A method and system for storing and managing data in an intelligent power plant

Abstract

The present invention relates to the technical field of power plant data storage, and specifically to a smart power plant data storage management method and system, comprising the following steps: S1, data acquisition and preprocessing: real-time power plant data is collected through sensors, equipment and information platforms in smart power plants; S2, data classification and storage: preprocessed power plant data is classified and stored in a distributed database; S3, data compression: differential evolution algorithm is used to compress and optimize multiple types of power plant data; S4, data access control and encryption: access control strategies are set for various types of power plant data, and encryption technology is used to encrypt and store power plant data; S5, data retrieval and intelligent analysis: intelligent retrieval functions based on metadata are provided to support complex queries in multiple dimensions. The present invention not only improves the response speed of data storage, but also ensures that real-time data can be quickly accessed.

Description

Technical Field

[0001] The present invention relates to the technical field of power plant data storage, and in particular to an intelligent power plant data storage management method and system. Background Art

[0002] With the development of smart power plants, the amount of data generated in the process of power production and management has increased exponentially. In order to ensure the safety, reliability and efficiency of power plant operation, these massive data must be effectively collected, stored, processed and analyzed. Smart power plants have many types of data, including real-time operation data, historical data and external environment data. These data are not only used for daily monitoring and operation optimization, but also play a key role in fault diagnosis, predictive maintenance and decision support. Therefore, how to effectively manage and utilize these data has become an important challenge for smart power plants to achieve intelligence and informatization.

[0003] Existing power plant data storage and management systems usually face multiple deficiencies. First, data compression and storage efficiency are low, and storage space and computing resources cannot be fully utilized, resulting in high data storage costs and slow access speeds. Second, data security and privacy protection measures are relatively weak, and they cannot be dynamically adjusted according to data sensitivity and system load, posing potential safety hazards. In addition, the existing systems are not intelligent enough in data retrieval and analysis, and it is difficult to support multi-dimensional complex queries and real-time data analysis, resulting in low data utilization efficiency and unable to meet the needs of efficient operation and accurate decision-making of smart power plants.

[0004] In order to solve the above problems, the present invention provides a smart power plant data storage management method and system, which aims to improve the data management level of the smart power plant, enable it to better utilize data resources in a complex operating environment, support the stable operation and efficient management of the power plant, and provide strong technical guarantee for the operation optimization and decision support of the smart power plant. Summary of the invention

[0005] The invention provides a smart power plant data storage management method and system.

[0006] A method for storing and managing data in an intelligent power plant comprises the following steps:

[0007] S1, data collection and preprocessing: collect real-time power plant data, including operation data, historical data and external environment data, through sensors, equipment and information platforms in smart power plants, and preprocess the collected power plant data, including data cleaning, denoising, format conversion and standardization;

[0008] S2, data classification and storage: the pre-processed power plant data is classified according to the data type, source and purpose, and stored in a distributed database;

[0009] S3, data compression: Differential evolution algorithm is used to compress and optimize various types of power plant data, including:

[0010] S31, initial population generation: for various types of power plant data, generate initial populations of multiple compression strategies, each individual represents a compression scheme, including the selection of compression algorithm, compression ratio, and block size;

[0011] S32, differential mutation and crossover operation: through the mutation operation of the differential evolution algorithm, the differences between different individuals in the population are used to generate new candidate compression schemes (mutation vectors), and the new candidate compression schemes (mutation vectors) are cross-operated with the individuals in the initial population to generate new candidate solutions;

[0012] S33, fitness evaluation and optimal strategy selection: according to the quality of compressed data, compression rate and resource consumption, set the fitness function, evaluate all candidate solutions, and eliminate the solutions with low fitness through multiple iterations, and finally select the compression strategy with the highest fitness;

[0013] S4, data access control and encryption: set access control policies for various types of power plant data, and use encryption technology to encrypt and store power plant data. The encryption and access control policies are dynamically adjusted according to the classification and compression of the data;

[0014] S5, data retrieval and intelligent analysis: provides metadata-based intelligent retrieval functions, supports multi-dimensional complex queries, and performs real-time analysis and visualization of stored power plant data.

[0015] Optionally, the data collection and preprocessing in S1 includes:

[0016] S11, data collection: collect real-time power plant data, including operation data, historical data and external environment data, through sensors, equipment and information platforms in smart power plants;

[0017] The operating data includes the output power of the power generation equipment, equipment temperature, pressure, speed, voltage, current, and power factor;

[0018] The historical data includes accumulated equipment operation records, fault history, maintenance logs, production scheduling records and historical energy consumption data;

[0019] The external environmental data includes external meteorological data (such as temperature, humidity, wind speed, wind direction), power grid load demand data, and environmental monitoring data;

[0020] S12, data cleaning: cleaning the collected power plant data to remove noise data and abnormal values;

[0021] S13, format conversion: convert the cleaned power plant data into a standard format;

[0022] S14, data standardization: standardize the power plant data after format conversion.

