Enterprise management data processing method and device based on big data analysis

Abstract

The invention discloses an enterprise management data processing method and device based on big data analysis, and relates to the technical field of power enterprise management data processing. The method comprises the following steps: acquiring parameters of user access desensitization data, and acquiring power enterprise management historical parameters; performing power enterprise management data security first evaluation analysis on the desensitized data access parameters; adjusting the user identity according to the comparison and analysis result; performing power enterprise management data security second evaluation analysis on the desensitization blood relationship data access parameters; and performing comprehensive analysis to obtain a power enterprise management data security comprehensive negative index, and performing adjustment according to a comprehensive comparative analysis result. According to the method, the power enterprise management desensitization data parameters are analyzed, evaluated and judged, and the power enterprise management data processing is adjusted according to the comprehensive comparison and analysis result, so that the effect of improving the safety comprehensiveness of the power enterprise management data is achieved, and the problem of insufficient safety comprehensiveness of the power enterprise management data in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power enterprise management data processing, and in particular to an enterprise management data processing method and device based on big data analysis. Background Art

[0002] With the development of information technology, the power industry, as a data-intensive industry, is particularly important for the application of big data. The generation, storage and transmission of a large amount of power enterprise management data have posed new challenges to the management efficiency of power enterprises. As a key technology for mining the value of these data, big data analysis has received widespread attention and application from power enterprises.

[0003] The existing enterprise management data processing methods based on big data analysis are implemented in the following ways, including: data collection and processing. The big data technology of power companies involves the comprehensive processing of production, transportation, sales, operation and management data; power grid planning and optimization. By establishing power grid planning data mining and diagnostic evaluation models, the precision of power grid planning can be achieved, and it is also applied to power grid construction, maintenance, operation and power marketing. The development of smart grids. The construction and operation of smart grids are important application areas of big data technology in the power industry. By integrating various business and information resources, deep integration of business applications is achieved, and the unity of power flow, business flow and information flow is promoted, thereby realizing data sharing and business integration.

[0004] For example, the data information management system based on big data configuration of the power system announced by the Chinese invention patent with announcement number: CN117852848B includes: a data acquisition unit, a data processing unit, a calculation and analysis unit, and a dispatch processing unit. By collecting the power generation of multiple target power stations in a target period and the power consumption of all target users in several target areas in the target period, the data is integrated, processed and analyzed. By comparing the dispatch priority coefficients, the specific dispatch output of each power station and the specific dispatch inclusion of each area can be determined to ensure the balance of supply and demand and meet the safe operation requirements of the power grid.

[0005] For example, the Chinese invention patent announcement with announcement number: CN117787664B discloses a smart enterprise management platform based on big data, including: specifically involving power data acquisition module, power data preprocessing module, power parameter acquisition module, current load processing module, voltage fluctuation processing module, load power processing module, comprehensive analysis module, and monitoring and early warning module. The power data acquisition module collects relevant power data, the power data preprocessing module preprocesses the data to remove abnormal values ​​that do not meet the data specifications and exceed the reasonable range, the power parameter acquisition module obtains parameters that affect the power data, the current load processing module, the voltage fluctuation processing module, and the load power processing module respectively calculate the current load coefficient, the voltage fluctuation coefficient and the load power coefficient, the comprehensive analysis module calculates the comprehensive power quality index, and the monitoring and early warning module monitors whether the power consumption meets the standards.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, the data managed by power companies is huge and involves extensive and complex content. The security of data management in power companies is an important issue. The existing data security is mainly achieved through data encryption, access control, and data backup and recovery. However, for users with higher access rights, the constraints on data security management are weak, and there is a problem of insufficient comprehensiveness in the security of power company management data. Summary of the invention

[0008] The embodiments of the present application provide a method and device for processing enterprise management data based on big data analysis, thereby solving the problem of insufficient security and comprehensiveness of power enterprise management data in the prior art and achieving the effect of improving the security and comprehensiveness of power enterprise management data.

[0009] An embodiment of the present application provides an enterprise management data processing method based on big data analysis, comprising the following steps: collecting parameters for user access to desensitized data to obtain desensitized data access parameters, collecting power enterprise management history parameters to obtain power enterprise management history data; performing a first assessment analysis on the power enterprise management data security of the desensitized data access parameters to obtain a first negative index for power enterprise management data security; comparing and analyzing the first negative index for power enterprise management data security with a preset first threshold for power enterprise management data security, and adjusting the user identity according to the comparison and analysis results; collecting bloodline data parameters for user access to desensitized data to obtain desensitized bloodline data access parameters, performing a second assessment analysis on the desensitized bloodline data access parameters to obtain a second negative index for power enterprise management data security; performing a comprehensive analysis to obtain a comprehensive negative index for power enterprise management data security, performing a comparison analysis based on the comprehensive negative index for power enterprise management data security and the corresponding threshold, and making adjustments according to the comprehensive comparison and analysis results.

