value is combined with the timestamp and node identifier, and written into the block header of the initial surface ledger. The surface ledger uses a chain structure to maintain, and each new block contains a previous block hash pointer, forming an unalterable sequential record. When updating, the surface ledger state of each node is synchronized through a consistency algorithm (such as Raft), ensuring that all participants store consistent ledger versions.

[0110] Then, extract the transaction hash value and the device unique code, and construct a hierarchical relationship through the hash pointer chain technology: link the current transaction hash as the parent pointer to the historical transaction record corresponding to the device unique code (such as a tree structure). The intermediate layer ledger stores these pointer chains and associated composite verification marks. When updating, use a distributed consistency protocol (such as Paxos) to ensure consensus on the pointer chain among all regional processing nodes, and if verification fails, trigger a data rollback mechanism to ensure logical consistency.

[0111] According to the time window (such as the data validity period) and the device unique code, perform secondary encryption on the original encrypted data packet: the outer layer uses a dynamic key (a temporary key generated based on the time window) for AES-256 encryption, and the inner layer uses a device binding key generated by combining the device unique code and the national encryption algorithm for encryption. The generated double-layer encrypted data packet is stored in a distributed cluster of edge storage nodes. The bottom layer ledger records the encryption algorithm parameters, key versions, and data packet storage paths, and uses a Merkle tree structure to maintain, which can quickly verify data integrity through the root hash value.

[0112] Integrate the updated surface ledger (containing transaction order), intermediate layer ledger (containing pointer chain and verification logic), and bottom layer ledger (storing physical data packet), and combine multi-level mapping relationship (such as device code-storage node-shard location). Through cross-layer hash verification (such as the surface hash pointing to the intermediate layer pointer chain, and the intermediate layer pointer chain associating the bottom layer data packet hash), a logical closed loop is established. Finally, the ledger is presented in a nested structure (such as a Bloom Filter index), supporting efficient query and audit, while sensitive operation details are hidden through zero-knowledge proof technology.

[0113] For ease of understanding, an example scenario of power plant boiler state monitoring and trusted management is described. In a thermal power plant, the boiler as the core equipment, its operating parameters (such as steam temperature, pressure, vibration frequency) need to be collected in real time and analyzed with grid dispatching, environmental supervision and other multi-system data. Assume the following scenarios exist:

[0114] Multi-source data integration: The vibration sensor of the boiler (SCADA system), thermal parameter (DCS system) and environmental monitoring data (independent Internet of Things platform) need to be collected across protocols (Modbus TCP / IP, OPC UA). Through the standardized interface of the blockchain light node, the unique code of the equipment (such as "BOILER-001") is mapped to each data field, and the original data is hashed after being formatted, and is encrypted in segments to generate encrypted data packets containing time windows (such as 2025-03-09 20:00:00 to 20:05:00), which are transmitted to the management platform through the national SSL / TLS channel. For example, vibration data is collected at a frequency of 1000 sampling points per second, and after AES-256 segment encryption, it is packaged as a 5MB data packet, with a timestamp and equipment code as metadata.

[0115] Distributed storage: The encrypted data packet is written to the distributed ledger by the alliance chain node. The core coordination node verifies the integrity of the data packet based on the PBFT consensus algorithm, the regional processing node confirms the legality of the source by comparing the device fingerprint (such as the check value of "BOILER-001"), and the edge storage node stores the data fragments into the static encryption cluster. For example, the data packet is divided into 3 redundant storage parts by SM4 algorithm, each part is about 1.6MB, and is stored in the storage nodes of North China, East China and South China regions. Finally, a storage record containing the equipment code, time window and consensus signature is generated, anchored to the hash pointer chain of the blockchain, ensuring that the data is tamper-proof and traceable. If subsequent audit needs to verify the authenticity of the vibration data in a certain period, the original data packet and the node signature participating in the consensus can be quickly located through the hash pointer chain of the storage record.