[0023] Optionally, the data classification and storage in S2 includes:

[0024] S21, data classification: classify the pre-processed power plant data according to the type, source and purpose of the data;

[0025] The types include operational data, historical data, and external environment data;

[0026] Said sources include power generation equipment, sensors and external information platforms;

[0027] Said uses include real-time monitoring, historical analysis and environmental assessment;

[0028] S22, data storage: storing the classified power plant data in a distributed database.

[0029] Optionally, the initial population generation in S31 includes:

[0030] S311, compression algorithm selection: Select a compression algorithm according to the data type and data characteristics, including a lossless compression algorithm and a lossy compression algorithm, and select each compression algorithm through an empirical formula, which is expressed as:

[0031]

[0032] Among them, C alg Choose a fitness score for the algorithm, w i is the weight coefficient of each compression algorithm, R comp is the compressed data rate, R uncomp is the original data rate before compression;

[0033] S312, compression ratio determination: set an initial compression ratio for different power plant data types. The initial compression ratio is calculated based on data redundancy and information entropy and is expressed as:

[0034]

[0035] Among them, R ratio is the compression ratio, H(X) is the actual information entropy of the data, and H max is the information entropy with maximum probability;

[0036] S313, block size setting: For various types of power plant data, the block size is set according to the data access mode and storage requirements, expressed as:

[0037]

[0038] Among them, B size is the block size, D total is the total size of the data, and N is the frequency coefficient of data access.

[0039] Optionally, the differential mutation and crossover operation in S32 includes:

[0040] S321, differential mutation operation: using the differential evolution algorithm, by randomly selecting three individuals x from the population 1 、x 2 、x 3 Perform differential mutation operation to generate a new candidate compression scheme (mutation vector) v, expressed as:

[0041] v=x 1 +F·(x 2 -x 3 );

[0042] Where v is the generated candidate compression scheme (mutation vector), and F is the scaling factor;

[0043] S322, crossover operation: cross the generated candidate compression scheme (mutation vector) v with the individual x in the initial population 0 Perform a crossover operation to generate a new candidate solution u. The crossover operation is expressed as:

[0044]

[0045] Among them, u i is the i-th component in the candidate solution, C r is the crossover probability and j is the randomly selected component index.

[0046] Optionally, the scaling factor F is set including:

[0047] Difference calculation: Calculate the average difference D among all individuals in the current population var , expressed as:

[0048]

[0049] Among them, n is the number of individuals in the population, m is the number of dimensions of each individual, and x i,j is the jth component of the ith individual, is the average value of the jth component;

[0050] Maximum difference setting: Set the maximum probability value D of population difference max-prob , D max-probis the maximum difference degree when generating the initial population;

[0051] Scaling factor range setting: Set the minimum value F min and the maximum value F max , with the range 0 < F ≤ 1, used to control the amplification degree of the differential vector;

[0052] Adaptive calculation of the scaling factor: According to the current difference degree D var of the population and the set maximum difference degree D max-prob , adaptively calculate the value of the scaling factor F, expressed as:

[0053]

[0054] Optionally, the fitness evaluation and optimal strategy selection in S33 include:

[0055] S331, Fitness function setting: For the compressed data quality, compression ratio, and resource consumption situation, set the fitness function f(x) to evaluate the candidate compression schemes, expressed as:

[0056]

[0057] where Q(x) is the quality score of the compressed data, R ratio is the compression ratio, C(x) is the resource consumption situation, w 1 , w 2 , w 3 are weight coefficients;

[0058] S332, Candidate scheme evaluation and sorting: Use the fitness function to evaluate all current candidate compression schemes, calculate the fitness value of each scheme, sort the schemes according to the fitness value, eliminate the schemes with the lowest 50% fitness values, and retain the top 50% of the schemes for iteration;

[0059] S333, Multiple iterations and optimization: Repeat the processes of candidate scheme generation, fitness calculation, sorting, and selection, and gradually improve the overall fitness of the population through multiple iterations;

[0060] S334, Optimal strategy selection: When the iteration process reaches the predetermined condition or the maximum number of iterations, select the candidate scheme with the highest fitness value as the final optimal compression strategy.