[0010] Furthermore, the specific process of collecting parameters for users to access desensitized data is as follows: setting the power enterprise management data security level, and setting the corresponding access identity authentication level according to the corresponding security level; obtaining the corresponding access identity authentication level when the user passes the identity authentication, desensitizing high-sensitive words of the corresponding level according to the access identity authentication level, obtaining desensitized data and allowing the user to view the desensitized data; recording and collecting parameters when the user accesses the desensitized data.

[0011] Furthermore, highly sensitive words of corresponding levels are desensitized according to the access authentication level, specifically including: the user verifies his identity on the authentication device by providing identity credentials, and queries the user authority database based on the identity verification result to determine the user's corresponding access permission level; according to preset sensitive data identification rules, sensitive information in the data is automatically identified, the identified sensitive information is matched with the preset sensitivity level, and corresponding desensitization rules are applied according to the user's access permission level; the sensitive information is desensitized and the desensitized data is displayed to the user.

[0012] Furthermore, the specific process of obtaining the power enterprise management historical data is: obtaining the power enterprise management historical data, the power enterprise management historical data including the power enterprise management historical firewall parameters, the power enterprise management historical access time parameters, the power enterprise management historical copy data volume parameters and the power enterprise management historical copy time parameters, the power enterprise management historical access data volume parameters and the power enterprise management historical access times parameters.

[0013] Furthermore, the specific process of obtaining the first negative index of power enterprise management data security is: monitoring the access situation parameters in the power enterprise management data through the power enterprise management data security software to obtain the power enterprise management data firewall parameters, power enterprise management data access time parameters, power enterprise management data copy data parameters and power enterprise management data copy time parameters; comparing the power enterprise management data firewall parameters and power enterprise management data access time parameters with the power enterprise management historical firewall parameters and power enterprise management historical access time parameters to obtain the power enterprise management data security first negative index indicator value; combining the power enterprise management data copy data parameters, the power enterprise management data copy time parameters and the power enterprise management data security first negative index indicator value; comprehensive analysis to obtain the power enterprise management data security first negative index.

[0014] Furthermore, the user identity is adjusted according to the comparative analysis results, specifically including: if the first negative index of the power enterprise management data security is less than the first threshold of the power enterprise management data security, then the corresponding user is judged to have no sensitive data operations and is recorded as a normal user; if the first negative index of the power enterprise management data security is not less than the first threshold of the power enterprise management data security, then the corresponding user is judged to have sensitive data operations and is recorded as a monitoring user.

[0015] Furthermore, the specific process of obtaining the second negative index of power enterprise management data security is as follows: data lineage analysis is performed on desensitized data access parameters by a data lineage analysis method to obtain desensitized lineage data access parameters, wherein the desensitized lineage data access parameters are used to indicate the access status of lineage-related data of users accessing desensitized data; various access status parameters of monitored users in power enterprise management data are monitored by power enterprise management data security software to obtain desensitized lineage data access data type parameters, desensitized lineage data access number parameters and desensitized lineage data access data volume parameters; comprehensive comparative analysis is performed in combination with power enterprise management historical access data volume parameters and power enterprise management historical access number parameters to obtain the second negative index of power enterprise management data security.

[0016] Furthermore, the specific process of comparing and analyzing the comprehensive negative index of power enterprise management data security with the corresponding threshold is: comprehensively analyzing the first negative index of power enterprise management data security and the second negative index of power enterprise management data security to obtain the comprehensive negative index of power enterprise management data security; if the user is judged as a monitored user and the corresponding comprehensive negative index of power enterprise management data security is not greater than the preset comprehensive threshold of power enterprise management data security, the user is judged as a normal user; if the user is judged as a monitored user and the corresponding comprehensive negative index of power enterprise management data security is greater than the preset comprehensive threshold of power enterprise management data security, the user is judged as a warning user.

[0017] Furthermore, the specific process of adjusting according to the comprehensive comparative analysis results is as follows: if the user is judged as a warning user, the starting time of judging the user as a warning user and the user-related security index are recorded; the user-related security index includes the user's first negative index of power enterprise management data security, the user's second negative index of power enterprise management data security and the user's comprehensive negative index of power enterprise management data security; immediately notify relevant personnel to conduct real-time background monitoring of the user's access identity authentication level and all the user's power enterprise management historical data; after the preset warning user monitoring observation time, comprehensively analyze again to obtain the comprehensive negative index of power enterprise management data security and compare it with the corresponding threshold; if the comprehensive negative index of power enterprise management data security of the warning user is not greater than the preset comprehensive threshold of power enterprise management data security, then issue a permission warning notification to the corresponding user and maintain the user's access identity authentication level; if the comprehensive negative index of power enterprise management data security of the warning user is greater than the preset comprehensive threshold of power enterprise management data security, then issue a permission adjustment notification to the corresponding user and lower the user's access identity authentication level.

