A data authority management and data isolation method for power database

Abstract

The present invention proposes a data authority management and data isolation method for an electric power database. The data authority management method for an electric power database includes: retrieving employee information; sequentially reorganizing employee information and converting it into XML tree data; identifying and extracting feature categories to generate an information data set; and allocating employee authority based on the authority binary vector of the feature category in the information data set. The data isolation method for an electric power database includes: preprocessing electric power data information; extracting feature categories after preprocessing; classifying feature categories; encrypting each feature category and isolating and storing them in the electric power database; and limiting the access and data operation scope of the electric power database. Thus, by directly allocating authority to the department and position category of employees, automated authority allocation is achieved, efficiency is improved, and feature category identification and analysis of electric power data information are performed to determine the sensitivity of the electric power data information, thereby completing the isolation and division of the electric power data information.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power databases, and in particular to a data authority management and data isolation method, device, electronic device and storage medium for an electric power database. Background Art

[0002] The establishment of power data requires authority management of power data to improve data security, and to isolate power data to ensure that the data will not be interfered with or contaminated during storage and processing.

[0003] In the related art, when assigning permissions to operators, it is always done by management personnel. However, because there are too many operators, and when management personnel assign permissions, it is easy to make mistakes and generate a large workload. In addition, the isolation of power data information is also done by managers setting different sensitivity levels. Due to working hours, isolation is not timely enough, which makes power information vulnerable to attacks. In addition, the artificial setting will be affected by personal factors, resulting in inaccurate division. Therefore, there is an urgent need for a more reliable data permission management and data isolation method for power databases. Summary of the Invention

[0004] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the first object of the present invention is to propose a data authority management and data isolation method for an electric power database.

[0006] The second objective of the present invention is to provide a data isolation method for an electric power database.

[0007] The third object of the present invention is to provide a data authority management device for an electric power database

[0008] A fourth objective of the present invention is to provide a data isolation device for an electric power database.

[0009] A fifth object of the present invention is to provide an electronic device.

[0010] A sixth object of the present invention is to provide a non-transitory computer-readable storage medium storing computer instructions.

[0011] To achieve the above objectives, a first embodiment of the present invention provides a data authority management and data isolation method for a power database, comprising the following steps:

[0012] Retrieving employee information of operating employees from the employee information database of the controllable power database;

[0013] Reorganizing the employee information according to a preset information arrangement order to obtain sorted employee information, and converting the sorted employee information into XML format to obtain XML tree data of the employee information;

[0014] Identifying and extracting feature categories from the information in the XML tree data, generating a feature category list from the data information identified and extracted, and sorting the data in the feature category list in the order in which the employee information is sorted to generate a data set of the employee information;

[0015] Obtain the target information data set after the data set is preprocessed for integrity and consistency, and encode the feature category list in the target information data set to obtain a one-hot encoding vector for each feature category in the feature category list, and then generate a permission binary vector for the feature category based on the one-hot encoding vector, and use the permission binary vector to allocate permissions to employees.

[0016] To achieve the above objectives, a second objective of the present invention is to provide a data isolation method for a power database, comprising the following steps:

[0017] Preprocessing the initial power data information to be stored to eliminate abnormal information in the initial power data information, and performing mean filling on the initial power data information with missing or abnormal values ​​to obtain power data information;

[0018] Extract feature categories from power data information and use random forest or gradient boosting tree methods to rank the extracted feature categories according to their importance;

[0019] Use support vector machines to analyze and process the feature categories that are ranked by category importance, and to identify and classify public data, internal data, sensitive data, and confidential data in power data information;

[0020] Encrypting the internal data, sensitive data and confidential data, logically isolating the power database storing the internal data, sensitive data and confidential data, and providing a dedicated API access interface;

[0021] When accessing through a dedicated API access interface, the employee's identity is identified by detecting the permission binary vector of the feature category, and the permission is obtained. Then, the power database is accessed and data operations are performed by verifying the password.