[0061] Optionally, the data access control and encryption in S4 include:

[0062] S41, access control policy setting: according to the type of power plant data (such as operating data, historical data, external environment data) and its sensitivity level, set different access control policies, including user authority classification, data access records and role-based access control (RBAC);

[0063] S42, data encryption storage: using AES encryption algorithm to encrypt and store power plant data;

[0064] S43, Dynamically adjust the strategy: dynamically adjust the access control strategy and encryption strategy according to the data classification, compression ratio and real-time security requirements, including:

[0065] S431, access control adjustment coefficient: according to the sensitivity of real-time data and the current platform load, the access control adjustment coefficient is calculated, which is expressed as:

[0066]

[0067] Among them, S is the sensitivity level of the data, L is the current platform load, and U is the user authority level;

[0068] S432, encryption strength adjustment coefficient: Calculate the encryption strength adjustment coefficient E according to the data compression ratio and the current environment response time. c , expressed as:

[0069]

[0070] Among them, S is the sensitivity level of the data, R ratio is the compression ratio (the ratio of the compressed data size to the original data size), T is the parameter of the environmental response time or real-time processing capability;

[0071] S433, comprehensive adjustment decision: adjust coefficient A according to access control c and encryption strength adjustment factor E c The value of is adjusted comprehensively to calculate the final dynamic adjustment coefficient D f , if D f If the threshold is exceeded, policy adjustments are triggered, including improving encryption strength or tightening access rights, as shown in the following example:

[0072] D f =w 1 ×A c +w 2 ×E c ;

[0073] Among them, w 1 and w 2 is the weight coefficient.

[0074] Optionally, the data retrieval and intelligent analysis in S5 includes:

[0075] S51, intelligent retrieval based on metadata: Provides intelligent retrieval based on metadata to annotate and index stored power plant data. The metadata includes data type, timestamp, source device, data format and data content summary;

[0076] S52, multi-dimensional complex query support: supports users to conduct multi-dimensional complex queries, allowing users to combine query conditions based on multiple metadata attributes, including filtering by time period, device type, and data type;

[0077] S53, real-time analysis and visualization: Real-time analysis of the retrieved power plant data, including data aggregation analysis, trend analysis and prediction, and anomaly detection. Graphical tools are used to visualize the analysis results, including real-time curve graphs, historical trend graphs, and pie charts.

[0078] An intelligent power plant data storage management system, used to implement the above-mentioned intelligent power plant data storage management method, includes the following modules:

[0079] Data acquisition module: collects real-time power plant data, including operation data, historical data and external environment data, through sensors, equipment and information platforms in smart power plants, and pre-processes the collected power plant data, including data cleaning, denoising, format conversion and standardization;

[0080] Data classification and storage module: classifies the pre-processed power plant data according to the data type, source and purpose, and stores it in a distributed database;

[0081] Data compression module: uses differential evolution algorithm to compress and optimize various types of power plant data;

[0082] Data access control and encryption module: Set access control policies for various types of power plant data, use encryption technology to encrypt and store data, and make dynamic adjustments based on the classification and compression of power plant data;

[0083] Data retrieval and intelligent analysis module: provides metadata-based intelligent retrieval functions, supports multi-dimensional complex queries, and performs real-time analysis and visualization of stored power plant data.

[0084] Beneficial effects of the present invention:

[0085] The present invention, by adopting differential evolution algorithm to perform data compression optimization, effectively improves the efficiency of data storage and processing, ensures that data reaches the best balance between storage space, transmission speed and computing resource utilization, and by classifying and storing different types of power plant data, the system not only improves the response speed of data storage, but also ensures that real-time data can be quickly accessed, providing a solid foundation for subsequent data analysis and decision support.

[0086] The present invention adopts dynamic access control and encryption strategies, combines the sensitivity of real-time data, platform load and compression ratio, and intelligently adjusts access control and encryption strength to ensure data security and privacy under different operating conditions. The dynamic adjustment mechanism effectively enhances the adaptability and security of the system through real-time calculation and historical data analysis, ensuring that the smart power plant can still operate efficiently and safely in a complex environment.

[0087] The present invention greatly improves the availability and operational convenience of power plant data through metadata-based intelligent retrieval, multi-dimensional complex query, real-time analysis and visual display. Users can quickly locate and analyze required data, and intuitively understand data change trends and abnormal situations through visualization tools, thereby supporting the operation optimization and accurate decision-making of smart power plants, and further improving the overall operation efficiency and management level of smart power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0089] Figure 1 A schematic diagram of a management method flow chart of an embodiment of the present invention;

[0090] Figure 2 Schematic diagram of system function modules according to an embodiment of the present invention. DETAILED DESCRIPTION

[0091] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0092] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0093] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0094] like Figure 1 As shown, a smart power plant data storage management method includes the following steps:

