Dynamic desensitization method for photovoltaic equipment data

Abstract

The invention discloses a dynamic desensitization method for photovoltaic equipment data, and belongs to the field of data desensitization, and the method comprises the following steps: obtaining sensitive data based on the photovoltaic equipment data; setting a photovoltaic equipment data desensitization rule, and desensitizing the sensitive data based on the photovoltaic equipment data desensitization rule to obtain desensitized data; and packaging and transmitting the desensitized data. The sensitive data can be recognized and processed in real time, new sensitive data can be found and desensitized in time by monitoring the data flow in real time, and continuous safety of the data is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of data desensitization, and in particular relates to a dynamic desensitization method for photovoltaic equipment data. Background Art

[0002] The research on data desensitization technology has also attracted the attention of domestic universities and research institutions, and a number of papers and works have been published. Duan Yaxin and others from Inspur Software Co., Ltd. proposed a dynamic data desensitization technology based on FlinkSQL and big data components to address the problem that the traditional dynamic data desensitization SQL rewriting technology route has low applicability and cannot adapt to complex statements, so as to respond simply and quickly when facing high concurrency, large traffic, and low latency demand scenarios. Zhang Huzi and others from the Chinese Academy of Sciences Big Data Research Institute proposed a dynamic data desensitization method based on Presto, which can dynamically configure desensitization rules for data sharing services, realize aql query desensitization of a single database, and realize cross-database joint query desensitization based on Presto. Chai Senchun and others from Beijing Institute of Technology proposed a method to desensitize different types of data in real time according to data access requirements and the identity of the visitor, so as to achieve the purpose of improving the security of sensitive data. He Ming and others from China Telecom Corporation Limited provided a 5G capability opening security control method, device, equipment and storage medium. The method desensitizes the data returned by the API service provider according to the limited sensitive data control policy of the service subscriber to avoid the risk of sensitive data leakage due to capability abuse, misuse, etc.

[0003] With the continuous development and evolution of technology, new attack methods and technologies continue to emerge, and data security and privacy protection face continuous challenges. In the photovoltaic industry, equipment data often contains sensitive information, such as equipment performance parameters, geographic location, user information, etc. If this data is obtained by unauthorized personnel, it may bring serious security risks, such as malicious attacks on equipment and abuse of information, which may cause serious losses to the company and even affect the national energy security and social stability. Therefore, a dynamic desensitization method for photovoltaic equipment data is urgently needed. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a dynamic desensitization method for photovoltaic equipment data to solve the problems existing in the above prior art.

[0005] To achieve the above object, the present invention provides a dynamic desensitization method for photovoltaic equipment data, comprising:

[0006] Acquire sensitive data based on photovoltaic equipment data;

[0007] Setting a photovoltaic equipment data desensitization rule, and desensitizing the sensitive data based on the photovoltaic equipment data desensitization rule to obtain desensitized data;

[0008] The desensitized data is encapsulated and transmitted.

[0009] Optionally, the process of obtaining sensitive data based on photovoltaic equipment data includes:

[0010] Splitting the photovoltaic equipment data to obtain split data;

[0011] Selecting a data source for the split data, and determining whether to add a sensitive data source to obtain the data source;

[0012] Based on the data source, a user-defined principle is adopted to perform sensitivity judgment on the split data to obtain sensitive data.

[0013] Optionally, the photovoltaic equipment data desensitization rules include:

[0014] The desensitization rules corresponding to the first-level permissions are: aggregate data and open to the target population according to the photovoltaic equipment data regulations;

[0015] The desensitization rules corresponding to the second-level permissions are: data is de-identified and only statistical information is displayed;

[0016] The desensitization rules corresponding to the third-level permissions are: mask sensitive data fields and control user access rights.

[0017] Optionally, the process of desensitizing the sensitive data includes detecting and processing conflicts in desensitization policies;

[0018] The process of detecting and handling conflicts in desensitization strategies includes:

[0019] Determine the desensitization strategy for sensitive data based on the policy library;

[0020] When there is no corresponding desensitization strategy in the strategy, add a new desensitization strategy and update the strategy library;

[0021] When there is a corresponding desensitization policy in the strategy, perform desensitization policy conflict detection; when there is no desensitization policy conflict, modify the desensitization rules; when there is a desensitization policy conflict, modify the desensitization policy and update the strategy library.

