A cross-regional clean energy Internet data storage system

Abstract

The present invention discloses a cross-regional clean energy Internet data storage system, which relates to the field of intelligent distributed energy storage technology, including pre-processing multi-dimensional data; constructing an energy supply and demand prediction model based on the pre-processed multi-dimensional data, and predicting the energy supply and demand status in combination with environmental factors; dynamically adjusting the data storage strategy according to the predicted energy supply and demand status; based on the adjusted data storage strategy, using data encryption and consensus mechanism to perform distributed data storage, and in the data storage process, monitoring the energy supply and demand status in real time, and performing cross-regional data scheduling and energy allocation optimization based on the energy supply and demand status. The present invention integrates real-time data collection, LSTM prediction, and dynamic storage strategy, uses distributed storage and consensus mechanism, optimizes energy allocation, ensures data security and efficiency, and provides a stable data management solution for the cross-regional clean energy Internet.

Description

Technical Field

[0001] The present invention relates to the field of intelligent distributed energy storage technology, and in particular to a cross-regional clean energy Internet data storage system. Background Art

[0002] In recent years, with the global pursuit of sustainable energy and the rapid development of information technology, the cross-regional clean energy internet (RECI) has become a key infrastructure to promote energy transformation. RECI aims to integrate clean energy resources in different regions and achieve efficient distribution and utilization of energy. In this context, the importance of data storage systems as the core components of RECI has become increasingly prominent. The main challenges faced by traditional data storage systems include massive growth of data, unsustainable energy consumption, and data access delays. In order to meet these challenges, researchers have proposed a variety of solutions, such as using distributed storage technology to improve data reliability and access speed, using intelligent algorithms to optimize energy management, and improving the effective utilization of data through data preprocessing and prediction models. However, despite the progress made in these technologies, there are still many shortcomings.

[0003] Existing cross-regional data storage systems often ignore the impact of dynamic changes in energy supply and demand on data storage strategies when processing large-scale, multi-dimensional clean energy data. Traditional data storage strategies are static and fixed, and cannot adapt to the intermittent and uncertain nature of clean energy production, resulting in inefficient energy utilization, especially in energy-scarce areas. This inefficiency is particularly prominent. In addition, when processing massive amounts of data, existing systems lack effective preprocessing mechanisms, making it difficult to ensure data quality, which in turn affects the accuracy of energy supply and demand forecasts. Furthermore, although some systems attempt to improve data access efficiency through distributed storage, they lack a sophisticated scheduling mechanism in terms of energy management, and fail to fully utilize the low-cost advantage of energy-abundant periods. Instead, they increase energy consumption and operating costs during energy-scarce periods. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a cross-regional clean energy Internet data storage system to solve the problems of poor adaptability to changes in energy supply and demand, lack of data preprocessing mechanism and imprecise energy management scheduling.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] An embodiment of the present invention provides a cross-regional clean energy Internet data storage system, which includes:

[0008] Data collection module: deploy intelligent terminals to collect multi-dimensional data in real time;

[0009] Data preprocessing module: preprocess multi-dimensional data;

[0010] Energy supply and demand forecasting module: Based on the pre-processed multi-dimensional data, an energy supply and demand forecasting model is constructed, and the energy supply and demand status is predicted in combination with environmental factors;

[0011] Storage strategy generation module: dynamically adjusts data storage strategies based on predicted energy supply and demand conditions;

[0012] Distributed storage and consensus module: Based on the adjusted data storage strategy, data encryption and consensus mechanism are used to perform distributed data storage;

[0013] Data scheduling and energy optimization module: During the data storage process, the energy supply and demand conditions are monitored in real time, and cross-regional data scheduling and energy allocation optimization are performed based on the energy supply and demand conditions.

[0014] As a preferred solution of the cross-regional clean energy Internet data storage system of the present invention, wherein: the deployed intelligent terminal includes a wind energy monitoring device, a solar energy monitoring device, a bioenergy detection device, a water conservancy detection device and a geothermal detection device;

[0015] The multi-dimensional data includes clean energy production status, environmental conditions, and equipment operating status.