[0022] To achieve the above objectives, a third object of the present invention is to provide a data rights management device for an electric power database, comprising the following steps:

[0023] A retrieval module, used to retrieve the employee information of the operating employee from the employee information database of the controllable power database;

[0024] a conversion module, configured to reorganize the employee information according to a preset information arrangement order to obtain sorted employee information, and convert the sorted employee information into XML format to obtain XML tree data of the employee information;

[0025] an identification and extraction module for identifying and extracting feature categories from the information in the XML tree data, generating a feature category list from the data information identified and extracted by the feature categories, and sorting the data information in the feature category list in the order in which the employee information is sorted to generate a data set of the employee information;

[0026] The encoding module is used to obtain the target information data set after the data set is preprocessed for integrity and consistency, and encode the feature category list in the target information data set to obtain a one-hot encoding vector for each feature category in the feature category list, and then generate a permission binary vector for the feature category based on the one-hot encoding vector, and use the permission binary vector to allocate permissions to employees.

[0027] To achieve the above objectives, a fourth object of the present invention is to provide a data isolation device for a power database, comprising the following steps:

[0028] A preprocessing module is used to preprocess the initial power data information to be stored to eliminate abnormal information in the initial power data information and perform mean filling on the initial power data information with missing or abnormal values ​​to obtain power data information;

[0029] A sorting module is used to extract feature categories from power data information and sort the feature categories by importance using random forest or gradient boosting tree methods;

[0030] An analysis and processing module, configured to use a support vector machine to analyze and process feature categories for ranking category importance, and to identify and classify public data, internal data, sensitive data, and confidential data in power data information;

[0031] An isolation module, configured to encrypt the internal data, sensitive data, and confidential data, and to logically isolate the power database storing the internal data, sensitive data, and confidential data, and provided with a dedicated API access interface;

[0032] The access module is used to identify the employee's identity by detecting the permission binary vector of the feature category when accessing through the dedicated API access interface, and obtain permissions, and then access the power database and perform data operations by verifying the password.

[0033] To achieve the above-mentioned purpose, the fifth aspect embodiment of the present invention proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect.

[0034] In order to achieve the above-mentioned objectives, the sixth aspect of the present invention proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0035] The data authority management and data isolation method, device, electronic device and storage medium of the power database provided by the embodiment of the present invention are as follows: employee information retrieval; employee information is sequentially reorganized and converted into XML tree data; feature category identification and extraction are performed to generate an information data set; and employee authority is allocated according to the authority binary vector of the feature category in the information data set. The data isolation method of the power database includes: preprocessing of power data information; feature category extraction after preprocessing; feature category classification; encryption of each feature category and isolation and storage in the power database; and authority restriction on access to the power database and data operation scope. Therefore, by directly allocating authority to the department and position category of the employee, automatic authority allocation is achieved, efficiency is improved, and feature category identification and analysis of power data information are performed to determine the sensitivity of the power data information, thereby completing the isolation and division of the power data information.

[0036] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0038] Figure 1 A schematic diagram of a flow chart of a data authority management method for a power database provided by an embodiment of the present invention;

[0039] Figure 2 A schematic flow chart of a data isolation method for a power database provided by an embodiment of the present invention;

[0040] Figure 3 A schematic structural diagram of a data rights management device for a power database provided by an embodiment of the present invention;

[0041] Figure 4A schematic structural diagram of a data isolation device for an electric power database provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0043] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of the present invention comply with the relevant provisions of relevant laws and regulations.

[0044] The following describes data authority management and data isolation method, device, electronic device, and storage medium for a power database according to embodiments of the present invention with reference to the accompanying drawings.

[0045] Figure 1 A flow chart of a data authority management and data isolation method for an electric power database provided by an embodiment of the present invention.

[0046] like Figure 1 As shown, the method includes the following steps:

[0047] Step 101: retrieve the employee information of the operating employee from the employee information database of the controllable power database.