[0022] Optionally, the desensitization strategy includes encryption, obfuscation, and replacement.

[0023] Optionally, after desensitizing the sensitive data, the method further includes performing consistency verification on the desensitized data.

[0024] To achieve the above-mentioned purpose, the present invention also provides an electronic device, including: a memory and a processor; the memory is used to store programs; the processor is used to execute the programs to implement various steps of a dynamic desensitization method for photovoltaic device data.

[0025] To achieve the above-mentioned purpose, the present invention also provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of a dynamic desensitization method for photovoltaic device data are implemented.

[0026] Compared with the prior art, the present invention has the following advantages and technical effects:

[0027] Traditional data desensitization methods usually use static desensitization, that is, a one-time desensitization process is performed before the data is stored or transmitted. It cannot cope with possible changes in the data during its life cycle and it is difficult to ensure the continuous security of the data. 5G+PV equipment data dynamic desensitization technology can identify and process sensitive data in real time. By real-time monitoring of data streams, new sensitive data can be discovered and desensitized in a timely manner to ensure the continuous security of data. At the same time, flexible desensitization strategies and rule engines are adopted to dynamically adjust desensitization strategies according to changes in business needs and security risks. In the management of 5G+PV equipment data, it is often necessary to obtain data from multiple sources. Dynamic desensitization technology can support the fusion and processing of multi-source data to ensure the consistency and accuracy of data. At the same time, the system can also set different desensitization strategies and rules according to different data sources and data types. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0029] Figure 1 This is a schematic diagram of dynamic desensitization according to an embodiment of the present invention;

[0030] Figure 2 This is a flow chart of a dynamic desensitization method for photovoltaic equipment data according to an embodiment of the present invention;

[0031] Figure 3 The present invention is a flowchart of a desensitization strategy selection process. DETAILED DESCRIPTION

[0032] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0033] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] Embodiment 1

[0035] A dynamic desensitization technology and method for 5G+photovoltaic device data aims to ensure data accuracy and security while protecting sensitive information from being leaked. Photovoltaic equipment collects various data through a series of sensors, such as voltage sensors, current sensors, and temperature sensors. The data generated contains sensitive information such as device location and power generation data. There is still a risk of leakage during 5G transmission, which poses a security risk. During the photovoltaic data transmission process, the transmitted data is first split, the split data segments are identified, the sensitive data in the data is identified, and desensitization is performed according to relevant rules. This process is completed in the "Photovoltaic Sensitive Data Identification" and "Photovoltaic Sensitive Data Desensitization" modules respectively. After the data is desensitized, it is packaged and enters the data transmission network for transmission.

[0036] like Figure 1-Figure 2 As shown, this embodiment provides a dynamic desensitization method for photovoltaic equipment data, including the following steps: obtaining sensitive data based on photovoltaic equipment data; setting photovoltaic equipment data desensitization rules, and desensitizing the sensitive data based on the photovoltaic equipment data desensitization rules to obtain desensitized data; and packaging and transmitting the desensitized data.

[0037] The process of obtaining sensitive data based on photovoltaic equipment data includes: splitting the photovoltaic equipment data to obtain split data; selecting the data source for the split data, and judging whether to add a new sensitive data source to obtain the data source; and judging the sensitivity of the split data based on the data source using the user-defined principle to obtain sensitive data. The specific implementation process includes:

[0038] Specifically, sensitive data includes user information, equipment performance parameters, etc. If these data are obtained by unauthorized personnel, they may bring serious security risks. In the initial stage of data processing, it is necessary to process user information, equipment performance parameters, production data, operating status, and test data. Different types of photovoltaic data are sorted according to user-defined rules to ensure the accuracy and processability of the data, laying the foundation for subsequent data processing. After data preprocessing, users can select data sources for desensitization operations. Due to the diversity and complexity of 5G+ photovoltaic equipment data, it may be necessary to obtain data from multiple sources, including real-time collected data, historical records, user input, etc. At this stage, it is also necessary to determine whether a new sensitive data source needs to be added. Whether a new sensitive data source is added is mainly determined by whether there are new production equipment. The new sensitive data source still needs to be preprocessed to adapt to changes in business needs.

