A new energy power generation data storage management method and system

Abstract

The present invention provides a new energy power generation data storage management method and system, which relates to the field of data processing technology, including: obtaining Q new energy power stations, obtaining Q power generation data and Q operation data; obtaining location information, performing regional division to obtain Q power consumption areas; obtaining Q power consumption data; performing power supply balance analysis based on a local data analysis module, generating Q power supply balance analysis results; transmitting to a data storage center, and uploading to a power generation data chain built based on a blockchain; performing distributed power station collaborative analysis through a distributed power station collaborative management model, generating a distributed power station collaborative management plan; sending to Q new energy power stations for collaborative management. The present invention solves the technical problem that each power station in a new energy power generation system in the prior art operates independently, and it is difficult to achieve resource coordination between multiple power stations, resulting in low energy utilization efficiency of the overall energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for managing data storage of renewable energy power generation. Background Art

[0002] Renewable energy power generation systems cover solar, wind, hydro and other power stations, which are usually distributed in different geographical locations. Each power station in the traditional renewable energy power generation system operates independently, making it difficult to achieve resource coordination and optimization among multiple power stations. How to achieve data sharing and collaborative optimization among power stations has become a key challenge. In addition, multiple renewable energy power stations generate a large amount of scattered data, such as power generation, grid status, equipment operation status, etc. Traditional methods are difficult to achieve unified and real-time data management, and traditional data storage methods may face the risk of data leakage, tampering or unauthorized access, resulting in poor data security. Summary of the invention

[0003] The present application provides a new energy power generation data storage and management method and system, aiming to solve the technical problems in the prior art that each power station in the new energy power generation system operates independently, it is difficult to achieve resource coordination among multiple power stations, and the large amount of scattered data generated by each power station is difficult to achieve unified and real-time data management, resulting in low energy utilization efficiency of the overall energy system.

[0004] The first aspect disclosed in the present application provides a method for managing data storage of renewable energy power generation, the method comprising: obtaining Q renewable energy power stations of a renewable energy power generation system to be stored with data, collecting power generation data and operation data at the Q renewable energy power stations according to a preset time period, and obtaining Q power generation data and Q operation data, where Q≥2; obtaining location information of the Q renewable energy power stations, dividing a target power consumption area into regions based on the location information, and obtaining Q power consumption areas; collecting power consumption data at the Q power consumption areas according to the preset time period, and obtaining Q power consumption data; analyzing the Q power generation data and the Q operation data based on a local data analysis module of the Q renewable energy power stations; and The Q power generation data, the Q operation data, the Q power consumption data, and the Q power balance analysis results are transmitted to the data storage center, and uploaded to the power generation data chain built based on the blockchain; through the distributed power station collaborative management model of the data storage center, based on the Q supply and demand deviations, the distributed power station collaborative analysis of the Q new energy power stations is performed to generate a distributed power station collaborative management plan; the distributed power station collaborative management plan is sent to the Q new energy power stations to perform collaborative management of the Q new energy power stations.

[0005] In a second aspect disclosed in the present application, a new energy power generation data storage management system is provided, the system being used for the above-mentioned new energy power generation data storage management method, the system comprising: a power generation data acquisition module, the power generation data acquisition module being used to acquire Q new energy power stations of the new energy power generation system to be stored with data, and collecting power generation data and operation data at the Q new energy power stations respectively according to a preset time period to obtain Q power generation data and Q operation data, where Q≥2; an area division module, the area division module being used to acquire location information of the Q new energy power stations, and dividing a target power consumption area into regions based on the location information to obtain Q power consumption areas; a power consumption data acquisition module, the power consumption data acquisition module being used to collect power consumption data at the Q power consumption areas respectively according to the preset time period to obtain Q power consumption data; a power supply balance analysis module being used to analyze the power supply balance based on the Q new energy power stations. A data analysis module local to the power station performs power supply balance analysis on the Q power generation data, the Q operation data, and the Q power consumption data, and generates Q power supply balance analysis results, wherein the Q power supply balance analysis results include Q supply and demand deviations; a data transmission module, the data transmission module is used to transmit the Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results to a data storage center, and upload them to a power generation data chain built based on blockchain; a collaborative analysis module, the collaborative analysis module is used to perform a distributed power station collaborative analysis of the Q new energy power stations based on the Q supply and demand deviations through a distributed power station collaborative management model of the data storage center, and generate a distributed power station collaborative management plan; a collaborative management module, the collaborative management module is used to send the distributed power station collaborative management plan to the Q new energy power stations, and perform collaborative management of the Q new energy power stations.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The power generation, operation and power consumption data are collected from multiple renewable energy power stations at the same time through a preset time period, and are centrally managed and stored through regional division and distributed data storage centers; a local data analysis module is introduced to perform power supply balance analysis, and combined with power generation, operation and power consumption data, supply and demand deviation analysis results are generated to achieve more accurate power supply balance management; through the distributed power station collaborative management model, collaborative analysis and management are performed based on real-time supply and demand deviation data, realizing real-time collaborative analysis and management among multiple renewable energy power stations, optimizing resource utilization efficiency, reducing operating costs, and improving the overall response speed and stability of the system. In general, this new energy power generation data storage management method effectively solves the problems of data integration difficulties, insufficient power supply balance and poor collaborative management in the management of traditional renewable energy power generation systems through system integration, and improves the operating efficiency and sustainable development capabilities of renewable energy power generation systems.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of a new energy power generation data storage management method provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of the structure of a new energy power generation data storage management system provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: power generation data collection module 10 , area division module 20 , power consumption data collection module 30 , power supply balance analysis module 40 , data transmission module 50 , collaborative analysis module 60 , collaborative management module 70 . DETAILED DESCRIPTION

