Method and equipment for stratification and classification of traceability data based on genetic bionic model algorithm

Abstract

The present application discloses a method and device for stratifying and grading traceable data based on a genetic bionic model algorithm, which draws on the principles of genetic evolution in nature to optimize data management and processing, and combines a data management method that combines genetic algorithms with the idea of ​​stratified and graded data management. This method aims to optimize the data classification and grading process by simulating genetic mechanisms in the biological evolution process, such as selection, crossover, mutation, etc., thereby improving the efficiency and security of data management, and can be applied to data classification, storage, retrieval and other aspects in a big data environment to improve data processing efficiency and accuracy. In addition, it is also possible to form traceable basic model data with obvious label attributes, combined with stratified and graded data management, which brings great convenience to the regional power grid.

Description

Technical Field

[0001] The present application relates to the technical field of data management, and in particular to a method and device for stratifying and grading traceability data based on a genetic bionic model algorithm. Background Art

[0002] In the related technologies, the hierarchical and graded management of data mostly relies on work experience, and manual configuration, custom settings and other methods are used to perform hierarchical maintenance and hierarchical management of data. Manual division of multiple levels is of general practical value for a variety of data with relatively simple data features and similar data attributes. However, for large quantities of data with complex data features, many data attributes and few common properties, hierarchical and graded management of data through manual configuration or simple noun matching will result in many errors in data hierarchical and graded management, and the classification is rough and the grading is unclear, which is not conducive to rapid data retrieval and data security requirements.

[0003] Therefore, the existing data stratification and grading scheme has the problem of inaccurate stratification and grading. Summary of the invention

[0004] The embodiment of the present application provides a method and device for stratification and classification of traceability data based on a genetic bionic model algorithm, aiming to improve the accuracy of data stratification and classification.

[0005] On the one hand, the present application provides a method for stratifying and grading traceability data based on a genetic bionic model algorithm, and the method for stratifying and grading traceability data based on a genetic bionic model algorithm comprises:

[0006] Obtain target data to be managed;

[0007] Determining data hierarchical classification information of each of the target data;

[0008] Using the data hierarchical classification information of each target data as an initial individual in the genetic algorithm;

[0009] Utilizing the genetic algorithm, optimizing the plurality of initial individuals to obtain optimized data stratification and classification information;

[0010] According to the optimized data hierarchical and graded information, the corresponding target data is stored in a hierarchical and graded manner.

[0011] In some embodiments, the data hierarchical information includes data hierarchical information and data classification information, and the step of determining the data hierarchical information of each target data includes:

[0012] Acquire a data source and a data type of each target data, wherein the data source includes at least one of enterprise data, personal data, and public data, and the data type includes a first-level subclass, a second-level subclass, a third-level subclass, a fourth-level subclass, and a fifth-level subclass;

[0013] According to a preset stratification strategy, based on the data source and the data type, determine the data stratification information of each of the target data;

[0014] Data classification information of each of the target data is determined.

[0015] In some embodiments, determining the data classification information of each target data includes:

[0016] Obtaining the data value and data impact of each target data, wherein the data value includes at least one of public interest, industry interest, personal interest, and organizational interest;

[0017] According to a preset classification strategy, based on the data value and the data impact degree, the data classification information of each target data is determined.

[0018] In some embodiments, the use of the genetic algorithm to optimize the plurality of initial individuals to obtain optimized data stratification and classification information includes:

[0019] For each of the initial individuals, obtaining a storage performance index when the corresponding target data is stored according to the corresponding data hierarchical information, wherein the storage performance index includes at least one of query speed, storage cost, and data redundancy;

[0020] Based on the storage performance indicator, determining the data stratification and grading score corresponding to the initial individual;

[0021] The genetic algorithm is used to optimize the plurality of initial individuals according to the data stratification and grading scores to obtain optimized data stratification and grading information.

[0022] In some embodiments, the use of the genetic algorithm to optimize the plurality of initial individuals according to the data stratification and grading scores to obtain optimized data stratification and grading information includes:

[0023] According to the data stratification and grading, a selection operation is performed on the plurality of initial individuals to obtain a plurality of first individuals;

[0024] Performing a crossover operation on a plurality of the first individuals to obtain a plurality of second individuals;

[0025] Performing mutation operation on the plurality of the second individuals to obtain a plurality of third individuals;

[0026] Based on the plurality of third entities, optimized data hierarchical classification information is determined.

[0027] In some embodiments, the data layering and grading information includes data layering information and data grading information, and the cross-operation on the plurality of first individuals to obtain the plurality of second individuals includes:

[0028] According to a preset crossover probability, selecting a plurality of the first individuals;

[0029] Among the selected multiple first individuals, the data layering information and / or the data classification information between different first individuals are exchanged to obtain multiple second individuals;

[0030] The performing mutation operation on the plurality of the second individuals to obtain a plurality of third individuals comprises:

[0031] According to a preset mutation probability, selecting a plurality of the second individuals;

[0032] The data layering information and / or the data classification information of each selected second individual is adjusted to obtain a plurality of the third individuals.

[0033] In some embodiments, after storing the target data in a hierarchical manner according to the optimized data hierarchical information, the method further includes:

[0034] For each of the target data, obtaining associated data having a blood relationship with the target data;

[0035] Using the optimized data hierarchical information of the target data as the data hierarchical information of the associated data;

[0036] The associated data are stored according to data hierarchical classification information of the associated data.