[0095] S1, data collection and preprocessing: Through the sensors, equipment and information platform in the smart power plant, real-time power plant data, including operation data, historical data and external environment data, are collected, and the collected power plant data is preprocessed, including data cleaning, denoising, format conversion and standardization processing to ensure data consistency and integrity;

[0096] S2, data classification and storage: the pre-processed power plant data is classified according to the data type, source and purpose, and stored in a distributed database;

[0097] S3, data compression: Differential evolution algorithm is used to compress and optimize various types of power plant data, including:

[0098] S31, initial population generation: for various types of power plant data, generate initial populations of multiple compression strategies, each individual represents a compression scheme, including the selection of compression algorithm, compression ratio, and block size;

[0099] S32, differential mutation and crossover operation: through the mutation operation of the differential evolution algorithm, the differences between different individuals in the population are used to generate new candidate compression schemes (mutation vectors), and the new candidate compression schemes (mutation vectors) are cross-operated with the individuals in the initial population to generate new candidate solutions;

[0100] S33, fitness evaluation and optimal strategy selection: according to the quality of compressed data, compression rate and resource consumption, set the fitness function, evaluate all candidate solutions, and eliminate the solutions with low fitness through multiple iterations, and finally select the compression strategy with the highest fitness;

[0101] S4, data access control and encryption: To ensure data security and privacy, access control policies are set for various types of power plant data, and encryption technology is used to encrypt and store power plant data. The encryption and access control policies are dynamically adjusted according to the classification and compression of the data to ensure the security of data during storage and transmission;

[0102] S5, data retrieval and intelligent analysis: Provides metadata-based intelligent retrieval functions, supports multi-dimensional complex queries, and performs real-time analysis and visualization of stored power plant data to support operation optimization and decision support for smart power plants;

[0103] Through the above steps, not only the efficiency of data storage and processing is improved, but also the security and integrity of the data are ensured, providing strong technical guarantee for the efficient operation and decision support of smart power plants.

[0104] Data collection and preprocessing in S1 include:

[0105] S11, data collection: collect real-time power plant data, including operation data, historical data and external environment data, through sensors, equipment and information platforms in smart power plants;

[0106] Operation data include output power of power generation equipment, equipment temperature, pressure, speed, voltage, current, and power factor;

[0107] Historical data includes accumulated equipment operation records, fault history, maintenance logs, production scheduling records, and historical energy consumption data;

[0108] External environmental data include external meteorological data (such as temperature, humidity, wind speed, wind direction), power grid load demand data, and environmental monitoring data;

[0109] S12, data cleaning: cleaning the collected power plant data to remove noise data and outliers to ensure the accuracy and reliability of the data;

[0110] S13, format conversion: convert the cleaned power plant data into a standard format to facilitate subsequent storage and processing;

[0111] S14, data standardization: standardize the power plant data after format conversion to ensure the consistency of the data in terms of dimension, scope, etc.;

[0112] Through the above content, not only the operating data, historical data and external environmental data are effectively integrated, providing a high-quality foundation for subsequent data storage, analysis and decision-making, but also the data processing efficiency and the overall intelligent level of power plant management are improved by eliminating noise and standardizing processing, ensuring that the power plant can obtain reliable and accurate data support under various operating conditions.

[0113] Data classification and storage in S2 include:

[0114] S21, data classification: classify the pre-processed power plant data according to the type, source and purpose of the data;

[0115] Types include operational data, historical data, and external environment data;

[0116] Sources include power generation equipment, sensors, and external information platforms;

[0117] Uses include real-time monitoring, historical analysis, and environmental assessment;

[0118] S22, data storage: storing the classified power plant data in a distributed database;

[0119] Through the above content, not only the efficiency and response speed of data storage are improved, but also the application of distributed databases ensures that real-time data can be quickly accessed.

[0120] The initial population generation in S31 includes:

[0121] S311, compression algorithm selection: Select a compression algorithm according to the data type and data characteristics, including a lossless compression algorithm and a lossy compression algorithm, and select each compression algorithm through an empirical formula, which is expressed as:

[0122]

[0123] Among them, C alg Choose a fitness score for the algorithm, w i is the weight coefficient of each compression algorithm, R comp is the compressed data rate, R uncomp is the original data rate before compression;

[0124] S312, compression ratio determination: set an initial compression ratio for different power plant data types. The initial compression ratio is calculated based on data redundancy and information entropy and is expressed as:

[0125]

[0126] Among them, R ratio is the compression ratio, H(X) is the actual information entropy of the data, and H maxis the information entropy of maximum probability, that is, the theoretical maximum information entropy based on the highest probability distribution in the data;

[0127] S313, block size setting: For various types of power plant data, the block size is set according to the data access mode and storage requirements, expressed as:

[0128]

[0129] Among them, B size is the block size, D total is the total size of the data, N is the frequency coefficient of data access;

[0130] Through the above content, not only the efficiency and effect of data compression are improved, but also a solid foundation is provided for the subsequent differential evolution algorithm, ensuring that the globally optimal compression strategy can be achieved in the processing of various types of power plant data, thereby improving the intelligence level of the entire data storage management system.

