Electric power material information safety management system and method thereof

Abstract

The invention discloses an electric power material information safety management method, and the method comprises the steps: 1, collecting the management data of electric power materials, and carrying out the preprocessing of the management data; 2, disturbing the preprocessed data, defining a privacy budget dynamic adjustment mechanism according to the query times, the user permission and the data sensitivity, outputting the privacy budget of the next moment, and determining the disturbance degree according to the output privacy budget of the next moment; and according to the disturbed data, constructing a query result credibility evaluation mechanism, and outputting a distribution parameter value used for determining the next round of privacy budget. According to the method, the data privacy is protected, meanwhile, the availability of the data is ensured, and the data access permission can be dynamically adjusted according to the actual use condition.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power material management, and in particular to an electric power material information security management system and method thereof. Background Art

[0002] In the power industry, material management involves multiple links such as procurement, inventory, and supply chain coordination. Data security and privacy protection in these links are crucial. With the advancement of digital transformation, the power material management system has gradually adopted information technology to improve efficiency and optimize material scheduling and cost control through data analysis.

[0003] Existing power material management methods mainly rely on static access control and fixed encryption mechanisms for data security management, and are unable to dynamically adjust privacy protection strategies based on query behavior. During the data query process, existing methods find it difficult to balance data availability and privacy security, and lack a data perturbation mechanism based on dynamic adjustment of the privacy budget. At the same time, the means of credibility assessment of query results are limited, making it difficult to ensure data reliability. Summary of the Invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0005] In view of the above problems existing in the existing power material information security management system and method, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide an electric power material information security management system and method thereof, which is suitable for solving the problems of difficulty in balancing data availability and privacy security, lack of a data perturbation mechanism based on dynamic adjustment of the privacy budget, and limited means of credibility assessment of query results.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a method for managing power material information security, the method comprising:

[0009] Collect management data of power materials and pre-process the management data;

[0010] Perturb the preprocessed data, define a dynamic privacy budget adjustment mechanism based on the number of queries, user permissions, and data sensitivity, and output the privacy budget for the next moment;

[0011] The degree of the perturbation is determined according to the privacy budget of the output at the next moment;

[0012] Based on the perturbed data, a query result credibility evaluation mechanism is constructed to output the allocation parameter value for determining the next round of privacy budget.

[0013] As a preferred solution of the electric power material information security management method of the present invention, the privacy budget dynamic adjustment mechanism is defined as follows:

[0014] Define initial privacy budget values ​​for all data types;

[0015] When the number of queries for a certain data category exceeds a first preset threshold, the privacy budget value of the data category is reduced according to the ratio of the actual number of queries to the maximum allowed number of queries;

[0016] When the data utility score is greater than a second preset threshold, the privacy budget value of the data category is increased according to the ratio of the actual utility score to the maximum utility score;

[0017] The budget decay caused by the number of queries and the budget recovery results caused by data utility are combined to generate the privacy budget value used to control the data perturbation intensity at the next moment.

[0018] As a preferred solution of the electric power material information security management method described in the present invention, a low privacy budget threshold and a high privacy budget threshold are set according to the privacy budget value, and the privacy protection level is divided into a high privacy protection interval, a moderate privacy protection interval, and a low privacy protection interval according to the set low privacy budget threshold and high privacy budget threshold. The division criteria are as follows:

[0019] If the privacy budget value at the next moment is less than or equal to the low privacy budget threshold, the privacy budget at the next moment enters the high privacy protection interval, and the privacy protection strategy adopts the first protection measure;

[0020] If the privacy budget value at the next moment is not less than the low privacy budget threshold and not greater than the high privacy budget threshold, then the privacy budget at the next moment enters the moderate privacy protection interval, and the privacy protection strategy adopts the second protection measure;

[0021] If the privacy budget value at the next moment is greater than the high privacy budget threshold, the privacy budget at the next moment enters the low privacy protection interval, and the privacy protection strategy adopts the third protection measure.

[0022] As a preferred solution of the electric power material information security management method of the present invention, the construction process of the query result credibility evaluation mechanism is as follows:

[0023] The construction process of the query result credibility evaluation mechanism is as follows:

[0024] Obtaining a noise level added to the current query data, calculating a ratio of the noise level added to the current query data to a preset maximum allowable noise level, and generating a negative impact value of the noise on the credibility of the query result based on the ratio;

[0025] generating a positive gain value of the security operation on the credibility of the query result according to the ratio of the number of security queries to the total number of queries;

[0026] The baseline credibility value is subtracted from the negative impact of noise and added to the positive gain of security operation to generate the allocation parameter value used to determine the privacy budget for the next round.