[0039] After the data source is determined, the system needs to make a sensitivity judgment on the data. This is mainly to identify whether the data from a specific source is sensitive data. Sensitive data is set by the user based on the original data. After the sensitive data is identified, corresponding desensitization rules need to be set for different data. Users can customize the desensitization strategy of the data. These strategies may include encryption, obfuscation, replacement and other methods to protect sensitive data from being leaked.

[0040] Furthermore, the specific processes of encryption, obfuscation, and replacement include: ① Encryption: Encryption converts data into a format that cannot be directly read with the help of a key. Only authorized users with the corresponding decryption key can restore the data, ensuring that sensitive information is not accessed by unauthorized third parties during storage or transmission. ② Obfuscation: Obfuscation is a technology that reduces the direct identifiability of data by changing its format, retaining part of the data but hiding or replacing key details, such as using a mask to hide part of the serial number of a device. The obfuscation method reduces the direct exposure of sensitive information. ③ Replacement: Replacement is to replace specific parts of sensitive data with fictitious information. For example, replacing real place names with pseudonyms can quickly remove sensitive elements in the data while keeping the structure and format of the data unchanged for subsequent processing or analysis.

[0041] At the same time, in order to ensure the effectiveness and practicality of the strategy, the system also needs to detect and process conflicts in the strategy. For desensitization rules that are triggered at the same time, the user sets the priority of the desensitization rules to solve possible policy conflicts, as shown in Table 1.

[0042] The process of detecting and handling desensitization policy conflicts includes: determining the desensitization policy for sensitive data based on the policy library; when there is no corresponding desensitization policy in the policy, adding a new desensitization policy and updating the policy library; when there is a corresponding desensitization policy in the policy, performing desensitization policy conflict detection; when there is no desensitization policy conflict, modifying the desensitization rules; when there is a desensitization policy conflict, modifying the desensitization policy and updating the policy library. The specific implementation process includes:

[0043] like Figure 3 As shown in the figure, if a conflict occurs, the conflicting policy will be modified and the data will be desensitized again. All desensitization policies will be stored in the policy library. When the corresponding data appears next time, the desensitization policy library will automatically match the corresponding desensitization policy to desensitize the data. During the desensitization process, the system also needs to verify the consistency of the data to ensure that the desensitized data still meets business requirements and standards.

[0044] Furthermore, the consistency check of desensitized data is to conduct a comprehensive comparison of the data before and after the desensitization process to verify whether the desensitization operation maintains the consistency of the internal logical relationship and external presentation format of the data. By comparing the value, quantity, structure and business relevance of the original data and the desensitized data, it is ensured that the desensitization process does not damage the integrity of the data, thereby ensuring that the desensitized data can meet the privacy protection requirements and maintain its effectiveness and accuracy in practical applications.

[0045] If there is an error in the consistency of the desensitized data, the error information will be logged and fed back to the administrator. After the desensitization operation is completed, the system will encapsulate the data and transmit it to the required location. The encapsulation process needs to ensure the security and integrity of the data to prevent the data from being tampered with or leaked during transmission.

[0046] Furthermore, in the process of data encapsulation, ensuring the security and integrity of data is mainly achieved through strict access control and data hiding mechanisms. By declaring data as private attributes and providing controlled access paths only through public methods, encapsulation effectively prevents external code from directly accessing and modifying data, thereby ensuring data security. At the same time, these public methods often contain data validation logic to ensure that any operation on the data complies with business rules and data integrity requirements, thus maintaining data integrity.