[0012] The embodiment of the present application provides a method for storing and managing new energy power generation data, which solves the technical problems in the prior art that each power station in the new energy power generation system operates independently, making it difficult to achieve resource coordination among multiple power stations, and that the large amount of scattered data generated by each power station is difficult to achieve unified and real-time data management, resulting in low energy utilization efficiency of the overall energy system.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] like Figure 1As shown, an embodiment of the present application provides a new energy power generation data storage management method, the method comprising:

[0015] Acquire Q new energy power stations of the new energy power generation system to be stored with data, collect power generation data and operation data at the Q new energy power stations respectively according to a preset time period, and obtain Q power generation data and Q operation data, where Q≥2;

[0016] Determine the number of renewable energy power stations Q for which data needs to be collected, where Q ≥ 2. These power stations can be renewable energy power generation facilities such as solar energy and wind energy distributed in different geographical locations. Determine a preset time period, such as hourly, daily, or weekly, so that data can be collected and stored according to this period.

[0017] Data acquisition equipment is deployed inside each new energy power station to collect power generation data and operation data in real time. The power generation data includes the actual power generation in each cycle, and the operation data includes the operating status and performance parameters of the power generation equipment, and may also include temperature, humidity, speed, power output and other related data.

[0018] Acquire location information of the Q new energy power stations, and divide the target power consumption area into regions based on the location information to obtain Q power consumption areas;

[0019] For the Q determined new energy power stations, the specific location information of each power station is obtained. This information can be obtained through the global positioning system (GPS). Based on the obtained location information of the Q new energy power stations, the target power consumption area is divided into Q sub-areas. These sub-areas are centered on the new energy power station and determine a certain regional range. Each divided power consumption area includes one or more power users or power networks served by the new energy power station. The regional division provides the necessary spatial reference for power management and collaborative management.

[0020] According to the preset time period, respectively collect power consumption data in the Q power consumption areas to obtain Q power consumption data;

[0021] In each of the Q divided power consumption areas, power consumption data collection equipment is deployed. These devices can be installed at key nodes of major power consumption facilities or power consumption networks to ensure comprehensive collection of power consumption data. Power consumption data collection is performed according to preset time periods. The collected power consumption data includes actual power consumption information in each period, wherein the collected power consumption data has a time correspondence with the aforementioned power generation data.

[0022] Based on the local data analysis modules of the Q new energy power stations, power supply balance analysis is performed on the Q power generation data, the Q operation data, and the Q power consumption data to generate Q power supply balance analysis results, wherein the Q power supply balance analysis results include Q supply and demand deviations;

[0023] Integrate the data collected locally at each renewable energy power station, including power generation data, operation data and power consumption data, conduct power supply balance analysis in the local data analysis module of each renewable energy power station, and generate Q power supply balance analysis results. Each result includes the supply and demand deviation data of the power station within a specific time period, reflecting the balance between actual power supply and power consumption.

[0024] Further, performing power supply balance analysis on the Q power generation data, the Q operation data, and the Q power consumption data to generate Q power supply balance analysis results includes:

[0025] Calculating a basic supply-demand deviation on the first power generation data and the first power consumption data to generate a first basic supply-demand deviation;

[0026] Performing an operation stability analysis based on the first operation data to generate a first stability coefficient;

[0027] Compensating the first basic supply-demand deviation based on the first stability coefficient to generate a first supply-demand deviation;

[0028] By analogy, Q supply-demand deviations are generated, and the Q power supply balance analysis results are obtained.

[0029] For the first new energy power station, a basic supply-demand deviation between the first power generation data and the first power consumption data is calculated. The basic supply-demand deviation is the difference between the actual power generation and the actual power consumption. This calculation is obtained by a simple subtraction operation.

[0030] Based on the first operating data, an operation stability analysis is performed. This analysis aims to evaluate the operating stability and reliability of the power generation equipment under specific conditions. The stability coefficient involves a comprehensive evaluation of indicators such as equipment availability, operating efficiency, and failure rate. After the stability analysis, the first stability coefficient is generated.

[0031] The first basic supply-demand deviation is compensated according to the first stability coefficient. The compensation process can adjust the size of the basic supply-demand deviation according to the stability coefficient to more accurately reflect the actual power supply capacity and demand situation.

[0032] Repeat the above steps to calculate and analyze the supply and demand deviation for each renewable energy power station in turn, until Q corresponding supply and demand deviation data are generated for all Q power stations. These data constitute Q power supply balance analysis results, each of which reflects the power supply and demand situation of the corresponding power station within a specific time period, providing a basis for subsequent management decisions and optimization.

[0033] The Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results are transmitted to a data storage center, and uploaded to a power generation data chain built based on blockchain;

[0034] The power generation data, operation data, power consumption data and power balance analysis results collected locally at each renewable energy power station are transmitted to the data storage center through a secure network. The data storage center is used to receive, store and manage large amounts of data from multiple power stations.

[0035] The power generation data, operation data, electricity consumption data and power supply balance analysis results are uploaded to the power generation data chain built on the blockchain. The characteristics of blockchain technology include decentralization, immutability and transparency, which can provide higher data security and trust.

