Multi-source energy data fusion traceability analysis method, system, equipment and medium

Abstract

The invention relates to the technical field of multi-source energy data fusion traceability analysis, in particular to a multi-source energy data fusion traceability analysis method, system and device and a medium, and the method comprises the steps: uploading multi-source energy data to a platform, and inputting target data according to a preset template; verifying the authenticity of the source of the data, generating a data hash value, and storing the data hash value to a block chain main chain; and based on the data flow path recorded by the block chain, performing traceability analysis by adopting a visualization method, and dynamically allocating role permissions through an intelligent contract to realize operation auditing. The beneficial effect of the invention is that accurate verification and full-process traceability of the data source are realized by constructing the traceability model based on the block chain. The operation authority is dynamically managed by using the intelligent contract, the compliance and security of data access are guaranteed, and the risk of data leakage and abuse is effectively prevented.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-source energy data fusion traceability analysis, in particular to a multi-source energy data fusion traceability analysis method, system, device and medium. BACKGROUND

[0002] With the acceleration of the digitalization and intelligentization transformation of the energy industry, the energy data center has become the core platform for converging massive data in multiple fields such as electricity, coal, oil and natural gas. Multi-source energy data fusion is considered as a key means to improve energy management and optimize decision-making, and its application is increasingly widespread.

[0003] However, there are many problems in the processing of multi-source energy data. On the one hand, due to the diversity of data sources, the type structure and storage path differ significantly, resulting in a very complex data flow process, making it difficult to achieve comprehensive and accurate data traceability. On the other hand, the current data management method has a big loophole in data security. Data is easy to be tampered with or forged in the transmission and storage link, but it is difficult to be effectively detected and traced, and the responsibility is difficult to define. At the same time, there is a lack of efficient traceability mechanism. Once the data is problematic, it is difficult to quickly locate the source and track the flow, which brings great risks and uncertainties to energy management and decision-making, seriously restricting the further promotion and application of energy data fusion technology. SUMMARY

[0004] To solve the above technical problems, the present application provides the following technical solutions:

[0005] In a first aspect, the present application provides a multi-source energy data fusion traceability analysis method, comprising uploading multi-source energy data to a platform, and entering target data according to a preset template.

[0006] Verifying the authenticity of the data source, generating a data hash value and storing it to the main chain of the blockchain;

[0007] Based on the data flow path recorded by the blockchain, a visual method is used for traceability analysis, and the role permission is dynamically allocated by the smart contract to realize operation audit.

[0008] As a preferred scheme of the multi-source energy data fusion traceability analysis method of the present application, wherein: verifying the authenticity of the data source, generating a data hash value and storing it to the main chain of the blockchain, comprising,

[0009] The data provider processes the data to generate a digital signature;

[0010] The platform verifies the validity of the digital signature;

[0011] For the data that passes the verification, a unique hash value is generated and stored to the main chain of the blockchain.

[0012] As a preferred scheme of the multi-source energy data fusion traceability analysis method of the application, the traceability analysis is performed by using a visualization method, including using a directed acyclic graph for traceability analysis or using a data flow conversion ordered tree for traceability analysis.

[0013] As a preferred scheme of the multi-source energy data fusion traceability analysis method of the application, the traceability analysis is performed by using a directed acyclic graph, including,

[0014] The upstream dependent nodes are traced back from the target data as a starting point.

[0015] A preset algorithm is used to locate the full-link dependent relationship, and a visual traceability path is generated.

[0016] As a preferred scheme of the multi-source energy data fusion traceability analysis method of the application, the traceability analysis is performed by using a data flow conversion ordered tree, including,

[0017] The root node represents the original data state, and the child nodes record the data change history in a preset order.

[0018] Supporting layer-by-layer backtracking from any child node to the root node and associating the change log stored in the blockchain.

[0019] As a preferred scheme of the multi-source energy data fusion traceability analysis method of the application, the smart contract is used for,

[0020] Verifying data life cycle compliance, dynamically assigning role permissions, and recording violation operations to the blockchain attached chain, and generating an audit log.

[0021] As a preferred scheme of the multi-source energy data fusion traceability analysis method of the application, the preset template includes,

[0022] Data state information field, operation log field, and mapping rules and standardized entry specifications of multi-source heterogeneous data.

[0023] In a second aspect, the application provides a multi-source energy data fusion traceability analysis method, including: an upload entry module for uploading multi-source energy data to a platform and entering target data according to a preset template;

[0024] A verification storage module for verifying the authenticity of the data source, generating a data hash value and storing it in the main chain of the blockchain;

[0025] An analysis audit module for performing traceability analysis by using a visualization method based on the data flow conversion path recorded in the blockchain, and dynamically assigning role permissions through a smart contract to realize operation audit.

