Power grid data interaction and auditing method based on multi-source heterogeneous distributed data

Abstract

The invention belongs to the technical field of power grid data processing, and particularly relates to a power grid data interaction and auditing method based on multi-source heterogeneous distributed data, which comprises the following steps of: identifying and accessing data sources; designing a data authentication protocol; compressing the multi-source heterogeneous distributed data; constructing a representation model of the multi-source heterogeneous distributed data; performing data processing on the data of different data sources to extract data features; on the basis of a cross validation idea of deep learning, compressed data records of different sources are compared through an algorithm, and the data consistency is verified so as to evaluate the authenticity of the data; establishing an audit log and a chain structure; deploying auditing rules and algorithms; an evaluation mechanism of audit results; according to the method, a charging facility and power grid multi-source heterogeneous data high-performance sharing interaction model is constructed, heterogeneous interfaces and protocols, multi-type data representation and compression, cross check and auditing are supported, and high-throughput and high-concurrency data interaction and cooperative execution of multiple types of nodes are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid data processing, and in particular relates to a power grid data interaction and auditing method based on multi-source heterogeneous distributed data. Background Art

[0002] In recent years, the country has accelerated the construction of a high-quality charging infrastructure system and built the world's largest charging infrastructure network with the widest service scope and the most complete varieties. By the end of October 2023, a total of 7.95 million charging piles have been built nationwide, of which more than 2.5 million are public charging piles. With the gradual large-scale promotion and application of electric vehicles, the scale and frequency of multi-source heterogeneous data sharing and interaction between charging facilities and power grids have increased sharply. On the one hand, due to the large number of charging facility investment entities and service platforms, which are scattered and operate independently, electric vehicle users face problems such as insufficient compatibility and mutual trust of heterogeneous network architectures, and numerous network and data barriers in the process of participating in power grid data sharing and interaction. On the other hand, since charging facilities involve large amounts of multi-terminal data that are scattered and highly concurrent, and lack unified data interaction interfaces and protocols, it is difficult to ensure the full-link circulation efficiency and security control of multi-source scattered data.

[0003] In the process of data sharing and interaction between charging facilities and power grids, as the scale and frequency of data and business interactions such as transactions and settlements between multiple entities increase sharply, the problems of trustworthy perception of massive information, secure interactive integration of data, and efficient and trustworthy collaboration of business are becoming increasingly prominent, and a series of technical challenges are faced. First, in terms of heterogeneous network integration architecture, the charging operator network is heterogeneous and diverse, and the scalability and compatibility of the existing network are insufficient, making it difficult to support the rapid expansion and high concurrent access needs of multiple entities and nodes such as massive charging operators and personal charging piles in the future. Second, in terms of data interaction and circulation, the amount of data in the operation of charging facilities is large and scattered, and the traditional database docking method is difficult to ensure the efficiency of the full-link flow and interaction of multi-source data of scattered entities, and it is difficult to adapt to the needs of large-scale data interaction and processing in the future. Third, in terms of data security control, charging operators involve multiple entities that do not trust each other, and contain user personal privacy data. Existing encryption algorithms and other technical means are difficult to ensure the elasticity and verifiability of data under privacy protection conditions, and data cross-platform security control is difficult. Summary of the invention

[0004] The purpose of the present invention is to provide a power grid data interaction and auditing method based on multi-source heterogeneous distributed data to address the problems existing in the prior art. The method constructs a high-performance shared interaction model for multi-source heterogeneous data between charging facilities and power grids, supports heterogeneous interfaces and protocols, multi-type data representation and compression, cross-checking and auditing, and realizes high-throughput, high-concurrency data interaction and collaborative execution of multiple types of nodes.