[0027] As a preferred solution of the power material information security management method described in the present invention, wherein: three-level thresholds C1, C2 and C3 are set according to the output result of the query credibility formula to adjust the privacy budget allocation strategy;

[0028] Setting a credibility threshold, a medium credibility threshold, and a maximum credibility threshold according to the allocation parameter value to trigger a corresponding privacy budget redistribution strategy;

[0029] If the allocation parameter value is less than the minimum credibility threshold, it means that the reliability of the query data is lower than the available standard, and the privacy budget reallocation strategy executes the first adjustment measure;

[0030] If the allocation parameter value is not greater than the minimum credibility threshold and less than the medium credibility threshold, it means that the reliability of the query data is close to the usable standard, and the privacy budget reallocation strategy executes the second adjustment measure;

[0031] If the allocation parameter value is not greater than the medium confidence threshold and less than the maximum confidence threshold, it means that the reliability of the query data meets the availability standard, and the privacy budget redistribution strategy executes the third adjustment measure;

[0032] If the allocation parameter value is greater than or equal to the maximum credibility threshold, it means that the data credibility exceeds the acceptable range and there is a risk of privacy leakage. The privacy budget reallocation strategy implements the fourth adjustment measure.

[0033] As a preferred solution of the electric power material information security management method of the present invention, the first adjustment measure includes:

[0034] S303-1-1: Increase the privacy budget of current data categories;

[0035] S303-1-2: Adjust the corresponding data disturbance parameters to the preset minimum disturbance level;

[0036] S303-1-3: Delete data records that do not meet the data consistency verification rules;

[0037] The second adjustment measures include:

[0038] S303-2-1: Increase the privacy budget value of the current data category by less than the maximum privacy budget threshold that can be allocated to the current data category;

[0039] S303-2-2: Limit the maximum number of queries a user can make within a preset time window;

[0040] S303-2-3: Adjust the privacy budget decay parameter of the current data category based on its call frequency in continuous queries;

[0041] The third adjustment measure includes:

[0042] S303-3-1: Maintain the current privacy budget value;

[0043] S303-3-2: Verify the permissions of the current user and allow them to access data categories within the corresponding permission range;

[0044] S303-3-3: Setting a user query time interval threshold for the same data category;

[0045] The fourth adjustment measure includes:

[0046] S303-4-1: Reduce the privacy budget corresponding to the current data category;

[0047] S303-4-2: Suspend the current user's access rights to the data category and update the current user's access status label to restricted;

[0048] S303-4-3: Enable the query behavior recognition program to analyze the current user's historical query path and frequency characteristics.

[0049] As a preferred solution of the electric power material information security management method of the present invention, wherein: in said S303-3-2 and said S303-3-3, an upper limit threshold of the number of queries is set;

[0050] If the current user's cumulative query times are less than or equal to the query limit, and the shortest time interval between consecutive queries is greater than or equal to the query time interval threshold, the privacy budget value of the current data category remains unchanged;

[0051] If the current user's cumulative query times exceed the query limit, and the shortest interval between consecutive queries is greater than or equal to the query interval threshold, the user's access rights to the data category for the current query session will be frozen, and subsequent query requests for the same category will be rejected.

[0052] If the current user's cumulative query count is less than or equal to the query count limit, and the shortest interval between consecutive queries is less than the query interval threshold, a time control mechanism is applied to delay the execution time of subsequent query response operations;

[0053] If the current user's cumulative query times are greater than the query times limit, and the shortest time interval between consecutive queries is less than the query time interval threshold, it is considered to have triggered a privacy leak judgment, and the privacy budget reallocation strategy will execute the fourth adjustment measure.

[0054] Secondly, to further address the problems of existing management methods, such as the difficulty in balancing data availability and privacy security, the lack of a data perturbation mechanism based on dynamic adjustment of privacy budgets, and the limited means of evaluating the credibility of query results, this embodiment provides an electric power material information security management system, including:

[0055] Data acquisition module: collects management data of power materials and pre-processes the management data;

[0056] Privacy protection and dynamic adjustment module: This module perturbs preprocessed data and defines a dynamic privacy budget adjustment mechanism based on the number of queries, user permissions, and data sensitivity. It outputs the privacy budget for the next moment and controls the degree of data perturbation.