[0036] Through the distributed power station collaborative management model of the data storage center, based on the Q supply and demand deviations, a distributed power station collaborative analysis of the Q new energy power stations is performed to generate a distributed power station collaborative management plan;

[0037] The data storage center centrally manages the data of Q new energy power stations and establishes a distributed power station collaborative management model in the data storage center. This model is constructed based on the supply and demand deviation data and historical data sets of each power station. The model is used to analyze the supply and demand deviation data of the Q new energy power stations, evaluate the power supply capacity and electricity demand of each power station in different time periods, and generate a distributed power station collaborative management plan based on the analysis results of the distributed power station collaborative management model. The plan includes adjusting the operation strategy of the power station according to the supply and demand deviation data, thereby optimizing power generation and supply.

[0038] The distributed power station collaborative management plan is sent to the Q new energy power stations to perform collaborative management of the Q new energy power stations.

[0039] Use secure communication channels, such as encrypted network connections or dedicated communication protocols, to send the distributed power station collaborative management plan to each of the Q renewable energy power stations. According to the received collaborative management plan, the operations specified in the implementation plan of each renewable energy power station, including power dispatching, etc., can improve the overall coordination and operation efficiency of the power generation system and ensure the stability and reliability of power supply.

[0040] Furthermore, through the distributed power station collaborative management model of the data storage center, based on the Q supply and demand deviations, the distributed power station collaborative analysis of the Q new energy power stations is performed to generate a distributed power station collaborative management plan, including:

[0041] Collecting a set of historical supply and demand deviations of the Q new energy power stations within a preset historical time;

[0042] According to the plurality of historical supply and demand deviations in the historical supply and demand deviation set, respectively formulate distributed power station supply and demand balancing plans to generate a distributed power station supply and demand balancing plan set;

[0043] Constructing a distributed power station collaborative management model based on a neural network, using the historical supply and demand deviation set as input data and the distributed power station supply and demand balance solution set as output data, and performing model training until the model converges;

[0044] The Q supply-demand deviations are input into the distributed power station collaborative management model to obtain Q distributed power station supply-demand balance plans, which are combined to generate the distributed power station collaborative management plan.

[0045] Use a data storage center or related data storage system to extract supply and demand deviation data from the historical records of each power station. This data may include supply and demand deviations every hour, every day, or other preset time periods. Analyze the historical supply and demand deviation collection to explore the trend of supply and demand changes in different time periods. Based on the analysis results, formulate supply and demand balancing plans for each power station. These plans include adjusting the operating mode and time period of the power generation equipment, adjusting the power supply and load management strategy, etc., and generating multiple distributed power station supply and demand balancing plans to cover various situations in different time periods and operating conditions.

[0046] Select a neural network structure suitable for processing time series data and complex relationships, such as a recurrent neural network. Take the set of historical supply and demand deviations as the input of the model. These data include the supply and demand conditions in different time periods. Take the set of distributed power station supply and demand balance solutions as the target output of the model. The task of the model is to learn how to predict future supply and demand balance solutions based on historical data.

[0047] The model is trained using labeled historical data. During the training process, the model adjusts the weights and parameters of the neural network through the back-propagation algorithm and optimizer to minimize the error between the predicted output and the actual output. The training is continued iteratively until the model converges, that is, the model performs stably on the validation set and the error reaches an acceptable level.

[0048] After the model training is completed, the supply and demand deviations of the Q power stations are used as the input of the model. The model processes the input supply and demand deviation data to predict the optimal supply and demand balance plan for each power station in the future time period. These plans include strategies such as adjusting power generation and optimizing load distribution to ensure the stability and efficient operation of the overall power system. The distributed power station collaborative management plan is generated by combining the supply and demand balance plans of the Q distributed power stations.

[0049] Furthermore, the distributed power station collaborative management solution is generated by combination, including:

[0050] Extracting X power supply plans and Y power supply plans from the Q distributed power station supply and demand balancing plans;

[0051] Based on the location information of the Q new energy power stations, the X power acquisition plans and the Y power supply plans are matched with a goal of shortest power dispatch distance to generate the distributed power station collaborative management plan.

[0052] X power withdrawal plans and Y power supply plans are extracted from the supply and demand balancing plans of Q distributed power stations. X and Y are the number of plans in each part after the plan is divided, which are determined according to the specific situation. Among them, the power withdrawal plan specifies the amount of power that each power station needs to withdraw to meet the expected demand, and the power supply plan specifies the amount of power that each power station is expected to supply to meet the needs of the overall system.

[0053] Using the specific location information of each power station, the distance between power stations is calculated. For each power transfer plan, the distance from the supply site to the demand site is determined. Through path planning optimization methods, such as the shortest path algorithm, the combination of power transfer and supply plans that minimizes the total dispatch distance is selected. Combined with the optimal combination of power transfer and supply plans, new distributed power station collaborative management plans are generated. These plans not only meet the balance between electricity demand and supply, but also optimize the efficiency and cost of energy distribution.

[0054] Furthermore, the Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results are transmitted to a data storage center and uploaded to a power generation data chain built based on blockchain, and further include:

[0055] Based on preset evaluation indicators, data sensitivity evaluation is performed on the Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results, and data levels are divided according to the sensitivity evaluation results to obtain N data sensitivity levels;

[0056] Performing data association analysis on the Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results to obtain M data association coefficients, performing compensation for the N data sensitivity levels based on the M data association coefficients to obtain N data levels, and obtaining N hierarchical data sets corresponding to the N data levels;

[0057] The N hierarchical data sets are uploaded to the N-level nodes of the power generation data chain built based on the blockchain, and the encryption level and access permission level of the N-level nodes are set to establish a basic level set to complete data storage.