[0005] The technical solution of the present invention is:

[0006] Grid data interaction and auditing method based on multi-source heterogeneous distributed data, including the following steps:

[0007] S1. Data source identification and access: Identify and define various heterogeneous distributed data sources, and establish a mechanism to enable the data from these sources to be securely accessed by the auditing system;

[0008] S2. Design of data authentication protocol: Design a data authentication protocol to establish the authentication path of the data. This protocol should cover the verification of data sources, encryption during data transmission, and integrity verification mechanisms;

[0009] S3. Compress multi-source heterogeneous distributed data;

[0010] S4. Construct a characterization model for multi-source heterogeneous distributed data;

[0011] S5. Verify the effectiveness and robustness of the constructed characterization model, and adjust the model parameters and structure according to the evaluation results to improve the performance of the characterization model;

[0012] S6. Extract data features by processing the data of different data sources according to the above characterization model and compressed data;

[0013] S7. Based on the cross-validation idea of deep learning, verify the data consistency by comparing the compressed data records of different sources through an algorithm to evaluate the authenticity of the data;

[0014] S8. Establishment of audit logs and chain structure: Construct an audit log recording system to record all data operations and audit decisions, and adopt a chain structure to improve the immutability of the logs;

[0015] S9. Deployment of audit rules and algorithms: Set up and deploy a set of audit rules and algorithms that can be automatically executed to detect data authenticity and consistency;

[0016] S10. Evaluation mechanism for audit results: Establish a mechanism to evaluate audit results to quantify the credibility of the data, and determine whether to take further data cleaning or correction measures according to the evaluation results of data authenticity.

[0017] Specifically, the compression of multi-source heterogeneous distributed data includes the following steps:

[0018] Heterogeneity analysis: First, thoroughly analyze the heterogeneous characteristics of multi-source data, including data format, scale, sparsity, and distribution characteristics. This step is to identify the compressible data parts and design adaptive compression strategies;

[0019] Compression Algorithm Selection: Based on the results of heterogeneity analysis, select or design a suitable compression algorithm. Common algorithms include lossless compression (such as Huffman coding, LZ77), lossy compression (such as DCT, the data compression algorithm based on VAEs mentioned in 2.1.2), and compression algorithms designed specifically for certain data types (such as H.264 for video data);

[0020] Distributed Compression Framework Design: Design a framework that can distribute and merge compression tasks among different nodes, and optimize the communication between nodes to reduce transmission redundancy and execute the compression algorithm in parallel;

[0021] Data Synchronization and Integration: To ensure data consistency and integrity, synchronize and integrate the compressed data segments, and design a data checksum and reconstruction mechanism to maintain the integrity and accuracy of the data during the compression and decompression processes;

[0022] Performance Evaluation: Evaluate the performance of the compression technology by estimating metrics such as compression ratio, compression speed, and data recovery accuracy. It is necessary to test on multiple dimensions, including but not limited to different data types, different network conditions, and different system load scenarios.

[0023] Optimization Iteration: Based on the results of performance evaluation, perform necessary iterations and optimizations on the compression algorithm and the distributed framework to achieve the purpose of improving compression efficiency and transmission speed.

[0024] Specifically, constructing a representation model for multi-source heterogeneous distributed data includes the following steps:

[0025] Data Preprocessing: Due to the heterogeneity of data sources, it is necessary to standardize, denoise, and handle missing values for the data of each source to ensure the consistency of data quality;

[0026] Feature Extraction and Fusion: Extract features from each data source through data mining and machine learning algorithms, using techniques such as principal component analysis (PCA), autoencoders, and then use various data fusion techniques, such as Kalman filtering, Bayesian networks, to synthesize the extracted features into a unified representation form;

[0027] Heterogeneous Data Representation Model: Design a representation model according to data characteristics and application requirements. For graph-structured data, use graph neural networks (GNNs); for sequence data, use recurrent neural networks (RNNs) or long short-term memory networks (LSTMs);

[0028] Selection and Optimization of Modeling Techniques: Based on the characteristics of the problem domain, select a suitable modeling technique. In a distributed environment with limited resources, consider the computational complexity and communication overhead of the model, and optimize the model structure and learning strategy to achieve effective information extraction and transmission among the data of each source.

[0029] Specifically, a cross-verification method based on smart contracts can also be used to cross-verify multi-source heterogeneous distributed data.

[0030] Specifically, the effectiveness and robustness of the model are tested through cross-validation and A / B testing methods, and the model parameters and structure are adjusted according to the evaluation results to improve the performance of the representation model.

[0031] Specifically, the cross-verification idea based on deep learning uses specific methods such as holdout validation, k-fold cross-validation, stratified k-fold cross-validation, leave-one-out cross-validation, leave-p-out cross-validation, Monte Carlo cross-validation, and time series cross-validation. By comparing compressed data records from different sources through algorithms, the data consistency is verified to evaluate the authenticity of the data.