[0037] In some embodiments, after storing the target data in a hierarchical manner according to the optimized data hierarchical information, the method further includes:

[0038] For each of the target data, based on the optimized data layering and grading information of the target data, determine a security access policy for the target data;

[0039] According to the security access policy, access requests to the target data are restricted.

[0040] On the other hand, the present application also provides a traceability data stratification and grading device based on a genetic bionic model algorithm, the traceability data stratification and grading device based on a genetic bionic model algorithm comprises:

[0041] A first acquisition module, used to acquire target data to be managed;

[0042] A first determining module, used to determine data hierarchical classification information of each target data;

[0043] A second determination module is used to use the data stratification and classification information of each target data as an initial individual in a genetic algorithm;

[0044] A genetic algorithm module, used to optimize the plurality of initial individuals using the genetic algorithm to obtain optimized data stratification and classification information;

[0045] The data storage module is used to perform hierarchical and graded storage of the corresponding target data according to the optimized data hierarchical and graded information.

[0046] On the other hand, the present application also provides a computer device, the computer device comprising:

[0047] one or more processors;

[0048] Memory; and

[0049] One or more applications, wherein the one or more applications are stored in the memory and are configured to be executed by the processor to run any one of the traceability data stratification and classification methods based on the genetic bionic model algorithm.

[0050] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is loaded by a processor to run any one of the traceability data stratification and grading methods based on the genetic bionic model algorithm.

[0051] On the other hand, the present application also provides a computer program product, including a computer program or instructions, which are executed by a processor to implement the traceability data stratification and grading method based on the genetic bionic model algorithm as described in any of the above items.

[0052] The traceability data hierarchical grading method and device based on the genetic bionic model algorithm provided in the embodiment of the present application draws on the genetic evolution principle in nature to optimize data management and processing, and combines the data management method that combines genetic algorithm with the idea of ​​hierarchical and hierarchical data management. This method aims to optimize the data classification and grading process by simulating the genetic mechanisms in the biological evolution process, such as selection, crossover, mutation, etc., so as to improve the efficiency and security of data management, and can be applied to data classification, storage, retrieval and other aspects in a big data environment to improve data processing efficiency and accuracy. In addition, it is also possible to form traceable basic model data with obvious label attributes, which, combined with hierarchical and graded data management, brings great convenience to the regional power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 It is a schematic flow chart of an embodiment of a method for stratifying and grading traceability data based on a genetic bionic model algorithm provided in an embodiment of the present application;

[0055] Figure 2 It is a schematic flow chart of another embodiment of the traceability data stratification and grading method based on the genetic bionic model algorithm provided in the embodiments of the present application;

[0056] Figure 3 It is a schematic flow chart of another embodiment of the method for stratifying and grading traceability data based on the genetic bionic model algorithm provided in the embodiments of the present application;

[0057] Figure 4 It is a schematic diagram of the terminal structure of an embodiment of the traceability data stratification and grading device based on the genetic bionic model algorithm provided in the embodiments of the present application;

[0058] Figure 5 It is a schematic diagram of the terminal structure of an embodiment of the computer device provided in the embodiments of the present application. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0060] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0061] Any embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0062] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data for processing by the computer device. The details will not be repeated here.

[0063] First, the definitions of terms that may be involved in the embodiments of the present application are explained as follows:

[0064] (1) Data inheritance: Genetic Algorithm (GA) is an optimization algorithm that simulates the biological evolution process. It draws on Darwin's theory of evolution and Mendel's genetics to search for the optimal solution through operations such as selection, crossover (or recombination) and mutation. In data science, genetic algorithms are applied to tasks such as feature selection, cluster analysis, and predictive modeling. These processes can be regarded as a manifestation of "data inheritance".

[0065] (2) Data bionics: This refers to the design and manufacture of engineering systems or products by imitating the principles and behaviors of biological systems. In the field of data science, data bionics uses the data processing and storage mechanisms of biological systems to improve and optimize data technology. This includes drawing inspiration from organisms to design more efficient and intelligent data processing algorithms and storage solutions.

[0066] (3) Stratification and classification: In data analysis, stratification and classification is an important analytical method. By classifying and organizing data, it helps people better understand the distribution and characteristics of data and make more accurate decisions.

[0067] (4) Grid dispatch data: Grid dispatch data refers to various types of data used for grid dispatch and management, including but not limited to real-time information on power production, dispatch instructions, control information, etc. These data are transmitted through the power dispatch data network to ensure the safe, stable and economical operation of the power grid.

[0068] (5) Data life cycle: The data life cycle refers to the entire process from the generation to the disappearance of data, which covers various stages such as data creation, collection, storage, processing, analysis, archiving and deletion.

[0069] (6) Public data: data disclosed in accordance with relevant requirements and included in the scope of national public data (except data involving secrets and personal privacy). In principle, the company's public data does not involve original detailed data, but mainly covers indicators, statements, reports and other data submitted in accordance with relevant regulatory requirements.

[0070] (7) Personal data: personal information data related to an identified or identifiable natural person recorded electronically or otherwise, excluding information that has been anonymized.

[0071] (8) Enterprise data: original data and derived data collected and generated by the company in its activities such as power generation, business operations and customer service. Enterprise data is classified and graded according to business lines and does not overlap with personal data or public data.

[0072] (9) Dataset: refers to the data format in which data records are aggregated.