[0048] In some embodiments, employee information includes but is not limited to name, gender, date of birth, date of employment, department employed, and the order of position, which is not specifically limited in this embodiment.

[0049] Step 102 : reorganize the employee information according to a preset information arrangement order to obtain sorted employee information, and convert the sorted employee information into XML format to obtain XML tree data of the employee information.

[0050] In some embodiments, employee information is reorganized according to a preset information arrangement order to obtain sorted employee information. One implementation method may be to set a specific sorting sequence, in which the sorting sequence is arranged and reorganized in the order of the employee's name, gender, date of birth, date of employment, department of employment, and position; and the employee information is arranged and reorganized according to the sorting sequence using the KEY parameters in the employee information database to generate sorted employee information according to the specific sorting sequence.

[0051] Furthermore, one implementation method of converting the sorted employee information into XML format to obtain XML tree data of the employee information may be to directly output the sorted employee information into XML tree data through the xml.etree.ElementTree module in the employee information database, and store the XML tree data in a buffer.

[0052] It should be noted that, when the sorted employee information is exported, the sorted employee information can be directly generated into XML tree data through the xml.etree.ElementTree module in the database, so as to facilitate subsequent direct use.

[0053] Step 103, identify and extract feature categories from the information in the XML tree data, generate a feature category list from the feature category identified and extracted data, and sort the data in the feature category list in the order of sorting employee information to generate a data set of employee information.

[0054] In some embodiments, the data set X of employee information is generated in the following form:

[0055] .

[0056] It should be noted that the information in the XML tree data is subjected to feature category identification and extraction, and is converted into a data set according to a reorganized and sorted sequence, so as to improve the clarity of employee information and facilitate feature extraction.

[0057] Step 104: Obtain the target information data set after the data set is preprocessed for integrity and consistency, and encode the feature category list in the target information data set to obtain a one-hot encoding vector for each feature category in the feature category list. Then, generate a permission binary vector for the feature category based on the one-hot encoding vector, and use the permission binary vector to allocate permissions to employees.

[0058] In some embodiments, a target information data set is obtained after preprocessing the data set for integrity and consistency, and a feature category list in the target information data set is encoded to obtain a one-hot encoding vector for each feature category in the feature category list. Then, a permission binary vector for the feature category is generated based on the one-hot encoding vector. A real-time method for allocating employee permissions using the permission binary vector may be:

[0059] The data in the data set is preprocessed and cleaned to check the integrity and consistency of the data in the data set, remove duplicates and irrelevant information, and obtain the target information data set.

[0060] List the feature categories of the data set Extract, where

[0061]

[0062] After extracting the feature category list Then, the feature category list Perform encoding processing, wherein the encoding processing includes:

[0063] C is a categorical variable in the feature category list, and its value is , which represents the category of the department in which the employee works and the category of the position. For the case of the one-hot encoding vector of C, for each feature category , whose one-hot encoded vector Defined as .

[0064] in,

[0065] .

[0066] Through two categorical variables and By comparing the categories of the departments and positions, we can determine the categories of the employment departments and positions, and then generate feature categories. The permissions binary vector.

[0067] Employee permissions are assigned based on the permission binary vector, and employee identification is performed through the permission binary vector during access.

[0068] Therefore, by extracting features from the employee's department and position, and generating binary vectors based on the department and position as permission binary vectors for access permission conditions, it is easy to automatically allocate permissions, improve work efficiency, and reduce errors in manually setting permissions.

[0069] The data authority management method of the electric power database in the embodiment of the present invention calls employee information in the employee information database, reorganizes the called employee information sequence and converts it into XML format to obtain XML tree data of the employee information, identifies and extracts feature categories of the information in the XML tree data, and sorts it to generate a data set of the employee information, so as to facilitate the extraction of the categories of the employment department and position in the employee information, and then encodes the employment department and position, and then identifies them in conjunction with the coding information set when the electric power data information is stored, completes automatic authority allocation, improves work efficiency, and realizes accurate authority allocation.

