Power plant data full life cycle management method, system, device and storage medium

Abstract

Embodiments of the present disclosure provide a power plant data full life cycle management method, system, device and storage medium. First, the power plant data is sorted by assets, the data assets are clarified comprehensively and clearly, then an implementable data security classification method is formed, and finally the data full life cycle is protected and managed according to the classification results. Embodiments of the present disclosure reduce the cost, maintenance cost and labor cost of data protection construction by adopting a small sample sampling method, provide a new idea for the industry to provide low-cost and feasible data full life cycle security protection, propose an automatic classification method to standardize the grading and classification of power plant data, and first provide a complete and feasible scheme for the classification of power plant data in the power generation industry, provide theoretical support and technical support for the development of power plant data security related systems, and ensure the safety of the data full life cycle.

Description

Technical Field

[0001] The embodiments disclosed herein belong to the field of power plant data management technology, specifically relating to a method, system, equipment, and storage medium for the full lifecycle management of power plant data. Background Technology

[0002] With the rapid development of information networks, various data security issues are constantly emerging. How to deal with the data and asset losses caused by information security and how to achieve thorough security protection for data assets and information security have become the most pressing issues for power plants.

[0003] Currently, power generation companies face several key challenges in data protection: 1) Lack of industry standards for power plant classification and grading. There are no specific solutions or standards for classifying and grading power plant data, making it difficult to effectively implement data leakage prevention, tamper prevention, and data grading protection, which are crucial for power plant data security. This poses a risk of power system failures or major safety accidents. 2) Fragmented data security capabilities. Various production, management, operational, and personal data generated during power production management are scattered across numerous different information systems, office computers, maintenance equipment, and mobile terminals. Each power plant's business systems are isolated, and protection equipment management is fragmented, resulting in unclear data asset distribution, lack of inter-device coordination, and ineffective data management. 3) Unclear data access and flow. Data transferred from information systems to the physical world lacks access control mechanisms, and data flow and access are not effectively recorded, making it impossible to determine or track historical data access paths. 4) Power companies currently lack a system suitable for power production and management data security protection. Power plants lack data classification and grading capabilities and suitable tools, relying primarily on traditional IT asset management methods, lacking effective monitoring and protection technologies such as visualization, tracking, encryption, authentication, and data masking. Summary of the Invention

[0004] The embodiments disclosed herein aim to at least solve one of the technical problems existing in the prior art, and provide a method, system, device and storage medium for full life cycle management of power plant data.

[0005] One aspect of this disclosure provides a method for full lifecycle management of power plant data, the method comprising:

[0006] Data from power plants is collected by sampling; the power plant data includes general data and core data.

[0007] Encrypt power plant data during transmission, monitor for anomalies in the data transmission process, and generate a data flow diagram;

[0008] Power plant data is distributed and stored on corresponding storage media terminals. A data storage media management ledger is established, the usage of storage media is verified regularly, removable storage media are tracked and recorded, and a data distribution map is generated.

[0009] Core data is anonymized, and emergency response measures are taken for any unforeseen events during the anonymization process, including blocking the corresponding data interfaces.

[0010] Establish a data provision ledger, issue alerts for overdue data usage, and track and record data asset transfers;

[0011] Establish a data destruction ledger, use overwrite to erase general data, and use physical destruction to erase core data, and record and supervise the destruction process.

[0012] Furthermore, prior to the sampling and collection of power plant data, the method further includes:

[0013] Organize the power plant data into an asset inventory, generate a data asset management ledger, and establish a data asset management view;

[0014] Semi-supervised learning is used to automatically classify and grade power plant data, resulting in general data and core data.

[0015] Furthermore, the automated classification and grading of power plant data using semi-supervised learning includes:

[0016] Obtain the power plant dataset;

[0017] A portion of the power plant data was manually labeled, and field features and similar column features of the remaining power plant data were extracted through unsupervised learning to establish a classification and hierarchical label feature model.

[0018] The classification and grading label feature model is used to automatically classify and grade power plant data.

[0019] Furthermore, the power plant data lifecycle management method also includes:

[0020] Conduct a security assessment of power plant data, establish a data security compliance baseline, establish a data security privacy baseline, generate a data security assessment report, and generate a list of issues based on the data security assessment report.

