Multi-source power equipment power generation data storage method and device

Abstract

The invention provides a multi-source power equipment power generation data storage method and device, and relates to the technical field of data storage, and the method comprises the steps: obtaining power generation data of different power equipment, and sequentially carrying out the preprocessing and format conversion of the power generation data, and obtaining target power generation data; identifying the target power generation data, and determining an equipment identifier and equipment feature information in the power generation data; generating a power generation table associated with each device identifier based on the device identifier and the device feature information, and encrypting the power generation table to obtain an encrypted table; the encrypted table is subjected to region division and stored in the corresponding storage regions, and the technical problems that in the prior art, an electric power data storage mode occupies a large space and is not beneficial to electric power data query are solved.

Description

Technical Field

[0001] The present application relates to the technical field of data storage, and in particular to a method and device for storing power generation data of multi-source power equipment. Background Art

[0002] Power generation is the use of power generation devices to convert hydropower, thermal energy from fossil fuels (coal, oil, natural gas, etc.), nuclear energy, as well as solar energy, wind energy, geothermal energy, ocean energy, etc. into electrical energy; with the rapid development of electricity, the amount of data generated during the operation of the power grid has increased exponentially. The reasonable storage of power generation data can provide data support and subsequent data tracking for the development of electricity; the current power data storage method has high requirements on spatial resources and is not conducive to power data query. Summary of the invention

[0003] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, the first purpose of this application is to propose a multi-source power equipment power generation data storage method to achieve efficient data storage and convenient data query to provide data support for data analysis.

[0005] The second object of the present application is to provide a data storage device for power generation of electric equipment with multiple sources.

[0006] The third objective of the present application is to provide an electronic device.

[0007] A fourth objective of the present application is to provide a computer-readable storage medium.

[0008] A fifth object of the present application is to provide a computer program product.

[0009] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for storing power generation data of a multi-source power equipment, comprising:

[0010] Acquire power generation data of different power equipment, and perform preprocessing and format conversion on the power generation data in sequence to obtain target power generation data;

[0011] Identifying the target power generation data, and determining the device identification and device characteristic information in the power generation data;

[0012] Based on the device identification and device characteristic information, a power generation table associated with each device identification is generated, and the power generation table is encrypted to obtain an encrypted table;

[0013] The encrypted table is divided into regions and stored in corresponding storage areas.

[0014] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a multi-source power equipment power generation data storage device, comprising:

[0015] A data acquisition module is used to acquire power generation data of different power equipment, and pre-process and format-convert the power generation data in sequence to obtain target power generation data;

[0016] A feature recognition module, used to recognize the target power generation data and determine the device identification and device feature information in the power generation data;

[0017] A table acquisition module, used to generate a power generation table associated with each device identification based on the device identification and device characteristic information, and encrypt the power generation table to obtain an encrypted table;

[0018] The data storage module is used to divide the encrypted table into regions and store them in corresponding storage regions.

[0019] To achieve the above-mentioned purpose, the third aspect of the present application provides an electronic device, including:

[0020] A processor, and a memory communicatively connected to the processor;

[0021] The memory stores computer-executable instructions;

[0022] The processor executes the computer-executable instructions stored in the memory to implement the method as described in the embodiment of the first aspect.

[0023] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer execution instructions, and when the computer execution instructions are executed by a processor, the method described in the first aspect embodiment is implemented.

[0024] To achieve the above-mentioned purpose, the fifth aspect of the present application proposes a computer program product, including a computer program, which implements the method described in the first aspect when executed by a processor.

[0025] The method and device for storing power generation data of multi-source electric power equipment provided in the present application acquire power generation data of multi-source electric power equipment, and pre-process and convert the power generation data to improve the accuracy and precision of the power generation data, so as to facilitate data transmission and storage; extract features of target power generation data to obtain device identification and device feature information, reduce interference from irrelevant data, form a power generation table with the device identification and device feature information, encrypt the power generation table to obtain an encrypted table, improve data security, partition and store data in different areas of the encrypted table to prevent storage confusion and improve the regularity of power generation data storage.