[0070] also, Figure 2 A flow chart of a data authority management and data isolation method for an electric power database provided by an embodiment of the present invention.

[0071] Step 201 : pre-processing the initial power data information to be stored to eliminate abnormal information in the initial power data information, and performing mean filling on the initial power data information with missing values ​​or abnormal values ​​to obtain power data information.

[0072] In some embodiments, the initial power data information to be stored is preprocessed to eliminate abnormal information in the initial power data information, and the initial power data information with missing or abnormal values ​​is mean-filled to obtain the power data information, including:

[0073]

[0074] Among them, F is the outlier factor, D is the collected initial power data information, is the number of samples in the jth cluster group, Initial power data information The distance to the jth cluster center, is the jth cluster center.

[0075] For the distance away from the cluster center, it is set as the average distance from the initial power data information to the center position, and the average distance calculated by the cluster analysis algorithm is .

[0076] If the distance from the center position of the initial power data information is greater than 3d, the data signal is determined to be abnormal data information and removed, and then the missing and abnormal value initial power data information is mean-filled to obtain alternative power data information.

[0077] Normalization processing of alternative power data information , convert the data information to the same scale to obtain power data information of the same scale.

[0078] in, Represented as power data information, Indicates alternative power data information, is the minimum value among the alternative power data information, The maximum value among the candidate power data information is mapped between [0, 1].

[0079] Therefore, by cleaning the initial power data information, the accuracy of the power data information can be improved, which is convenient for subsequent calculation and analysis of the power data information, and normalization is performed to map the power data information between [0, 1], so as to reduce the pressure and complexity of the calculation.

[0080] Step 202 : extract feature categories from the power data information, and use a random forest or gradient boosting tree method to sort the feature categories extracted by category importance.

[0081] In some embodiments, the gradient boosting tree method includes: initializing the current predicted value of the feature category using the mean value model; calculating the residual of the current mean value model, where the residual is the difference between the true value of the feature category and the current predicted value, and fitting the residual with a decision tree, updating the next predicted value of the current mean value model, and completing the category importance sorting of the feature categories by adding the next predicted value to the current predicted value until a specified number of iterations is reached or the current mean value model converges.

[0082] Therefore, by sorting the feature categories, it is easy to identify the important features in the power data information, and sensitive judgment and analysis of the power data information can be achieved through the important features.

[0083] Step 203: Use a support vector machine to analyze and process the feature categories that have been sorted by category importance, so as to identify and classify public data, internal data, sensitive data, and confidential data in the power data information.

[0084] In some embodiments, a support vector machine is used to analyze and process the feature categories that are sorted by category importance to identify and classify public data, internal data, sensitive data, and confidential data in the power data information. One implementation method is:

[0085] Through the decision function of the support vector machine, the feature categories for category importance sorting are separated into hyperplanes of different categories. The hyperplane maximizes the interval between categories to complete the classification of public data, internal data, sensitive data and confidential data in power data information.

[0086] Among them, the decision function of the support vector machine is ,The goal of SVM is to maximize the interval between categories, which can be expressed as follows:

[0087] Minimize the class gap Satisfy constraints .

[0088] in, is the normal vector of the hyperplane, is the bias, is the category sample point in the feature category, is the category label of the i-th category sample point; Is the output of the decision function, used to predict the category sample point Category, if , the forecast is internal data; if , the prediction is public data, is the Lagrange multiplier, which is related to the support vector of the training sample. Is the kernel function, calculate the i-th category sample point and the current category sample point The similarity between them, N is the total number of sample points of the category; and the internal data is divided again using support vector machine, if , then the prediction is confidential data; if , it is predicted to be sensitive data.

[0089] Therefore, by isolating and dividing important feature categories, it is convenient to divide the sensitivity of power data information and implement isolated processing of power data information.

[0090] Step 204 , encrypt the internal data, sensitive data and confidential data, and logically isolate the power database storing the internal data, sensitive data and confidential data, and provide a dedicated API access interface.