[0021] Another aspect of this disclosure provides a power plant data lifecycle management system, the system comprising:

[0022] The data acquisition module is used to sample and collect power plant data; wherein, the power plant data includes general data and core data;

[0023] The data transmission module is used to encrypt and transmit power plant data, monitor anomalies during the data transmission process, and generate a data flow diagram.

[0024] The data storage module is used to distribute power plant data across corresponding storage media terminals, establish a data storage media management ledger, periodically verify the usage of storage media, track and record removable storage media, and generate data distribution maps.

[0025] The data processing module is used to desensitize core data, handle emergencies in the desensitization process, and block relevant data interfaces.

[0026] The data provision module is used to establish a data provision ledger, issue alerts for overdue use of provided data, and track and record the transfer of data assets.

[0027] The data destruction module is used to establish a data destruction ledger, perform overwrite erasure on general data, perform physical destruction on core data, and record and monitor the destruction process.

[0028] Furthermore, the power plant data lifecycle management system also includes:

[0029] The asset sorting module is used to sort out the power plant data, generate a data asset management ledger, and establish a data asset management view.

[0030] The classification and grading module is used to automatically classify and grade power plant data using semi-supervised learning, resulting in general data and core data.

[0031] Furthermore, the classification and grading module is specifically used for:

[0032] Obtain the power plant dataset;

[0033] A portion of the power plant data was manually labeled, and field features and similar column features of the remaining power plant data were extracted through unsupervised learning to establish a classification and hierarchical label feature model.

[0034] The classification and grading label feature model is used to automatically classify and grade power plant data.

[0035] Furthermore, the power plant data lifecycle management system also includes:

[0036] The security assessment module is used to conduct security assessments on power plant data, establish data security compliance baselines, establish data security privacy baselines, generate data security assessment reports, and generate a list of issues based on the data security assessment reports.

[0037] Another aspect of this disclosure provides an electronic device, comprising:

[0038] At least one processor; and,

[0039] A memory communicatively connected to the at least one processor is used to store one or more programs, which, when executed by the at least one processor, enable the at least one processor to implement the power plant data lifecycle management method described above.

[0040] Another aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power plant data lifecycle management method described above.

[0041] This disclosure discloses a method, system, device, and storage medium for full lifecycle management of power plant data. By employing a small sample sampling approach, it reduces the costs of data protection construction, maintenance, and manpower, providing the industry with a new approach for low-cost and implementable full lifecycle data security protection. It proposes an automated classification and grading method for standardized classification and grading of power plant data. For the first time, it provides a complete and feasible solution for the classification and grading of power plant data in the power generation industry, providing theoretical and technical support for the development of systems related to power plant data security, and ensuring data security throughout its entire lifecycle. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating a power plant data lifecycle management method according to an embodiment of the present disclosure;

[0043] Figure 2 This is a schematic diagram of the structure of a power plant data lifecycle management system according to another embodiment of this disclosure;

[0044] Figure 3 This is a schematic diagram of the structure of an electronic device according to another embodiment of the present disclosure. Detailed Implementation

[0045] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this disclosure.

[0046] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0047] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0048] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of this disclosure. As used in this disclosure, the term "and / or" includes all combinations of any and more of the associated listed items.

[0049] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing this disclosure, and therefore cannot be used to limit the scope of protection of this disclosure.

[0050] like Figure 1 As shown, one embodiment of this disclosure provides a method for full lifecycle management of power plant data, the method comprising:

[0051] Step S1: Conduct an asset inventory of the power plant data.

[0052] Specifically, a comprehensive review of the power plant's structured database, semi-structured interface information, and unstructured images and text is conducted to create a data asset repository. Data assets from different sources and of different types are standardized, systematically merged, and organized. New data asset information is added through various methods, including automated scanning of specified IP network segments, manual user addition, and batch import of asset catalogs. Existing data asset information can be queried, modified, deleted, and exported. A data asset management ledger is generated, clearly defining the types, sources, content, and uses of data assets, as well as their ownership and application scenarios. This clearly displays all data asset information within the power plant, facilitating management of various data assets by power plant personnel. A data asset management view is established, displaying information such as asset type, quantity, and distribution, providing users with an intuitive demonstration of management effectiveness.

[0053] Step S2: Use semi-supervised learning to automatically classify and grade the power plant data to obtain general data and core data.

[0054] Specifically, the embodiments of this disclosure abandon the drawbacks of traditional data classification and grading methods that require a large amount of manual labeling, and propose a semi-supervised data classification and grading method based on deep learning to complete the automated classification and grading of massive data in the power plant field.