[0116] Digital twin driven management: Based on the equipment code in the storage record, the digital twin platform constructs a virtual model of the boiler, and injects real-time parameters (such as vibration amplitude anomaly) after decryption. If the simulation result shows that the vibration exceeds the threshold (deviates from the historical baseline by 15%), the system automatically calls the preset label (such as "high risk working condition") and historical operation log (such as the latest maintenance record) in the storage record, triggers the intelligent contract to execute instructions: limits the boiler load and pushes the operation and maintenance order, and updates the storage record to solidify the disposal measures. For example, digital twin simulation can predict the trend of heat efficiency decline in the next 2 hours, generate optimization suggestions based on the historical energy efficiency data of the storage, guide fuel adjustment or equipment maintenance, and avoid the daily economic loss of more than 500,000 yuan caused by unplanned shutdown.

[0117] The embodiment proposes a three-level mapping mechanism of "physical layer-logical layer-encryption layer" based on composite verification marks. The frequency domain features of the encrypted data packet are extracted by wavelet transform, and then a dynamic disturbance factor is generated by using a chaotic mapping algorithm to realize dynamic adaptive adjustment of data fragmentation. The three-level digital ledger update mechanism includes a hierarchical update strategy of a surface ledger (transaction hash), an intermediate layer ledger (hash pointer chain), and a bottom layer ledger (double-layer encrypted data packet), which meets the evidence storage needs of different business scenarios, generates double-layer encrypted data packets based on a time window and a device unique code, and innovatively applies and extends the national encryption algorithm to dynamic data scenarios.

[0118] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the data management method of the present application. More forms of simple transformation based on this technical concept are within the protection scope of the present application.

[0119] The present application also provides a data management device, please refer to Figure 3 , the data management device comprises:

[0120] The data acquisition module 10 is configured to acquire original multi-source data of each service, and obtain encrypted data packets based on an encrypted transmission channel and the original multi-source data.

[0121] The evidence storage acquisition module 20 is configured to write the encrypted data packets into a distributed ledger to obtain target evidence records.

[0122] The data management module 30 is configured to manage the original multi-source data based on the target evidence records and a digital twin algorithm.

[0123] The data management device provided by the present application adopts the data management method in the above embodiment, which can solve the technical problems of multi-source data island and non-traceability caused by scattered data collection and lack of trusted management mechanism in the prior art. Compared with the prior art, the data management device provided by the present application has the same beneficial effects as the data management method provided by the above embodiment, and the other technical features in the data management device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0124] The present application provides a data management device, which comprises at least one processor and a memory connected with the at least one processor in communication. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the data management method in the above embodiment one.

[0125] The following will be described with reference to Figure 4The diagram illustrates a structural schematic of a data management device suitable for implementing embodiments of this application. The data management device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Desserts), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The data management device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0126] like Figure 4 As shown, the data management device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in the read-only memory 1002 or a program loaded from the storage device 1003 into the random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the data management device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the data management device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show data management devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0127] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program codes for executing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiments disclosed in the present application are executed.

[0128] The data management device provided by the present application adopts the data management method in the above-mentioned embodiments, and can solve the technical problems of multi-source data islands and non-traceability caused by scattered data collection and lack of trusted management mechanism in the prior art. Compared with the prior art, the data management device provided by the present application has the same beneficial effects as the data management method provided by the above-mentioned embodiments, and other technical features in the data management device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0129] It should be understood that parts of the present application can be realized by hardware, software, firmware or a combination thereof. In the description of the above-mentioned embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0130] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0131] The present application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e. computer program) for executing the data management method in the above-mentioned embodiments.

[0132] The computer readable storage medium provided in the application may be, for example, a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system or device, or any combination of the above. More specific examples of the computer readable storage medium may include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiment, the computer readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), and the like, or any suitable combination of the above.

[0133] The computer readable storage medium described above may be contained in the data management device, or may exist separately without being assembled into the data management device.