[0047] Table 1

[0048]

[0049] In summary, the dynamic desensitization technology and method for 5G+PV device data is a complex and effective data processing strategy. By setting appropriate desensitization strategies and execution processes, sensitive information in 5G+PV device data can be protected from being leaked or abused, while ensuring the accuracy and availability of data. In the photovoltaic industry, this technology and method has important application value and practical significance.

[0050] As a further illustration of the present invention, specific implementation cases are as follows:

[0051] Enterprise A monitors the device data transmitted in the 5G network in real time, intelligently identifies sensitive information such as device performance parameters and user information, and dynamically encrypts, masks or replaces these sensitive data according to preset desensitization rules to prevent data leakage and abuse. Enterprise A's dynamic desensitization policy library allows managers to customize and adjust desensitization rules according to different data types and sensitivity levels to achieve refined data management. In addition, the system's built-in conflict detection and processing mechanism ensures that the priority and consistency of desensitization operations are properly handled when multiple rules are triggered. Security measures during packaging and transmission further enhance data integrity and confidentiality. Through this dynamic and flexible data protection method, Enterprise A not only improves the security of photovoltaic equipment data, but also provides a data security management practice that can be used as a reference for the entire industry, demonstrating the company's forward-looking thinking and solutions for combining emerging technologies with data protection.

[0052] Embodiment 2

[0053] To achieve the above-mentioned purpose, this embodiment also provides an electronic device, including: a memory and a processor; the memory is used to store programs; the processor is used to execute the programs to implement various steps of a dynamic desensitization method for photovoltaic equipment data.

[0054] Embodiment 3

[0055] To achieve the above-mentioned purpose, this embodiment further provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, various steps of a dynamic desensitization method for photovoltaic device data are implemented.

[0056] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A dynamic desensitization method for photovoltaic equipment data, characterized in that: The following steps are involved: Acquire sensitive data based on photovoltaic equipment data; Setting a photovoltaic equipment data desensitization rule, and desensitizing the sensitive data based on the photovoltaic equipment data desensitization rule to obtain desensitized data; The desensitized data is encapsulated and transmitted.

2. The dynamic desensitization method for photovoltaic equipment data according to claim 1 is characterized in that: The process of obtaining sensitive data based on photovoltaic equipment data includes: Splitting the photovoltaic equipment data to obtain split data; Selecting a data source for the split data, and determining whether to add a sensitive data source to obtain the data source; Based on the data source, a user-defined principle is adopted to perform sensitivity judgment on the split data to obtain sensitive data.

3. The dynamic desensitization method for photovoltaic equipment data according to claim 1, characterized in that: The photovoltaic equipment data desensitization rules include: The desensitization rules corresponding to the first-level permissions are: aggregate data and open to the target population according to the photovoltaic equipment data regulations; The desensitization rules corresponding to the second-level permissions are: data is de-identified and only statistical information is displayed; The desensitization rules corresponding to the third-level permissions are: mask sensitive data fields and control user access rights.

4. The dynamic desensitization method for photovoltaic equipment data according to claim 3 is characterized in that: The process of desensitizing the sensitive data includes detecting and handling conflicts in desensitization policies; The process of detecting and handling conflicts in desensitization strategies includes: Determine the desensitization strategy for sensitive data based on the policy library; When there is no corresponding desensitization strategy in the strategy, add a new desensitization strategy and update the strategy library; When there is a corresponding desensitization policy in the strategy, perform desensitization policy conflict detection; when there is no desensitization policy conflict, modify the desensitization rules; when there is a desensitization policy conflict, modify the desensitization policy and update the strategy library.

5. The dynamic desensitization method for photovoltaic equipment data according to claim 4 is characterized in that: The desensitization strategy includes encryption, obfuscation, and replacement.

6. The dynamic desensitization method for photovoltaic equipment data according to claim 1, characterized in that: After desensitizing the sensitive data, the desensitized data is also subjected to consistency verification.

7. An electronic device, characterized in that: include: Memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the dynamic desensitization method for photovoltaic device data as described in any one of claims 1-6.

8. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the dynamic desensitization method for photovoltaic device data as described in any one of claims 1 to 6 is implemented.

Images