[0026] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A schematic flow chart of a method for storing power generation data of multi-source power equipment provided in an embodiment of the present application;

[0029] Figure 2 A schematic flow chart of another method for storing power generation data of multi-source power equipment provided in an embodiment of the present application;

[0030] Figure 3 A schematic structural diagram of a multi-source power equipment power generation data storage device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0032] The following describes a method and device for storing power generation data of multi-source electric power equipment according to an embodiment of the present application with reference to the accompanying drawings.

[0033] Figure 1 A schematic diagram of a flow chart of a method for storing power generation data of a multi-source power equipment provided in an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0034] S101, acquiring power generation data of different power equipment, and preprocessing and format conversion of the power generation data in sequence to obtain target power generation data.

[0035] In some implementations, the power equipment may include different types of power generation equipment such as wind power generation, thermal power generation, hydropower generation, and solar power generation, and power generation data of different power generation equipment are collected.

[0036] Optionally, the preprocessing method may include processing methods such as filtering and denoising to reduce errors and invalid data in the power generation data, improve data accuracy, and avoid resource occupation by erroneous data.

[0037] Optionally, the power generation data can be converted into a lightweight data exchange format JavaScript Object Notation (JSON) to make the power generation data easy to read and write, occupy a small bandwidth, and be suitable for transmission in the network; after the power generation data is preprocessed and format converted, the target power generation data in JSON format is obtained.

[0038] S102, identifying the target power generation data, and determining the equipment identification and equipment characteristic information in the power generation data.

[0039] In some implementations, the device identification and device characteristic information in the target power generation data can be identified based on keyword recognition or feature extraction algorithms, where the device identification can be a device identity document (ID), which uniquely identifies the corresponding power equipment with the device ID; the device characteristic information is the device-related information in the target power generation data, such as the equipment type (wind power, thermal power, hydropower or solar power), equipment installation location, corresponding collection time, power generation and environmental data, such as temperature or humidity.

[0040] Optionally, a term frequency-inverse document frequency (TF-IDF) algorithm may be used to extract and identify features, thereby determining the device identification and device feature information in the target power generation data.

[0041] S103, based on the device identification and the device characteristic information, generating a power generation table associated with each device identification, and encrypting the power generation table to obtain an encrypted table.

[0042] Optionally, a power generation table can be constructed based on the device identification and device characteristic information of different data types, for example, using the device identification as the column and different device characteristic information as the rows to form a power generation table. The number of columns of the power generation table is the same as the number of different device identifications, and the number of rows of the power generation table is the same as the number of different data types in the device characteristic information. For example, the first row is the device type, the second row is the device installation location, the first column is the device identification 1, and the second column is the device identification 2. The data in the first row and the first column is the device type corresponding to the device identification 1, and the data in the first row and the first column is the device installation location corresponding to the device identification 1.

[0043] Furthermore, the power generation table can be encrypted by using a symmetric encryption algorithm or an asymmetric encryption algorithm to ensure the security of data transmission and viewing.

[0044] S104, dividing the encrypted table into regions and storing them in corresponding storage areas.

[0045] Optionally, regional divisions can be made according to equipment types. For example, hydroelectric power equipment, thermal power equipment, wind power equipment and solar power equipment can be divided into different areas. The data information belonging to the equipment type in the encrypted table is stored in the corresponding storage area, that is, the hydroelectric power equipment data is stored in the hydroelectric power equipment area, etc. Regional storage makes data storage clearer and improves the efficiency of subsequent search and reference.

[0046] In this embodiment, power generation data of power equipment from multiple sources is acquired, and the power generation data is preprocessed and formatted, so as to improve the accuracy and precision of the power generation data and facilitate data transmission and storage; feature extraction is performed on the target power generation data to obtain device identification and device feature information, reduce interference from irrelevant data, improve the simplicity of the power generation data and reduce the storage space required for the power generation data; the device identification and device feature information are combined into a power generation table to improve the viewing effect of the power generation data, the power generation table is encrypted to obtain an encrypted table to improve data security, and the data in different areas of the encrypted table are partitioned and stored to prevent storage confusion, improve the regularity and neatness of power generation data storage, and facilitate search.