[0058] Define indicators for assessing data sensitivity, including data privacy, security needs, value, related requirements, etc., and conduct data sensitivity assessment based on these indicators. For example, classify data according to its nature and purpose, determine which data may involve sensitive information, assess the risks that may be caused by data leakage or tampering, assess the importance and value of data in business processes, and assess whether the data meets relevant requirements, such as personal information protection.

[0059] Based on the results of sensitivity assessment, data is divided into different sensitivity levels. For example, low-sensitivity data refers to data whose leakage will have little impact on the business or will not directly lead to major losses. Medium-sensitivity data includes business operation data, and the leakage of which may bring a certain degree of risk.

[0060] Analyze the correlation between Q power generation data, operation data, power consumption data and power balance analysis results, including the mutual influence of data, the interaction of data in business processes, etc., and quantitatively evaluate the correlation degree of each pair of data. Statistical methods such as the correlation coefficient method can be used to calculate M data correlation coefficients. M is determined according to the number of correlated data. The correlation coefficient reflects the correlation between data. A higher correlation coefficient indicates that the data is strongly correlated and requires higher security protection.

[0061] According to the calculated M data correlation coefficients, the N data sensitivity levels determined based on the preset evaluation indicators are adjusted and compensated. For example, if there is a high correlation coefficient between certain data, they can be classified as high sensitivity levels to enhance their security protection and management. On the contrary, if the correlation between the data is low, their sensitivity level can be lowered to reduce additional security management costs and complexity.

[0062] According to the sensitivity level after compensation, N data levels of Q power generation data, operation data, power consumption data and power supply balance analysis results are finally determined, and each determined data level corresponds to a hierarchical data set.

[0063] To ensure data security, set the encryption level for each N-level node, which includes selecting appropriate encryption algorithms and key management strategies to prevent unauthorized access and data leakage. Set corresponding access rights for each N-level node. These rights can be assigned based on user roles or identities to ensure that only authorized users or systems can access and operate data. According to the sensitivity level of the data and the access rights settings, establish basic level sets. These sets define the storage and access conditions of data at different levels, providing a basic framework for data management and security.

[0064] By uploading hierarchical data sets to blockchain nodes and setting corresponding encryption levels and access permission levels, the security, integrity and traceability of the data can be ensured. Blockchain technology provides decentralized storage and tamper-proof transaction records, which helps to enhance the transparency and trust of data storage while meeting data security and privacy protection requirements.

[0065] Furthermore, uploading the N hierarchical data sets to the N-level nodes of the power generation data chain built based on the blockchain includes:

[0066] Based on blockchain, build multiple initial nodes;

[0067] Performing hash algorithm processing on the N hierarchical data sets and combining with the timestamp information of data collection to obtain N data blocks, wherein the N data blocks correspond to N data levels;

[0068] In combination with the N data levels, the N data blocks are added to the multiple initial nodes to obtain N-level nodes, and updates are performed in the N-level nodes.

[0069] Based on blockchain, multiple initial nodes for carrying data are established, and N hierarchical data sets are processed by hash algorithms respectively. The hash algorithm converts the data into a unique hash value of fixed length, which helps to protect the integrity and security of the data. In order to ensure the timeliness and traceability of the data, each data block is also combined with the timestamp information of data collection. The timestamp can accurately record the specific time when the data is generated or updated. Each hashed data set generates a data block, which contains the processed data and its corresponding hash value to ensure that the data is not tampered with during transmission and storage.

[0070] According to the data level, the N generated data blocks are added to the multiple initial nodes constructed. Each node is responsible for managing and maintaining the hosted data blocks, and updating and managing the data within the node, including adding new data, modifying or deleting old data, etc. Through these steps, blockchain technology can be used to ensure the secure storage, immutability and distributed management of data, thereby enhancing the transparency and credibility of data.

[0071] Furthermore, the method further comprises:

[0072] When a user accesses data, based on the user characteristics, the adaptation authority characteristics of the user account and the N-level node are configured as an additional level, combined with the basic level set as the basic level, and a level evaluation is performed to generate a user account level of the user account;

[0073] Configuring sensitive access factors based on the user account level, wherein the sensitive access factors include access frequency factor, access time factor, data sensitivity factor, and behavior deviation factor;

[0074] When the user account accesses the N-level node, an access behavior data set of the user account is collected, access verification of the access behavior data set is performed based on the sensitive access factor, and an access verification result is generated;

[0075] Data access management is performed according to the access verification result.

[0076] Collect the user's basic information, including identity, role, department, etc., and define different user roles according to the user's identity and function, such as administrator, engineer, ordinary user, etc., and divide the corresponding permission level for each role to ensure that users of different roles can access data that matches their functions. According to the user role and permission requirements, configure the adaptation permission features of the user account and N-level nodes, and subdivide the user's access rights into different permission levels, such as read, write, modify, delete, etc. Map the user's basic permissions with the basic level set of the N-level node to ensure that the user can access the data in the basic level.

[0077] The access frequency factor is used to measure the frequency with which users access data within a certain period of time. Frequently accessed data has a higher sensitivity. Configure the access frequency factor. For example, high-level accounts are allowed a higher access frequency, while low-level accounts are restricted in access frequency. The access frequency restriction is dynamically adjusted based on the user's historical access records and behavioral characteristics.

[0078] The access time factor is used to measure the time period during which users access data. Access during non-working hours may be considered abnormal behavior. By configuring the access time factor, for example, looser access rights are allowed during normal working hours, and access rights are restricted during non-working hours. According to business needs, access rights for specific time periods can be set, for example, nighttime access requires additional verification.