[0057] Query result credibility assessment module: Based on the perturbed data, it outputs the allocation parameter value used to determine the next round of privacy budget;

[0058] Access rights management module: dynamically adjusts access rights based on the assigned parameter values ​​output by the query result credibility assessment module.

[0059] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the electric power material information security management method as described in the first aspect of the present invention.

[0060] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for secure management of electric power material information as described in the first aspect of the present invention is implemented.

[0061] Beneficial effects of the present invention: Through a dynamic privacy budget adjustment mechanism, the present invention dynamically allocates privacy budgets based on the number of queries, user permissions, and data sensitivity, enabling adaptive adjustment of the degree of data disturbance. This effectively prevents the risk of data leakage caused by frequent queries while ensuring the availability of low-risk queries. Based on noise impact factors and security query impact factors, the present invention establishes a query result credibility assessment mechanism, enabling the system to dynamically optimize the privacy budget allocation strategy based on the credibility of the query results. This not only ensures data security but also improves data reliability, providing higher-quality data support for subsequent decision-making.

[0062] By introducing privacy protection parameter initialization, Laplace mechanism perturbation, privacy budget adjustment and credibility assessment mechanism, the present invention ensures data availability while protecting data privacy, and can dynamically adjust data access rights according to actual usage, thus achieving an effective balance between security and business needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0064] Figure 1 This is a schematic diagram of the overall process of a method for managing power material information security proposed by the present invention;

[0065] Figure 2 This is a schematic diagram of the privacy budget allocation strategy judgment process of the power material information security management method proposed by the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0067] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0068] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0069] Furthermore, the present invention is described in detail with reference to schematic diagrams. For ease of illustration, when describing the embodiments of the present invention, cross-sectional views illustrating device structures may be partially enlarged and not to scale. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of protection of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0070] Example 1

[0071] Reference Figure 1-2 , as an embodiment of the present invention, provides a method for managing power material information security, the management method comprising:

[0072] Collect management data of power materials and pre-process the management data;

[0073] Specifically, the system collects management data of power materials, including procurement records, inventory information, and supplier information. It also pre-processes the management data (i.e., data structuring and initialization of privacy protection parameters) to provide privacy protection for subsequent data query and analysis.

[0074] Data structuring is achieved through the following means:

[0075] Data format standardization: Obtain raw data, use Pandas and PySpark for field mapping and format conversion, store structured data in a SQL database, use regular expressions (Regex) to parse unstructured text data, unify field naming conventions, and convert all data into a unified JSON, CSV, or SQL table format;

[0076] Key field extraction and data cleaning: We use SQL SELECT+CASE WHEN statements to extract key fields, use Python dictionary mapping to unify field values, perform mean padding on numeric data, and perform mode padding on categorical data. We remove abnormal purchase amounts and inventory outliers to obtain clean, high-quality structured data, reducing the impact of erroneous data on subsequent analysis.

[0077] Data tiered storage and index optimization: Data from the past three months is stored in MySQL / InnoDB to support high-frequency queries. Historical data (over three months) is stored in Hadoop / Hive / S3 for large-scale offline analysis. B+ tree indexing accelerates queries related to material IDs and supplier IDs. Full-text indexing improves the query speed of text data. Vector indexing is used for recommending similar materials, providing a foundation for subsequent differential privacy protection and data analysis.

[0078] In the present invention, privacy protection parameter initialization is a key step in building a dynamic privacy budget adjustment mechanism and data perturbation process. This initialization process provides the system with an initial privacy budget and related parameters to ensure that the privacy of power material management data is protected while taking into account the availability of data. The initialization process comprehensively considers the sensitivity of the data, user access patterns, and system security requirements.

[0079] The Laplace mechanism is used to perturb the data after structuring and initialization of privacy protection parameters. A dynamic adjustment mechanism for the privacy budget is defined based on the number of queries, user permissions, and data sensitivity. The privacy budget at time t+1 is output to control the degree of data perturbation, allowing the data to adaptively adjust the privacy protection level during the query process.