[0047] Figure 2 A flowchart of another method for storing power generation data of multiple sources of electric power equipment provided in an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0048] S201, acquiring power generation data of different power equipment, and preprocessing and format conversion of the power generation data in sequence to obtain target power generation data.

[0049] Optionally, power generation data can be collected through a star topology, with each power device as a child node. The child nodes are searched regularly through an object-oriented heuristic search algorithm, and the power generation data in the child nodes are acquired to obtain the power generation data of different power devices, thereby facilitating rapid query of the power generation data and improving the efficiency of acquiring the power generation data.

[0050] In some implementations, the power generation data may be filtered in sequence to obtain first processed data; and the first processed data may be subjected to outlier elimination and mean padding to obtain second processed data.

[0051] Optionally, the power generation data can be subjected to weighted moving average filtering, which can be expressed as:

[0052]

[0053] Among them, w1, w2 and w nare the weight values ​​of each power generation data; x(t), x(t-1), …, x(t-n+1) are the power generation data at time t, and the power generation data before time t, where t is the time point at which the power generation data needs to be calculated; X is the power generation data output after filtering, that is, the first processed data.

[0054] Furthermore, after filtering the power generation data to obtain the first processed data, the filtered power generation data is cleaned, including abnormal value detection: Where μ is the mean, σ is the standard deviation, x is the input power generation data, and z is the value of the calculated standard deviation. Values ​​of |z|>3 are considered outliers.

[0055] After the outliers are detected, they are removed, and the removed outliers and missing values ​​in the power generation data are filled with the mean to obtain the second processed data, which can maintain the accuracy and integrity of the power generation data; the mean is expressed as:

[0056]

[0057] Wherein, n is the total number of power generation data.

[0058] Furthermore, the data format of the second processed data and the target format to be converted are obtained; the data format of the second processed data is converted into the target format to obtain the target power generation data; in this embodiment, the target format is the JSON format, that is, the data format of the second processed data is converted into a JSON format text to obtain the target power generation data.

[0059] In the embodiment of the present application, the implementation method of step S201 can be implemented by any of the methods in the embodiments of the present disclosure, which is not limited here and will not be repeated.

[0060] S202, obtaining feature values ​​of different phrases in the target power generation data.

[0061] Optionally, this embodiment uses the TF-IDF algorithm to extract features, and the feature value can be expressed as:

[0062]

[0063] Among them, TF(x i ,d) reflects the importance of a word in the target power generation data text. The more frequently it appears, the higher the TF value; x i The word representing the calculation; d represents the word x i The number of times it appears in the target power generation data text; It is used to measure the prevalence of a word in the entire corpus. The higher the value, the less common the word is in the corpus, and thus the stronger the ability to distinguish the target power generation data text. N represents the total number of texts in the corpus. DF(x i ) means it contains the phrase x i The number of texts.

[0064] It is understandable that if a word appears frequently in the target power generation data to be detected and calculated, but appears less frequently in other texts, TF IDF(x i ,d) will be very high, indicating that the word is more important to the target power generation data text that needs to be detected and calculated; on the contrary, if the word frequency is low or the word appears in many documents, then TF IDF (x i ,The value of d) will be low, indicating that the word is not important to the JSON format text that needs to be detected and calculated, so the importance of the phrase is represented according to the feature value to achieve feature detection in the target power generation data.

[0065] S203, filtering the phrases in the target power generation data according to the characteristic values ​​to obtain the device identification and device characteristic information in the target power generation data.

[0066] Optionally, phrases with characteristic values ​​greater than or equal to a preset threshold can be obtained for extraction, thereby obtaining characteristic phrases in the target power generation data, identifying and classifying the characteristic phrases, and obtaining device identification and device characteristic information in the target power generation data; wherein the device characteristic information includes equipment type, installation location, status, timestamp, power generation, efficiency, and one or more characteristic categories in environmental data.

[0067] S204, based on the device identification and the device characteristic information, generating a power generation table associated with each device identification, and encrypting the power generation table to obtain an encrypted table.