[0018] The embodiment of the present application provides an enterprise management data processing device based on big data analysis, including an electric power enterprise management data acquisition module, an electric power enterprise management data security first analysis module, an electric power enterprise management data security first judgment module, an electric power enterprise management data security second analysis module and an electric power enterprise management data security second judgment module; the electric power enterprise management data acquisition module is used to collect parameters for user access to desensitized data to obtain desensitized data access parameters, collect electric power enterprise management historical parameters to obtain electric power enterprise management historical data; the electric power enterprise management data security first analysis module is used to perform a first evaluation and analysis of the electric power enterprise management data security on the desensitized data access parameters to obtain a first negative index of the electric power enterprise management data security; the electric power enterprise management data The safety first judgment module is used to compare and analyze the first negative index of power enterprise management data security with the preset first threshold of power enterprise management data security, and adjust the user identity according to the comparison and analysis results; the power enterprise management data security second analysis module is used to collect the bloodline data parameters of users accessing desensitized data, obtain desensitized bloodline data access parameters, perform a second assessment and analysis of power enterprise management data security on the desensitized bloodline data access parameters, and obtain the second negative index of power enterprise management data security; the power enterprise management data security second judgment module is used to comprehensively analyze and obtain the comprehensive negative index of power enterprise management data security, compare and analyze the comprehensive negative index of power enterprise management data security with the corresponding threshold, and make adjustments according to the comprehensive comparison and analysis results.

[0019] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. Collect the parameters of users’ access to desensitized data and the historical management parameters of electric power enterprises; conduct a first assessment and analysis on the security of electric power enterprise management data for the desensitized data access parameters; adjust the user identity based on the comparative analysis results; conduct a second assessment and analysis on the security of electric power enterprise management data for the desensitized lineage data access parameters; obtain the comprehensive negative index of power enterprise management data security through comprehensive analysis, and make adjustments based on the comprehensive comparative analysis results, thereby achieving the effect of improving the comprehensiveness of power enterprise management data security, and effectively solving the problem of insufficient comprehensiveness of power enterprise management data security in the existing technology.

[0021] 2. By comparing and analyzing the comprehensive negative index of power enterprise management data security with the corresponding threshold, potential data security risks can be identified in a timely manner, and risks can be graded for easy management and response. By adjusting the permissions of early warning users, potential data leakage or abuse can be prevented, thereby achieving the effect of improving the security of power enterprise management data.

[0022] 3. By making adjustments based on comprehensive comparative analysis results and notifying relevant personnel in a timely manner, potential security threats can be responded to quickly. By setting an observation period, user behavior and security status can be evaluated more reasonably to prevent misjudgments based on single events or short-term behaviors. Based on the analysis results after the observation period, user permissions and authentication levels can be dynamically adjusted, thereby achieving the effect of improving the accuracy of power company management data. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of a method for processing enterprise management data based on big data analysis provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of a function image of an analytical formula for the second negative index of power enterprise management data security provided in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of the structure of an enterprise management data processing device based on big data analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application solve the problem of insufficient security and comprehensiveness of power enterprise management data in the prior art by providing a method and device for processing enterprise management data based on big data analysis. By analyzing, evaluating and judging the parameters of desensitized data of power enterprise management, the processing of power enterprise management data is adjusted according to the results of comprehensive comparative analysis, thereby improving the security and comprehensiveness of power enterprise management data.

[0027] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0028] like Figure 1 As shown, it is a flow chart of the enterprise management data processing method based on big data analysis provided by an embodiment of the present application. The method is applied to an enterprise management data processing device based on big data analysis, and the method includes the following steps: collecting parameters for user access to desensitized data to obtain desensitized data access parameters, collecting power enterprise management history parameters to obtain power enterprise management history data; performing a first assessment analysis on the power enterprise management data security of the desensitized data access parameters to obtain a first negative index for power enterprise management data security; comparing and analyzing the first negative index for power enterprise management data security with a preset first threshold for power enterprise management data security, and adjusting the user identity according to the comparison and analysis results; collecting bloodline data parameters for user access to desensitized data to obtain desensitized bloodline data access parameters, performing a second assessment analysis on the desensitized bloodline data access parameters to obtain a second negative index for power enterprise management data security; performing a comprehensive analysis to obtain a comprehensive negative index for power enterprise management data security, comparing and analyzing the comprehensive negative index for power enterprise management data security with the corresponding threshold, and making adjustments according to the comprehensive comparison and analysis results.

[0029] In this embodiment, the specific forms of identity authentication are various, including: user name, password, digital certificate, biometric information; through identity authentication, the authenticity and legitimacy of the user's identity are ensured.