[0032] Specifically, the cross-verification method based on smart contracts includes the following steps:

[0033] Smart contract design and development: Write smart contract code for data cross-verification, which should be automatically executed under specific conditions, including defining contract rules, verification logic, and event trigger mechanisms;

[0034] Selection of consensus mechanism: Select a suitable blockchain consensus mechanism to ensure consistency among multiple nodes in the network; for example, mechanisms such as Proof of Work (POW) or Proof of Stake (POS) can be adopted.

[0035] Construction of nodes and network: Build a blockchain network, and its nodes will deploy the previously developed smart contract and complete data submission, verification, and synchronization according to the design;

[0036] Cross-system data access and formatting: Ensure that the data input from internal and external data sources of the system undergoes standardized processing to meet the requirements of the smart contract for data format and structure;

[0037] Deployment and integration: Deploy the smart contract to the blockchain network and integrate it with the existing system through an adapter or API interface to automatically trigger the cross-verification operation;

[0038] Testing and debugging: Test the execution results of the smart contract through a series of simulations and actual operations to ensure that it can work properly under various conditions and scenarios.

[0039] It also includes performance optimization: evaluating and optimizing system performance, including aspects such as the execution efficiency of smart contracts, the transmission speed of the blockchain network, and the processing capacity of nodes; security assessment and enhancement: comprehensively assessing the security of smart contracts and the network to identify potential security risks and implementing necessary security enhancement measures such as encryption, access control, and data privacy protection.

[0040] The beneficial effects of the present invention are: an efficient data compression method to reduce storage requirements, optimize data transmission speed, and handle data heterogeneity from different sources; a modular and scalable design concept to ensure its flexibility in adapting to future technological advancements and growing data demands. The multi-source data audit based on deep learning is a system solution aimed at ensuring the integrity, credibility, and consistency of data from different sources, which can effectively evaluate and verify the trustworthiness of data sources, provide strong data support for decision-making, improve the accuracy of data analysis and decision-making, fully consider the actual application background, and ensure the flexibility and scalability of the model to adapt to changing data environments and application requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the smart contract of the blockchain provided in Embodiment 3 of the present invention;

[0042] Figure 2 It is a schematic diagram of the consensus mechanism of the blockchain provided in Embodiment 3. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The technical solutions of the present invention will be described in detail below in conjunction with the specific embodiments.

[0044] The technical solution of the present invention is:

[0045] A power grid data interaction and audit method based on multi-source heterogeneous distributed data, comprising the following steps:

[0046] S1. Data source identification and access, identifying and defining various heterogeneous distributed data sources and establishing a mechanism to enable the data from these sources to be securely accessed by the audit system;

[0047] S2. Design of data authentication protocol: Designing a data authentication protocol to establish the authentication path of the data, and this protocol should cover the verification of the data source, the encryption and integrity verification mechanisms during data transmission;

[0048] S3. Compressing the multi-source heterogeneous distributed data;

[0049] S4. Constructing a characterization model for the multi-source heterogeneous distributed data;

[0050] S5. Examine the effectiveness and robustness of the constructed representation model, and adjust the model parameters and structure according to the evaluation results to improve the performance of the representation model;

[0051] S6. Based on the above representation model and compressed data, perform data processing on data from different data sources to extract data features;

[0052] S7. Based on the cross - validation idea of deep learning, compare the compressed data records from different sources through algorithms to verify the data consistency and evaluate the authenticity of the data;

[0053] S8. Establishment of audit logs and chain structure: Construct an audit log recording system to record all data operations and audit decisions, and adopt a chain structure to improve the immutability of the logs;

[0054] S9. Deployment of audit rules and algorithms: Set up and deploy a set of audit rules and algorithms that can be automatically executed to detect data authenticity and consistency;

[0055] S10. Evaluation mechanism for audit results: Establish a mechanism to evaluate audit results to quantify the credibility of the data, and determine whether to take further data cleaning or correction measures according to the evaluation results of data authenticity.