[0068] In some implementations, the extracted device identification and device characteristic information can be combined into a power generation data set, and the power generation data set can be transmitted and processed to improve the efficiency of power generation data transmission and storage, and a power generation table associated with the corresponding device identification can be generated according to the power generation data set.

[0069] Optionally, a preset table can be obtained, each row of which is a different feature category; the feature data in the equipment feature information is filled in the preset table according to the feature category to obtain a power generation table, in which each column of data is the same power generation equipment.

[0070] In some implementations, the power generation data set can be split and each feature data can be filled into a preset table. The data set can be generated by Python and saved as a comma-separated values ​​(CSV) file to generate a power generation table. The CSV file is a common data exchange format that is easy to analyze and manage. For example, the form of the power generation table can be as follows:

[0071] Table 1

[0072] Equipment identification Device ID1 Device ID2 …… Device IDn Device Type *** *** …… *** Equipment installation location *** *** …… *** …… …… …… …… …… Environmental data *** *** …… ***

[0073] In some implementations, in the process of analyzing power generation data to generate power generation tables, in response to the power generation data being the data of one power device, the power generation data is stream processed; in response to the power generation data being the data of at least two power devices, the power generation data is batch processed; that is, when the power generation data is small, real-time stream processing is performed to reduce latency, and when the power generation data is large, data is collected and batch processed to avoid data loss during transmission or processing due to excessive data.

[0074] Furthermore, the hash value of each device identification can be obtained; a corresponding key-value pair is generated based on the hash value, and each column of data in the power generation data is associated with the device identification of the corresponding power generation device according to the key-value pair to obtain a power generation table associated with each device identification.

[0075] The process of determining the hash value of the device ID in this embodiment may be: converting the device ID into a binary format and filling the device ID; determining a fixed initial hash value; dividing the filled device ID into blocks of a fixed size, applying a compression function to each data block, combining the current initial hash value and the data of the data block, and generating a new hash value; after processing all database blocks, obtaining a final hash value, which can be expressed as:

[0076] H=f(H prev ,M)

[0077] Where H is the current hash value, H prev is the initial hash value, and M is the data block currently being processed.

[0078] The hash value based on the device ID is used as a key to connect to the power generation table, that is, the hash value is used as a key-value pair between the device ID and the power generation table, so that when querying the power generation table, it can be directly indexed by the device ID, and when querying the device ID, the power generation table is cached to improve the efficiency of index query.

[0079] Furthermore, the power generation table is encrypted. In this embodiment, the power generation table can be encrypted by the Secure Sockets Layer (SSL) encryption protocol, and the power generation table is encrypted by a symmetric encryption algorithm. The encryption process includes selecting a key: selecting three keys K1, K2 and K3; initial permutation: performing an initial permutation IP on the plain text P; loop processing: performing 16 rounds of processing, each round including splitting the data into a left half L and a right half R; using the F function to calculate the new right half and perform an XOR operation with the left half; exchanging the left and right parts, which is only performed at the end of each round; quasi-permutation: after all rounds are completed, performing an inverse initial permutation IP -1 .

[0080] For example, assuming that P is plaintext, C is ciphertext, E and D are encryption and decryption functions respectively, the encryption process can be expressed as:

[0081] C=E K3 (D K2 (E K1 (P)))

[0082] Correspondingly, the decryption process is the inverse process of the encryption process, and the decryption process is:

[0083] P=D K1 (E K2 (D K3 (C)))

[0084] By encrypting the power generation table to obtain an encrypted table, the security of power generation data storage is improved to avoid data loss caused by attacks. Optionally, when the power generation data is transmitted, it can be an encrypted table that has been encrypted, or the power generation data is encrypted when querying the data. When the account querying the data passes the security verification, the encrypted table is decrypted to obtain the data to ensure data security.

[0085] S205, obtaining the power equipment type corresponding to each equipment identification in the encrypted table.