[0073] (10) Wide table data: refers to a database table that associates indicators, dimensions, and attributes related to a business topic.

[0074] (11) Personal sensitive information: refers to personal information data that, if leaked or used in violation of regulations, may easily cause infringement on the personal dignity of a natural person or endanger the personal or property safety, including biometrics, specific identity, medical health, financial accounts, whereabouts and other information, as well as personal data of minors under the age of fourteen.

[0075] (12) Biometric information: refers to personal data obtained through technical processing of a natural person's physical, biological or behavioral characteristics that can be used alone or in combination with other information to identify the natural person. Biometric information includes personal facial recognition features, irises, fingerprints, genes, voiceprints, gait, palm prints, ear prints, eye prints, etc.

[0076] (13) Basic personal information: refers to basic information about a natural person, such as name, birthday, age, gender, ethnicity, nationality, place of origin, political affiliation, marital status, family relationships, address, personal telephone number, email address, interests and hobbies, etc.

[0077] (14) Important power users: refers to customers who play an important role in the society, economy, life, etc. of a country or a region (city), and whose power interruption may cause personal injury, serious environmental pollution, great economic losses, serious disruption of social public order, or customers who have special requirements for power supply reliability. The identification of important power customers is generally proposed by the customers and approved by the power authorities at or above the county level.

[0078] (15) Raw data: refers to data that has not been processed.

[0079] (16) Derivative data: refers to data generated through processing activities such as statistics, association, mining, aggregation, and de-identification.

[0080] In the related art, the following schemes can be adopted for data stratification and classification:

[0081] Solution 1: Relying on work experience, using manual configuration, custom settings and other methods to perform hierarchical maintenance and hierarchical management of data, it has general practical value for manually dividing multiple data with relatively simple data features and similar data attributes. However, for large quantities of data with complex data features, many data attributes and few common properties, data hierarchical management through manual configuration or simple noun matching will result in many data hierarchical and hierarchical errors, and the classification is rough and the classification is unclear, which is not conducive to rapid data retrieval and data security requirements.

[0082] Solution 2: Use a simple and clear label system or knowledge graph technology to achieve automatic stratification and grading of data. This technology relies on mature data labels or knowledge graphs, and automatically stratifies and grades data through intelligent judgment of data. This method eliminates the risk of human intervention, reduces the cost of manual configuration, and improves the accuracy of data stratification and grading. However, for unknown data labels or data content related to incomplete knowledge graphs, it is impossible to better identify and judge, and it is still necessary to improve the relevant data labels and knowledge graphs before data stratification and grading can be performed. There is a lack of self-updating and self-judgment data stratification and grading management.

[0083] For Solution 1, manual data stratification and classification management relies on manual operation and configuration, as well as personal data management experience, and has four main defects: First, the efficiency is low. As the amount of data continues to increase, the workload of manual classification and classification also increases. In the context of big data, manual classification and classification may take a lot of time and manpower, resulting in low efficiency; second, there are many errors. There may be subjectivity and inconsistency in the manual classification and classification process, resulting in inaccurate or biased classification results (in addition, human factors such as fatigue and negligence may also cause classification errors); third, it is difficult to sustain. Data is constantly generated and changed, and manual classification and classification can usually only be operated on the current data set. When new data is generated, classification and classification work needs to be re-performed, which increases the difficulty of sustainability; fourth, the cost is high. In order to maintain the accuracy and sustainability of data classification and classification, enterprises need to invest a lot of human and material resources (this includes recruiting and training professional data classification personnel, purchasing and maintaining related tools and equipment, etc.), thereby increasing the operating costs of enterprises.

[0084] For Solution 2, a simple and clear labeling system or knowledge graph technology is used to automatically stratify and grade data. This method has the following main defects: First, it relies on the built-in labeling system and knowledge graph algorithm. If the built-in algorithm is not designed properly or the rules are not set accurately, it may lead to deviations or errors in the classification and grading results (this requires that the system should fully consider the diversity and complexity of data, as well as the specific needs of different industries and scenarios when designing). Second, it has poor adaptability to new types of data. With the continuous generation and change of data, new data types and data patterns emerge in an endless stream (when facing these new types of data, the system may need to readjust the algorithms and rules for automatic classification and grading, or even retrain the model to adapt to the new data characteristics, which increases the complexity and maintenance cost of the system). Third, it lacks security and privacy. During the data classification and grading process, the system may come into contact with sensitive or private data (if the system's security and privacy protection measures are not in place, it may lead to risks such as data leakage or abuse).

[0085] The traceability data stratification and grading method and device based on the genetic bionic model algorithm provided in the embodiment of the present application has the following advantages over Solution 1: First, it improves efficiency. It relies on data genetics and data bionic algorithms, adopts artificial intelligence technology, identifies various characteristics of data, and automatically manages data in a hierarchical manner, which greatly improves data management efficiency; second, it improves accuracy. By extracting data characteristics, attributes, internal and external correlations, etc., based on big data analysis, it can better and faster realize automatic stratification and grading of data, and improves the accuracy of data stratification and grading based on the analysis and comparison of massive historical data; third, it is sustainable development. Based on data genetics and bionic algorithms, through artificial intelligence analysis, it can continuously learn and explore. As the amount of data continues to expand, the attributes and data characteristics of the data are mastered more and more, which is more conducive to the hierarchical and hierarchical management of data; fourth, it reduces costs. The hierarchical and hierarchical management of data implemented by machines and related data algorithms requires little or no human intervention, which greatly reduces labor costs, improves the efficiency of data stratification and grading management, and reduces operating costs.