[0055] First, the data source to be scanned is entered, using mainstream databases such as DM, Massive, PostgreSQL, and MySQL. Then, the acquired power plant dataset is fed into a semi-supervised learning engine for data asset assessment and labeling. A small portion of the data is manually labeled, while unsupervised learning is used to analyze similar fields in the remaining large dataset, automatically extracting database field features and similar column features for batch labeling, thus establishing a classification and grading label feature model. This model employs a multi-layer convolutional neural network and embeds the Word2Vec algorithm to train word vectors for short sentences, mining features of relationships between samples while learning the correlation between input and output labels. This automatically extracts database field features and similar column features, effectively extracting semantic and contextual information to establish a classification and grading label feature model, achieving automated classification and grading of power plant data. This method can handle various data types, including numbers, dates, and text, and can also process multi-dimensional and complex data. For unstructured data such as text, Natural Language Processing (NLP) technology is used, employing a deep learning model to understand and analyze the data, dynamically achieving automatic classification and grading of power plant business data. Power plant data can be divided into general data and core data.

[0056] Step S3: Sampling and collecting power plant data.

[0057] Specifically, power plant data acquisition includes three forms: data acquisition between internal power plant systems, data acquisition from external systems, and data acquisition from the power plant by the management system. This embodiment uses a sampling method to collect the power plant data obtained through the preceding steps, including general data and core data. A certain proportion of the latest small sample data is collected periodically during certain processes (such as data access). Since various types of data have the same table and field structure, sampling and periodic updates can reduce subsequent data management costs and save resources.

[0058] Step S4: Encrypt the power plant data during transmission, monitor for anomalies in the data transmission process, and generate a data flow diagram.

[0059] Specifically, the system encrypts and protects the small sample data transmission, including API encryption and transmission protocol encryption. Differentiated encryption measures are used for core and important data to prevent the leakage of critical information during transmission. Anomalies in the data transmission process are monitored, including system connection drops and malicious traffic requests, generating an alarm list to prevent data transmission from being affected by system failures, network failures, or malicious attacks. A data flow diagram is generated to reflect the flow of data between various systems, recording the time, method, and other operational information of data transmission, helping power plant personnel to promptly identify potential risks such as unauthorized data transfers and risky transfers. Relevant systems such as identity authentication, data encryption, and monitoring and early warning are established to ensure the security of the data transmission link. Data flow comparison is performed by formulating data transmission rules, generating alarm notifications for abnormal data flows, and further collecting and analyzing all data within that time period to improve low-cost, implementable data management.

[0060] Step S5: Distribute the power plant data to corresponding storage media terminals, establish a data storage media management ledger, regularly verify the usage of storage media, track and record removable storage media, and generate a data distribution map.

[0061] Specifically, the data storage media within a power plant encompasses data servers and data management platforms from various systems, as well as computer terminals, mobile terminals, and portable hard drives used by power plant employees. This dispersed data storage presents significant management challenges. Traditional data storage methods involve storing the collected data in a unified data lake or data center.

[0062] This embodiment uses a single integrated device to store data, while all data remains stored in individual application systems. A data storage media management ledger is established to clearly define the media type, storage method, deployment location, encryption status, business ownership, and responsible entity for data storage. This supports both automatic identification and manual entry, enabling unified management of the power plant's data storage media. The usage of storage media is regularly verified, including user information, usage cycle, copying activity, and borrowing information. Removable storage media are tracked and recorded. Database vulnerability scanning is performed, or the system integrates with third-party vulnerability management platforms to achieve unified database security management. A data distribution map is generated, reflecting the distribution of data across various storage media. This map can statistically analyze the total number of database tables, fields, and files, and supports viewing single or multiple data distributions. Simultaneously, data storage rules and regulations are established to periodically monitor for anomalies and generate alarm notifications.

[0063] Step S6: Desensitize the core data, take emergency measures for any unforeseen events during the desensitization process, and block the corresponding data interfaces.

[0064] Specifically, unlike traditional data processing that analyzes data by calling platform interfaces, this embodiment uses platform-defined processing rules to anonymize core and important data. While ensuring data availability, it removes sensitive information from the data, preventing the leakage of important information during processing. It also audits and tracks major emergencies during data processing, creating an alarm list and implementing emergency response measures. This includes timely isolation and blocking of data interfaces of relevant systems to prevent the escalation of data security incidents. Furthermore, it establishes systems for data access control, data anonymization, and operational auditing to ensure the security of the data processing environment and process.