[0091] In some embodiments, internal data, sensitive data, and confidential data are encrypted, and the power database storing the internal data, sensitive data, and confidential data is logically isolated, and a dedicated API access interface is provided. One implementation method may be:

[0092] Internal data, sensitive data and confidential data are encrypted and stored in the power database.

[0093] Configure the permissions of the permission binary vectors of different feature categories in the power database, create a data permission view of the power database, and use the data permission view to limit the access to the power database and the scope of data operations of the dedicated API access interface.

[0094] Among them, by writing stored procedures and functions, stored procedures and functions define a series of SQL statements and logical processing, and operators access and operate data by calling SQL statements and logical processing.

[0095] Therefore, the power data information is isolated and divided, and logically isolated and stored to improve the security of the power data information. The operator's permission binary vector is identified through the data permission view, which facilitates the determination of the operator's access rights. The security of the power data information is improved by calling access and encrypted storage.

[0096] Step 205 , when accessing through a dedicated API access interface, the employee identity is identified by detecting the permission binary vector of the feature category, and the permission is obtained, and then the power database is accessed and data operations are performed by verifying the password.

[0097] In some embodiments, when access is made through a dedicated API access interface, identity and permission identification are performed through a permission binary vector to implement permission management of isolated data.

[0098] The data isolation method of the power database of the embodiment of the present invention pre-processes the initial power data information to improve the accuracy of the power data information, then extracts feature categories from the power data information, and then sorts the extracted feature categories by importance through a random forest or gradient boosting tree method, and then analyzes and processes the sorted feature categories through a support vector machine to identify and classify public data, internal data, sensitive data and confidential data in the power data information, thereby dividing the power data information into isolation ranges, and in order to further improve the security of isolation, passwords are set for important internal data, sensitive data and confidential data to improve the security of the power data information, and when the operator accesses, the employee identity is identified through the permission binary vector of the feature category and the permission is obtained, and then the power database is accessed and data operations are performed by verifying the password to realize the permission management of the isolated data.

[0099] In order to implement the above embodiment, the present invention also proposes a data authority management device for an electric power database.

[0100] Figure 3 A schematic structural diagram of a data rights management device for an electric power database provided by an embodiment of the present invention.

[0101] like Figure 3 As shown, the data authority management device 30 of the power database includes: a calling module 31 , a conversion module 32 , an identification and extraction module 33 and an encoding module 34 .

[0102] A retrieval module 31 is used to retrieve the employee information of the operating employee from the employee information database of the controllable power database;

[0103] a conversion module 32 for reorganizing the employee information according to a preset information arrangement order to obtain sorted employee information, and converting the sorted employee information into XML format to obtain XML tree data of the employee information;

[0104] an identification and extraction module 33 for identifying and extracting feature categories from the information in the XML tree data, generating a feature category list from the data information identified and extracted by the feature categories, and sorting the data information in the feature category list in the order in which the employee information is sorted to generate a data set of the employee information;

[0105] The encoding module 34 is used to obtain the target information data set after the data set is preprocessed for integrity and consistency, and to encode the feature category list in the target information data set to obtain a one-hot encoding vector for each feature category in the feature category list, and then generate a permission binary vector for the feature category based on the one-hot encoding vector, and use the permission binary vector to allocate permissions to employees.

[0106] In order to implement the above embodiment, the present invention further provides a data isolation device for a power database.

[0107] Figure 4 A schematic structural diagram of a data isolation device for an electric power database provided by an embodiment of the present invention.

[0108] like Figure 4 As shown, the data isolation device 40 of the power database includes: a pre-processing module 41 , a sorting module 42 , an analysis and processing module 43 , an isolation module 44 and an access module 45 .