[0086] The type of electric power equipment includes at least one of solar power generation, wind power generation, thermal power generation and hydropower generation; optionally, a query may be performed based on the device ID to determine the type of electric power equipment corresponding to the device ID.

[0087] S206: Determine a corresponding target storage area based on the type of electric power equipment identified by the equipment identifier.

[0088] Optionally, the storage area may include solar power generation, wind power generation, thermal power generation and hydropower generation; based on the type of power equipment identified by the equipment, the corresponding storage area is determined, for example, the target storage area corresponding to the hydropower equipment is the hydropower storage area.

[0089] S207, storing the encrypted table corresponding to the device identification into the corresponding target storage area.

[0090] The encrypted table corresponding to the device identification is stored in the corresponding target storage area. Different storage areas can be indexed according to the partition field (solar power generation, wind power generation, thermal power generation and hydropower generation) to distinguish the storage areas. That is, when querying the data, the corresponding target storage area can be determined by the regional index. By indexing the corresponding encrypted table by the device ID, it can effectively prevent the storage disorder of the power generation table and facilitate the management of the power generation table. By indexing the partition field and the device ID, the indexing efficiency of the power generation table can be improved.

[0091] In this embodiment, power generation data is collected through a star topology relationship, and the accuracy of power generation data is improved through filtering and outlier identification. Data filling is performed based on the mean, and the integrity of the power generation data is ensured at the same time. The power generation data is converted into a JSON format text for easy editing and transmission. Feature identification is performed on the target power generation data, and a power generation data set is composed based on the device identification and device feature information for transmission and analysis, thereby improving transmission efficiency and reducing interference and resource occupation of non-critical information. A corresponding power generation table is generated based on the power generation data set and encrypted, and the power generation table is indexed by the device ID to obtain the device feature information corresponding to the device ID to ensure data security. The storage area is partitioned and field indexed, and the power generation data of the corresponding device type is stored in the corresponding area. The storage area can be indexed and viewed according to the partition field, and the device feature information in the area can be indexed and viewed by the device ID, thereby improving the efficiency of viewing the power generation data in the storage area, effectively preventing storage confusion of the power generation table, and achieving better storage effect.

[0092] On the basis of the above embodiments, the present application also proposes a power generation data storage system for the above-mentioned multi-source power equipment, including: a power generation data acquisition unit, an edge cloud preprocessing unit, a power generation data structure optimization unit, a power generation data feature extraction unit, an Internet of Things transmission unit, a power generation data storage optimization unit, a power generation data table generation unit, a key-value pair generation unit, a power generation data encryption unit and a power generation data partition storage unit;

[0093] The power generation data collection unit is used to collect power generation data of power equipment from different sources, and is used to collect power generation data of different types of power generation equipment such as solar power generation, wind power generation, thermal power generation and hydropower generation;

[0094] The edge cloud preprocessing unit is used to process the multi-source power generation data collected by the power generation data collection unit, including filtering the power generation data and cleaning abnormal values;

[0095] The power generation data structure optimization unit is used to convert the format of the processed multi-source power generation data and convert all the power generation data into JSON format text;

[0096] The IoT transmission unit is used to transmit the power generation data processed by the edge cloud pre-processing unit;

[0097] The power generation data feature extraction unit is used to extract features from the power generation data converted into JSON format text, obtain the device ID, device type, installation location, status, timestamp, power generation, efficiency and environmental data in the power generation data, and convert the extracted features into a power generation data set;

[0098] The power generation data storage optimization unit is used to select the storage method for the power generation data set. The power generation data storage optimization unit combines relational and non-relational databases (Not Only Structured Query Language, NoSQL). The relational database is used for structured data storage, and the NoSQL database is used for non-structured and semi-structured data storage.

[0099] The power generation data table generation unit is used to generate a power generation table in a tabular form from the transmitted power generation data set, and select batch processing or stream processing for data processing and transmission according to actual data conditions;

[0100] The key-value pair generating unit is used to extract the device ID in the power generation data set, generate a key-value pair from the device ID through a hash function, and connect the device ID with the power generation table through the key-value pair;

[0101] The power generation data encryption unit is used to encrypt the stored power generation table;

[0102] The power generation data partition storage unit is used to store power generation data from different sources in different regions.