[0065] Step S7: Establish a data provision ledger, issue alerts for overdue data usage, and track and record data asset transfers.

[0066] Specifically, data provision involves the transfer of data between internal systems or from internal systems to external organizations and individuals. Potential risks include a lack of approval and management for data provision transactions, insufficient monitoring and documentation of data interaction processes, and inadequate security assessment of submitted data.

[0067] This embodiment establishes a data provision ledger, clearly defining the content, scope, and usage period of the provided data. It provides alerts for expired data usage, facilitating data management by power plant personnel. The provided data is encrypted and protected, supporting domestically produced encryption and implementing a one-key-one-key system to prevent excessive sharing and forwarding. Core and important data are anonymized to prevent leakage of critical information during the data provision process. Data asset transfer is tracked and recorded, with the data asset ledger updated synchronously when ownership changes, ensuring consistency between the ledger, personnel, and asset information. A data provision management system is established, clearly defining the principles, conditions, implementation methods, approval processes, filing processes, and security responsibilities for data provision. Process comparison is performed using a system data asset map, and alerts are promptly issued for any abnormal data. Security protection plans are developed for high-risk and sensitive operations such as data sharing, publishing, transfer, and downloading.

[0068] Step S8: Establish a data destruction ledger, use overwrite to erase general data, use physical destruction to erase core data, and record and supervise the destruction process.

[0069] Specifically, by establishing a data destruction ledger, the types, sources, content, uses, ownership, and application scenarios of the data to be destroyed are clearly defined, enabling the updating of the data asset ledger and management of data assets that meet the destruction conditions or require destruction. A data destruction tool ledger is also established, clearly defining the types, methods, deployment locations, business ownership, and responsible entities of destruction tools, enabling unified management of these tools. Appropriate destruction measures are selected for different levels of data, including overwrite erasure for general data and physical destruction for core and important data, ensuring that destroyed data cannot be recovered. Dedicated personnel or designated qualified service providers are assigned to destroy data assets, and the destruction process is recorded and supervised. A data destruction management system is established, clearly defining the conditions, methods, approval processes, destruction procedures, and archiving processes for data destruction, forming a closed-loop confirmation. For data from other systems, management is implemented through API access and approval processes, strengthening the effective management of data assets by power plant personnel.

[0070] For example, in the above steps S1 to S8, a power plant data lifecycle management method according to an embodiment of the present disclosure further includes: conducting a security assessment of power plant data, establishing a data security compliance baseline, establishing a data security privacy baseline, generating a data security assessment report, and generating a problem list based on the data security assessment report.

[0071] Specifically, the system establishes a data security compliance baseline, clearly defining the content and boundaries of data compliance, supporting multi-dimensional baseline definitions, and allowing for free combination and personalized configuration of baselines, providing a benchmark for data access control and violations; it also establishes a data security privacy baseline, clearly defining the objects and methods of data privacy protection, supporting multi-dimensional baseline definitions, and allowing for free combination and personalized configuration of baselines, providing a benchmark for data leakage prevention and privacy protection; it generates a data security assessment report, comprehensively displaying data security risk-related information from different perspectives such as management personnel, maintenance personnel, and users, supporting the export of reports in multiple formats, helping power plant safety management personnel to identify problems early, rectify deficiencies in a timely manner, and reduce the serious harm caused by potential risks to the power plant; it can generate a problem list based on the data security assessment report, including the number of problems, problem status, processing time, involved assets, and personnel involved, providing a reference for the power plant to understand the data security protection situation through the distribution of work orders; and it establishes a data security assessment management system, clearly defining the basis standards, assessment requirements, quantitative indicators, assessment methods, assessment processes, and feedback processes for data security assessment, ensuring the implementation of data security work in the power plant's data asset management, data classification and grading management, and data lifecycle management processes.

[0072] In summary, the first step was to conduct an asset inventory of the power plant data to comprehensively and clearly define the data assets. Then, an implementable data security classification and grading method was developed. Finally, based on the classification and grading results, data protection and management were carried out throughout the entire data lifecycle to ensure data security throughout the entire lifecycle.