[0080] The definition process of the privacy dynamic adjustment mechanism is as follows:

[0081] Define the initial privacy budget value for all data types, the initial privacy budget is ∈ glo :

[0082] The data sensitivity Δf is calculated using the maximum variation range method;

[0083] Δf=max(|χ i -χ j |);

[0084] Among them, χ i and χ j are any two possible values ​​in the data set;

[0085]

[0086] in, is the privacy budget of data category i in the initial state;

[0087] Specifically, the perturbation using Laplace mechanism data is achieved by the following means:

[0088] First, we need to calculate the global and local sensitivities, and then calculate the Laplace noise distribution scale parameter based on the privacy budget. The formula is as follows:

[0089]

[0090] Where b is the scale parameter of the Laplace noise distribution, ∈ is the privacy budget, and Δf is the data sensitivity;

[0091] The Laplace noise η follows the following distribution:

[0092] η~Lap(0,b);

[0093] Its probability density function (PDF) is:

[0094]

[0095] Add Laplace noise to the original data X to obtain the disturbed data X';

[0096] X'=X+η;

[0097] Where η is the noise value sampled from Lap(0,b).

[0098] When the number of queries for a certain data category exceeds the first preset threshold, the privacy budget value of the data category is reduced according to the ratio of the actual number of queries to the maximum allowed number of queries, and the query number impact factor α is set. It is assumed that at time t, the number of queries for a certain data category i is Q t , the maximum acceptable number of queries is Q max , then the partial expression of the privacy budget decaying due to the increase in the number of queries is as follows:

[0099]

[0100] When the data utility score is greater than the second preset threshold, the privacy budget value of the data category is increased according to the ratio of the actual utility score to the maximum utility score, and the data availability impact factor β is set. At time t, the utility score of the current data category is U t , the maximum acceptable data utility score is U max , then the partial expression of the privacy budget recovered due to the improvement of data utility is as follows:

[0101]

[0102] The budget decay caused by the number of queries and the budget recovery caused by the data utility are combined to generate the privacy budget value used to control the data perturbation intensity at the next moment. Its expression is as follows:

[0103]

[0104] It is the privacy budget value used to control the intensity of data disturbance at the next moment. It is used to control the degree of data disturbance so that the privacy protection level can be adaptively adjusted during the query process.

[0105] A low privacy budget threshold and a high privacy budget threshold are set based on the privacy budget value. The privacy protection level is divided into high privacy protection interval, moderate privacy protection interval and low privacy protection interval based on the set low privacy budget threshold and high privacy budget threshold. The division criteria are as follows:

[0106] The low privacy budget threshold ∈ low Determined by the query count influencing factor:

[0107]

[0108] The high privacy budget threshold ∈ high Determined by the data availability impact factor:

[0109]

[0110] If the privacy budget value at the next moment is less than or equal to the low privacy budget threshold, the privacy budget at the next moment enters the high privacy protection interval, and the privacy protection strategy adopts the first protection measure;

[0111] If the low privacy budget threshold is less than the privacy budget value at the next moment and is less than or equal to the high privacy budget threshold, the privacy budget at the next moment enters the moderate privacy protection interval, and the privacy protection strategy adopts the second protection measure;

[0112] If the privacy budget value at the next moment is greater than the high privacy budget threshold, the privacy budget at the next moment enters the low privacy protection interval, and the privacy protection strategy adopts the third protection measure.

[0113] Specifically, when the privacy protection strategy takes the first protection measure, the system will restrict access to sensitive data to ensure that the privacy of the data can be fully protected even in high-risk query scenarios.

[0114] The first protection measure is as follows:

[0115] Increase data desensitization processing, blur or generalize highly sensitive data, and reduce the risk of original data leakage;

[0116] Implement access control policies to allow only authorized users to access specific data sets and enhance security through multi-factor authentication;

[0117] Record and monitor all access behaviors, set up abnormal access warning mechanisms, and promptly detect and respond to potential data leaks.

[0118] Secondly, when the privacy protection strategy takes the second protection measure, the system will improve data availability under controlled conditions, while limiting the frequency and scope of access behavior and reducing the fluctuation range of the privacy budget.

[0119] The second protection measure is specifically implemented as follows:

[0120] Adjust the data presentation method, apply field-level desensitization to medium-sensitivity data, and control data granularity to reduce the scope of leakage;

[0121] Adjust access control parameters to only allow access to authorized data sets within the authorized time window, and dynamically adjust authentication strength based on user behavior scores;

[0122] Record and analyze access frequency, set access frequency control rules, automatically delay responses when high-frequency operations are detected, and limit short-term repeated query behaviors.

[0123] Again, when the privacy protection policy is about to take the third protection measure, at this time, the system determines that the current access behavior and the data disturbance state are in a balanced state, and can keep the existing privacy protection policy unchanged while implementing stability control.