[0079] The data sensitivity factor is used to measure the sensitivity of data. Sensitive data requires stricter access control. Configure the data sensitivity factor. For example, for highly sensitive data, such as user personal information and key operating parameters, set higher access rights and stricter encryption measures. According to the data sensitivity level, configure different access rights. Highly sensitive data is only accessible to high-authorized users.

[0080] The behavior deviation factor is used to measure whether the user's access behavior deviates from the normal pattern. Abnormal behavior may represent a potential security risk. Configure the behavior deviation factor. For example, establish a baseline of the user's normal behavior pattern, including the type of data, time period, and frequency of common access. Monitor the user's access behavior and detect deviations from the baseline. The greater the deviation, the higher the risk. Mark the deviation behavior to trigger additional verification or restrict access rights.

[0081] When a user account accesses an N-level node, a detailed log of each user access is recorded, including access time, access frequency, access data type, access IP address, etc. The user's behavioral characteristics are extracted from the access log, such as access mode, data operation type, access path, etc.

[0082] Access verification is performed based on sensitive access factors. Specifically, the current access frequency is compared with the access frequency allowed by the user account level. If the user's access frequency exceeds the preset threshold, an exception is recorded and a warning is triggered; check whether the current access time is within the allowed time period. If the user accesses during the non-permitted time period, an exception is recorded and additional identity verification is triggered; confirm whether the accessed data matches the user account level. If the user accesses highly sensitive data without the corresponding authority, the access is blocked and an exception is recorded; detect whether the current access behavior deviates from the user's normal behavior pattern. If the user behavior deviates significantly from its normal behavior pattern, an exception is recorded and further verification is performed. The generated verification results include three types: pass, warning, and block, which serve as the basis for subsequent access management and auditing.

[0083] Data access management is performed based on the access verification results. Specifically, if all verifications are passed, the user is allowed to access the data. If there are minor anomalies, a warning is recorded and the user is prompted. Further verification may be required, such as multi-factor authentication and auxiliary authentication. If there are serious anomalies, access is immediately blocked and corresponding measures are taken, such as notifying the administrator, locking the account, initiating a security audit, etc., to ensure data security and the effectiveness of access control.

[0084] Furthermore, the data analysis module includes a basic supply-demand deviation calculation channel and a basic supply-demand deviation compensation channel, and the method further includes:

[0085] The basic supply and demand deviation calculation channel has a built-in basic supply and demand deviation calculation function, and the formula is as follows:

[0086]

[0087] Among them, B i (t) represents the basic supply and demand deviation of the i-th renewable energy power station, G i (t) represents the actual power generation of the i-th renewable energy power station at the tth moment, D i (t) represents the actual power consumption of the jth power-consuming device in the ith power-consuming area at the tth moment, and m is the total number of power-consuming devices in the ith power-consuming area.

[0088] Performing basic supply and demand deviation calculation on the first power generation data and the first power consumption data based on the basic supply and demand deviation calculation function to generate a first basic supply and demand deviation;

[0089] The basic supply and demand deviation compensation channel has a built-in basic supply and demand deviation compensation function, and the formula is as follows:

[0090] B′ i (t) = B i (t)+α·(1-C i )·B i (t);

[0091] Among them, B′ i (t) represents the i-th supply-demand deviation after compensation, C i Characterizes the i-th stability coefficient, α is the adjustment parameter used to control the degree of compensation;

[0092] The first basic supply-demand deviation is compensated based on the first stability coefficient by using the basic supply-demand deviation compensation function to generate a first supply-demand deviation.

[0093] Specifically, the data analysis module includes a basic supply and demand deviation calculation channel, and the basic supply and demand deviation calculation channel has an embedded basic supply and demand deviation calculation function, and the formula is as follows:

[0094]

[0095] This formula calculates the basic supply-demand deviation by comparing the actual power generation of any power station with the total power consumption of the power consumption area corresponding to the power station, where the total power consumption is the sum of the power consumption of all power-consuming equipment in the power consumption area. This calculation is used to evaluate the supply and demand balance of the power generation system and provide basic data for subsequent stability analysis and optimization.

[0096] The first power generation data and the first power consumption data are substituted into the basic supply-demand deviation calculation function to perform basic supply-demand deviation calculation to generate a first basic supply-demand deviation.

[0097] The data analysis module also includes a basic supply and demand deviation compensation channel, in which a basic supply and demand deviation compensation function is embedded, and the formula is as follows:

[0098] B′ i (t) = B i (t)+α·(1-C i )·B i (t);

[0099] Among them, α is an adjustment parameter used to control the degree of compensation, which can be set according to actual needs and specific conditions. i is the stability coefficient, which reflects the stability of the system. Generally speaking, the higher the stability coefficient, the smaller the compensation amount; the lower the stability coefficient, the larger the compensation amount. (1-C i ) reflects the impact of the stability coefficient on the compensation amount. This formula dynamically adjusts the basic supply and demand deviation according to the actual stability of the system to more accurately reflect the actual supply and demand deviation of the power generation system.

[0100] In summary, the new energy power generation data storage and management method provided by the embodiment of the present application has the following technical effects:

[0101] The power generation, operation and power consumption data are collected from multiple renewable energy power stations at the same time through a preset time period, and are centrally managed and stored through regional division and distributed data storage centers; a local data analysis module is introduced to perform power supply balance analysis, and combined with power generation, operation and power consumption data, supply and demand deviation analysis results are generated to achieve more accurate power supply balance management; through the distributed power station collaborative management model, collaborative analysis and management are performed based on real-time supply and demand deviation data, realizing real-time collaborative analysis and management among multiple renewable energy power stations, optimizing resource utilization efficiency, reducing operating costs, and improving the overall response speed and stability of the system. In general, this new energy power generation data storage management method effectively solves the problems of data integration difficulties, insufficient power supply balance and poor collaborative management in the management of traditional renewable energy power generation systems through system integration, and improves the operating efficiency and sustainable development capabilities of renewable energy power generation systems.