[0056] Example 1

[0057] This example provides an efficient data compression method to reduce storage requirements, optimize data transmission speed, and handle data heterogeneity from different sources. The compression of multi - source heterogeneous distributed data includes the following steps:

[0058] Heterogeneity analysis: First, thoroughly analyze the heterogeneous characteristics of multi - source data, including data format, scale, sparsity, and distribution characteristics. This step is to identify compressible data parts and design adaptive compression strategies;

[0059] Compression algorithm selection: Based on the results of heterogeneity analysis, select or design a suitable compression algorithm. Common algorithms include lossless compression (such as Huffman coding, LZ77), lossy compression (such as DCT, the data compression algorithm based on VAEs mentioned in 2.1.2), and compression algorithms designed specifically for specific data types (such as H.264 for video data);

[0060] Design of distributed compression framework: Design a framework that can distribute and merge compression tasks among different nodes, and optimize the communication between nodes to reduce transmission redundancy and execute compression algorithms in parallel;

[0061] Data Synchronization and Integration: To ensure data consistency and integrity, the compressed data fragments are synchronized and integrated, and a data verification and reconstruction mechanism is designed to maintain the integrity and accuracy of the data during the compression and decompression processes;

[0062] Performance Evaluation: The performance of the compression technology is evaluated by estimating metrics such as compression ratio, compression speed, and data recovery accuracy. Testing needs to be carried out in multiple dimensions, including but not limited to different data types, different network conditions, and different system load scenarios.

[0063] Optimization and Iteration: Based on the results of performance evaluation, necessary iterations and optimizations are performed on the compression algorithm and distributed framework to achieve the goal of improving compression efficiency and transmission speed.

[0064] This embodiment is based on a modular and extensible design concept to ensure its flexibility in adapting to future technological advancements and growing data requirements.

[0065] Embodiment 2

[0066] This embodiment provides the following specific steps for constructing a representation model of multi-source heterogeneous distributed data:

[0067] Data Preprocessing: Due to the heterogeneity of data sources, the data of each source needs to be standardized, denoised, and missing value processed to ensure the consistency of data quality;

[0068] Feature Extraction and Fusion: Feature extraction is performed on each data source through data mining and machine learning algorithms, using techniques such as principal component analysis (PCA) and autoencoders. Then, various data fusion techniques, such as Kalman filtering and Bayesian networks, are used to synthesize the extracted features into a unified representation form;

[0069] Heterogeneous Data Representation Model: A representation model is designed according to data characteristics and application requirements. For graph-structured data, graph neural networks (GNNs) are used; for sequence data, recurrent neural networks (RNNs) or long short-term memory networks (LSTMs) are used;

[0070] Selection and Optimization of Modeling Techniques: Based on the characteristics of the problem domain, appropriate modeling techniques are selected. In a distributed environment with limited resources, considering the computational complexity and communication overhead of the model, the model structure and learning strategy are optimized to achieve effective information extraction and transmission between data sources of each source. The actual application background needs to be fully considered to ensure the flexibility and scalability of the model to adapt to changing data environments and application requirements.

[0071] Embodiment 3

[0072] This embodiment provides a cross - verification method based on smart contracts for cross - verifying multi - source heterogeneous distributed data, aiming to ensure the integrity and consistency of data across multiple scenarios and entities. The effectiveness and robustness of the model are tested through cross - validation and A / B testing methods, and the model parameters and structure are adjusted according to the evaluation results to improve the performance of the representation model.

[0073] The specific implementation plan may include the following steps:

[0074] Smart contract design and development: Write smart contract code for data cross - verification. The contract should be automatically executed under specific conditions. The key points include defining contract rules, verification logic, and event - triggering mechanisms.

[0075] Selection of consensus mechanism: Select a suitable blockchain consensus mechanism to ensure consistency among multiple nodes in the network. For example, mechanisms such as Proof of Work (POW) or Proof of Stake (POS) can be adopted.

[0076] Construction of nodes and network: Build a blockchain network. Its nodes will deploy the previously developed smart contract and complete data submission, verification, and synchronization according to the design.

[0077] Cross - system data access and formatting: Ensure that the data input from internal and external data sources of the system undergoes standardization processing to meet the requirements of the smart contract for data format and structure, as Figure 1 shown.

[0078] Deployment and integration: Deploy the smart contract to the blockchain network and integrate it with the existing system through an adapter or API interface to automatically trigger the cross - verification operation.