[0016] As a preferred solution of the cross-regional clean energy Internet data storage system of the present invention, the multi-dimensional data is pre-processed in the following specific steps:

[0017] Linear interpolation is used to fill missing values ​​in multidimensional data;

[0018] Use the Z-score method to identify outliers in the filled multi-dimensional data;

[0019] Use the STL method to extract the periodic characteristics of time series;

[0020] Through standardization, multiple dimensions are adjusted to a unified scale.

[0021] As a preferred solution of the cross-regional clean energy Internet data storage system of the present invention, wherein: the energy supply and demand prediction model is constructed based on the pre-processed multi-dimensional data, and the specific steps are as follows:

[0022] The energy supply and demand forecasting model is constructed based on LSTM. The input layer of the model receives preprocessed multi-dimensional data, the LSTM layer captures the long-term dependencies of the time series, the Dropout layer prevents overfitting, and the output layer outputs the clean energy supply. The energy supply and demand forecasting model expression is:

[0023] Y' t =σ(W out ·h t +b out )

[0024] Among them, Y' t is the predicted value of energy demand output by the model at time point t, σ is the Sigmoid activation function, which is used to map the output of the linear combination to an appropriate value range, and W out is the weight matrix of the output layer, which connects the hidden state ht of the LSTM layer and the output layer, h t represents the hidden state of the LSTM layer at time point t. It is a vector whose dimension depends on the hidden layer size of the LSTM unit. out is the bias term of the output layer, which is a scalar used to adjust the zero point of the activation function of the output layer so that the model can fit the offset in the data;

[0025] The energy supply and demand prediction model is trained using mean square error as the loss function, and the Adam optimizer is used to update the weights.

[0026] As a preferred solution of the cross-regional clean energy Internet data storage system of the present invention, the specific steps of predicting energy supply and demand conditions in combination with environmental factors are as follows:

[0027] The energy supply and demand is predicted based on the seasonal fluctuations of energy supply and demand, weather condition index and historical energy supply and demand ratio. The expression is:

[0028]

[0029] Among them, P(Y' t ) represents the forecast value of energy supply and demand after comprehensive adjustment at time point t, F i represents the seasonal adjustment factor at time point i, W i represents the weather condition index at time point i, H represents the historical energy supply and demand ratio at time point t, and Y' t is the predicted value of energy demand output by the model at time point t, W represents the number of past time points considered by the model, t represents the time point at which energy supply and demand forecast is currently desired, i is an index variable, max(H) represents the maximum value of the historical supply-demand ratio, and min(H) represents the minimum value of the historical supply-demand ratio;

[0030] Defining energy abundance threshold T based on historical energy supply-demand ratio ES and energy shortage threshold T ST , by comparing P(Y' t ) and T ES and T ST The size of the energy supply and demand is determined by the

[0031] When P(Y' t ) is higher than T ES When , the predicted energy supply and demand is at a sufficient level, and it is considered that the current energy supply is in excess;

[0032] When P(Y' t ) is lower than T ST When , the predicted energy supply and demand is at a shortage level, and it is believed that the current energy supply is insufficient.

[0033] As a preferred solution of the cross-regional clean energy Internet data storage system of the present invention, the data storage strategy is dynamically adjusted according to the predicted energy supply and demand, and the specific steps are as follows:

[0034] When energy supply is in excess, the physical storage space of data is reduced through advanced data compression algorithms, while intelligent caching technology is used to store hot data;

[0035] Distribute data on storage media with different energy efficiencies based on energy costs and data access frequency to reduce energy consumption;

[0036] Enable the intelligent hibernation mechanism to hibernate storage devices that contain cold data that has not been accessed for a long time;

[0037] When energy supply is insufficient, increase the number of data copies and store them in different geographical locations. At the same time, establish cloud backup as a disaster recovery point.

[0038] Use authentication mechanisms to strengthen data encryption and access control;

[0039] Load balancing technology, automatic failover mechanism and redundant system design are used to distribute traffic, seamlessly switch and prepare backup resources.