[0030] The specific rules for determining the access rights level corresponding to a user are set independently by the power company. The access rights level may be related to factors such as the user's position, responsibilities, work content, power projects in charge, and project progress.

[0031] Furthermore, the specific process of collecting parameters for users to access desensitized data is as follows: setting the power enterprise management data security level, and setting the corresponding access identity authentication level according to the corresponding security level; obtaining the corresponding access identity authentication level when the user passes the identity authentication, desensitizing high-sensitive words of the corresponding level according to the access identity authentication level, obtaining desensitized data and allowing the user to view the desensitized data; recording and collecting parameters when the user accesses the desensitized data.

[0032] Furthermore, highly sensitive words of corresponding levels are desensitized according to the access authentication level, specifically including: the user verifies his identity on the authentication device by providing identity credentials, and queries the user authority database based on the identity verification result to determine the user's corresponding access permission level; according to preset sensitive data identification rules, sensitive information in the data is automatically identified, the identified sensitive information is matched with the preset sensitivity level, and corresponding desensitization rules are applied according to the user's access permission level; the sensitive information is desensitized and the desensitized data is displayed to the user.

[0033] In this embodiment, the specific contents of the desensitization processing include: data masking, partially hiding or replacing sensitive data, such as replacing part of the numbers in the ID number with asterisks; data encryption, encrypting the sensitive data to ensure that only users with corresponding decryption permissions can view the original data; data generalization, generalizing specific data into general information, such as generalizing specific age into age range; data artifacts, replacing the original sensitive data with fictitious data to keep the statistical characteristics of the data unchanged.

[0034] Furthermore, the specific process of obtaining the power enterprise management historical data is: obtaining the power enterprise management historical data, the power enterprise management historical data includes the power enterprise management historical firewall parameters, the power enterprise management historical access time parameters, the power enterprise management historical copy data volume parameters and the power enterprise management historical copy time parameters, the power enterprise management historical access data volume parameters and the power enterprise management historical access times parameters.

[0035] Furthermore, the specific process of obtaining the first negative index of power enterprise management data security is as follows: monitoring the access situation parameters in the power enterprise management data through the power enterprise management data security software to obtain the power enterprise management data firewall parameters, power enterprise management data access time parameters, power enterprise management data copy data parameters and power enterprise management data copy time parameters; comparing the power enterprise management data firewall parameters and power enterprise management data access time parameters with the power enterprise management historical firewall parameters and power enterprise management historical access time parameters to obtain the power enterprise management data security first negative index indicator value; combining the power enterprise management data copy data parameters, the power enterprise management data copy time parameters and the power enterprise management data security first negative index indicator value; comprehensive analysis is performed to obtain the power enterprise management data security first negative index.

[0036] In this embodiment, the first negative index of power enterprise management data security corresponds to each user one by one, and the first negative index of power enterprise management data security is used to describe the relative level of sensitive data of power enterprise management data being accessed by users;

[0037]

[0038] like but And set like but

[0039]

[0040] The first detection time periods for data security management are numbered in sequence, A0 represents the number of the first detection time period for data security management, A0=1, 2, ..., A, and A represents the total number of the first detection time periods for data security management.

[0041] The first detection time points of data security management are numbered in sequence, J0 represents the number of the first detection time point of data security management, J0=1, 2, ..., J, and J represents the total number of the first detection time points of data security management.

[0042] Indicates the negative index indicator value of the first level of power enterprise management data security.

[0043] It represents the first negative index of power enterprise management data security in the first detection time period of data security management A0.

[0044] It indicates the maximum number of attacks on the power enterprise management data firewall in the first detection time period of data security management A0.

[0045] F Y It represents the average number of firewall attacks in the historical data of power enterprise management.

[0046] Indicates the maximum value of the power enterprise management data access time in the first detection time period of the A0th data security management.

[0047] W Y Represents the average access time in the power enterprise management historical data.

[0048] Represents the amount of copied data in the power enterprise management data at the J0th data security management first detection time point in the A0th data security management first detection time period.

[0049] S Y It represents the average value of the copy behavior data in the historical data managed by the power enterprise.

[0050] Indicates the cumulative amount of time for copying the power enterprise management data in the first detection period of the A0th data security management.

[0051] K Y It represents the average time of the accumulated copies in the historical data managed by the power enterprise.

[0052] Table 1 is an example table of the first negative index of data security management of electric power enterprises. The example parameters in Table 1 only take the parameters of one data security management first detection time period and one data security management first detection time point for illustration. Table 1 is shown below.

[0053] Table 1 Example of the first negative index of power enterprise management data security

[0054]

[0055]

[0056] According to Table 1, when the indicator value of the first negative index of power enterprise management data security is greater than 0 and less than 1, the first negative index of power enterprise management data security is always 0.