[0079] Testing and debugging: Test the execution results of the smart contract through a series of simulations and actual operations to ensure that it can work properly under various conditions and scenarios.

[0080] Performance optimization: Evaluate and optimize system performance, including aspects such as smart contract execution efficiency, blockchain network transmission speed, and node processing capacity.

[0081] Security assessment and enhancement: Conduct a comprehensive security assessment of the smart contract and the network to identify potential security risks and implement necessary security enhancement measures, such as encryption, access control, and data privacy protection.

[0082] Monitoring and maintenance: Establish a system monitoring mechanism to continuously track the running status of the smart contract, regularly check data integrity, and perform necessary upgrades and maintenance on the system.

[0083] While implementing the above solutions, it is necessary to ensure compliance with relevant industry standards and legal requirements to guarantee the legal compliance, universality, and long-term effectiveness of the solutions. By adopting such a technical implementation solution, a transparent, reliable, and automated data verification mechanism can be achieved, such as Figure 2 as shown.

[0084] Example 4

[0085] This example provides the cross-validation idea based on deep learning, aiming to ensure the integrity, credibility, and consistency of data from different sources. Specific methods such as holdout validation, k-fold cross-validation, stratified k-fold cross-validation, leave-one-out cross-validation, leave-p-out cross-validation, Monte Carlo cross-validation, and time series cross-validation are used to verify the data consistency by comparing the compressed data records from different sources through algorithms to evaluate the authenticity of the data.

[0086] Specifically, it includes the following steps: Establishment of audit logs and chain structure: Construct an audit log recording system that can automatically record all data operations and audit decisions, and a chain structure can be adopted to improve the immutability of the logs.

[0087] Application of distributed ledger technology: Utilize distributed ledger technology (such as blockchain) to ensure the transparency and immutability of the data audit process and enhance the trustworthiness of the entire system.

[0088] Deployment of audit rules and algorithms: Set up and deploy a set of audit rules and algorithms that can be automatically executed to detect data authenticity and consistency.

[0089] Evaluation mechanism for audit results: Establish a mechanism to evaluate audit results to quantify the credibility of the data. Based on the evaluation results of data authenticity, determine whether to take further data cleaning or correction measures.

[0090] System monitoring and update: Implement system monitoring, evaluate the effectiveness of the audit system and data processing capabilities, and regularly update audit rules, verification algorithms, and system security measures to adapt to new data sources and audit requirements.

[0091] Consideration of compliance and ethical standards: Ensure that the entire audit process complies with current legal, compliance requirements, and ethical standards for data processing, especially in terms of data protection and privacy.

[0092] The cross-validation idea based on deep learning can effectively evaluate and verify the trustworthiness of data sources, provide strong data support for decision-making, and improve the accuracy of data analysis and decision-making.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific implementation manners of the present invention or perform equivalent replacements for some technical features; without departing from the spirit of the technical solutions of the present invention, they should all be covered within the scope of the technical solutions claimed by the present invention.

Claims

1. A power grid data interaction and auditing method based on multi-source heterogeneous distributed data, characterized in that: The steps include: S1. Data source identification and access: identifying and defining various heterogeneous distributed data sources, and establishing mechanisms to enable these data to be securely accessed into the audit system; S2. Data authentication protocol design: Design a data authentication protocol and establish the authentication path for data. The protocol should cover the verification of data sources, encryption of data during transmission, and integrity verification mechanism; S3. Compress multi-source heterogeneous distributed data; S4. Construct a representation model for multi-source heterogeneous distributed data; S5. Verify the effectiveness and robustness of the constructed representation model, and adjust the model parameters and structure according to the evaluation results to improve the performance of the representation model; S6. According to the above characterization model and compressed data, data from different data sources are processed to extract data features; S7. Based on the cross-validation idea of ​​deep learning, the compressed data records from different sources are compared through algorithms to verify the consistency of the data to evaluate the authenticity of the data; S8. Audit log and chain structure establishment: Build an audit log recording system to record all data operations and audit decisions, and use a chain structure to improve the immutability of the log; S9. Audit rules and algorithm deployment: Establish and deploy a set of audit rules and algorithms that can be automatically executed to detect data authenticity and consistency; S10. Audit results evaluation mechanism: Establish a mechanism to evaluate audit results to quantify the credibility of the data and determine whether to take further data cleaning or correction measures based on the evaluation results of data authenticity.