[0103] In order to implement the above-mentioned embodiment, the present application also proposes a multi-source power equipment power generation data storage device.

[0104] Figure 3 A schematic structural diagram of a multi-source power equipment power generation data storage device provided in an embodiment of the present application.

[0105] like Figure 3 As shown, the multi-source power equipment power generation data storage device 300 includes:

[0106] The data acquisition module 301 is used to acquire power generation data of different power equipment, and perform preprocessing and format conversion on the power generation data in sequence to obtain target power generation data;

[0107] The feature recognition module 302 is used to recognize the target power generation data and determine the device identification and device feature information in the power generation data;

[0108] The table acquisition module 303 is used to generate a power generation table associated with each device identification based on the device identification and device characteristic information, and encrypt the power generation table to obtain an encrypted table;

[0109] The data storage module 304 is used to divide the encrypted table into regions and store them in corresponding storage regions.

[0110] In some implementations, the feature recognition module 302 includes:

[0111] Obtaining feature values ​​of different phrases in target power generation data;

[0112] The phrases in the target power generation data are filtered according to the characteristic values ​​to obtain the device identification and device characteristic information in the target power generation data, wherein the device characteristic information includes one or more characteristic categories in the device type, installation location, status, timestamp, power generation, efficiency and environmental data.

[0113] In some implementations, the table acquisition module 303 includes:

[0114] Get a preset table, each row of which is a different feature category;

[0115] Fill the preset table with the characteristic data in the equipment characteristic information according to the characteristic category to obtain a power generation table, in which each column of data in the power generation table is the same power generation equipment;

[0116] Each column of data in the power generation data is associated with the equipment identification of the corresponding power generation equipment to obtain a power generation table associated with each equipment identification.

[0117] In some implementations, the table acquisition module 303 includes:

[0118] Get the hash value of each device ID;

[0119] A corresponding key-value pair is generated based on the hash value, and each column of data in the power generation data is associated with the equipment identification of the corresponding power generation equipment according to the key-value pair.

[0120] In some implementations, the table acquisition module 303 includes:

[0121] The power generation table is encrypted based on a symmetric encryption algorithm to obtain an encrypted table.

[0122] In some implementations, the data storage module 304 includes:

[0123] Obtaining the type of electric power equipment corresponding to each equipment identifier in the encrypted table, where the type of electric power equipment includes at least one of solar power generation, wind power generation, thermal power generation and hydropower generation;

[0124] Determine the corresponding target storage area based on the type of power equipment identified by the equipment;

[0125] The encrypted table corresponding to the device identification is stored in the corresponding target storage area.

[0126] In some implementations, the data acquisition module 301 includes:

[0127] Performing filtering processing on the power generation data in sequence to obtain first processed data;

[0128] Outliers are eliminated and mean values ​​are filled for the first processed data to obtain second processed data.

[0129] In some implementations, the data acquisition module 301 includes:

[0130] Acquire a data format of the second processed data and a target format to be converted;

[0131] The data format of the second processed data is converted into a target format to obtain target power generation data.

[0132] In some implementations, the apparatus 300 further includes:

[0133] In response to the power generation data being data of one electric power device, stream processing is performed on the power generation data;

[0134] In response to the power generation data being data of at least two electric power devices, the power generation data is batch-processed.

[0135] In the embodiment of the present application, power generation data is collected through a star topology relationship, and the accuracy of the power generation data is improved through filtering and outlier identification, and data filling is performed based on the mean, while ensuring the integrity of the power generation data; the power generation data is formatted to obtain JSON format text for easy editing and transmission, feature identification is performed on the target power generation data, and a power generation data set is composed based on the device identification and device feature information for transmission and analysis, thereby improving transmission efficiency and reducing interference and resource occupation of non-critical information, generating a corresponding power generation table based on the power generation data set and encrypting it, and indexing the power generation table through the device ID to obtain the device feature information corresponding to the device ID to ensure data security, partitioning the storage area and performing field indexing, storing the power generation data of the corresponding device type in the corresponding area, and the storage area can be indexed and viewed according to the partition field, and the device feature information in the area can be indexed and viewed through the device ID, thereby improving the efficiency of viewing the power generation data in the storage area, effectively preventing storage confusion of the power generation table, and achieving better storage effect.