[0102] Based on the same inventive concept as the method for data storage management of new energy power generation in the aforementioned embodiment, Figure 2 As shown, the embodiment of the present application provides a new energy power generation data storage management system, the system comprising:

[0103] A power generation data collection module 10, the power generation data collection module 10 is used to obtain Q new energy power stations of the new energy power generation system to be stored with data, and to collect power generation data and operation data at the Q new energy power stations respectively according to a preset time period to obtain Q power generation data and Q operation data, where Q≥2;

[0104] A region division module 20, the region division module 20 is used to obtain the location information of the Q new energy power stations, and divide the target power consumption area into regions based on the location information to obtain Q power consumption areas;

[0105] The power consumption data collection module 30 is used to collect power consumption data in the Q power consumption areas according to the preset time period to obtain Q power consumption data;

[0106] A power balance analysis module 40, the power balance analysis module 40 is used to perform power balance analysis on the Q power generation data, the Q operation data, and the Q power consumption data based on the local data analysis modules of the Q new energy power stations, and generate Q power balance analysis results, wherein the Q power balance analysis results include Q supply and demand deviations;

[0107] A data transmission module 50, which is used to transmit the Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results to a data storage center, and upload them to a power generation data chain built based on blockchain;

[0108] A collaborative analysis module 60, which is used to perform a distributed power station collaborative analysis of the Q new energy power stations based on the Q supply and demand deviations through a distributed power station collaborative management model of a data storage center, and generate a distributed power station collaborative management plan;

[0109] The collaborative management module 70 is used to send the distributed power station collaborative management plan to the Q new energy power stations to perform collaborative management of the Q new energy power stations.

[0110] Furthermore, the system further includes a power balance analysis result acquisition module to perform the following operation steps:

[0111] Calculating a basic supply-demand deviation on the first power generation data and the first power consumption data to generate a first basic supply-demand deviation;

[0112] Performing an operation stability analysis based on the first operation data to generate a first stability coefficient;

[0113] Compensating the first basic supply-demand deviation based on the first stability coefficient to generate a first supply-demand deviation;

[0114] By analogy, Q supply-demand deviations are generated, and the Q power supply balance analysis results are obtained.

[0115] Furthermore, the system further includes a distributed power station collaborative management solution acquisition module to perform the following operation steps:

[0116] Collecting a set of historical supply and demand deviations of the Q new energy power stations within a preset historical time;

[0117] According to the plurality of historical supply and demand deviations in the historical supply and demand deviation set, respectively formulate distributed power station supply and demand balancing plans to generate a distributed power station supply and demand balancing plan set;

[0118] Constructing a distributed power station collaborative management model based on a neural network, using the historical supply and demand deviation set as input data and the distributed power station supply and demand balance solution set as output data, and performing model training until the model converges;

[0119] The Q supply-demand deviations are input into the distributed power station collaborative management model to obtain Q distributed power station supply-demand balance plans, which are combined to generate the distributed power station collaborative management plan.

[0120] Furthermore, the system further includes a collaborative management solution generation module to perform the following operation steps:

[0121] Extracting X power supply plans and Y power supply plans from the Q distributed power station supply and demand balancing plans;

[0122] Based on the location information of the Q new energy power stations, the X power acquisition plans and the Y power supply plans are matched with a goal of shortest power dispatch distance to generate the distributed power station collaborative management plan.

[0123] Furthermore, the system further includes a data storage module to perform the following operation steps:

[0124] Based on preset evaluation indicators, data sensitivity evaluation is performed on the Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results, and data levels are divided according to the sensitivity evaluation results to obtain N data sensitivity levels;

[0125] Performing data association analysis on the Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results to obtain M data association coefficients, performing compensation for the N data sensitivity levels based on the M data association coefficients to obtain N data levels, and obtaining N hierarchical data sets corresponding to the N data levels;

[0126] The N hierarchical data sets are uploaded to the N-level nodes of the power generation data chain built based on the blockchain, and the encryption level and access permission level of the N-level nodes are set to establish a basic level set to complete data storage.

[0127] Furthermore, the system also includes an N-level node building module to perform the following operation steps:

[0128] Based on blockchain, build multiple initial nodes;

[0129] Performing hash algorithm processing on the N hierarchical data sets and combining with the timestamp information of data collection to obtain N data blocks, wherein the N data blocks correspond to N data levels;

[0130] In combination with the N data levels, the N data blocks are added to the multiple initial nodes to obtain N-level nodes, and updates are performed in the N-level nodes.

[0131] Furthermore, the system further includes a data access management module to perform the following operation steps:

[0132] When a user accesses data, based on the user characteristics, the adaptation authority characteristics of the user account and the N-level node are configured as an additional level, combined with the basic level set as the basic level, and a level evaluation is performed to generate a user account level of the user account;

[0133] Configuring sensitive access factors based on the user account level, wherein the sensitive access factors include access frequency factor, access time factor, data sensitivity factor, and behavior deviation factor;

[0134] When the user account accesses the N-level node, an access behavior data set of the user account is collected, access verification of the access behavior data set is performed based on the sensitive access factor, and an access verification result is generated;

[0135] Data access management is performed according to the access verification result.