[0026] In a third aspect, the present application provides a computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the method as described above when executing the computer program.

[0027] In a fourth aspect, the present application provides a computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the steps of the method as described above.

[0028] Compared with the prior art, the present application has the following beneficial effects: by constructing a traceability model based on a block chain, accurate verification of data sources and full-process traceability are achieved. By using a smart contract to dynamically manage operation permissions, the compliance and security of data access are ensured, and the risk of data leakage and misuse is effectively prevented. At the same time, based on a directed acyclic graph (DAG) and a data flow conversion ordered tree, the data flow direction and state change are clearly displayed, and the data problem source is quickly located. This method provides an intelligent solution for efficient management and trusted sharing of energy data, reduces data management costs, improves energy data utilization efficiency, lays a solid foundation for energy digital transformation, and has important significance for promoting the intelligentization of the energy industry and optimizing energy management decision-making processes. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0030] Figure 1 It is a flowchart of a multi-source energy data fusion traceability analysis method.

[0031] Figure 2 It is a traceability information diagram.

[0032] Figure 3 It is an example diagram of data traceability analysis based on a directed acyclic graph.

[0033] Figure 4 It is an example diagram of data traceability analysis based on a data flow conversion ordered tree. DETAILED DESCRIPTION

[0034] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0035] Embodiment 1, refer to Figure 1 As a first embodiment of the present application, the embodiment provides a multi-source energy data fusion traceability analysis method, comprising:

[0036] S100: uploading multi-source energy data to a platform, and entering target data according to a preset template;

[0037] S200: verifying the authenticity of the data source, generating a data hash value and storing it to a blockchain main chain;

[0038] S300: based on the data flow path recorded by the blockchain, using a visualization method for traceability analysis, and dynamically allocating role permissions through a smart contract to realize operation audit.

[0039] It should be noted that in the process of energy data collection, transmission and processing, the data sources are extensive and the types are complex and diverse, including but not limited to data in multiple fields such as electricity, coal, oil and natural gas. When these data flow between different systems, due to the differences in system architecture and technical standards, there are many difficulties in data integration and traceability. At the same time, with the acceleration of energy digital transformation, the security and credibility of data are facing severe challenges, and the traditional data management method cannot meet the current needs of energy data fusion and traceability. The risk of data leakage and tampering is increasing, and it is difficult to quickly locate the source and track the flow when data problems occur, which brings great uncertainty and risk to energy management and decision-making.

[0040] Therefore, in view of the above-mentioned problems of energy data traceability and security management, through the steps of S100-S300, a multi-source energy data fusion traceability system based on blockchain is constructed. First, multi-source energy data is uploaded to the platform and the target data is entered according to the preset template to ensure the standardization and integrity of the data; then the authenticity of the data source is strictly verified, the data hash value is generated and stored to the blockchain main chain, and the security and credibility of the data are guaranteed by the tamper-proof feature of the blockchain; finally, based on the data flow path recorded by the blockchain, a visualization method is used for traceability analysis, and role permissions are dynamically allocated through a smart contract to realize operation audit, realizing the whole process traceability and fine management of energy data from generation to use, effectively improving the efficiency and reliability of energy data management, and providing strong support for the intelligent development of the energy industry.

[0041] Embodiment 2, refer to Figures 1 to 4 As an embodiment of the present application, based on the above embodiment, a multi-source energy data fusion traceability analysis method is provided.

[0042] In the embodiment of the present application, in step S100, the multi-source energy data is uploaded to the platform, and target data is entered according to a preset template, wherein the preset template includes a data state information field, an operation log field, and a mapping rule and standardized entry specification of multi-source heterogeneous data.

[0043] It can be understood that the data state information field includes data owner, data current stage, and data access strategy and other parameter information, and the operation log records operation behavior and timestamp.

[0044] In an optional embodiment, in step S100, the multi-source energy data uploaded to the platform can be directly connected through a data interface, that is, the platform side cooperates with the system supplier or operation and maintenance team of each energy data source, develops a customized data interface for each energy data source according to the data receiving standards and requirements of the platform.

[0045] In another optional embodiment, in step S100, the multi-source energy data uploaded to the platform can also be collected and transmitted through Internet of Things devices, that is, the platform sets a special Internet of Things data access module responsible for receiving data sent from different Internet of Things devices, and after identity authentication, security verification and other operations on the accessed data, integrates the data into the multi-source energy data resource pool of the platform, and uniformly manages and subsequently analyzes and processes the data together with the data obtained through other ways.