[0040] As a preferred solution of the cross-regional clean energy Internet data storage system of the present invention, wherein: based on the adjusted data storage strategy, data encryption and consensus mechanism are used to perform distributed data storage, the specific steps are as follows:

[0041] According to the adjusted data storage strategy, the system uses the hash ring algorithm to disperse and store data in various nodes in the network, and at the same time, encrypts the data through the AES encryption algorithm;

[0042] Based on the dynamic characteristics and security requirements of data in distributed networks, a data integrity assessment algorithm is designed, which is expressed as:

[0043]

[0044] Where T(t) represents the data integrity check coefficient at time point t, X(D t ) represents the data D at time point t t The hash value of X(D t-1 ) represents the data D at the previous time point t-1 t-1 The hash value of A(D t represents the data encryption strength at time point t, V(D t ) 2 Represents the data verification result V(D t ) squared;

[0045] By conducting statistical analysis on the historical records of data integrity transmitted and stored in the distributed network, the mean value and standard deviation of T(t) under normal operating conditions are determined, and the normal range of data integrity is identified;

[0046] A data integrity security threshold DISB is defined based on the normal range of data integrity, and data security and integrity are optimized by comparing the size of DISB with T(t);

[0047] When T(t)≥DISB, the data integrity is considered to be threatened, and the system automatically enhances the encryption strength and the execution of the consensus mechanism;

[0048] When T(t)<DISB, the data integrity is considered to be within a safe range and the system maintains the current encryption and consensus mechanism configuration.

[0049] As a preferred solution of the cross-regional clean energy Internet data storage system of the present invention, the real-time monitoring of energy supply and demand conditions includes the following specific steps:

[0050] In the process of executing distributed data storage, multi-dimensional data including clean energy production status, environmental parameters, and equipment operation status are continuously obtained from smart terminals;

[0051] The HTTPS protocol is used to transmit the collected multi-dimensional data to the data prediction processing module and the energy supply and demand prediction module to evaluate the energy supply and demand situation.

[0052] As a preferred solution of the cross-regional clean energy Internet data storage system of the present invention, wherein: based on data integrity, cross-regional data scheduling and energy allocation optimization are performed, the specific steps are as follows:

[0053] Dynamically migrate data to cost-effective storage locations based on energy supply and demand, while taking into account fast data access and high availability, dynamically adjust power supply, and prioritize high-priority and urgent data processing needs;

[0054] Increase local caching of hot data in energy-sufficient areas to reduce the need for remote access to the main storage system;

[0055] In areas where energy is scarce, the storage load of non-hotspot data is reduced, access efficiency of key data is prioritized, energy consumption is reduced through intelligent scheduling, and the stability and economy of data services are ensured;

[0056] During periods of high energy costs, hibernate storage devices that contain inactive data.

[0057] The beneficial effects of the present invention are: real-time collection of clean energy multi-dimensional data through intelligent terminals, and ensuring data quality through preprocessing modules, and then using LSTM models combined with environmental factors to predict energy supply and demand, and dynamically generate storage strategies. The system reduces energy consumption through data compression, intelligent caching, and energy-efficient storage allocation when energy is abundant, and enhances data redundancy and security measures to ensure data integrity when energy is scarce. With the help of distributed storage and consensus mechanisms, the system enhances data dispersion and fault tolerance, while optimizing energy distribution through real-time monitoring and intelligent scheduling, ultimately achieving high efficiency, security, and economic efficiency of data storage, and providing a stable and reliable data management and optimization solution for the cross-regional clean energy Internet. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0059] Figure 1 This is a flow chart of the cross-regional clean energy Internet data storage system in Example 1.

[0060] Figure 2 This is a flow chart for predicting energy supply and demand conditions in combination with environmental factors in Example 1. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a cross-regional clean energy Internet data storage system, comprising the following steps:

[0065] S1. Deploy smart terminals to collect multi-dimensional data in real time.

[0066] Furthermore, smart terminals are deployed at each clean energy site, including wind energy monitoring devices, solar energy monitoring devices, bioenergy detection devices, water conservancy detection devices, and geothermal detection devices;

[0067] The multi-dimensional data includes clean energy production status, environmental conditions, equipment operating status, etc.