[0136] Furthermore, the data analysis module includes a basic supply-demand deviation calculation channel and a basic supply-demand deviation compensation channel to perform the following operation steps:

[0137] The basic supply and demand deviation calculation channel has a built-in basic supply and demand deviation calculation function, and the formula is as follows:

[0138]

[0139] Among them, B i (t) represents the basic supply and demand deviation of the i-th renewable energy power station, G i (t) represents the actual power generation of the i-th renewable energy power station at the tth moment, D i (t) represents the actual power consumption of the jth power-consuming device in the ith power-consuming area at the tth moment, and m is the total number of power-consuming devices in the ith power-consuming area.

[0140] Performing basic supply and demand deviation calculation on the first power generation data and the first power consumption data based on the basic supply and demand deviation calculation function to generate a first basic supply and demand deviation;

[0141] The basic supply and demand deviation compensation channel has a built-in basic supply and demand deviation compensation function, and the formula is as follows:

[0142] B′ i (t) = B i (t)+α·(1-C i )·B i (t);

[0143] Among them, B′ i (t) represents the i-th supply-demand deviation after compensation, C i Characterizes the i-th stability coefficient, α is the adjustment parameter used to control the degree of compensation;

[0144] The first basic supply-demand deviation is compensated based on the first stability coefficient by using the basic supply-demand deviation compensation function to generate a first supply-demand deviation.

[0145] Through the above detailed description of a new energy power generation data storage management method, those skilled in the art can clearly understand a new energy power generation data storage management system in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0146] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for storing and managing new energy power generation data, characterized in that: The method comprises: Acquire Q new energy power stations of the new energy power generation system to be stored with data, collect power generation data and operation data at the Q new energy power stations respectively according to a preset time period, and obtain Q power generation data and Q operation data, where Q≥2; Acquire location information of the Q new energy power stations, and divide the target power consumption area into regions based on the location information to obtain Q power consumption areas; According to the preset time period, respectively collect power consumption data in the Q power consumption areas to obtain Q power consumption data; Based on the local data analysis modules of the Q new energy power stations, power supply balance analysis is performed on the Q power generation data, the Q operation data, and the Q power consumption data to generate Q power supply balance analysis results, wherein the Q power supply balance analysis results include Q supply and demand deviations; The Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results are transmitted to a data storage center, and uploaded to a power generation data chain built based on blockchain; Through the distributed power station collaborative management model of the data storage center, based on the Q supply and demand deviations, the distributed power station collaborative analysis of the Q new energy power stations is performed to obtain the supply and demand balance solutions of the Q distributed power stations, and the distributed power station collaborative management solutions are combined to generate; Sending the distributed power station collaborative management plan to the Q new energy power stations to perform collaborative management of the Q new energy power stations; The performing power supply balance analysis on the Q power generation data, the Q operation data, and the Q power consumption data to generate Q power supply balance analysis results includes: Calculating a basic supply-demand deviation on the first power generation data and the first power consumption data to generate a first basic supply-demand deviation; Performing an operation stability analysis based on the first operation data to generate a first stability coefficient; Compensating the first basic supply-demand deviation based on the first stability coefficient to generate a first supply-demand deviation; By analogy, Q supply-demand deviations are generated, and the Q power supply balance analysis results are obtained; Wherein, the data analysis module includes a basic supply and demand deviation calculation channel and a basic supply and demand deviation compensation channel, and the method further includes: The basic supply and demand deviation calculation channel has a built-in basic supply and demand deviation calculation function, and the formula is as follows: in, Characterizes the basic supply and demand deviation of the i-th renewable energy power station, Represents the actual power generation of the i-th renewable energy power station at the t-th moment, represents the actual power consumption of the jth power-consuming device in the i-th power-consuming area at the tth moment, and m is the total number of power-consuming devices in the i-th power-consuming area; Based on the basic supply-demand deviation calculation function, the first power generation data and the first power consumption data are subjected to basic supply-demand deviation calculation to generate a first basic supply-demand deviation; the basic supply-demand deviation compensation channel is embedded with a basic supply-demand deviation compensation function, and the formula is as follows: in, Characterizes the compensated i-th supply-demand deviation, Characterizes the i-th stability coefficient, is an adjustment parameter for controlling the degree of compensation; through the basic supply-demand deviation compensation function, the first basic supply-demand deviation is compensated based on the first stability coefficient to generate a first supply-demand deviation; The combination generates the distributed power station collaborative management solution, including: Extracting X power supply plans and Y power supply plans from the Q distributed power station supply and demand balancing plans; Based on the location information of the Q new energy power stations, the X power acquisition plans and the Y power supply plans are matched with a goal of shortest power dispatch distance to generate the distributed power station collaborative management plan.

2. A method for storing and managing new energy power generation data as claimed in claim 1, characterized in that: Through the distributed power station collaborative management model of the data storage center, based on the Q supply and demand deviations, the distributed power station collaborative analysis of the Q new energy power stations is performed to obtain the supply and demand balance solutions of the Q distributed power stations, including: Collecting a set of historical supply and demand deviations of the Q new energy power stations within a preset historical time; According to the plurality of historical supply and demand deviations in the historical supply and demand deviation set, respectively formulate distributed power station supply and demand balancing plans to generate a distributed power station supply and demand balancing plan set; Constructing a distributed power station collaborative management model based on a neural network, using the historical supply and demand deviation set as input data and the distributed power station supply and demand balance solution set as output data, and performing model training until the model converges; The Q supply-demand deviations are input into the distributed power station collaborative management model to obtain supply-demand balancing solutions for the Q distributed power stations.