[0046] In the embodiment of the present application, in step S200, the authenticity of the data source is verified, a data hash value is generated, and the data hash value is stored in a blockchain main chain, including the following steps A1-A3:

[0047] A1: The data provider processes the data to generate a digital signature;

[0048] It can be understood that before the data is uploaded, the data provider signs the data using its own private key to generate a digital signature to prove the source of the data and the identity of the data provider. The private key signature ensures that the data has not been tampered with and proves that the data provider is responsible for the data content.

[0049] A2: The platform verifies the validity of the digital signature;

[0050] It can be understood that during the data uploading process, the platform uses the public key of the data provider to verify the validity of the data signature to ensure that the data is indeed uploaded by a reasonable data provider and the data content has not been tampered with. If the signature verification fails, the data uploading request is rejected.

[0051] A3: For the data that passes the verification, a unique hash value is generated, and the unique hash value is stored in the blockchain main chain.

[0052] It can be understood that each piece of uploaded data will generate a corresponding unique hash value through a hash algorithm, and the hash value will be recorded on the blockchain for subsequent data integrity verification.

[0053] It should be noted that for data uploaded to the blockchain affiliated chain, the smart contract is automatically called to verify the entire life cycle of the data. If it has a legal source, the data can be stored in the blockchain, otherwise, it stays in the blockchain affiliated chain and rejects the data upload request. All records of the operator will exist in the blockchain affiliated chain.

[0054] In an optional embodiment, the verification of the authenticity of the data source in step S200 can be verified by big data analysis and machine learning model verification, that is, the relevant feature information of the data provider corresponding to the data is extracted and input into the trained machine learning model. The model outputs the probability or credibility score of the data source being real according to the learned mapping relationship. According to the output result of the model, combined with the preset credibility threshold, it is judged whether the data source is real. If the credibility score is higher than the threshold, it is considered that the data source is real; otherwise, it is considered that the data source is at risk and needs to be further investigated or rejected.

[0055] In another optional embodiment, the verification of the authenticity of the data source in step S200 can also be verified by multi-factor authentication and data cross comparison. That is, the data provider provides multiple identity information when first registering or accessing the platform. For each data upload, in addition to requiring the data provider to perform regular username and password login verification, an additional dynamic verification factor is added, such as sending an SMS verification code to the data provider's reserved mobile phone number, or sending a verification link to its associated email. Only after the data provider correctly enters the verification code or clicks the verification link is the data upload operation allowed, and the data provided by the data provider is further compared and analyzed with the same type or associated data provided by known authoritative data sources or other trusted data providers.

[0056] In the embodiments of the present application, the data flow path recorded based on the blockchain in step S300 is analyzed by a visual method, and the role permission is dynamically allocated by the smart contract to realize operation audit, including the following steps B1 or B2:

[0057] It can be understood that traceability analysis is a comprehensive tracking and recording of the source, transmission path, and application method of data in the flow process. Each link in the data flow process is recorded in detail to ensure that when a data security problem occurs, the problem source can be quickly located and the data flow direction can be tracked, providing strong support for data rights protection and responsibility investigation.

[0058] It should be noted that whenever data is transmitted between different nodes, the blockchain platform records the data flow path, the nodes passed through, the transmission time, etc. All data flow records are written into the blockchain, ensuring that the flow path of each piece of data cannot be tampered with, and the credibility of the data flow record is guaranteed.

[0059] Further, at each link of the data flow, relevant traceability information needs to be recorded, including the stage to which the data belongs, the data provider, the timestamp, the hash value, etc. (see FIG. 6) Figure 2 All data flow records are stored in the data storage module after being encrypted by the blockchain, facilitating the calling of traceability analysis. Business personnel obtain the traceability information of the data through the traceability analysis module to understand the source, flow path, processing node, etc. of each piece of data.

[0060] B1: using a directed acyclic graph for traceability analysis;

[0061] B2: using a data flow ordered tree for traceability analysis.

[0062] Further, in the embodiments of the present application, step B1 uses a directed acyclic graph for traceability analysis, including the following steps B11-B12:

[0063] B11: reversing the original DAG arrow direction to trace back the upstream dependent nodes from the target data as the starting point;

[0064] B12: using a preset algorithm to locate the full-link dependency relationship to generate a visual traceability path, wherein the preset algorithm includes a breadth-first search algorithm.