[0068] Solar energy monitoring devices, bioenergy detection devices, water conservancy detection devices and geothermal detection devices. These intelligent terminals not only collect real-time clean energy production conditions, such as wind speed, light intensity, biomass, water flow and geothermal temperature and other key indicators, but also comprehensively monitor environmental conditions, including temperature, humidity, air pressure and weather changes, as well as equipment operation conditions, such as equipment load, fault warning and maintenance requirements and other multi-dimensional data;

[0069] Through the comprehensive use of these intelligent terminals, the system can fully grasp the real-time operating status of clean energy sites and provide detailed data support for intelligent prediction, data storage strategy generation and system optimization.

[0070] S2. Preprocess the multi-dimensional data.

[0071] Furthermore, linear interpolation is used to fill missing values ​​in multidimensional data;

[0072] For example, suppose that on a certain day, the temperature data is missing due to equipment failure. In this case, linear interpolation is used to estimate the temperature value of the missing day by analyzing the actual temperature two days before and after the missing date. Specifically, if the temperature is 15℃ on the previous day and 20℃ on the next day, it can be assumed that the temperature changes linearly between the two days, and the temperature of the missing day can be inferred to be about 17.5℃. This method itself has a certain degree of continuity and smoothness.

[0073] The Z-score method is used to identify outliers in the filled multi-dimensional data, where values ​​with a Z-score greater than 3 or less than -3 are considered outliers;

[0074] Specifically: If the absolute value of a data point's Z-score is greater than 3 or less than -3, it is usually considered an outlier because it is at least 3 standard deviations away from the mean of the data set, and such deviations are rare in a normal distribution. For example, in a data set that records monthly sales, if the sales in a particular month are much higher or lower than the average, then the sales in that month can be considered an outlier.

[0075] Use the STL method to extract the periodic characteristics of time series;

[0076] For example, suppose there is a time series data set that records the monthly sales of a certain product. The sales volume may be affected by seasonal factors (such as holidays, seasonal changes) and long-term trends (such as market growth or recession). Using the STL method, these complex time series data can be decomposed into three parts: the trend component shows the long-term trend of sales volume over time; the seasonal component reveals the cyclical fluctuations of sales volume; and the residual component captures the remaining random noise. Through this decomposition, the structure of the data can be understood more clearly, providing deeper insights for predictive modeling.

[0077] Through standardization, multiple dimensions are adjusted to a unified scale;

[0078] For example, consider a data set containing many different types of measurement data (such as temperature, humidity, and air pressure), which may have completely different dimensions and magnitudes. Before machine learning modeling, standardization can eliminate the impact of different feature dimensions, making each feature equally important during model training. This step is critical for many machine learning algorithms, especially when using gradient descent for optimization. Standardization can accelerate the convergence of the model and improve the predictive performance of the model.

[0079] S3. Based on the pre-processed multi-dimensional data, an energy supply and demand forecasting model is constructed, and the energy supply and demand situation is predicted in combination with environmental factors.

[0080] Furthermore, an energy supply and demand forecasting model is constructed based on LSTM. The input layer of the model receives preprocessed multi-dimensional data, the LSTM layer captures the long-term dependencies of the time series, the Dropout layer prevents overfitting, and the output layer outputs the clean energy supply. The energy supply and demand forecasting model expression is:

[0081] Y' t =σ(W out ·h t +b out )

[0082] Among them, Y' t is the predicted value of energy demand output by the model at time point t, σ is the Sigmoid activation function, which is used to map the output of the linear combination to an appropriate value range, and W out is the weight matrix of the output layer, which connects the hidden state ht of the LSTM layer and the output layer, h t represents the hidden state of the LSTM layer at time point t. It is a vector whose dimension depends on the hidden layer size of the LSTM unit. out is the bias term of the output layer, which is a scalar used to adjust the zero point of the activation function of the output layer so that the model can fit the offset in the data;

[0083] The energy supply and demand forecasting model was trained using mean square error as the loss function, and the Adam optimizer was used for weight update;