[0131] The differential mutation and crossover operations in S32 include:

[0132] S321, differential mutation operation: using the differential evolution algorithm, by randomly selecting three individuals x from the population 1 、x 2 、x 3 Perform differential mutation operation to generate a new candidate compression scheme (mutation vector) v, expressed as:

[0133] v=x 1 +F·(x 2 -x 3 );

[0134] Among them, v is the generated candidate compression scheme (mutation vector), and F is the scaling factor, which is used to control the degree of amplification of the differential vector;

[0135] S322, crossover operation: cross the generated candidate compression scheme (mutation vector) v with the individual x in the initial population 0 Perform a crossover operation to generate a new candidate solution u. The crossover operation is expressed as:

[0136]

[0137] Among them, u i is the i-th component in the candidate solution, C r is the crossover probability, which controls the degree of feature exchange between the mutation vector and the original individual, j is the randomly selected component index, ensuring that at least one component comes from the mutation vector;

[0138] Through the above content, the differences of different individuals in the population are effectively utilized, enhancing the global search ability of the algorithm. While maintaining the population diversity, the balance between the search breadth and precision is ensured, further improving the optimization effect of candidate solutions, providing a more intelligent and optimized solution for the data compression strategy of intelligent power plants, and contributing to more efficient data management.

[0139] The setting of the scaling factor F includes:

[0140] Calculation of the difference degree: Calculate the average difference degree D among all individuals in the current population var , expressed as:

[0141]

[0142] where n is the number of individuals in the population, m is the dimension number of each individual, x i,j is the j-th component of the i-th individual, is the average value of the j-th component;

[0143] Setting of the maximum difference degree: Set the maximum probability value D of the population difference degree max-prob , D max-prob is the maximum difference degree when the initial population is generated, serving as a reference benchmark for the scaling factor setting;

[0144] Setting of the scaling factor range: Set the minimum value F min and the maximum value F max of the scaling factor, with the range 0 < F ≤ 1, used to control the amplification degree of the differential vector;

[0145] Adaptive calculation of the scaling factor: According to the current difference degree D var of the population and the set maximum difference degree D max-prob , adaptively calculate the value of the scaling factor F, expressed as:

[0146]

[0147] Through the above content, the balance between the global search ability and local refinement is optimized, significantly improving the optimization effect of the data compression strategy.

[0148] The fitness evaluation and optimal strategy selection in S33 include:

[0149] S331, Setting of the fitness function: For the data quality, compression ratio, and resource consumption of the compressed data, set the fitness function f(x) to evaluate the candidate compression schemes, expressed as:

[0150]

[0151] Among them, Q(x) is the quality score of the compressed data. The higher the score, the better the integrity of the data recovery. ratio is the compression ratio, which is defined as the ratio of the compressed data size to the original data size. The smaller the better. C(x) is the resource consumption, which indicates the computing resource consumption during the compression and decompression process. The lower the better. w 1 ,w 2 ,w 3 is the weight coefficient, which is used to balance the importance of different evaluation indicators;

[0152] S332, candidate scheme evaluation and sorting: use the fitness function to evaluate all current candidate compression schemes, calculate the fitness value of each scheme, sort the schemes according to the fitness value, eliminate the bottom 50% of the schemes in the fitness value ranking, and retain the top 50% of the schemes for iteration;

[0153] S333, multiple iterations and optimization: Repeat the generation, fitness calculation, sorting and selection process of candidate solutions, and gradually improve the overall fitness of the population through multiple iterations;

[0154] S334, optimal strategy selection: when the iteration process reaches a predetermined condition or a maximum number of iterations, the candidate solution with the highest fitness value is selected as the final optimal compression strategy;

[0155] Through the above content, the transparency and efficiency of the optimization process are ensured, so that each round of iteration can continuously improve the compression strategy. Finally, the solution with the highest fitness is selected as the optimal strategy, which not only improves the data compression effect, but also optimizes the data management of the smart power plant, ensuring that the system achieves the best balance between data quality and resource utilization.