[0136] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0137] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0138] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0139] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0140] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0141] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0142] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0143] In addition, 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 the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0144] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0145] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0146] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0147] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0148] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0149] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for storing power generation data of multi-source power equipment, characterized in that: include: Acquire power generation data of different power equipment, and perform preprocessing and format conversion on the power generation data in sequence to obtain target power generation data; Identifying the target power generation data, and determining the device identification and device characteristic information in the power generation data; Based on the device identification and device characteristic information, a power generation table associated with each device identification is generated, and the power generation table is encrypted to obtain an encrypted table; The encrypted table is divided into regions and stored in corresponding storage areas.

2. The method according to claim 1, characterized in that The identifying the target power generation data and determining the device identification and device characteristic information in the power generation data includes: Acquiring characteristic values ​​of different phrases in the target power generation data; The phrases in the target power generation data are filtered according to the characteristic values ​​to obtain device identification and device characteristic information in the target power generation data, wherein the device characteristic information includes one or more characteristic categories in device type, installation location, status, timestamp, power generation, efficiency and environmental data.

3. The method according to claim 2, characterized in that The generating a power generation table associated with each device identification based on the device identification and the device characteristic information includes: Obtain a preset table, where each row of the preset table is a different feature category; Filling the preset table with the characteristic data in the equipment characteristic information according to the characteristic category to obtain a power generation table, wherein each column of data in the power generation table is for the same power generation equipment; Each column of data in the power generation data is associated with the equipment identification of the corresponding power generation equipment to obtain a power generation table associated with each equipment identification.

4. The method according to claim 3, characterized in that The associating each column of data in the power generation data with the equipment identification of the corresponding power generation equipment includes: Get the hash value of each device ID; A corresponding key-value pair is generated based on the hash value, and each column of data in the power generation data is associated with the equipment identification of the corresponding power generation equipment according to the key-value pair.

5. The method according to any one of claims 1 to 4, characterized in that: The step of encrypting the power generation table to obtain an encrypted table includes: The power generation table is encrypted based on a symmetric encryption algorithm to obtain an encrypted table.

6. The method according to claim 5, characterized in that The step of dividing the encrypted table into regions and storing the regions into corresponding storage areas includes: Obtaining the type of electric power equipment corresponding to each equipment identifier in the encrypted table, wherein the type of electric power equipment includes at least one of solar power generation, wind power generation, thermal power generation and hydropower generation; Based on the type of electric power equipment identified by the equipment, determining a corresponding target storage area; The encrypted table corresponding to the device identification is stored in the corresponding target storage area.

7. The method according to claim 5, characterized in that Preprocessing the power generation data includes: Performing filtering processing on the power generation data in sequence to obtain first processed data; Outliers are eliminated and mean values ​​are filled for the first processed data to obtain second processed data.

8. The method according to claim 7, characterized in that The acquisition of the target power generation data includes: Acquire a data format of the second processed data and a target format to be converted; The data format of the second processed data is converted into the target format to obtain the target power generation data.

9. The method according to claim 1, characterized in that: The method further comprises: In response to the power generation data being data of one of the electric power devices, performing stream processing on the power generation data; In response to the power generation data being data of at least two of the electric power devices, batch processing is performed on the power generation data.

10. A multi-source power equipment power generation data storage device, characterized in that: include: A data acquisition module is used to acquire power generation data of different power equipment, and pre-process and format-convert the power generation data in sequence to obtain target power generation data; A feature recognition module, used to recognize the target power generation data and determine the device identification and device feature information in the power generation data; A table acquisition module, used to generate a power generation table associated with each device identification based on the device identification and device characteristic information, and encrypt the power generation table to obtain an encrypted table; The data storage module is used to divide the encrypted table into regions and store them in corresponding storage regions.

Images