[0109] A preprocessing module 41 is used to preprocess the initial power data information to be stored to eliminate abnormal information in the initial power data information and perform mean filling on the initial power data information with missing or abnormal values ​​to obtain power data information;

[0110] A sorting module 42 is used to extract feature categories from the power data information and sort the feature categories by importance using a random forest or gradient boosting tree method;

[0111] An analysis and processing module 43 is configured to analyze and process the feature categories for ranking the importance of the categories using a support vector machine, so as to identify and classify public data, internal data, sensitive data, and confidential data in the power data information;

[0112] an isolation module 44 for encrypting the internal data, sensitive data, and confidential data, and for logically isolating the power database storing the internal data, sensitive data, and confidential data, and provided with a dedicated API access interface;

[0113] The access module 45 is used to identify the employee identity by detecting the permission binary vector of the feature category when accessing through the dedicated API access interface, and obtain the permission, and then access the power database and perform data operations by verifying the password.

[0114] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment and will not be repeated here.

[0115] The data authority management and data isolation device of the power database in the embodiment of the present invention realizes automatic authority allocation and improves efficiency by directly allocating authority to employees' departments and job categories, and also identifies and analyzes the feature categories of power data information, determines the sensitivity of the power data information, and completes the isolation and division of the power data information.

[0116] In order to implement the above embodiment, the present invention further provides an electronic device, including:

[0117] at least one processor; and

[0118] a memory communicatively connected to the at least one processor; wherein,

[0119] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the aforementioned method.

[0120] In order to implement the above embodiment, the present invention further proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable the computer to execute the above method.

[0121] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0122] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0123] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0124] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" is any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (not exhaustive) of computer-readable media include: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0125] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any of the following technologies known in the art, or a combination thereof, may be used: a discrete logic circuit having logic gates for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gates, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0126] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0127] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0128] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A data isolation method for an electric power database, characterized in that: The steps include: Preprocessing the initial power data information to be stored to eliminate abnormal information in the initial power data information, and performing mean filling on the initial power data information with missing or abnormal values ​​to obtain power data information; Extract feature categories from power data information and use random forest or gradient boosting tree methods to rank the extracted feature categories according to their importance; Use support vector machines to analyze and process the feature categories that are ranked by category importance, and to identify and classify public data, internal data, sensitive data, and confidential data in power data information; encrypting the internal data, sensitive data, and confidential data, and storing the internal data, sensitive data, and confidential data in a power database; Configuring permissions for permission binary vectors of different feature categories in the power database, creating a data permission view for the power database, and limiting the scope of access to the power database and data operations of the dedicated API access interface through the data permission view; Among them, by writing stored procedures and functions, the stored procedures and functions define a series of SQL statements and logical processing, and operators access and operate data by calling SQL statements and logical processing; When accessing through a dedicated API, the system detects the permission binary vector of the feature category to identify the employee's identity and obtain permissions. Then, the system verifies the password to access the power database and perform data operations, thus achieving isolated data permission management. The step of creating a data permission view for the power database includes: Retrieving employee information of operating employees from the employee information database of the controllable power database; Setting a specific sorting sequence, wherein the sorting sequence is arranged and reorganized according to the order of the employee's name, gender, date of birth, date of employment, department employed, and position; Arrange and reorganize the employee information according to the sorting sequence using the KEY parameter in the employee information database to generate sorted employee information according to the specific sorting sequence, and directly output the sorted employee information as XML tree data using the xml.etree.ElementTree module in the employee information database, and store the XML tree data in a buffer; Feature category identification and extraction are performed on the information in the XML tree data, and then the data information of the feature category identification and extraction is generated in the form of a feature category list, and the data information in the feature category list is sorted according to the order of sorting employee information to generate a data set of employee information, wherein: The data set X for generating employee information is in the following form: ； Preprocessing and cleaning the data in the data set to check the integrity and consistency of the data in the data set, remove duplicates and irrelevant information, and obtain a target information data set; List the feature categories of the data set Extract, where After extracting the feature category list Then, the feature category list Performing an encoding process, wherein the encoding process includes: C is a categorical variable in the feature category list, and its value is , which represents the category of the department in which the employee works and the category of the position. For the case of the one-hot encoding vector of C, for each feature category , whose one-hot encoded vector Defined as ; in, ； Represents the binary vector of authority generated based on the categories of employment department and position, through two categorical variables and By comparing the categories of the departments and positions, we can determine the categories of the departments and positions, and then generate feature categories. The permission binary vector of ; The rights of employees are allocated according to the rights binary vector, and the employees are identified by the rights binary vector during access.