[0084] It should be noted that when training energy supply and demand forecasting models, Mean Squared Error (MSE) is one of the most commonly used loss functions, especially for regression problems. The MSE loss function measures the average squared difference between the model prediction value and the actual target value, and the expression is:

[0085]

[0086] Among them, y i is the actual target value of the ith sample, is the model's predicted value for the sample, n is the total number of samples, and the MSE loss function encourages the model to reduce the prediction error as much as possible, especially when there is a large prediction error. Due to the square of the error, MSE will give a greater penalty, thereby prompting the model to pay more attention to reducing large prediction deviations;

[0087] The Adam optimizer maintains two independent caches for each weight, namely the first-order moment mt and the second-order moment vt, and updates these caches in each iteration. These caches help the optimizer understand the changing trend and magnitude of each weight during the training process. By correcting these caches, the Adam optimizer can more accurately estimate the size and direction of the gradient.

[0088] Finally, based on the corrected cache, the Adam optimizer calculates the weight update amount according to the preset learning rate α and the first and second moments of the gradient, expressed as:

[0089]

[0090] in, is the corrected first-order moment sum, is the corrected second-order moment, ∈ is a very small positive value used to prevent the denominator in the division operation from being zero, and Δw represents the weight update amount;

[0091] The energy supply and demand is predicted based on the seasonal fluctuations of energy supply and demand, weather condition index and historical energy supply and demand ratio. The expression is:

[0092]

[0093] Among them, P(Y' t ) represents the forecast value of energy supply and demand after comprehensive adjustment at time point t, F i represents the seasonal adjustment factor at time point i, W i represents the weather condition index at time point i, H represents the historical energy supply and demand ratio at time point t, and Y' t is the predicted value of energy demand output by the model at time point t, W represents the number of past time points considered by the model, t represents the time point at which energy supply and demand forecast is currently desired, i is an index variable used to index historical time points within the time window, max(H) represents the maximum value of the historical supply-demand ratio, and min(H) represents the minimum value of the historical supply-demand ratio;

[0094] It should be noted that the weather condition index W i It is calculated by integrating historical weather data and real-time weather forecasts, and after preprocessing steps such as data cleaning and feature extraction, using statistical analysis or machine learning techniques. This index quantifies the impact of weather conditions on energy demand, taking into account multiple meteorological parameters such as temperature, humidity, wind speed, etc., and by analyzing the relationship between these parameters and historical energy consumption patterns, a value that reflects the immediate effect of weather on energy demand is generated. The expression is:

[0095] W i =w1T+w2H+w3V;

[0096] Where T is temperature, H is humidity, V is wind speed, w1 is the weight coefficient of temperature, w2 is the weight coefficient of humidity, and w3 is the weight coefficient of wind speed;

[0097] Defining energy abundance threshold T based on historical energy supply-demand ratio ES and energy shortage threshold T ST , by comparing P(Y' t ) and T ES and T ST The size of the energy supply and demand is determined by the

[0098] When P(Y' t ) is higher than T ES When , the predicted energy supply and demand is at a sufficient level, and it is considered that the current energy supply is in excess;

[0099] When P(Y' t ) is lower than T ST When , the predicted energy supply and demand is at a shortage level, and it is considered that the current energy supply is insufficient;

[0100] S4. Dynamically adjust data storage strategies based on predicted energy supply and demand conditions.

[0101] Furthermore, when there is an oversupply of energy, the physical storage space of data is reduced through advanced data compression algorithms, while intelligent caching technology is used to store hot data;

[0102] According to energy costs and data access frequency, data is distributed on storage media with different energy efficiencies to reduce energy consumption. The intelligent hibernation mechanism is enabled to hibernate storage devices where cold data that has not been accessed for a long time is located, so as to achieve the goal of energy saving and emission reduction.