[0065] It can be understood that during the data flow process, different data entities may generate new data entities after multiple flows. This flow process can be represented by a directed acyclic graph. Data traceability analysis reverses the arrow of the original directed acyclic graph and finds the data entity nodes related to the target traceability data to achieve the purpose of data traceability. Each node in the directed acyclic graph represents a data entity, and the lines between the nodes represent the dependency relationship between two data entities.

[0066] For example, referring to FIG. 6, Figure 3 The result of data traceability on data entity D8 can be described as D8 depending on D6 and D7, D6 depending on D4, D7 depending on D5, and D4 and D5 being respectively processed by D1 and D2.

[0067] Further, in the embodiments of the present application, step B2 uses a data flow ordered tree for traceability analysis, including the following steps B21-B22:

[0068] B21: The root node represents the original data state, and the child nodes record the data change history in a preset order, wherein the preset order includes time sequence;

[0069] B22: Support from any child node to layer backtracking to the root node, and associate the change log stored in the blockchain.

[0070] It can be understood that each node in the ordered tree represents a data state, and the data state records data field information, data provider information, third-party access to the data, modification, and other behavior operation information. The root node represents the original data state, and each of the remaining nodes represents a data state evolved from the parent node through some changes. The child nodes of the same parent node are sorted from left to right according to the data change time.

[0071] For example, in combination with the accompanying drawings Figure 4 As shown in the figure, node D1.3.1.1 represents a new data state obtained after data flow from D1.3.1 data state, and the traceability data ordered tree before flow is stored in the corresponding blockchain of D1.3.1.1, and according to the editable block structure condition, the change of D1.3.1→D1.3.1.1 is recorded in the existing D1.3.1 data state behavior log. When performing data traceability, only need to query the process information of the newly added data flow from the root node of the traceability data ordered tree stored in the corresponding blockchain, add D1.3.1.1 data state change time to the traceability time range, and get the current complete traceability result.

[0072] Further, the smart contract is used to verify data lifecycle compliance, dynamically allocate role permissions, and record violations to the blockchain auxiliary chain, and generate audit logs.

[0073] It can be understood that dynamically allocating role permissions includes that the data provider has the upload, view, modify, download, and delete permissions of the data; the data transmission party has the data viewing permission; the data cleaning party has the data viewing and modifying permissions; the data storage party has the data viewing permission; and the data usage party includes the data viewing and downloading permissions.

[0074] It should be noted that each time a user attempts to access data, the platform will verify the user's role and permissions. If the user does not have the right to perform an operation, the platform will reject the access request and record the reason for the rejection. As business needs and user roles change, the platform can dynamically update or revoke user permissions through smart contracts, ensuring flexibility in permission management. Further energy data fusion analysis and processing involves multiple participants, including data providers, transmission parties, cleaning parties, storage parties, and usage parties. For these different participants, data access, modification, and usage permissions vary. Therefore, traceability analysis needs to be able to implement strict audit and permission control for different data operations. The platform should be able to record the operation behavior of each user and record and verify data access, modification, deletion, and other behaviors.

[0075] In an alternative embodiment, the dynamic allocation of role permissions in step S300 to implement operation audit can be achieved through dynamic permission management based on a zero-trust architecture, i.e., introducing a zero-trust security architecture into the platform, abandoning the traditional boundary-based security model, assuming that the network environment is not secure by default, and strictly authenticating and authorizing each user access request. According to the user's identity, device status, access time, access location, operation type, and other real-time factors, the system assesses the risk of the user's access request and dynamically grants the user appropriate permissions based on the risk assessment results. For high-risk requests, the system can limit the user's operation permissions or require the user to provide higher-level identity verification.

[0076] In another alternative embodiment, the dynamic allocation of role permissions in step S300 to implement operation audit can also be achieved through dynamic permission adjustment and operation audit based on risk assessment, i.e., considering various factors that affect data security and operation compliance, building a risk assessment index system, covering whether the user login location is a strange environment, whether the login time is abnormal, whether the past operation has violated the rules, the sensitivity of the requested operation, and other indicators. Each time a user initiates a data access or operation request, the system calculates the risk value in real-time based on the risk assessment index system. If the risk value is below the set threshold, the operation is performed according to the normal permissions; if the risk value exceeds the threshold, some high-risk operation permissions are dynamically limited.