[0073] This disclosure presents a power plant data lifecycle management method that reduces the cost of data protection construction, maintenance, and manpower by employing small-sample sampling. It provides a new approach to low-cost, implementable data lifecycle security protection for the industry. Furthermore, it proposes an automated classification and grading method for standardized classification and grading of power plant data. For the first time, it provides a complete and feasible solution for classifying and grading power plant data in the power generation industry, offering theoretical and technical support for developing systems related to power plant data security and ensuring data security throughout its entire lifecycle.

[0074] like Figure 2 As shown, another embodiment of this disclosure provides a power plant data lifecycle management system, the system comprising:

[0075] The data acquisition module 230 is used to sample and collect power plant data; wherein, the power plant data includes general data and core data;

[0076] The data transmission module 240 is used to encrypt and transmit power plant data, monitor anomalies during the data transmission process, and generate a data flow diagram.

[0077] The data storage module 250 is used to distribute power plant data across corresponding storage media terminals, establish a data storage media management ledger, periodically verify the usage of storage media, track and record removable storage media, and generate a data distribution map.

[0078] Data processing module 260 is used to desensitize core data, handle emergencies in the desensitization process, and block corresponding data interfaces.

[0079] The data provision module 270 is used to establish a data provision ledger, issue alerts for overdue use of provided data, and track and record the transfer of data assets.

[0080] The data destruction module 280 is used to establish a data destruction ledger, perform overwrite erasure on general data, perform physical destruction on core data, and record and supervise the destruction process.

[0081] For example, such as Figure 2 As shown, the power plant data lifecycle management system also includes:

[0082] The asset sorting module 210 is used to sort out the power plant data, generate a data asset management ledger, and establish a data asset management view.

[0083] The classification and grading module 220 is used to automatically classify and grade power plant data using semi-supervised learning to obtain general data and core data.

[0084] For example, Figure 2 As shown, the classification and grading module is specifically used for:

[0085] Obtain the power plant dataset;

[0086] A portion of the power plant data was manually labeled, and field features and similar column features of the remaining power plant data were extracted through unsupervised learning to establish a classification and hierarchical label feature model.

[0087] The classification and grading label feature model is used to automatically classify and grade power plant data.

[0088] For example, such as Figure 2 As shown, the power plant data lifecycle management system also includes:

[0089] The security assessment module 290 is used to conduct security assessments on power plant data, establish data security compliance baselines, establish data security privacy baselines, generate data security assessment reports, and generate a list of issues based on the data security assessment reports.

[0090] Specifically, the power plant data lifecycle management system of this disclosure is used to implement the power plant data lifecycle management method described in the above embodiments, and the specific implementation process has been described in detail in the above embodiments.

[0091] As an example, a power plant data lifecycle management system can be divided into four layers: data source layer, data processing layer, security measures layer, and application layer. The data source layer categorizes data according to its source and interface type, including traffic probes, application probes, data security logs, network security logs, and threat intelligence. The data processing layer collects data using methods such as syslog, JDBC, and APIs. After preprocessing such as data cleaning, integration, and transformation, the data is stored in databases such as Elasticsearch, MongoDB, and HDFS. The security measures layer employs corresponding sensitive data identification, indicator calculation, and correlation analysis techniques for each stage of the data lifecycle to achieve data asset management, threat awareness, and analysis. The application layer provides users with data security visualization functions such as data asset dashboards and data flow dashboards, as well as event management functions such as event alerts and aggregation, enabling daily data security operations and coordinated response in the event of a data security incident. The JAVA Spring framework can be used as the development framework for the power plant data lifecycle management system. The front end uses JSP+AJAX to implement backend interaction, and the back end uses SpringMVC. It can be deployed in the power plant. The production environment includes business servers and production databases, etc. The deployment method is bypass deployment, which only requires network communication with the database and does not affect the business network.

[0092] This disclosure discloses a power plant data lifecycle management system that reduces the cost of data protection construction, maintenance, and manpower by employing a small sample sampling method, providing the industry with a new approach to low-cost and implementable data lifecycle security protection. It also proposes an automated classification and grading method for standardized classification and grading of power plant data. For the first time, it provides a complete and feasible solution for classifying and grading power plant data in the power generation industry, offering theoretical and technical support for developing systems related to power plant data security and ensuring data security throughout its entire lifecycle.

[0093] like Figure 3 As shown, another embodiment of this disclosure provides an electronic device, including:

[0094] At least one processor 301; and a memory 302 communicatively connected to the at least one processor 301 for storing one or more programs that, when executed by the at least one processor 301, enable the at least one processor 301 to implement the power plant data lifecycle management method described in the above embodiments.