[0103] It should be noted that energy costs mainly come from two aspects: one is the market price of energy, and the other is the energy consumption cost of storage devices during operation. Market prices can be obtained by accessing real-time energy trading market data or cooperating with local energy suppliers. It varies according to energy type (such as wind energy, solar energy, etc.), time and geographical area. Energy consumption costs are related to the power consumption of storage devices. Different storage media (such as SSD, HDD, tape library, etc.) have different energy consumption under the same data read and write operations. These data can be obtained through manufacturer specifications, laboratory tests or actual operation records. The system will continuously monitor this cost information, and dynamically adjust the data storage strategy based on the predicted energy supply and demand to minimize costs;

[0104] Data access frequency is collected through system logs and performance monitoring tools. Whenever data is accessed, the system records the access event, including the access time, data ID, and access type (read or write). Over time, these records can be aggregated and analyzed to identify which data is hot data (frequently accessed data) and which is cold data (rarely accessed data). Hot data typically needs to be stored on high-performance, low-latency storage media to support fast access, while cold data can be stored on lower-cost, more energy-efficient storage media, such as tape libraries or deep storage solutions, which can reduce energy consumption without affecting the user experience.

[0105] When energy supply is insufficient, increase the number of data copies and store them in different geographical locations. At the same time, establish cloud backup as a disaster recovery point.

[0106] Use authentication mechanisms to strengthen data encryption and access control;

[0107] For example, in a cross-regional clean energy internet data storage system, when a user attempts to access sensitive data within the system, the system will first require the user to provide a username and password, or pass a more secure multi-factor authentication, such as a text message verification code or biometric information. Once the user's identity is verified, the system only allows them to access pre-authorized data. At the same time, all data in transit will be protected by encryption technology (such as TLS / SSL) to ensure that even if the data is intercepted during transmission, it cannot be interpreted by a third party.

[0108] Load balancing technology, automatic failover mechanism and redundant system design are used to distribute traffic, seamlessly switch and prepare backup resources to ensure service continuity and reliability.

[0109] For example, the platform uses load balancing technology to distribute user requests to multiple servers to avoid overloading a single server. If one of the servers fails, the automatic failover mechanism will immediately redirect traffic to a healthy server, and users will hardly feel the service interruption. In addition, key components in the system, such as databases, have redundant backups. Even if there is a problem with the primary database, the standby database can quickly take over to ensure the integrity of transaction data and the continuity of transactions. Through these measures, the system can maintain stable operation and provide high-quality services even during peak hours or when encountering unexpected failures.

[0110] S5. Based on the adjusted data storage strategy, data encryption and consensus mechanism are used to perform distributed data storage.

[0111] Through the hash ring algorithm, a distributed network of cross-regional clean energy Internet data storage system is built to enhance the dispersion and fault tolerance of data;

[0112] It should be noted that all storage nodes in the system are virtualized and mapped to a hash ring to form a logical ring structure. Each node occupies one or more positions on the ring, which are calculated by the node identifier through the hash function, ensuring the uniqueness of the node position and the uniformity of distribution. When data needs to be stored, the system also uses the hash function to calculate the data key to obtain a hash value. Then, search in a clockwise direction along the hash ring until the first node after the hash value is found. This node is the storage location of the data. In this way, data is evenly distributed to each node in the network, avoiding the generation of data hotspots.

[0113] According to the adjusted data storage strategy, the system uses the hash ring algorithm to disperse and store data in various nodes in the network to ensure the uniform distribution and high availability of data. At the same time, the data is encrypted through the AES encryption algorithm, and the consensus mechanism is combined to ensure the security and consistency of data during transmission and storage.

[0114] Based on the dynamic characteristics and security requirements of data in distributed networks, a data integrity assessment algorithm is designed, which is expressed as:

[0115]

[0116] Where T(t) represents the data integrity check coefficient at time point t, X(D t ) represents the data D at time point t t The hash value of X(D t-1 ) represents the data D at the previous time point t-1 t-1 The hash value of A(D t represents the data encryption strength at time point t, V(D t ) 2 Represents the data verification result V(D t ) squared;

[0117] It should be noted that A(D t ) is determined by the encryption algorithm AES and the key length;

[0118] V(D t ) is to process the data through a secure hash algorithm (such as SHA-256) to generate a unique hash identifier. Subsequently, this hash value is strictly compared with the previously recorded or expected hash value to verify whether the data has not been tampered with during transmission or storage. In addition, the system will check whether the data conforms to the predefined format and structure specifications to ensure the validity and consistency of the data. By performing these verification operations, the system can comprehensively evaluate the credibility of the data and ultimately derive the value of V(Dt);