[0057] When the indication value of the first negative index of power enterprise management data security is greater than 1, the first negative index of power enterprise management data security is positive. At the same time, as the amount of copied data in the power enterprise management data and the cumulative copying time in the power enterprise management data increase, the first negative index of power enterprise management data security increases. Comprehensive evaluation of the above factors makes it easier to quantitatively evaluate the first negative index of power enterprise management data security.

[0058] Furthermore, the user identity is adjusted according to the comparative analysis results, specifically including: if the first negative index of the power enterprise management data security is less than the first threshold of the power enterprise management data security, the corresponding user is judged to have no sensitive data operations and is recorded as a normal user; if the first negative index of the power enterprise management data security is not less than the first threshold of the power enterprise management data security, the corresponding user is judged to have sensitive data operations and is recorded as a monitoring user.

[0059] Furthermore, the specific process of obtaining the second negative index of power enterprise management data security is as follows: data lineage analysis is performed on desensitized data access parameters through data lineage analysis methods to obtain desensitized lineage data access parameters, which are used to represent the access status of lineage-related data of users accessing desensitized data; various access status parameters of monitored users in power enterprise management data are monitored through power enterprise management data security software to obtain desensitized lineage data access data type parameters, desensitized lineage data access number parameters and desensitized lineage data access data volume parameters; comprehensive comparative analysis is performed based on power enterprise management historical access data volume parameters and power enterprise management historical access number parameters to obtain the second negative index of power enterprise management data security.

[0060] In this embodiment, data lineage analysis is a method for tracking the flow and conversion process of data in an information system. The specific steps of the data lineage analysis method are as follows:

[0061] Identify the origin point (such as source system, database, file, etc.) and final destination point (such as target system, database, file, etc.) of the desensitized data.

[0062] List all the transformation steps that the masked data goes through as it flows, including how the data is read, processed, transformed, combined, or aggregated.

[0063] Graphically display the path of masked data from source to destination, usually in the form of a flowchart or directory tree.

[0064] Record various attributes related to the masked data, such as data type, data length, creation time, modification time, owner, data quality, etc.

[0065] Reveal the business rules and logic applied in the process of desensitized data processing, including calculation formulas, business logic codes, etc., and identify the dependencies between different data elements, including one-to-one, one-to-many or many-to-many relationships.

[0066] When the desensitized data or process changes, it is possible to evaluate the impact of these changes on the entire data flow and business process. That is, when the desensitized data changes, the amount of associated data will also change accordingly, thereby obtaining the desensitized lineage data access parameters.

[0067] The second index of power enterprise management data security is used to describe the degree to which the associated data of the power enterprise management data, which is desensitized, is accessed by the monitored users.

[0068]

[0069] The second detection time periods for data security management are numbered in sequence, B0 represents the number of the second detection time periods for data security management, B0=1, 2, ..., B, and B represents the total number of the second detection time periods for data security management.

[0070] The data security management second detection time points are numbered in sequence, G0 represents the number of the data security management second detection time point, G0=1, 2, ..., G, and G represents the total number of the data security management second detection time points.

[0071] Represents the second negative index of power enterprise management data security in the B0th data security management second detection time period.

[0072] The types of desensitized bloodline data access data are numbered in sequence, Z0 represents the number of the desensitized bloodline data access data type, Z0=1, 2, ..., Z, and Z represents the total number of desensitized bloodline data access data types.

[0073] Represents the total number of desensitized bloodline data access data types at the G0th data security management second detection time point in the B0th data security management second detection time period.

[0074] Z Y Indicates the preset standard value of the total number of data types for access to desensitized bloodline data.

[0075] It represents the amount of access data of the desensitized bloodline data at the G0th data security management second detection time point in the B0th data security management second detection time period.

[0076] L Y It represents the average amount of access data of desensitized bloodline data in the historical data managed by electric power enterprises.

[0077] Represents the comprehensive number of accesses to the Z0th desensitized bloodline data type in the B0th data security management second detection time period.

[0078] Represents the average value of the comprehensive access times in the power enterprise management historical data of the Z0th desensitized lineage data access data type.

[0079] like Figure 2 As shown, it is a schematic diagram of the analysis formula function image of the second negative index of power enterprise management data security provided by the embodiment of the present application; the total number of types of desensitized blood data access data is used as the independent variable, and the second negative index of power enterprise management data security is used as the dependent variable;

[0080] Figure 2 The function curve a in a=5,1,1 represents Z Y =5 and and Function curve a represents when Z Y =5 and and The analytical formula function graph of the second negative index of power enterprise management data security is shown in the figure.

[0081] Figure 2 The function curve b in b=5,1,2 represents Z Y =5 and and Function curve b represents when Z Y =5 and and The analytical formula function graph of the second negative index of power enterprise management data security is shown in the figure.