[0086] The embodiment of the present application provides a traceability data hierarchical grading method and device based on a genetic bionic model algorithm, which has the following advantages over Solution 2: First, it does not rely on a built-in labeling system or knowledge graph algorithm, but uses data genetics and data bionics algorithms to automatically analyze and identify data features, attributes, and associations, and adapt to the diversity and complexity of data; second, it has high adaptability to new types of data. In response to the continuous generation and change of data, it can automatically realize data inheritance, crossover and mutation based on data genetics and data bionics algorithms, obtain data blood relationship, and realize hierarchical and graded management of data; third, the data security and privacy are relatively high. Due to the use of a data genetic algorithm, based on data blood relationship, the hierarchical and graded attributes of the data are inherited, which can ensure the security and privacy of the data.

[0087] In summary, the traceability data stratification and classification method and device based on the genetic bionic model algorithm provided in the embodiment of the present application adopts data genetics and data bionics algorithms to automatically identify the various characteristics of power grid dispatching data, and optimizes the data classification and classification process by simulating the genetic mechanisms in the biological evolution process, such as selection, crossover, mutation, etc., so as to improve the efficiency and security of data management. It mainly includes the following contents:

[0088] 1. Obtain power grid dispatching data, analyze the correlation between various types of data according to the various attributes of the data, use data genetic algorithm, and realize data feature selection and clustering analysis through operations such as data selection, reorganization and mutation. Combined with the requirements of hierarchical management of data, automatically optimize the hierarchical process of power grid dispatching data, realize hierarchical management of data, use hierarchical result data, and use data bionic technology to realize the full life cycle management of data, as well as improve data management efficiency and data security protection management.

[0089] 2. Construct traceable basic model data, use data genetic bionic algorithm to define the characteristics of the acquired regional power grid dispatching data, formulate data labels, and form identifiable and traceable basic model data. This data has obvious characteristic attributes and cannot be changed. All kinds of data derived from the basic data automatically inherit the relevant characteristic attributes of the basic data, which is conducive to the rapid realization of hierarchical management of data and data traceability tracking.

[0090] 3. Divide data into different layers and levels according to factors such as data value, data impact, and frequency of use, and adopt different management and control measures for data at different layers and levels. This approach helps ensure the confidentiality, integrity, and availability of data and prevent data leaks and attacks.

[0091] 4. The data layering and grading management implemented by data genetics and bionic algorithms has the characteristics of strong adaptability, high robustness and good optimization effect. In terms of adaptability, it can adaptively adjust the classification and grading rules according to different data sets and business needs; in terms of robustness, it can effectively deal with problems such as data noise and outliers, and improve the stability and reliability of data management; in terms of optimization effect, it simulates the biological evolution process for global search and can find better data classification and grading solutions.

[0092] Specifically, refer to Figure 1 and Figure 3 In one embodiment, the method for stratifying and grading traceability data based on the genetic bionic model algorithm may include:

[0093] 101. Obtain target data to be managed.

[0094] In the embodiment of the present application, the target data to be managed is generally power grid dispatching data that needs to be stored in a preset database in a hierarchical manner. Due to the complexity and importance of power grid dispatching data, it is necessary to manage the power grid dispatching data in a hierarchical manner.

[0095] In some embodiments of the present application, after acquiring the target data to be managed, it may also include: pre-processing the target data such as cleaning, conversion and standardization to form traceable basic model data with obvious label attributes and ensure the quality of the data.

[0096] 102. Determine data layering and grading information for each target data.

[0097] In the embodiments of the present application, stratification refers to dividing the target data into different levels according to factors such as data value, data impact, and data access frequency. Grading refers to further dividing the data level within each layer in order to manage the target data more finely. Therefore, the data stratification and classification information may include data stratification information and data classification information. By dividing the data into different levels and grades, and taking different management and control measures for data at different levels and grades. This method helps to ensure the confidentiality, integrity and availability of data and prevent data leakage and attacks.

[0098] In some embodiments of the present application, the target data is preliminarily classified according to its characteristics to obtain corresponding data stratification and classification information. For example, a data stratification and classification strategy can be formulated according to business needs and data characteristics, and then the target data is preliminarily classified according to the data stratification and classification strategy. These strategies may include factors such as data source, data type, data value, and data impact.

[0099] The following example shows a data stratification and classification strategy. The overall strategy can be summarized as "three dimensions, five layers, and six levels", where "three dimensions" refers to data stratification from three data sources: enterprise data, personal data, and public data; "five layers" refers to the data types of the five subclasses (including first-level subclasses, second-level subclasses, third-level subclasses, fourth-level subclasses, and fifth-level subclasses) that can be referred to when classifying target data layer by layer; "six levels" refers to dividing the data level of power grid dispatching data from high to low into six levels (including level 6, level 5, level 4, level 3, level 2, and level 1) according to the value of the data and the degree of impact of the data after leakage or misuse, in accordance with the power grid company's confidential items catalog and data sharing negative list.