2. The data isolation method for the electric power database according to claim 1 is characterized in that The pre-processing of the initial power data information to be stored to eliminate abnormal information in the initial power data information and performing mean filling on the initial power data information with missing and abnormal values ​​to obtain the power data information includes: ， Among them, F is the outlier factor, D is the collected initial power data information, is the number of samples in the jth cluster group, Initial power data information The distance to the jth cluster center, is the jth cluster center; For the distance away from the cluster center, it is set as the average distance from the initial power data information to the center position, and the average distance calculated by the cluster analysis algorithm is ; If the distance from the center position of the initial power data information is greater than 3d, the data signal is determined to be abnormal data information and removed, and then the missing and abnormal value initial power data information is mean-filled to obtain alternative power data information; Normalization processing of alternative power data information , converting the data information to the same scale to obtain power data information of the same scale; in, Represented as power data information, Indicates alternative power data information, is the minimum value among the alternative power data information, The maximum value among the candidate power data information is mapped between [0, 1].

3. The data isolation method for the electric power database according to claim 1, characterized in that: The gradient boosting tree method includes: Initialize the current predicted value of the feature category using the mean model; Calculate the residual of the current average model, which is the difference between the true value of the feature category and the current predicted value, and use the decision tree to fit the residual. Update the next predicted value of the current average model. By adding the next predicted value to the current predicted value, until the specified number of iterations is reached or the current average model converges, the feature categories are sorted by category importance.

4. The data isolation method for the electric power database according to claim 1, characterized in that: The support vector machine is used to analyze and process the feature categories for ranking the importance of categories, so as to identify and classify public data, internal data, sensitive data and confidential data in the power data information, including: By using the decision function of the support vector machine, the feature categories for category importance sorting are separated into hyperplanes of different categories. The hyperplanes maximize the intervals between categories to complete the classification of public data, internal data, sensitive data, and confidential data in the power data information. Among them, the decision function of the support vector machine is ,The goal of SVM is to maximize the interval between categories, which can be expressed as follows: ； Minimize the class gap Satisfy constraints ; in, is the normal vector of the hyperplane, is the bias, is the category sample point in the feature category, is the category label of the i-th category sample point; Is the output of the decision function, used to predict the category sample point Category, if , the forecast is internal data; if , the prediction is public data, is the Lagrange multiplier, which is related to the support vector of the training sample. Is the kernel function, calculate the i-th category sample point and the similarity between the current category sample point x, N is the total number of category sample points; and the internal data is divided again using the support vector machine, if , the prediction is confidential data; if , it is predicted to be sensitive data. represents the inverted weight vector.

5. A data isolation device for an electric power database, characterized in that: The device is used to implement the data isolation method for the power database according to claim 1, and the device includes: A preprocessing module is used to preprocess the initial power data information to be stored to eliminate abnormal information in the initial power data information and perform mean filling on the initial power data information with missing or abnormal values ​​to obtain power data information; A sorting module is used to extract feature categories from power data information and sort the feature categories by importance using a random forest or gradient boosting tree method; An analysis and processing module, configured to use a support vector machine to analyze and process feature categories for ranking category importance, and to identify and classify public data, internal data, sensitive data, and confidential data in power data information; An isolation module, configured to encrypt the internal data, sensitive data, and confidential data, and to logically isolate the power database storing the internal data, sensitive data, and confidential data, and provided with a dedicated API access interface; The access module is used to identify the employee's identity by detecting the permission binary vector of the feature category when accessing through the dedicated API access interface, and obtain permissions, and then access the power database and perform data operations by verifying the password.

6. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

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