[0156] Data access control and encryption in S4 include:

[0157] S41, access control policy setting: according to the type of power plant data (such as operating data, historical data, external environment data) and its sensitivity level, set different access control policies, including user authority classification, data access records and role-based access control (RBAC), to ensure that only authorized users can access the corresponding data;

[0158] S42, data encryption storage: using AES encryption algorithm to encrypt and store power plant data;

[0159] S43, Dynamically adjust the strategy: dynamically adjust the access control strategy and encryption strategy according to the data classification, compression ratio and real-time security requirements, including:

[0160] S431, access control adjustment coefficient: according to the sensitivity of real-time data and the current platform load, the access control adjustment coefficient is calculated, which is expressed as:

[0161]

[0162] Among them, S is the sensitivity level of the data, the higher the value, the more sensitive the data; L is the current platform load, the higher the value, the heavier the platform load; I is the user authority level, the higher the value, the higher the user authority;

[0163] S432, encryption strength adjustment coefficient: Calculate the encryption strength adjustment coefficient E according to the data compression ratio and the current environment response time. c , expressed as:

[0164]

[0165] Among them, S is the sensitivity level of the data, R ratio is the compression ratio (the ratio of the compressed data size to the original data size), T is the parameter of the environmental response time or real-time processing capability;

[0166] S433, comprehensive adjustment decision: adjust coefficient A according to access control c and encryption strength adjustment factor E c The value of is adjusted comprehensively to calculate the final dynamic adjustment coefficient D f , if D f If the threshold is exceeded, policy adjustments are triggered, including improving encryption strength or tightening access rights, as shown in the following example:

[0167] D f =w 1 ×A c +w 2 ×E c ;

[0168] Among them, w 1 and w 2 is the weight coefficient;

[0169] The predetermined threshold is determined based on statistical analysis of historical data, current platform load, data sensitivity, and compression ratio, including:

[0170] Historical data analysis: Collect and analyze historical platform operation data, record situations that trigger policy adjustments under different platform loads and data types, and use statistical methods, including standard deviation analysis or distribution-based analysis, to determine the platform's response under different load and data sensitivity conditions;

[0171] Weighted average of load and sensitivity: Based on historical data, the weighted average of load L and data sensitivity level S of different platforms is calculated as the basis for the predetermined threshold, expressed as:

[0172]

[0173] Among them, T base is the basic threshold, L i is the load level recorded at the i-th time, S i is the data sensitivity level recorded for the i-th time, T i is the adjustment trigger point of the i-th record, and n is the number of records;

[0174] Dynamic adjustment factor: combined with the current compression ratio R ratio and environmental response time T, calculate the dynamic adjustment factor F d , so that the threshold is automatically adjusted according to the real-time situation, expressed as:

[0175]

[0176] Among them, T max The maximum response time recorded in history;

[0177] Comprehensive threshold calculation: The final predetermined threshold θ is calculated by comprehensive calculation of the basic threshold and the dynamic adjustment factor, expressed as:

[0178] θ=T base ×F d ;

[0179] Through the above contents, the security of various types of data in the smart power plant during storage and transmission is ensured, the intelligence level of data management is enhanced, and the power plant can maintain efficient and safe data processing and management under complex operating conditions.

[0180] Data retrieval and intelligent analysis in S5 include:

[0181] S51, intelligent retrieval based on metadata: Provides intelligent retrieval function based on metadata to annotate and index the stored power plant data. The metadata includes data type, timestamp, source device, data format and data content summary. Through the analysis and matching of metadata, it supports rapid location of relevant data and provides intelligent recommended query paths.

[0182] S52, Multi-dimensional Complex Query Support: Supports users to conduct multi-dimensional complex queries, allowing users to combine query conditions based on multiple metadata attributes, including filtering by time period, device type, and data type, ensuring efficient query response on large-scale data sets;

[0183] S53, real-time analysis and visualization: real-time analysis of the retrieved power plant data, including data aggregation analysis, trend analysis and prediction, and anomaly detection, and visualization of the analysis results using graphical tools, including real-time curve graphs, historical trend graphs, and pie charts, to support operation optimization and decision support of smart power plants;

[0184] Through the above content, the data management and decision support capabilities of smart power plants are improved, enabling users to use data more efficiently in complex environments and make wise operational decisions.

[0185] like Figure 2 As shown, a smart power plant data storage management system is used to implement the above-mentioned smart power plant data storage management method, including the following modules:

[0186] Data acquisition module: collects real-time power plant data, including operation data, historical data and external environment data, through sensors, equipment and information platforms in smart power plants, and pre-processes the collected power plant data, including data cleaning, denoising, format conversion and standardization;

[0187] Data classification and storage module: classifies the pre-processed power plant data according to the data type, source and purpose, and stores it in a distributed database;

[0188] Data compression module: uses differential evolution algorithm to compress and optimize various types of power plant data;

[0189] Data access control and encryption module: Set access control policies for various types of power plant data, use encryption technology to encrypt and store data, and make dynamic adjustments based on the classification and compression of power plant data;

[0190] Data retrieval and intelligent analysis module: provides metadata-based intelligent retrieval functions, supports multi-dimensional complex queries, and performs real-time analysis and visualization of stored power plant data.