[0124] The specific operations of the third protection measure are as follows:

[0125] Maintain the current data output structure and processing method, and do not perform additional desensitization processing on low-sensitivity data to maintain information integrity;

[0126] Verify the validity of the current user's permissions, confirm that they have completed authentication and are within the authorization period, and do not need to be re-authenticated;

[0127] Continuously record access behavior,

[0128] In the embodiment of the present application, low-sensitivity data includes routine information such as power material procurement records and inventory statistics; medium-sensitive data covers core information such as supplier details and inventory warning thresholds for specific power materials; and high-sensitivity data includes precise maintenance or construction material lists and operation cycles of core substations and high-voltage transmission lines, as well as high-authority data access logs. By accurately grading user identities and needs, the system can ensure that only authorized users can access the data categories they need, while avoiding the leakage of sensitive information to unnecessary personnel.

[0129] Access control policies specifically include:

[0130] (1) Classify user query needs and identities, and users of different levels can only access specific data categories;

[0131] Specifically, users are classified as follows:

[0132] Level 1 permission: can query low-sensitivity data, subject to privacy budget constraints;

[0133] Level 2 permissions: can query moderately sensitive data, with limited access frequency;

[0134] Level 3 authority: can query highly sensitive data and is subject to system audit.

[0135] (2) Adjusting the privacy budget based on query patterns:

[0136] Specifically, set query frequency and content thresholds:

[0137] For example, if the same user continuously queries similar data within a short period of time, the "permission adjustment logic" will be triggered to calculate the user's historical query pattern. If the user queries highly sensitive data for a long time, the privacy budget will be reduced. Based on the sensitivity level of the queried data, the query permission will be adjusted.

[0138] In the embodiment of the present application, the system defines short time as the time interval between query operations not exceeding 5 minutes, which is suitable for identifying high-frequency access behaviors; and defines "long term" as the statistical period of user access behaviors within 24 consecutive hours, which is suitable for judging their concentrated access trends to specific data categories. The system can dynamically adjust the privacy budget, access frequency limit and authority level based on the above time window. Specifically, according to national data security standards such as "GB / T 35273-2020 Personal Information Security Specification", real-time identification and policy response of access frequency and abnormal behavior are required. In the field of data desensitization and privacy protection, minute level is often used as "behavior suddenness identification threshold", and hourly or daily level behavior is used as "behavior trend judgment window".

[0139] (3) High-privilege users require multi-factor authentication, access time limits, and behavior log audits;

[0140] The hierarchical user authentication mechanism is as follows:

[0141] Level 1 authority: only basic identity authentication is required;

[0142] Second level authority: requires two-factor authentication;

[0143] Level 3 authority: requires multi-factor authentication (such as biometrics + dynamic password + IP address matching);

[0144] For example, when users access highly sensitive data categories such as high-privilege access logs, multi-factor authentication authorization is required, and access time limits are set. If a user queries too much highly sensitive data in a short period of time, the system will automatically restrict access and issue a security warning, record the query history of high-privilege users, and perform regular log analysis. If abnormal behavior is found (such as concentrated query of key data), it will trigger permission downgrade or temporary ban.

[0145] Based on the perturbed data, a query result credibility evaluation mechanism is constructed to output the allocation parameter value used to determine the next round of privacy budget. This allocation parameter value is the query credibility score.

[0146] The construction process of the query result credibility evaluation mechanism is as follows:

[0147] Get the noise level added to the current query data, calculate the ratio of the noise level added to the current query data to the preset maximum allowable noise level, generate the negative impact value of the noise on the credibility of the query result based on the ratio, set the noise impact factor γ, and calculate the noise impact degree ΔC of the current query data σ :

[0148]

[0149] in, is the noise variance of the current query data, is the maximum acceptable noise variance;

[0150] According to the ratio of the number of security queries to the total number of queries, the positive gain value of the security operation on the credibility of the query result is generated according to the ratio, the security query impact factor δ is set, and the impact degree of the security query ratio ΔC is calculated. safe ;

[0151]

[0152] Among them, Q safe Indicates the number of queries that are judged to be safe on the current data category i, Q T Indicates the total number of queries for the current data category at time T;

[0153] The baseline credibility value is subtracted from the negative noise value and the positive security operation gain value is added to generate the allocation parameter value for determining the next round of privacy budget. σ And the impact of security query ratio ΔC safe Construct the query credibility formula, which is expressed as follows:

[0154] C t =1-ΔC σ +ΔC safe ;

[0155] Among them, C t Score the query credibility.