[0134] The computer readable storage medium described above carries one or more programs, when the one or more programs are executed by the data management device, the data management device: obtains original multi-source data of each service, and obtains encrypted data packets based on an encrypted transmission channel and the original multi-source data, writes the encrypted data packets into a distributed ledger, obtains a target record, and manages the original multi-source data based on the target record and a digital twin algorithm.

[0135] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0136] The readable storage medium provided by the present application is a computer readable storage medium, which stores computer readable program instructions (i.e. computer programs) for executing the above-mentioned data management method, and can solve the technical problems of multi-source data islands and non-traceability caused by scattered data collection and lack of trusted management mechanism in the prior art. Compared with the prior art, the computer readable storage medium provided by the present application has the same beneficial effects as the data management method provided by the above-mentioned embodiments, which will not be repeated here.

[0137] The present application also provides a computer program product comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned data management method.

[0138] The computer program product provided by the present application can solve the technical problems of multi-source data islands and non-traceability caused by scattered data collection and lack of trusted management mechanism in the prior art. Compared with the prior art, the computer program product provided by the present application has the same beneficial effects as the data management method provided by the above-mentioned embodiments, which will not be repeated here.

[0139] The above-mentioned only some embodiments of the present application, and not limit the patent scope of the present application, any equivalent structural transformation made by using the contents of the present application specification and drawings, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A data management method characterized by, The method comprises the following steps: Obtain the original multi-source data of each service, and obtain encrypted data packets based on an encrypted transmission channel and the original multi-source data; Write the encrypted data packets into a distributed ledger to obtain a target record; Manage the original multi-source data based on the target record and a digital twin algorithm; Before the step of writing the encrypted data packets into the distributed ledger to generate a record, the method further comprises: Deploy a consortium chain node through a blockchain algorithm, and divide the consortium chain node into a core coordination node, a regional processing node, and an edge storage node; On the core coordination node, perform consensus processing on the encrypted data packets through a consensus algorithm and quantum secure communication to obtain a consensus processing result; Verify the encrypted data packets based on the regional processing node to obtain a verification result; On the edge storage node, deploy a distributed storage cluster in combination with a static encryption algorithm; Generate a distributed ledger according to the consensus processing result, the verification result, and the distributed storage cluster; The step of writing the encrypted data packets into the distributed ledger to obtain a target record comprises: Write the encrypted data packets into the distributed ledger through a consistent hashing algorithm to generate a first record; According to a multi-level mapping relationship, associate the first record with the encrypted data packets to obtain a structured associated evidence chain; Based on the structured associated evidence chain, generate an intermediate layer verification record of the distributed ledger in combination with a signature algorithm and a hash pointer chain, and obtain a second record based on the intermediate layer verification record; Aggregate the first record and the second record through a zero-knowledge proof algorithm to generate a target record; The step of managing the original multi-source data based on the target record and a digital twin algorithm comprises: Establish a mapping relationship between a device unique code in the target record and the original multi-source data through a digital twin algorithm; Construct a simulation scenario based on the mapping relationship, and obtain a digital twin simulation result according to the simulation scenario; If the digital twin simulation result deviates from an expected threshold, call a preset label and a historical operation log in the target record, and manage the original multi-source data in combination with a smart contract protocol.

2. The data management method of claim 1, wherein, The step of generating a distributed ledger according to the consensus processing result, the verification result, and the distributed storage cluster comprises: Nonlinearly combine consensus elements of the consensus processing result and verification elements of the verification result based on a weighted fusion algorithm to obtain a composite verification mark, wherein the composite verification mark comprises a device unique code and a time window; Based on the composite verification mark, establish a multi-level mapping relationship between the encrypted data packets and a corresponding sharded storage unit in the distributed storage cluster; Generate a distributed ledger according to the consensus elements, the composite verification mark, and the multi-level mapping relationship.