[0191] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0192] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for data storage and management of smart power plants, characterized in that: The following steps are involved: S1, data collection and preprocessing: collect real-time power plant data, including operation data, historical data and external environment data, through sensors, equipment and information platforms in smart power plants, and preprocess the collected power plant data, including data cleaning, denoising, format conversion and standardization; S2, data classification and storage: the pre-processed power plant data is classified according to the data type, source and purpose, and stored in a distributed database; S3, data compression: Differential evolution algorithm is used to compress and optimize various types of power plant data, including: S31, initial population generation: for various types of power plant data, generate initial populations of multiple compression strategies, each individual represents a compression scheme, including the selection of compression algorithm, compression ratio, and block size; S32, differential mutation and crossover operation: through the mutation operation of the differential evolution algorithm, the differences between different individuals in the population are used to generate new candidate compression schemes, and the new candidate compression schemes are cross-operated with the individuals in the initial population to generate new candidate solutions; S33, fitness evaluation and optimal strategy selection: according to the quality of compressed data, compression rate and resource consumption, set the fitness function, evaluate all candidate solutions, and eliminate the solutions with low fitness through multiple iterations, and finally select the compression strategy with the highest fitness; S4, data access control and encryption: set access control policies for various types of power plant data, and use encryption technology to encrypt and store power plant data. The encryption and access control policies are dynamically adjusted according to the classification and compression of the data; S5, data retrieval and intelligent analysis: provides metadata-based intelligent retrieval functions, supports multi-dimensional complex queries, and performs real-time analysis and visualization of stored power plant data; The initial population generation in S31 includes: S311, compression algorithm selection: Select a compression algorithm according to the data type and data characteristics, including a lossless compression algorithm and a lossy compression algorithm, and select each compression algorithm through an empirical formula, which is expressed as: Among them, C alg Choose a fitness score for the algorithm, w i is the weight coefficient of each compression algorithm, R comp is the compressed data rate, R uncomp is the original data rate before compression; S312, compression ratio determination: set an initial compression ratio for different power plant data types. The initial compression ratio is calculated based on data redundancy and information entropy and is expressed as: Among them, R ratio is the compression ratio, H(X) is the actual information entropy of the data, and H max is the information entropy with maximum probability; S313, block size setting: For various types of power plant data, the block size is set according to the data access mode and storage requirements, expressed as: Among them, B size is the block size, D total is the total size of the data, N is the frequency coefficient of data access; The differential mutation and crossover operation in S32 includes: S321, differential mutation operation: using the differential evolution algorithm, by performing differential mutation operations on three individuals x1, x2, and x3 randomly selected from the population, a new candidate compression scheme v is generated, which is expressed as: v = x1 + F·(x2-x3); Where v is the generated candidate compression scheme, and F is the scaling factor; S322, crossover operation: perform a crossover operation on the generated candidate compression solution v and the individual x0 in the initial population to generate a new candidate solution u. The crossover operation is expressed as: Among them, u i is the i-th component in the candidate solution, C r is the crossover probability, j is the randomly selected component index; The scaling factor F setting includes: Difference calculation: Calculate the average difference D among all individuals in the current population var , expressed as: Among them, n is the number of individuals in the population, m is the number of dimensions of each individual, and x i,j is the jth component of the ith individual, is the average value of the jth component; Maximum difference setting: Set the maximum probability value D of population difference max-prob , D max-prob The maximum difference when the initial population is generated; Scaling factor range setting: Set the minimum value F of the scaling factor min and the maximum value F max , with the range 0 < F ≤ 1, used to control the amplification degree of the differential vector; The scaling factor is adaptively calculated based on the current difference D of the population. var The maximum difference D max-prob , adaptively calculate the value of the scaling factor F, expressed as: The fitness evaluation and optimal strategy selection in S33 include: S331, fitness function setting: according to the quality of compressed data, compression rate and resource consumption, set the fitness function f(x) to evaluate the candidate compression schemes, which is expressed as: Among them, Q(x) is the quality score of the compressed data, R ratio is the compression ratio, C(x) is the resource consumption, w1, w2, w3 are weight coefficients; S332, candidate scheme evaluation and sorting: use the fitness function to evaluate all current candidate compression schemes, calculate the fitness value of each scheme, sort the schemes according to the fitness value, eliminate the bottom 50% of the schemes in the fitness value ranking, and retain the top 50% of the schemes for iteration; S333, multiple iterations and optimization: Repeat the generation, fitness calculation, sorting and selection process of candidate solutions, and gradually improve the overall fitness of the population through multiple iterations; S334, optimal strategy selection: when the iteration process reaches a predetermined condition or a maximum number of iterations, the candidate solution with the highest fitness value is selected as the final optimal compression strategy.