[0100] In the data stratification strategy, following relevant standards and relevant operating guidelines, combined with the actual business management of power grid companies, it can be divided into five levels (including professional fields, business themes, business objects, data entities, and data objects) for stratification. Among them, the first, second, and third levels are classified according to business characteristics, divided into professional fields, business themes, and business objects; the fourth and fifth levels are classified according to data characteristics, divided into data entities and data attributes. The data stratification strategy is shown in Table 1 below:

[0101] Table 1

[0102]

[0103] It can be seen that determining the data hierarchical grading information of each target data may include: obtaining the data source and data type of each target data, the data source including at least one of enterprise data, personal data, and public data, and the data type including first-level subclasses, second-level subclasses, third-level subclasses, fourth-level subclasses, and fifth-level subclasses; according to a preset hierarchical strategy, the data hierarchical information of each target data is determined based on the data source and data type.

[0104] In the data classification strategy, in accordance with relevant regulations, according to the importance of data in economic and social development and the impact on public interests, industry fields, or the rights and interests of individuals and organizations once tampered, destroyed, leaked, or illegally obtained or used, the data is generally divided into three levels from high to low: core data, important data, and general data. That is, data classification information may include at least one level of core data, important data, and general data. The data classification strategy is shown in Table 2 below:

[0105] Table 2

[0106]

[0107] It can be seen that determining the data layering and grading information of each target data may include: obtaining the data value and data impact degree of each target data, the data value including at least one of public interests, industry interests, personal rights and interests, and organizational rights and interests, and the data impact degree including at least one of particularly serious harm, serious harm, general harm, and no harm; according to the preset classification strategy, based on the data value and the data impact degree, determine the data classification information of each target data.

[0108] 103. The data stratification and classification information of each target data is used as an initial individual in the genetic algorithm.

[0109] In the embodiment of the present application, the data hierarchical information of the target data is used as the initial population in the genetic algorithm, and the data hierarchical information of each target data is encoded as an initial individual in the genetic algorithm for subsequent processing, and each initial individual is also associated with the corresponding target data. The encoding method can be set based on actual needs, for example, binary encoding rules can be used for encoding. By automatically generating genetic coding, traceable basic model data is formed, which is conducive to the full life cycle management of data.

[0110] 104. Genetic algorithms are used to optimize multiple initial individuals to obtain optimized data stratification and grading information.

[0111] In the embodiment of the present application, the genetic algorithm is a search heuristic algorithm, which is an optimization algorithm that simulates the biological evolution process in nature. It searches for the optimal solution in the solution space through operations such as selection, crossover and mutation. In data management, the genetic algorithm can be used to optimize data hierarchical information so that the target data can be divided and managed according to more reasonable data hierarchical information.

[0112] 105. According to the optimized data stratification and grading information, the corresponding target data is stored in a stratified and graded manner.

[0113] In the embodiments of the present application, the corresponding target data is stored in a hierarchical manner according to the optimized data hierarchical and graded information, that is, the optimized data hierarchical and graded information is applied to the actual data management work to realize hierarchical and graded management and control of the data.

[0114] In some embodiments of the present application, the efficiency of hierarchical management of related data can be further improved based on the data biomimetic algorithm. Specifically, after the corresponding target data is hierarchically stored according to the optimized data hierarchical information, it can also include: for each target data, obtaining the associated data that has a blood relationship with the target data; using the optimized data hierarchical information of the target data as the data hierarchical information of the associated data; storing the associated data according to the data hierarchical information of the associated data. Taking the target data as power grid power generation data as an example, the optimized data hierarchical information of the power grid power generation data can be obtained through the genetic algorithm, and the associated data that has a blood relationship with the power grid power generation data can be obtained at this time. For example, based on the attribute data such as power, power, time of generation, load rate, peak-to-valley difference, maximum value, minimum value, year-on-year, and month-on-month in the power grid power generation data, the associated data of the categories such as power grid power consumption and power plant power generation can be derived, and then the optimized data hierarchical information of the power grid power generation data is used as the optimized data hierarchical information of these associated data to improve the hierarchical efficiency and accuracy of the related data. In addition, when retrieving data, data bionics technology (such as ant colony algorithm, simulating the data information transmission mechanism during ant foraging) can be used to optimize the data retrieval path to improve data retrieval efficiency.

[0115] In some embodiments of the present application, different management and control measures can also be taken for data at different levels and grades to ensure the confidentiality, integrity and availability of the data and prevent data leakage and attacks. Specifically, after the corresponding target data is stored in a hierarchical manner according to the optimized data hierarchical information, it can also include: for each target data, based on the optimized data hierarchical information of the target data, determining the security access policy of the target data; according to the security access policy, restricting access requests to the target data, for example, by setting the security access policy, restricting the access and use of the target data by different users.

[0116] It can be seen that the traceability data stratification and grading method based on the genetic bionic model algorithm provided in the embodiment of the present application draws on the genetic evolution principle in nature to optimize data management and processing, and combines the data management method that combines genetic algorithm with the idea of ​​data stratification and grading management. This method aims to optimize the data classification and grading process by simulating the genetic mechanisms in the biological evolution process, such as selection, crossover, mutation, etc., thereby improving the efficiency and security of data management, and can be applied to data classification, storage, retrieval and other aspects in a big data environment to improve data processing efficiency and accuracy.

[0117] Reference Figure 2 and Figure 3 ,exist Figure 1 Based on the above, a genetic algorithm is used to optimize multiple initial individuals to obtain optimized data stratification and classification information, which may include:

[0118] 201. For each initial individual, obtain a storage performance indicator of corresponding target data when storing the corresponding data hierarchical information.