[0156] According to the allocation parameter value, the minimum credibility threshold C1, the medium credibility threshold C2, and the maximum credibility threshold C3 are set to trigger the corresponding privacy budget redistribution strategy;

[0157] If the allocation parameter value is less than the minimum credibility threshold, it means that the reliability of the query data is lower than the available standard, and the privacy budget reallocation strategy executes the first adjustment measure;

[0158] The first adjustment measures include:

[0159] S303-1-1: Increase the privacy budget of current data categories;

[0160] S303-1-2: Adjust the corresponding data disturbance parameters to the preset minimum disturbance level;

[0161] S303-1-3: Delete data records that do not meet the data consistency verification rules;

[0162] If the minimum credibility threshold is less than or equal to the allocation parameter value and is less than the medium credibility threshold, it means that the reliability of the query data is close to the usable standard, and the privacy budget reallocation strategy executes the second adjustment measure;

[0163] The second adjustment measures include:

[0164] S303-2-1: Increase the privacy budget value of the current data category by less than the maximum privacy budget threshold that can be allocated to the current data category;

[0165] S303-2-2: Limit the maximum number of queries a user can make within a preset time window;

[0166] S303-2-3: Adjust the privacy budget decay parameter of the current data category based on its call frequency in continuous queries;

[0167] If the medium confidence threshold is less than or equal to the allocation parameter value and less than the maximum confidence threshold, it means that the reliability of the query data meets the availability standard, and the privacy budget reallocation strategy executes the third adjustment measure;

[0168] The third adjustment measures include:

[0169] S303-3-1: Maintain the current privacy budget value;

[0170] S303-3-2: Verify the permissions of the current user and allow them to access data categories within the corresponding permission range;

[0171] S303-3-3: Set the user's query time interval threshold for the same data category, which is Δt min ;

[0172] Assume that the shortest time interval between two consecutive user queries is Δt. According to the above content, the cumulative number of queries is Q t The maximum number of queries is Q max , the query time interval threshold is Δt min ;

[0173] If the current user's cumulative query times are less than or equal to the query limit, and the shortest time interval between consecutive queries is greater than or equal to the query time interval threshold, the privacy budget value of the current data category remains unchanged;

[0174] If the current user's cumulative query times exceed the query limit, and the shortest interval between consecutive queries is greater than or equal to the query interval threshold, the user's access rights to the data category for the current query session will be frozen, and subsequent query requests for the same category will be rejected.

[0175] If the current user's cumulative query count is less than or equal to the query count limit, and the shortest interval between consecutive queries is less than the query interval threshold, a time control mechanism is applied to delay the execution time of subsequent query response operations;

[0176] In the present invention, the time control mechanism is implemented by the following:

[0177] When the system triggers the time control mechanism, it imposes a delay on subsequent query response operations. The delay time is T delay is calculated as follows:

[0178] T delay =T base +λ·(Δt min -Δt)

[0179] Among them, T base is the set basic delay time, and λ is the delay amplification coefficient, which is used to adjust the response suppression strength.

[0180] The system calculates T delay After that, the current query request is suspended for a specified period of time using asynchronous scheduling. Common methods include: in a multi-threaded / multi-process environment, using a timer or thread sleep control task to delay response.

[0181] If the current user's cumulative query times are greater than the query times limit, and the shortest time interval between consecutive queries is less than the query time interval threshold, it is considered to have triggered a privacy leak judgment, and the privacy budget reallocation strategy will execute the fourth adjustment measure.

[0182] If the allocation parameter value is greater than or equal to the maximum credibility threshold, it means that the data credibility exceeds the acceptable range and there is a risk of privacy leakage. The privacy budget reallocation strategy will execute the fourth adjustment measure;

[0183] The fourth adjustment measure includes:

[0184] S303-4-1: Reduce the privacy budget corresponding to the current data category;

[0185] S303-4-2: Suspend the current user's access rights to the data category and update the current user's access status label to restricted;

[0186] S303-4-3: Enable the query behavior recognition program to analyze the current user's historical query path and frequency characteristics.

[0187] In this embodiment, the allocation parameter value generated in step 3 is used to determine the next round of privacy budget. This allocation parameter value is used for interval comparison under a preset credibility threshold system. The system selects the first, second, third, and fourth adjustment measures for implementing the privacy budget reallocation strategy based on the interval in which the allocation parameter value falls.