[0082] Figure 2 The function curve c in the equation, c = 5, 2, 2, represents Z Y =5 and and Function curve c represents when Z Y =5 and and The analytical formula function graph of the second negative index of power enterprise management data security is shown in the figure.

[0083] As can be seen from the figure, when the total number of data types accessed by desensitized lineage data increases, it means that when the types of desensitized lineage data called and accessed by monitoring users increase, the second negative index of power enterprise management data security also increases.

[0084] when The value of is the same as When the values ​​are different, The larger the value, that is, the more times the monitored users access the desensitized bloodline data of the same type, the faster the upward trend of the second negative index of power enterprise management data security becomes.

[0085] when The value of is the same as When the values ​​are different, The larger the value, that is, the more desensitized bloodline data accessed by the monitoring users, the faster the upward trend of the second negative index of power enterprise management data security becomes.

[0086] Comprehensively evaluate and analyze the above parameters. If the amount of desensitized lineage data accessed by the monitoring user is small, but the number of times the same type of desensitized lineage data is accessed is large, the data security risk for the power enterprise will gradually increase. If the number of times the same type of desensitized lineage data accessed by the monitoring user is small, but the amount of desensitized lineage data accessed is large, the data security risk for the power enterprise will still gradually increase. Comprehensively evaluate the above factors and quantitatively evaluate the second negative index of power enterprise management data security, which is convenient for subsequent evaluation and adjustment. In specific applications, the potential confidentiality of the data can be obtained without obtaining the confidential data itself, but can be derived by obtaining a large amount of original data with a security level. Therefore, if users access a large amount of lineage data that reads high-security-level file data, it also needs to be taken into account in security risk considerations.

[0087] Furthermore, the specific process of comparing and analyzing the comprehensive negative index of power enterprise management data security with the corresponding threshold is as follows: comprehensively analyzing the first negative index of power enterprise management data security and the second negative index of power enterprise management data security to obtain the comprehensive negative index of power enterprise management data security; if the user is judged as a monitored user and the corresponding comprehensive negative index of power enterprise management data security is not greater than the preset comprehensive threshold of power enterprise management data security, the user is judged as a normal user; if the user is judged as a monitored user and the corresponding comprehensive negative index of power enterprise management data security is greater than the preset comprehensive threshold of power enterprise management data security, the user is judged as a warning user.

[0088] In this embodiment, the preset comprehensive security threshold of power enterprise management data is extracted from the power enterprise security database.

[0089] The specific constraint analysis formula for the comprehensive negative index of power enterprise management data security is:

[0090]

[0091] ψ represents the comprehensive negative index of power enterprise management data security, It represents the first negative index of power enterprise management data security in the first detection period of data security management A0, Represents the second negative index of power enterprise management data security in the B0th data security management second detection time period.

[0092] As the first negative index and the second negative index of power enterprise management data security continue to increase, the power enterprise management data security risk will continue to rise. The final assessment results of the power enterprise management data security risk in this process need to be quantitatively assessed in layers and stages.

[0093] By evaluating the access parameters of power enterprise management data and grading them according to the first negative index and the second negative index of power enterprise management data security, and through comprehensive analysis of multiple parameters, the access status of power enterprise management data can be judged more accurately, and the risk changes of power enterprise management data under actual work use can be simulated. The hierarchical judgment makes the specific changes in the risks of power enterprise management data more accurate.

[0094] Furthermore, the specific process of making adjustments based on the comprehensive comparative analysis results is as follows: if the user is determined to be a warning user, the starting time of determining the user as a warning user and the user-related security index are recorded; the user-related security index includes the user's first negative index of power enterprise management data security, the user's second negative index of power enterprise management data security and the user's comprehensive negative index of power enterprise management data security; immediately notify relevant personnel to conduct real-time background monitoring of the user's access identity authentication level and all the user's power enterprise management historical data; after the preset warning user monitoring observation time, conduct another comprehensive analysis to obtain the comprehensive negative index of power enterprise management data security and compare it with the corresponding threshold; if the comprehensive negative index of power enterprise management data security of the warning user is not greater than the preset comprehensive threshold of power enterprise management data security, then issue a permission warning notification to the corresponding user and maintain the user's access identity authentication level; if the comprehensive negative index of power enterprise management data security of the warning user is greater than the preset comprehensive threshold of power enterprise management data security, then issue a permission adjustment notification to the corresponding user and lower the user's access identity authentication level.

[0095] In this embodiment, the preset early warning user monitoring observation time is extracted from the power enterprise safety database.