[0119] In an embodiment of the present application, the storage performance index includes at least one of query speed, storage cost, and data redundancy. Query speed refers to the time taken for the target data to be successfully queried, storage cost refers to the hardware cost, software cost, etc. spent on storing the target data, and data redundancy refers to unnecessary repeated storage of data in the memory.

[0120] 202. Based on the storage performance indicator, determine the data stratification and grading scores of the corresponding initial individuals.

[0121] In the embodiment of the present application, the data stratification and grading score of the initial individual represents the classification accuracy and grading rationality of the data stratification and grading information of the corresponding target data, and is used to reflect the pros and cons of the corresponding initial individual. The data stratification and grading score can be calculated by a preset function, for example, the query speed, storage cost, and data redundancy can be weighted and summed to obtain the corresponding data stratification and grading score.

[0122] 203. Using genetic algorithms, multiple initial individuals are optimized according to data stratification and grading scores to obtain optimized data stratification and grading information.

[0123] In an embodiment of the present application, a genetic algorithm is used to optimize multiple initial individuals according to data stratification and grading scores to obtain optimized data stratification and grading information, which may include: performing a selection operation on multiple initial individuals according to the data stratification and grading scores to obtain multiple first individuals, that is, selecting excellent individuals as parents for subsequent crossover operations; performing a crossover operation on multiple first individuals to obtain multiple second individuals; performing a mutation operation on multiple second individuals to obtain multiple third individuals; and determining the optimized data stratification and grading information based on the multiple third individuals.

[0124] In a further embodiment, performing a crossover operation on multiple first individuals to obtain multiple second individuals may include: selecting multiple first individuals according to a preset crossover probability; exchanging data layering information and / or data classification information between different first individuals among the selected multiple first individuals to obtain multiple second individuals. It can be seen that the crossover operation specifically simulates the process of biological gene crossover, exchanging some genes of two parent individuals to generate new offspring individuals.

[0125] In a further embodiment, performing a mutation operation on multiple second individuals to obtain multiple third individuals may include: selecting multiple second individuals according to a preset mutation probability; adjusting the data layering information and / or data classification information of each selected second individual to obtain multiple third individuals, thereby completing the mutation operation on each second individual. It can be seen that the mutation operation is specifically a process of randomly changing certain genes of offspring individuals with a certain probability to increase the diversity of the population and avoid falling into a local optimal solution.

[0126] In a further embodiment, based on multiple third individuals, the optimized data stratification and grading information is determined, specifically, an iterative optimization process is performed, that is, the selection, crossover and mutation operations are repeated, and the data stratification and grading information is gradually optimized through an iterative process to finally obtain the optimized data stratification and grading information.

[0127] During the iteration process, the parameters of the genetic algorithm can be adjusted according to actual conditions (such as the population size requirements that need to be met during the selection operation, the crossover probability mutation that needs to be met during the crossover operation, the mutation probability requirements that need to be met during the mutation operation, etc.) to improve the performance of the genetic algorithm.

[0128] In a further embodiment, based on multiple third entities, the number of iterations for determining optimized data hierarchical and hierarchical information can be determined based on the target storage location of the corresponding target data (due to the privacy requirements of power grid dispatching data, power grid dispatching data with a higher degree of importance usually needs to be stored in the power grid intranet, while power grid dispatching data with a relatively lower degree of importance can be stored in the power grid extranet based on actual needs, but the data management and maintenance costs of the power grid intranet are usually higher than the data management and maintenance costs of the power grid extranet). Therefore, based on multiple third entities, determining optimized data hierarchical and hierarchical information can include: obtaining the target storage locations of target data associated with multiple third entities to obtain multiple target storage locations; among the multiple target storage locations, if the number of target storage locations located in the power grid intranet is greater than or equal to the number of target storage locations located in the power grid extranet, then, iterative optimization processing can be performed on the multiple third entities according to the first preset number of iterations to determine the optimized data hierarchical and hierarchical information, so as to give priority to ensuring the accuracy of the data hierarchical and hierarchical information of the power grid dispatching data stored in the power grid intranet, thereby minimizing the data management and maintenance costs within the power grid.

[0129] Among the multiple target storage locations, if the number of target storage locations located in the power grid intranet is less than the number of target storage locations located in the power grid extranet, then, iterative optimization processing can be performed on the multiple third individuals according to the second preset number of iterations to determine the optimized data stratification and classification information. The second preset number of iterations is less than the first preset number of iterations, so as to reduce the time consumption of the genetic algorithm and avoid excessively high cost of data stratification and classification under the premise that the data stratification and classification information of the power grid dispatching data has high accuracy.

[0130] It can be seen from the above embodiments that the traceability data stratification and grading method based on the genetic bionic model algorithm provided in the embodiments of the present application has the following technical effects:

[0131] 1. Improve data processing efficiency. The data genetic bionic algorithm can automatically and efficiently classify and grade massive amounts of data by simulating natural selection and genetic mechanisms. This algorithm can identify patterns and features in the data, thereby more accurately classifying and grading the data and improving the accuracy of data governance. The automated nature of the algorithm can reduce manual intervention and the possibility of human error, while improving the efficiency and speed of data processing. This is particularly important for processing large-scale data sets and can significantly shorten the cycle of data governance. The algorithm can dynamically adjust the hierarchy of the data according to changes in the data, thereby improving processing efficiency.