2. The method for storing and managing data in a smart power plant according to claim 1, characterized in that: The data collection and preprocessing in S1 include: S11, data collection: collect real-time power plant data, including operation data, historical data and external environment data, through sensors, equipment and information platforms in smart power plants; The operating data includes the output power of the power generation equipment, equipment temperature, pressure, speed, voltage, current, and power factor; The historical data includes accumulated equipment operation records, fault history, maintenance logs, production scheduling records and historical energy consumption data; The external environmental data includes external meteorological data, power grid load demand data, and environmental monitoring data; S12, data cleaning: cleaning the collected power plant data to remove noise data and abnormal values; S13, format conversion: convert the cleaned power plant data into a standard format; S14, data standardization: standardize the power plant data after format conversion.

3. The method for storing and managing data in a smart power plant according to claim 1, characterized in that: The data classification and storage in S2 include: S21, data classification: classify the pre-processed power plant data according to the type, source and purpose of the data; The types include operational data, historical data, and external environment data; Said sources include power generation equipment, sensors and external information platforms; Said uses include real-time monitoring, historical analysis and environmental assessment; S22, data storage: storing the classified power plant data in a distributed database.

4. The method for storing and managing data in a smart power plant according to claim 1, characterized in that: The data access control and encryption in S4 include: S41, access control policy setting: according to the type of power plant data and its sensitivity level, set different access control policies, including user authority classification, data access records and role-based access control; S42, data encryption storage: using AES encryption algorithm to encrypt and store power plant data; S43, Dynamically adjust the strategy: dynamically adjust the access control strategy and encryption strategy according to the data classification, compression ratio and real-time security requirements, including: S431, access control adjustment coefficient: according to the sensitivity of real-time data and the current platform load, the access control adjustment coefficient is calculated, which is expressed as: Among them, S is the sensitivity level of the data, L is the current platform load, and U is the user authority level; S432, encryption strength adjustment coefficient: Calculate the encryption strength adjustment coefficient E according to the data compression ratio and the current environment response time. c , expressed as: Among them, S is the sensitivity level of the data, R ratio is the compression ratio, T is the parameter of environmental response time or real-time processing capability; S433, comprehensive adjustment decision: adjust coefficient A according to access control c and encryption strength adjustment factor E c The value of is adjusted comprehensively to calculate the final dynamic adjustment coefficient D f , if D f If the threshold is exceeded, policy adjustments are triggered, including improving encryption strength or tightening access rights, as shown in the following example: D f =w1×A c +w2×E c ; Among them, w1 and w2 are weight coefficients.

5. The method for storing and managing data in a smart power plant according to claim 1, characterized in that: The data retrieval and intelligent analysis in S5 include: S51, intelligent retrieval based on metadata: Provides intelligent retrieval function based on metadata to annotate and index the stored power plant data. The metadata includes data type, timestamp, source device, data format and data content summary; S52, multi-dimensional complex query support: supports users to conduct multi-dimensional complex queries, allowing users to combine query conditions based on multiple metadata attributes, including filtering by time period, device type, and data type; S53, real-time analysis and visualization: Real-time analysis of the retrieved power plant data, including data aggregation analysis, trend analysis and prediction, and anomaly detection. Graphical tools are used to visualize the analysis results, including real-time curve graphs, historical trend graphs, and pie charts.

6. A smart power plant data storage management system, used to implement a smart power plant data storage management method as claimed in any one of claims 1 to 5, characterized in that: Includes the following modules: Data acquisition module: collects real-time power plant data, including operation data, historical data and external environment data, through sensors, equipment and information platforms in smart power plants, and pre-processes the collected power plant data, including data cleaning, denoising, format conversion and standardization; Data classification and storage module: classifies the pre-processed power plant data according to the data type, source and purpose, and stores it in a distributed database; Data compression module: uses differential evolution algorithm to compress and optimize various types of power plant data; Data access control and encryption module: Set access control policies for various types of power plant data, use encryption technology to encrypt and store data, and make dynamic adjustments based on the classification and compression of power plant data; Data retrieval and intelligent analysis module: provides metadata-based intelligent retrieval functions, supports multi-dimensional complex queries, and performs real-time analysis and visualization of stored power plant data.

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