[0077] In summary, the present application aims at multi-source energy data fusion traceability analysis, clearly defines the flow rules of multi-source energy data in the whole process of collection, transmission, storage and use, realizes accurate verification and full-process traceability of data sources by constructing a traceability model based on block chain. The intelligent contract is used to dynamically manage the operation permission, so as to ensure the compliance and safety of data access, effectively prevent the risk of data leakage and abuse. At the same time, based on the visualization methods such as directed acyclic graph (DAG) and data flow order tree, the data flow and state change are clearly displayed, and the data problem source is quickly located. This method provides an intelligent solution for efficient management and trusted sharing of energy data, reduces the data management cost, improves the energy data utilization efficiency, lays a solid foundation for energy digital transformation, and has important significance for promoting the intelligentization of energy industry and optimizing the energy management decision-making process.

[0078] In the embodiment 3, the above is a schematic scheme of a multi-source energy data fusion traceability analysis method. It should be noted that the technical scheme of the multi-source energy data fusion traceability analysis system belongs to the same concept as the technical scheme of the multi-source energy data fusion traceability analysis method described above. The technical scheme of the multi-source energy data fusion traceability analysis system in this embodiment is not described in detail, and the description of the technical scheme of the multi-source energy data fusion traceability analysis method can be referred to.

[0079] The embodiment also provides a multi-source energy data fusion traceability analysis system, which comprises:

[0080] The uploading and entering module is used for uploading multi-source energy data to the platform and entering target data according to a preset template.

[0081] The verification and storage module is used for verifying the authenticity of the data source, generating a data hash value and storing it in the block chain main chain.

[0082] The analysis and audit module is used for traceability analysis based on the data flow path recorded by the block chain, and dynamically allocates role permissions through the intelligent contract to realize operation audit.

[0083] The embodiment also provides an electronic device suitable for multi-source energy data fusion traceability analysis, which comprises a memory and a processor. The memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the multi-source energy data fusion traceability analysis method proposed in the above embodiment.

[0084] The embodiment also provides a storage medium having a computer program stored thereon. When the processor executes the program, the multi-source energy data fusion traceability analysis monitoring method proposed in the above embodiment is realized.

[0085] The storage medium proposed in the embodiment belongs to the same inventive concept as the method for realizing multi-source energy data fusion traceability analysis proposed in the above embodiment. Technical details not described in the embodiment can be seen from the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary general hardware, and of course can also be realized by hardware. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A multi-source energy data fusion traceability analysis method, characterized by: include, Upload multi-source energy data to the platform and enter target data according to the preset template; Verify the authenticity of the data source, generate a data hash value and store it on the blockchain main chain; Based on the data flow path recorded in the blockchain, a visualization method is used to conduct traceability analysis, and role permissions are dynamically allocated through smart contracts to achieve operational auditing.

2. The multi-source energy data fusion and traceability analysis method according to claim 1, characterized in that: The verification of the authenticity of the source of the data, generating a data hash value and storing it in the blockchain main chain, includes: The data provider processes the data and generates a digital signature; The platform verifies the validity of the digital signature; For the verified data, a unique hash value is generated and stored in the blockchain main chain.

3. The multi-source energy data fusion and traceability analysis method according to claim 2, characterized in that: The visualization method is used for source tracing analysis, including using a directed acyclic graph for source tracing analysis or a data flow ordered tree for source tracing analysis.

4. The multi-source energy data fusion and traceability analysis method according to claim 3 is characterized by: The directed acyclic graph is subjected to source tracing analysis. include, Starting from the target data, trace back to the upstream dependent nodes; Use preset algorithms to locate full-link dependencies and generate visual traceability paths.

5. The multi-source energy data fusion and traceability analysis method according to claim 3, characterized in that: The data flow is traced back to the ordered tree for analysis, including: The root node represents the original data state, and the child nodes record the data change history in a preset order; It supports tracing back from any child node to the root node layer by layer and associating the change log stored in the blockchain.

6. The multi-source energy data fusion and traceability analysis method according to claim 1, characterized in that: The smart contract is used to: Verify data lifecycle compliance, dynamically assign role permissions, record violations to the blockchain subsidiary chain, and generate audit logs.

7. The multi-source energy data fusion and traceability analysis method according to claim 1, characterized in that: The preset template includes: The mapping rules and standardized entry specifications of the data status information field, the operation log field and the multi-source heterogeneous data.

8. A multi-source energy data fusion traceability analysis system, applying the method according to any one of claims 1 to 7, characterized in that: include: The upload and entry module is used to upload multi-source energy data to the platform and enter target data according to the preset template; A verification and storage module is used to verify the authenticity of the source of the data, generate a data hash value and store it on the blockchain main chain; The analysis and audit module is used to perform traceability analysis based on the data flow path recorded in the blockchain using a visualization method, and dynamically assign role permissions through smart contracts to achieve operation auditing.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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