[0096] In addition to lowering the user's access authentication level, implementing permission adjustments also includes specifically modifying the user's permissions according to the adjustment plan by authorized administrators to ensure that all related systems synchronously update the user's permissions to prevent permission loopholes; monitoring and review, including monitoring user behavior after adjustment to ensure that the permission adjustment achieves the expected results; user training, data security awareness training for users to improve their understanding of data security; communication and feedback, maintaining communication with users, collecting their feedback on permission adjustments, and making necessary adjustments accordingly.

[0097] like Figure 3As shown, it is a structural schematic diagram of the enterprise management data processing device based on big data analysis provided by an embodiment of the present application, including: an electric power enterprise management data acquisition module, an electric power enterprise management data security first analysis module, an electric power enterprise management data security first judgment module, an electric power enterprise management data security second analysis module and an electric power enterprise management data security second judgment module; an electric power enterprise management data acquisition module, used to collect parameters for user access to desensitized data, obtain desensitized data access parameters, collect electric power enterprise management historical parameters, and obtain electric power enterprise management historical data; an electric power enterprise management data security first analysis module, used to perform an electric power enterprise management data security first assessment analysis on the desensitized data access parameters, and obtain an electric power enterprise management data security first negative index ... The first judgment module for power enterprise management data security is used to compare and analyze the first negative index of power enterprise management data security with the preset first threshold of power enterprise management data security, and adjust the user identity according to the comparison and analysis results; the second analysis module for power enterprise management data security is used to collect the bloodline data parameters of users accessing desensitized data, obtain desensitized bloodline data access parameters, perform a second assessment and analysis of power enterprise management data security on the desensitized bloodline data access parameters, and obtain the second negative index of power enterprise management data security; the second judgment module for power enterprise management data security is used to comprehensively analyze and obtain the comprehensive negative index of power enterprise management data security, compare and analyze the comprehensive negative index of power enterprise management data security with the corresponding threshold, and make adjustments according to the comprehensive comparison and analysis results.

[0098] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0102] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0103] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for processing enterprise management data based on big data analysis, characterized in that: The following steps are involved: Collect the parameters of users accessing desensitized data to obtain desensitized data access parameters, collect the power enterprise management history parameters to obtain the power enterprise management history data; Conduct the first assessment and analysis of power enterprise management data security on the desensitized data access parameters, and obtain the first negative index of power enterprise management data security; Compare and analyze the first negative index of power enterprise management data security with the preset first threshold of power enterprise management data security, and adjust the user identity according to the comparison and analysis results; Collect the blood relationship data parameters of users accessing desensitized data to obtain desensitized blood relationship data access parameters, conduct a second evaluation analysis on the desensitized blood relationship data access parameters for power enterprise management data security, and obtain a second negative index for power enterprise management data security; A comprehensive analysis is conducted to obtain the comprehensive negative index of power enterprise management data security. The comprehensive negative index of power enterprise management data security is compared with the corresponding threshold value and adjustments are made based on the results of the comprehensive comparative analysis.

2. The enterprise management data processing method based on big data analysis as claimed in claim 1, characterized in that: The specific process of collecting the parameters of the user's access to the desensitized data is as follows: Set the security level of power enterprise management data and set the corresponding access authentication level according to the corresponding security level; When a user passes identity authentication, the corresponding access identity authentication level is obtained, and the highly sensitive words of the corresponding level are desensitized according to the access identity authentication level to obtain desensitized data and allow the user to view the desensitized data; Record and collect parameters when users access desensitized data.

3. The enterprise management data processing method based on big data analysis as claimed in claim 2, characterized in that: Desensitizing highly sensitive words of corresponding levels according to the access identity authentication level specifically includes: The user verifies his identity on the identity authentication device by providing his identity credentials, and searches the user authority database based on the verification result to determine the access authority level corresponding to the user; Automatically identify sensitive information in data according to preset sensitive data identification rules, match the identified sensitive information with the preset sensitivity level, and apply corresponding desensitization rules according to the user's access permission level; Desensitize sensitive information and display the desensitized data to users.

4. The enterprise management data processing method based on big data analysis as claimed in claim 1, characterized in that: The specific process of obtaining the power enterprise management historical data is as follows: Obtain the power enterprise management historical data, which includes the power enterprise management historical firewall parameters, the power enterprise management historical access time parameters, the power enterprise management historical copy data volume parameters and the power enterprise management historical copy time parameters, the power enterprise management historical access data volume parameters and the power enterprise management historical access times parameters.

5. The enterprise management data processing method based on big data analysis as claimed in claim 1, characterized in that: The specific process of obtaining the first negative index of power enterprise management data security is as follows: Monitor the access parameters of the power enterprise management data through the power enterprise management data security software to obtain the power enterprise management data firewall parameters, power enterprise management data access time parameters, power enterprise management data copy data parameters and power enterprise management data copy time parameters; Compare and analyze the power enterprise management data firewall parameters and power enterprise management data access time parameters with the power enterprise management history firewall parameters and power enterprise management history access time parameters to obtain the first negative index indicator value of power enterprise management data security; Combined with the power enterprise management data copy data parameter, the power enterprise management data copy time parameter and the power enterprise management data security first negative index indicator value; Comprehensive analysis shows that the first negative index of power enterprise management data security is obtained.