2. The power grid data interaction and auditing method based on multi-source heterogeneous distributed data according to claim 1 is characterized in that: The method of compressing multi-source heterogeneous distributed data includes the following steps: Heterogeneity analysis: First, the heterogeneous characteristics of multi-source data are thoroughly analyzed, including data format, scale, sparsity, and distribution characteristics; Compression algorithm selection: Select or design a suitable compression algorithm based on the results of heterogeneity analysis; Distributed compression framework design: Design a framework that can distribute and merge compression tasks among different nodes, optimize inter-node communication to reduce transmission redundancy, and execute compression algorithms in parallel; Data synchronization and integration: synchronize and integrate the compressed data fragments, and design data verification and reconstruction mechanisms to maintain data integrity and accuracy during the compression and decompression process; Performance evaluation: Evaluate the performance of compression techniques by estimating compression ratio, compression speed, and data recovery accuracy metrics; Optimization iteration: Perform necessary iterations and optimizations on the compression algorithm and distributed framework to improve compression efficiency and transmission speed.

3. The power grid data interaction and auditing method based on multi-source heterogeneous distributed data according to claim 1 is characterized in that: Building a representation model for multi-source heterogeneous distributed data includes the following steps: Data preprocessing: Due to the heterogeneity of data sources, the data from each source needs to be standardized, denoised, and processed for missing values ​​to ensure consistency in data quality; Feature extraction and fusion: Extract features from various data sources through data mining and machine learning algorithms, using techniques such as principal component analysis (PCA) and autoencoders, and then use various data fusion techniques such as Kalman filtering and Bayesian networks to synthesize the extracted features into a unified representation form; Heterogeneous data representation model: Design a representation model based on data characteristics and application requirements. For graph-structured data, use graph neural network (GNN); for sequence data, use recurrent neural network (RNN) or long short-term memory network (LSTM); Selection and optimization of modeling technology: Select appropriate modeling technology based on the characteristics of the problem domain. In a distributed environment with limited resources, consider the computational complexity and communication overhead of the model, optimize the model structure and learning strategy, and achieve effective information extraction and transmission between source data.

4. The power grid data interaction and auditing method based on multi-source heterogeneous distributed data according to claim 1 is characterized in that: A cross-verification method based on smart contracts can also be used to cross-verify multi-source heterogeneous distributed data.

5. The power grid data interaction and auditing method based on multi-source heterogeneous distributed data according to claim 1 is characterized in that: The effectiveness and robustness of the model are tested through cross-validation and A / B testing methods, and the model parameters and structure are adjusted according to the evaluation results to improve the performance of the characterization model.

6. The power grid data interaction and auditing method based on multi-source heterogeneous distributed data according to claim 1 is characterized in that: The cross-validation idea based on deep learning uses specific methods such as retention validation, k-fold cross-validation, stratified k-fold cross-validation, leave-one-out cross-validation, leave-p-out cross-validation, Monte Carlo cross-validation and time series cross-validation to compare compressed data records from different sources through algorithms, verify data consistency, and evaluate the authenticity of the data.

7. The power grid data interaction and auditing method based on multi-source heterogeneous distributed data according to claim 4 is characterized in that: The cross-verification method based on smart contracts includes the following steps: Smart contract design and development: Write smart contract code for data cross-verification, which should be automatically executed under specific conditions, including defining contract rules, verification logic and event triggering mechanism; Consensus mechanism selection: Select a suitable blockchain consensus mechanism to ensure consistency among multiple nodes in the network; Node and network construction: Build a blockchain network whose nodes will deploy the previously developed smart contracts and complete data submission, verification and synchronization according to the design; Cross-system data access and formatting: Ensure that data input from internal and external data sources is standardized to meet the data format and structure requirements of smart contracts; Deployment and integration: Deploy smart contracts to the blockchain network and integrate with existing systems through adapters or API interfaces to automatically trigger cross-check operations; Testing and debugging: Test the execution results of smart contracts through a series of simulations and actual operations to ensure that they can work properly under various conditions and scenarios.

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