[0188] In each adjustment measure, the system will adopt corresponding privacy budget adjustment methods (such as increasing the privacy budget value, maintaining the privacy budget value, or lowering the privacy budget value) and link the permission verification and access control strategies, realizing the feedback control logic that drives the selection of privacy budget redistribution strategy with the allocation parameter value, and providing decision support for the next round of privacy budget allocation strategy. During operation, the system will dynamically adjust the privacy budget value according to the user's query behavior, and determine the privacy protection level applicable to the current data query operation in real time based on the budget value range, thereby triggering the corresponding privacy protection strategy, realizing the dynamic response capability of privacy control, and forming a closed-loop optimization between data usage security and privacy protection capabilities.

[0189] In summary, the present invention uses a dynamic adjustment mechanism of privacy budget to dynamically allocate privacy budget according to the number of queries, user permissions and data sensitivity, so that the degree of data disturbance can be adaptively adjusted, which can effectively prevent the risk of data leakage caused by frequent queries while ensuring the availability of low-risk queries. The present invention establishes a query result credibility assessment mechanism based on the noise impact factor and the security query impact factor, so that the system can dynamically optimize the privacy budget allocation strategy according to the credibility of the query results, which not only ensures the security of the data, but also improves the reliability of the data, providing higher quality data support for subsequent decision-making, while protecting data privacy, ensuring the availability of data, and being able to dynamically adjust data access rights according to actual usage, to achieve an effective balance between security and business needs.

[0190] Example 2, an embodiment of the present invention, provides an electric power material information security management system, including:

[0191] Data acquisition module: collects management data of power materials and pre-processes the management data;

[0192] Privacy protection and dynamic adjustment module: This module perturbs preprocessed data and defines a dynamic privacy budget adjustment mechanism based on the number of queries, user permissions, and data sensitivity. It outputs the privacy budget for the next moment and controls the degree of data perturbation.

[0193] Query result credibility assessment module: Based on the perturbed data, it outputs the allocation parameter value used to determine the next round of privacy budget;

[0194] Access rights management module: dynamically adjusts access rights based on the assigned parameter values ​​output by the query result credibility assessment module.

[0195] This embodiment also provides a computer device, which is suitable for a method for secure management of electric power material information, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a method for secure management of electric power material information as proposed in the above embodiment.

[0196] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus, wherein the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0197] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for implementing power material information security management as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0198] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for managing power material information security, characterized in that: The management method includes: Collect management data of power materials and pre-process the management data; Perturb the preprocessed data, define a dynamic privacy budget adjustment mechanism based on the number of queries, user permissions, and data sensitivity, and output the privacy budget for the next moment; The degree of the perturbation is determined according to the privacy budget of the output at the next moment; Based on the perturbed data, a query result credibility evaluation mechanism is constructed to output the allocation parameter value for determining the next round of privacy budget.

2. The method for managing power material information security according to claim 1, characterized in that: The definition process of the privacy budget dynamic adjustment mechanism is as follows: Define the initial privacy budget for all data types: When the number of queries for a certain data category exceeds a first preset threshold, the privacy budget value of the data category is reduced according to the ratio of the actual number of queries to the maximum allowed number of queries; When the data utility score is greater than the second preset threshold, the privacy budget value of the data category is increased according to the ratio of the actual utility score to the maximum utility score: The budget decay caused by the number of queries and the budget recovery results caused by data utility are combined to generate the privacy budget value used to control the data perturbation intensity at the next moment.

3. The method for managing power material information security according to claim 2, characterized in that: A low privacy budget threshold and a high privacy budget threshold are set according to the privacy budget value. The privacy protection level is divided into a high privacy protection interval, a moderate privacy protection interval, and a low privacy protection interval according to the set low privacy budget threshold and high privacy budget threshold. The division criteria are as follows: If the privacy budget value at the next moment is less than or equal to the low privacy budget threshold, the privacy budget at the next moment enters the high privacy protection interval, and the privacy protection strategy adopts the first protection measure; If the privacy budget value at the next moment is not less than the low privacy budget threshold and not greater than the high privacy budget threshold, then the privacy budget at the next moment enters the moderate privacy protection interval, and the privacy protection strategy adopts the second protection measure; If the privacy budget value at the next moment is greater than the high privacy budget threshold, the privacy budget at the next moment enters the low privacy protection interval, and the privacy protection strategy adopts the third protection measure.