[0132] 2. Strengthen data security and privacy protection. The traceability data stratification and grading method based on the genetic bionic model algorithm can implement different levels of security access strategies for different levels of data, which helps reduce the risk of data leakage and abuse and improve data security. Through the data genetic bionic algorithm, sensitive data with high data value and data impact can be more accurately identified and protected. For example, sensitive data can be encrypted for storage and transmission, and unauthorized access can be restricted, so as to ensure that sensitive data is not illegally obtained and used.

[0133] 3. Promote data sharing and analysis. The traceability data stratification and grading method based on the genetic bionic model algorithm can clarify the access and use rights of data at different levels and provide clear boundaries for data sharing, which helps to promote data sharing and exchange under the premise of compliance and improve the utilization value of data. Through the optimized data index structure, the data retrieval speed in the data analysis process is accelerated, providing decision makers with more accurate and timely data support to help make better business decisions.

[0134] 4. Strengthen compliance operation requirements. The traceability data stratification and grading method based on the genetic bionic model algorithm helps enterprises and organizations better comply with relevant regulations and industry standards. By clarifying the classification and grading standards of data and the corresponding security management and protection requirements, the risk of violations can be reduced and legal disputes can be reduced.

[0135] 5. Improve the economic benefits of data services. The traceable basic model data formed by the genetic bionic algorithm, combined with the hierarchical data management method, has high practical value in the regional power grid industry. It solves the difficulties of hierarchical grading of multi-department, multi-dimensional, and multi-business data related to the power grid, improves the management level of regional power grid data, and promotes the replacement of manual operation management models with advanced technology, which brings great convenience to the regional power grid.

[0136] In order to better implement the traceability data stratification and grading method based on the genetic bionic model algorithm in the embodiment of the present application, on the basis of the traceability data stratification and grading method based on the genetic bionic model algorithm, the embodiment of the present application also provides a traceability data stratification and grading device based on the genetic bionic model algorithm, such as Figure 4 As shown, the traceability data stratification and classification device 400 based on the genetic bionic model algorithm includes:

[0137] The first acquisition module 401 is used to acquire target data to be managed;

[0138] A first determining module 402, configured to determine data hierarchical classification information of each target data;

[0139] The second determination module 403 is used to use the data stratification and classification information of each target data as an initial individual in the genetic algorithm;

[0140] A genetic algorithm module 404 is used to optimize the plurality of initial individuals using the genetic algorithm to obtain optimized data stratification and classification information;

[0141] The data storage module 405 is used to perform data hierarchical storage on the corresponding target data according to the optimized data hierarchical and graded information.

[0142] The present application also provides a computer device that integrates any of the traceability data stratification and grading devices based on the genetic bionic model algorithm provided in the present application. Figure 5 As shown, it shows a schematic diagram of the structure of the computer device involved in the embodiment of the present application, specifically:

[0143] The computer device may include one or more processing core processors 501, one or more computer-readable storage media memories 502, a power supply 503, an input unit 504 and other components. Those skilled in the art will appreciate that Figure 5 The computer device structure shown in the figure is not intended to limit the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0144] The processor 501 is the control center of the computer device. It uses various interfaces and lines to connect various parts of the entire computer device. By running or executing software programs and / or modules stored in the memory 502 and calling data stored in the memory 502, it executes various functions of the computer device and processes data, thereby monitoring the computer device as a whole. Optionally, the processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 501.

[0145] The memory 502 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing by running the software programs and modules stored in the memory 502. The memory 502 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the computer device, etc. In addition, the memory 502 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 502 can also include a memory controller to provide the processor 501 with access to the memory 502.

[0146] The computer device also includes a power supply 503 for supplying power to various components. Preferably, the power supply 503 can be logically connected to the processor 501 through a power management system, so as to manage charging, discharging, power consumption and other functions through the power management system. The power supply 503 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.

[0147] The computer device may further include an input unit 504, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0148] Although not shown, the computer device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 501 in the computer device will load the executable files corresponding to the processes of one or more application programs into the memory 502 according to the following instructions, and the processor 501 will run the application programs stored in the memory 502, thereby realizing various functions, as follows:

[0149] Acquire the target data to be managed; determine the data stratification and grading information of each target data; use the data stratification and grading information of each target data as an initial individual in the genetic algorithm; use the genetic algorithm to optimize the multiple initial individuals to obtain the optimized data stratification and grading information; store the corresponding target data in a stratified and graded manner according to the optimized data stratification and grading information.

[0150] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0151] To this end, the embodiment of the present application provides a computer-readable storage medium, which may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in any of the traceability data hierarchical classification methods based on the genetic bionic model algorithm provided in the embodiment of the present application. For example, the computer program can be loaded by the processor to execute the following steps:

[0152] Acquire the target data to be managed; determine the data stratification and grading information of each target data; use the data stratification and grading information of each target data as an initial individual in the genetic algorithm; use the genetic algorithm to optimize the multiple initial individuals to obtain the optimized data stratification and grading information; store the corresponding target data in a stratified and graded manner according to the optimized data stratification and grading information.