6. The enterprise management data processing method based on big data analysis as claimed in claim 1, characterized in that: The adjusting of the user identity according to the comparative analysis result specifically includes: If the first negative index of power enterprise management data security is less than the first threshold of power enterprise management data security, it is judged that the corresponding user has no sensitive data operation and is recorded as a normal user; If the first negative index of power enterprise management data security is not less than the first threshold of power enterprise management data security, the corresponding user is judged to have sensitive data operations and is recorded as a monitoring user.

7. The enterprise management data processing method based on big data analysis as claimed in claim 1, characterized in that: The specific process of obtaining the second negative index of power enterprise management data security is as follows: Performing data lineage analysis on the desensitized data access parameter by a data lineage analysis method to obtain a desensitized lineage data access parameter, wherein the desensitized lineage data access parameter is used to indicate the access status of the user to the lineage-related data of the desensitized data; Through the power enterprise management data security software, the various access status parameters of the monitoring users in the power enterprise management data are monitored to obtain the desensitized bloodline data access data type parameters, desensitized bloodline data access number parameters and desensitized bloodline data access data volume parameters; A comprehensive comparative analysis of the power enterprise management historical access data volume parameters and the power enterprise management historical access number parameters was conducted to obtain the second negative index of power enterprise management data security.

8. The enterprise management data processing method based on big data analysis as claimed in claim 1, characterized in that: The specific process of comparing and analyzing the comprehensive negative index of power enterprise management data security with the corresponding threshold is as follows: The first negative index of power enterprise management data security and the second negative index of power enterprise management data security are comprehensively analyzed to obtain the comprehensive negative index of power enterprise management data security; If the user is judged as a monitoring user and the corresponding power enterprise management data security comprehensive negative index is not greater than the preset power enterprise management data security comprehensive threshold, the user is judged as a normal user; If the user is judged as a monitoring user and the corresponding power enterprise management data security comprehensive negative index is greater than the preset power enterprise management data security comprehensive threshold, the user is judged as a warning user.

9. The enterprise management data processing method based on big data analysis as claimed in claim 1, characterized in that: The specific process of adjusting according to the comprehensive comparative analysis results is as follows: If the user is determined to be a warning user, the starting time of determining the user as a warning user and the user-related security index are recorded; the user-related security index includes the user's power enterprise management data security first negative index, the user's power enterprise management data security second negative index and the user's power enterprise management data security comprehensive negative index; Immediately notify relevant personnel to conduct real-time backstage monitoring of the user's access identity authentication level and all the user's power enterprise management historical data; After the preset warning user monitoring observation time, the comprehensive negative index of power enterprise management data security is obtained through comprehensive analysis again and compared with the corresponding threshold value; If the comprehensive negative index of the power enterprise management data security of the warning user is not greater than the preset comprehensive threshold of the power enterprise management data security, a permission warning notification will be issued to the corresponding user and the user's access identity authentication level will be maintained. If the comprehensive negative index of the power enterprise management data security of the warning user is greater than the preset comprehensive threshold of the power enterprise management data security, a permission adjustment notification will be issued to the corresponding user and the user's access identity authentication level will be lowered.

10. An enterprise management data processing device based on big data analysis, characterized in that: It includes a power enterprise management data acquisition module, a power enterprise management data security first analysis module, a power enterprise management data security first judgment module, a power enterprise management data security second analysis module and a power enterprise management data security second judgment module; The power enterprise management data collection module is used to collect the parameters of the user accessing the desensitized data, obtain the desensitized data access parameters, collect the power enterprise management history parameters, and obtain the power enterprise management history data; The power enterprise management data security first analysis module is used to conduct a power enterprise management data security first assessment analysis on the desensitized data access parameters to obtain the power enterprise management data security first negative index; The power enterprise management data security first judgment module is used to compare and analyze the power enterprise management data security first negative index with the preset power enterprise management data security first threshold, and adjust the user identity according to the comparison and analysis results; The second analysis module for power enterprise management data security is used to collect the blood relationship data parameters of users accessing desensitized data, obtain the desensitized blood relationship data access parameters, perform the second evaluation analysis of power enterprise management data security on the desensitized blood relationship data access parameters, and obtain the second negative index of power enterprise management data security; The second judgment module for the power enterprise management data security is used to obtain the comprehensive negative index of the power enterprise management data security through comprehensive analysis, compare and analyze the comprehensive negative index of the power enterprise management data security with the corresponding threshold, and make adjustments based on the comprehensive comparative analysis results.

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