4. The method for managing power material information security according to claim 2, wherein: The construction process of the query result credibility evaluation mechanism is as follows: Obtaining a noise level added to the current query data, calculating a ratio of the noise level added to the current query data to a preset maximum allowable noise level, and generating a negative impact value of the noise on the credibility of the query result based on the ratio; generating a positive gain value of the security operation on the credibility of the query result according to the ratio of the number of security queries to the total number of queries; The baseline credibility value is subtracted from the negative impact of noise and added to the positive gain of security operation to generate the allocation parameter value used to determine the privacy budget for the next round.

5. The method for managing power material information security according to claim 4, characterized in that: Setting a minimum credibility threshold, a medium credibility threshold, and a maximum credibility threshold according to the allocation parameter value to trigger a corresponding privacy budget redistribution strategy; If the allocation parameter value is less than the minimum credibility threshold, it means that the reliability of the query data is lower than the available standard, and the privacy budget reallocation strategy executes the first adjustment measure; If the allocation parameter value is not greater than the minimum credibility threshold and less than the medium credibility threshold, it means that the reliability of the query data is close to the usable standard, and the privacy budget reallocation strategy executes the second adjustment measure; If the allocation parameter value is not greater than the medium confidence threshold and less than the maximum confidence threshold, it means that the reliability of the query data meets the availability standard, and the privacy budget redistribution strategy executes the third adjustment measure; If the allocation parameter value is greater than or equal to the maximum credibility threshold, it means that the data credibility exceeds the acceptable range and there is a risk of privacy leakage. The privacy budget reallocation strategy implements the fourth adjustment measure.

6. The method for managing power material information security according to claim 5, characterized in that: The first adjustment measures include: S303-1-1: Increase the privacy budget of current data categories; S303-1-2: Adjust the corresponding data disturbance parameters to the preset minimum disturbance level; S303-1-3: Delete data records that do not meet the data consistency verification rules; The second adjustment measures include: S303-2-1: Increase the privacy budget value of the current data category by less than the maximum privacy budget threshold that can be allocated to the current data category; S303-2-2: Limit the maximum number of queries a user can make within a preset time window; S303-2-3: Adjust the privacy budget decay parameter of the current data category based on its call frequency in continuous queries; The third adjustment measure includes: S303-3-1: Maintain the current privacy budget value; S303-3-2: Verify the permissions of the current user and allow them to access data categories within the corresponding permission range; S303-3-3: Setting the time interval threshold for user queries on the same data category; The fourth adjustment measure includes: S303-4-1: Reduce the privacy budget corresponding to the current data category; S303-4-2: Suspend the current user's access rights to the data category and update the current user's access status label to restricted; S303-4-3: Enable the query behavior recognition program to analyze the current user's historical query path and frequency characteristics.

7. The method for managing power material information security according to claim 6, characterized in that: In S303-3-2 and S303-3-3, an upper threshold value of the number of queries is set; If the current user's cumulative query times are less than or equal to the query limit, and the shortest time interval between consecutive queries is greater than or equal to the query time interval threshold, the privacy budget value of the current data category remains unchanged; If the current user's cumulative query times exceed the query limit, and the shortest interval between consecutive queries is greater than or equal to the query interval threshold, the user's access rights to the data category for the current query session will be frozen, and subsequent query requests for the same category will be rejected. If the current user's cumulative query count is less than or equal to the query count limit, and the shortest interval between consecutive queries is less than the query interval threshold, a time control mechanism is applied to delay the execution time of subsequent query response operations; If the current user's cumulative query times are greater than the query times limit, and the shortest time interval between consecutive queries is less than the query time interval threshold, it is considered to have triggered a privacy leak judgment, and the privacy budget reallocation strategy will execute the fourth adjustment measure.

8. An electric power material information security management system, based on the electric power material information security management method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module: collects management data of power materials and pre-processes the management data; Privacy protection and dynamic adjustment module: This module perturbs preprocessed data and defines a dynamic privacy budget adjustment mechanism based on the number of queries, user permissions, and data sensitivity. It outputs the privacy budget for the next moment and controls the degree of data perturbation. Query result credibility assessment module: Based on the perturbed data, it outputs the allocation parameter value used to determine the next round of privacy budget; Access rights management module: dynamically adjusts access rights based on the assigned parameter values ​​output by the query result credibility assessment module.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the electric power material information security management method described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electric power material information security management method according to any one of claims 1 to 7 are implemented.

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