[0095] The memory 302 and processor 301 are connected via a bus, which can include any number of interconnecting buses and bridges. The bus connects various circuits of one or more processors 301 and memory 302 together. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 301 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 301.

[0096] Processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 302 can be used to store data used by processor 301 during operation.

[0097] Another embodiment of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power plant data lifecycle management method described in the above embodiments.

[0098] The computer-readable storage medium may be included in the systems or electronic devices disclosed herein, or it may exist independently.

[0099] Computer-readable storage media can be any tangible medium that contains or stores a program, and can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, optical fibers, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0100] Computer-readable storage media may also include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code, specific examples of which include, but are not limited to, electromagnetic signals, optical signals, or any suitable combination thereof.

[0101] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.

Claims

1. A method for full lifecycle management of power plant data, characterized in that, The method includes: The structured database, semi-structured interface information, and unstructured images and text of the power plant are sorted out to form a data asset library, generate a data asset management ledger, and establish a data asset management view that displays asset type, asset quantity, and asset distribution. We acquire power plant datasets, manually label a portion of the power plant data, and extract field features and similar column features of the remaining power plant data through unsupervised learning. We then establish a classification and grading label feature model that uses a multi-layer convolutional neural network and embeds the Word2Vec algorithm. Using this model, we automatically classify and grade the power plant data to obtain general data and core data. The latest small sample of power plant data is collected by sampling at a predetermined proportion; wherein, the power plant data includes the general data and the core data; Encrypt power plant data during transmission, monitor for anomalies in the data transmission process, and generate a data flow diagram; Power plant data is distributed and stored on corresponding storage media terminals. A data storage media management ledger is established, the usage of storage media is regularly verified, removable storage media are tracked and recorded, and a data distribution map is generated. Core data is anonymized, and emergency response measures are taken for any unforeseen events during the anonymization process, including blocking the corresponding data interfaces. Establish a data provision ledger, issue alerts for overdue data usage, and track and record data asset transfers; Establish a data destruction ledger, use overwrite to erase general data, and use physical destruction to erase core data, and record and supervise the destruction process; Conduct security assessments on power plant data, establish data security compliance baselines and data security privacy baselines that support multi-dimensional definitions and personalized configurations, generate data security assessment reports, and generate a list of issues based on the reports, including the number of issues, status, processing time, assets involved, and personnel involved.

2. A power plant data lifecycle management system, characterized in that, The system includes: The asset sorting module is used to sort out the power plant's structured database, semi-structured interface information, unstructured images and text to form a data asset library, generate a data asset management ledger, and establish a data asset management view that displays asset types, asset quantities, and asset distribution. The classification and grading module is used to acquire power plant datasets, manually label a portion of the power plant data, extract field features and similar column features of the remaining power plant data through unsupervised learning, establish a classification and grading label feature model using a multi-layer convolutional neural network and embedding the Word2Vec algorithm, and use this model to automatically classify and grade the power plant data to obtain general data and core data. The data acquisition module is used to sample and collect a predetermined proportion of the latest small-sample power plant data; wherein, the power plant data includes the general data and the core data; The data transmission module is used to encrypt and transmit power plant data, monitor anomalies during the data transmission process, and generate a data flow diagram. The data storage module is used to distribute power plant data across corresponding storage media terminals, establish a data storage media management ledger, periodically verify the usage of storage media, track and record removable storage media, and generate data distribution maps. The data processing module is used to desensitize core data, handle emergencies in the desensitization process, and block relevant data interfaces. The data provision module is used to establish a data provision ledger, issue alerts for overdue use of provided data, and track and record the transfer of data assets. The data destruction module is used to establish a data destruction ledger, perform overwrite erasure on general data, perform physical destruction on core data, and record and supervise the destruction process. The security assessment module is used to conduct security assessments on power plant data, establish data security compliance baselines and data security privacy baselines that support multi-dimensional definitions and personalized configurations, generate data security assessment reports, and generate a list of issues based on the reports, including the number of issues, status, processing time, assets involved, and personnel involved.

3. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor is used to store one or more programs that, when executed by the at least one processor, enable the at least one processor to implement the power plant data lifecycle management method as described in claim 1 or 2.

4. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the power plant data lifecycle management method as described in claim 1 or 2.

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