[0153] The embodiment of the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. The processor of the electronic device reads the computer instruction from the computer-readable storage medium and executes the computer instruction, so that the electronic device executes to implement the traceability data hierarchical classification method based on the genetic bionic model algorithm as described in any one of the above items, for example:

[0154] Acquire the target data to be managed; determine the data stratification and grading information of each target data; use the data stratification and grading information of each target data as an initial individual in the genetic algorithm; use the genetic algorithm to optimize the multiple initial individuals to obtain the optimized data stratification and grading information; store the corresponding target data in a stratified and graded manner according to the optimized data stratification and grading information.

[0155] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the detailed description of other embodiments above, and will not be repeated here.

[0156] In specific implementation, the above units or structures can be implemented as independent entities, or can be arbitrarily combined to be implemented as the same or several entities. The specific implementation of the above units or structures can refer to the previous method embodiments, which will not be repeated here.

[0157] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0158] The above is a detailed introduction to the traceability data stratification and grading method and device based on the genetic bionic model algorithm provided in the embodiment of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for stratifying and grading traceability data based on a genetic bionic model algorithm, characterized in that: The traceability data stratification and grading method based on the genetic bionic model algorithm includes: Obtain target data to be managed; Determining data hierarchical classification information of each of the target data; Using the data hierarchical classification information of each target data as an initial individual in the genetic algorithm; Utilizing the genetic algorithm, optimizing the plurality of initial individuals to obtain optimized data stratification and classification information; According to the optimized data hierarchical and graded information, the corresponding target data is stored in a hierarchical and graded manner; The method of using the genetic algorithm to optimize the plurality of initial individuals to obtain optimized data stratification and grading information includes: for each of the initial individuals, obtaining a storage performance index when the corresponding target data is stored according to the corresponding data stratification and grading information, wherein the storage performance index includes at least one of query speed, storage cost, and data redundancy; determining a data stratification and grading score of the corresponding initial individual based on the storage performance index; and using the genetic algorithm to optimize the plurality of initial individuals according to the data stratification and grading score to obtain optimized data stratification and grading information. The method of using the genetic algorithm to optimize the plurality of initial individuals according to the data stratification and grading scores to obtain optimized data stratification and grading information includes: performing a selection operation on the plurality of initial individuals according to the data stratification and grading scores to obtain a plurality of first individuals; performing a crossover operation on the plurality of first individuals to obtain a plurality of second individuals; performing a mutation operation on the plurality of second individuals to obtain a plurality of third individuals; and determining the optimized data stratification and grading information based on the plurality of third individuals; The data layering and grading information includes data layering information and data grading information, and the crossover operation is performed on the plurality of first individuals to obtain the plurality of second individuals, including: selecting the plurality of first individuals according to a preset crossover probability; among the plurality of selected first individuals, exchanging the data layering information and / or the data grading information between different first individuals to obtain the plurality of second individuals; The method of determining optimized data hierarchical grading information based on multiple third entities includes: acquiring target storage locations of target data associated with multiple third entities to obtain multiple target storage locations; among the multiple target storage locations, if the number of target storage locations located in the power grid intranet is greater than or equal to the number of target storage locations located in the power grid extranet, iteratively optimizing the multiple third entities according to a first preset number of iterations to determine optimized data hierarchical grading information.

2. The method for stratifying and grading traceability data based on the genetic bionic model algorithm according to claim 1, characterized in that: The data layering and grading information includes data layering information and data grading information. The step of determining the data layering and grading information of each target data includes: Acquire a data source and a data type of each target data, wherein the data source includes at least one of enterprise data, personal data, and public data, and the data type includes a first-level subclass, a second-level subclass, a third-level subclass, a fourth-level subclass, and a fifth-level subclass; According to a preset stratification strategy, based on the data source and the data type, determine the data stratification information of each of the target data; Data classification information of each of the target data is determined.

3. The method for stratifying and grading traceability data based on the genetic bionic model algorithm according to claim 2, characterized in that: The determining of the data classification information of each target data includes: Obtaining the data value and data impact of each target data, wherein the data value includes at least one of public interest, industry interest, personal interest, and organizational interest; According to a preset classification strategy, based on the data value and the data impact degree, the data classification information of each target data is determined.

4. The method for stratifying and grading traceability data based on the genetic bionic model algorithm according to claim 1, characterized in that: The performing mutation operation on the plurality of the second individuals to obtain a plurality of third individuals comprises: According to a preset mutation probability, selecting a plurality of the second individuals; The data layering information and / or the data classification information of each selected second individual is adjusted to obtain a plurality of the third individuals.

5. The method for stratifying and grading traceability data based on the genetic bionic model algorithm according to claim 1, characterized in that: After storing the target data in a hierarchical manner according to the optimized data hierarchical and hierarchical information, the method further includes: For each of the target data, obtaining associated data having a blood relationship with the target data; Using the optimized data hierarchical information of the target data as the data hierarchical information of the associated data; The associated data are stored according to data hierarchical classification information of the associated data.

6. The method for stratifying and grading traceability data based on the genetic bionic model algorithm according to claim 1, characterized in that: After storing the target data in a hierarchical manner according to the optimized data hierarchical and hierarchical information, the method further includes: For each of the target data, based on the optimized data layering and grading information of the target data, determine a security access policy for the target data; According to the security access policy, access requests to the target data are restricted.

7. A computer device, characterized in that: The computer device comprises: one or more processors; Memory; and One or more applications, wherein the one or more applications are stored in the memory and are configured to be executed by the processor to run the traceability data stratification and classification method based on the genetic bionic model algorithm as described in any one of claims 1 to 6.

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