3. A method for storing and managing new energy power generation data as claimed in claim 1, characterized in that: The Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results are transmitted to a data storage center, and uploaded to a power generation data chain built based on blockchain, and further include: Based on preset evaluation indicators, data sensitivity evaluation is performed on the Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results, and data levels are divided according to the sensitivity evaluation results to obtain N data sensitivity levels; Performing data association analysis on the Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results to obtain M data association coefficients, performing compensation for the N data sensitivity levels based on the M data association coefficients to obtain N data levels, and obtaining N hierarchical data sets corresponding to the N data levels; The N hierarchical data sets are uploaded to the N-level nodes of the power generation data chain built based on the blockchain, and the encryption level and access permission level of the N-level nodes are set to establish a basic level set to complete data storage.

4. A method for storing and managing new energy power generation data as claimed in claim 3, characterized in that: The uploading of the N hierarchical data sets to the N-level nodes of the power generation data chain constructed based on the blockchain includes: Based on blockchain, build multiple initial nodes; Performing hash algorithm processing on the N hierarchical data sets and combining with the timestamp information of data collection to obtain N data blocks, wherein the N data blocks correspond to N data levels; In combination with the N data levels, the N data blocks are added to the multiple initial nodes to obtain N-level nodes, and updates are performed in the N-level nodes.

5. A method for storing and managing new energy power generation data as claimed in claim 3, characterized in that: The method further comprises: When a user accesses data, based on the user characteristics, the adaptation authority characteristics of the user account and the N-level node are configured as an additional level, combined with the basic level set as the basic level, and a level evaluation is performed to generate a user account level of the user account; Configuring sensitive access factors based on the user account level, wherein the sensitive access factors include access frequency factor, access time factor, data sensitivity factor, and behavior deviation factor; When the user account accesses the N-level node, an access behavior data set of the user account is collected, access verification of the access behavior data set is performed based on the sensitive access factor, and an access verification result is generated; Data access management is performed according to the access verification result.

6. A new energy power generation data storage and management system, characterized in that: A method for storing and managing new energy power generation data according to any one of claims 1 to 5, the system comprising: A power generation data collection module, the power generation data collection module is used to obtain Q new energy power stations of the new energy power generation system to be stored with data, and collect power generation data and operation data from the Q new energy power stations respectively according to a preset time period to obtain Q power generation data and Q operation data, where Q≥2; A region division module, the region division module is used to obtain the location information of the Q new energy power stations, and divide the target power consumption area into regions based on the location information to obtain Q power consumption areas; An electricity consumption data collection module, the electricity consumption data collection module is used to collect electricity consumption data in the Q electricity consumption areas respectively according to the preset time period to obtain Q electricity consumption data; A power supply balance analysis module, the power supply balance analysis module is used to perform power supply balance analysis on the Q power generation data, the Q operation data, and the Q power consumption data based on the local data analysis modules of the Q new energy power stations, and generate Q power supply balance analysis results, wherein the Q power supply balance analysis results include Q supply and demand deviations; A data transmission module, which is used to transmit the Q power generation data, the Q operation data, the Q power consumption data, and the Q power supply balance analysis results to a data storage center, and upload them to a power generation data chain built based on blockchain; A collaborative analysis module, the collaborative analysis module is used to perform a distributed power station collaborative analysis of the Q new energy power stations based on the Q supply and demand deviations through a distributed power station collaborative management model of a data storage center, obtain supply and demand balance plans for the Q distributed power stations, and combine and generate the distributed power station collaborative management plan; A collaborative management module, the collaborative management module is used to send the distributed power station collaborative management plan to the Q new energy power stations to perform collaborative management of the Q new energy power stations; The performing power supply balance analysis on the Q power generation data, the Q operation data, and the Q power consumption data to generate Q power supply balance analysis results includes: Calculating a basic supply-demand deviation on the first power generation data and the first power consumption data to generate a first basic supply-demand deviation; Performing an operation stability analysis based on the first operation data to generate a first stability coefficient; Compensating the first basic supply-demand deviation based on the first stability coefficient to generate a first supply-demand deviation; By analogy, Q supply-demand deviations are generated, and the Q power supply balance analysis results are obtained; Wherein, the data analysis module includes a basic supply and demand deviation calculation channel and a basic supply and demand deviation compensation channel, and the method further includes: The basic supply and demand deviation calculation channel has a built-in basic supply and demand deviation calculation function, and the formula is as follows: ;in, Characterizes the basic supply and demand deviation of the i-th renewable energy power station, Represents the actual power generation of the i-th renewable energy power station at the t-th moment, represents the actual power consumption of the jth power-consuming device in the i-th power-consuming area at the tth moment, and m is the total number of power-consuming devices in the i-th power-consuming area; Performing basic supply and demand deviation calculation on the first power generation data and the first power consumption data based on the basic supply and demand deviation calculation function to generate a first basic supply and demand deviation; The basic supply and demand deviation compensation channel has a built-in basic supply and demand deviation compensation function, and the formula is as follows: ;in, Characterizes the compensated i-th supply-demand deviation, Characterizes the i-th stability coefficient, It is an adjustment parameter used to control the degree of compensation; By using the basic supply-demand deviation compensation function, compensating the first basic supply-demand deviation based on the first stability coefficient to generate a first supply-demand deviation; The combination generates the distributed power station collaborative management solution, including: Extracting X power supply plans and Y power supply plans from the Q distributed power station supply and demand balancing plans; Based on the location information of the Q new energy power stations, the X power acquisition plans and the Y power supply plans are matched with a goal of shortest power dispatch distance to generate the distributed power station collaborative management plan.

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