3. The data management method of claim 2, wherein, The step of establishing a multi-level mapping relationship between the encrypted data packets and a corresponding sharded storage unit in the distributed storage cluster based on the composite verification mark comprises: Generate a space-time feature vector according to the device unique code and the time window; The key frequency domain features of the encrypted data packet are extracted by a wavelet transform function, and the key frequency domain features are mapped into dynamic disturbance factors by a chaotic mapping algorithm A multi-level mapping relationship is obtained according to the spatiotemporal feature vector and the dynamic disturbance factors. The step of generating the distributed ledger according to the consensus elements, the composite verification marks and the multi-level mapping relationship comprises:

4. The data management method of claim 3, wherein, The consensus elements and the composite verification marks are packaged into a transaction unit, and the transaction unit is verified by a signature algorithm to obtain a verified transaction unit; A transaction hash is generated according to the verified transaction unit, and an initial surface ledger is updated based on the transaction hash to obtain an updated surface ledger; A hash pointer chain is created by the transaction hash and the device unique code, and an initial intermediate layer ledger is updated based on the hash pointer chain to obtain an updated intermediate layer ledger; The encrypted data packet is encrypted according to the time window and the device unique code to obtain a double-layer encrypted data packet, and an initial bottom layer ledger is updated based on the double-layer encrypted data packet to obtain an updated bottom layer ledger; The distributed ledger is generated according to the updated surface ledger, the updated intermediate layer ledger, the updated bottom layer ledger and the multi-level mapping relationship. The step of obtaining the original multi-source data of each business and obtaining the encrypted data packet based on the encrypted transmission channel and the original multi-source data comprises:

5. The data management method according to any one of claims 1 to 4, wherein, The original multi-source data of each business is collected according to a preset mapping rule based on a standardized interface of a blockchain light node; The original multi-source data is preprocessed by hashing and encrypted in segments to obtain processed data; The processed data is encrypted by a national encryption algorithm and a zero-knowledge proof algorithm based on an encrypted transmission channel to obtain an encrypted data packet. The data management device comprises:

6. A data management apparatus characterized by comprising: A data acquisition module for obtaining the original multi-source data of each business and obtaining an encrypted data packet based on an encrypted transmission channel and the original multi-source data; A storage evidence acquisition module for writing the encrypted data packet into a distributed ledger to obtain a target storage evidence record; A data management module for managing the original multi-source data based on the target storage evidence record and a digital twin algorithm; The storage evidence acquisition module is further configured to deploy a consortium chain node by a blockchain algorithm, and divide the consortium chain node into a core coordination node, a regional processing node and an edge storage node; the encrypted data packet is consensus-processed by a consensus algorithm and quantum secure communication on the core coordination node to obtain a consensus processing result; the encrypted data packet is verified based on the regional processing node to obtain a verification result; a distributed storage cluster is deployed on the edge storage node in combination with a static encryption algorithm; and a distributed ledger is generated according to the consensus processing result, the verification result and the distributed storage cluster. ​ The evidence storage obtaining module is further configured to write the encrypted data packet into a distributed ledger by using a consistent hashing algorithm to generate a first evidence storage record; associate the first evidence storage record with the encrypted data packet according to a multi-level mapping relationship to obtain a structured associated evidence chain; generate an intermediate layer verification record of the distributed ledger based on the structured associated evidence chain, in combination with a signature algorithm and a hash pointer chain, and obtain a second evidence storage record based on the intermediate layer verification record; and aggregate the first evidence storage record and the second evidence storage record by using a zero-knowledge proof algorithm to generate a target evidence storage record. The data management module is further configured to establish a mapping relationship between a device unique code in the target evidence storage record and the original multi-source data by using a digital twin algorithm; construct a simulation scene based on the mapping relationship, and obtain a digital twin simulation result according to the simulation scene; and if the digital twin simulation result deviates from an expected threshold, invoke a preset label and a historical operation log in the target evidence storage record, and manage the original multi-source data in combination with a smart contract protocol.

7. A data management device, characterized by comprising: The data management device includes a memory, a processor, and a data management program stored on the memory and executable on the processor, and the data management program, when executed by the processor, implements the data management method of any one of claims 1 to 5.

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