[0119] By conducting statistical analysis on the historical records of data integrity transmitted and stored in the distributed network, the mean value and standard deviation of T(t) under normal operating conditions are determined, and the normal range of data integrity is identified;

[0120] A data integrity security threshold DISB is defined based on the normal range of data integrity, and data security and integrity are optimized by comparing the size of DISB with T(t);

[0121] When T(t)≥DISB, the data integrity is considered to be threatened, and the system automatically enhances the encryption strength and the execution of the consensus mechanism;

[0122] When T(t)<DISB, the data integrity is considered to be within a safe range, and the system maintains the current encryption and consensus mechanism configuration;

[0123] It should also be noted that the value range of T(t) is between 0 and 1, where: a value close to 0 indicates that the data integrity is good and the encryption strength and consensus mechanism do not need to be significantly adjusted; a value close to 1 indicates that the data integrity is threatened and the encryption strength and the enforcement of the consensus mechanism need to be enhanced.

[0124] The dynamic nature of data is reflected in its continuous evolution over time, environmental changes, and user behavior. In the cross-regional clean energy Internet data storage system, data generation, storage, access, and updating are all ongoing activities that are affected by a variety of factors, including fluctuations in energy supply and demand, changes in network conditions, the frequency of data access, and the expansion and contraction of the system itself. For example, energy supply and demand forecast data will change with seasonal changes, weather conditions, and market fluctuations, which requires the system to be able to adjust the data storage location and access strategy in real time to optimize energy efficiency and data access speed. At the same time, as the amount of data grows and new data is continuously added, the system must have the ability to elastically scale to ensure efficient storage and rapid retrieval of data.

[0125] The specific data security requirements are as follows: First, data encryption is the foundation to ensure that data is not interpreted by unauthorized parties during transmission and storage. Secondly, the access control mechanism ensures that only authenticated users can access data of the corresponding level to prevent illegal access. Furthermore, redundant storage and data backup strategies are adopted to prevent data loss and enable rapid recovery in the event of a failure. In addition, the system also needs to have the ability to monitor and respond to network attacks, and timely detect and block potential threats through technical means such as intrusion monitoring systems and firewalls. Finally, following data privacy regulations and industry standards to protect personal and corporate data from abuse is also an important part of security requirements. Through these measures, the system can build a solid security barrier to protect data from various security threats.

[0126] S6. During the data storage process, the energy supply and demand conditions are monitored in real time, and based on the energy supply and demand conditions, cross-regional data scheduling and energy allocation optimization are performed.

[0127] Furthermore, in the process of executing distributed data storage, key indicators such as clean energy production status (such as wind energy, solar energy, etc.), environmental parameters (temperature, humidity, etc.) and equipment operation status (such as disk health, read and write speed, storage capacity, etc.) are continuously obtained from smart terminals;

[0128] Using the HTTPS protocol, the collected multi-dimensional data is transmitted to the data prediction processing module and the energy supply and demand prediction module to evaluate the energy supply and demand situation;

[0129] It should be noted that the energy status assessment is to transmit the latest multi-dimensional data collected in real time to the pre-processing module during the distributed storage process. The multi-dimensional data processed by the pre-processing module is then used by the energy supply and demand forecasting module to calculate the predicted value of energy supply and demand P(Y' t ), then with T ES and T ST Make comparisons to assess current energy supply and demand;

[0130] Based on energy supply and demand conditions, data is dynamically migrated to cost-effective storage locations, while taking into account fast data access and high availability, and power supply is dynamically adjusted to prioritize high-priority and urgent data processing needs.

[0131] Increase local cache of hot data in energy-sufficient areas to reduce the need for remote access to the main storage system and save energy costs;

[0132] During periods of high energy costs, storage devices with inactive data can be hibernated to save energy.

[0133] It should also be noted that the determination of high-priority data processing needs is based on the importance and urgency of the business. In the cross-regional clean energy Internet data storage system, high priority usually involves the continuity of key business processes, customer service quality, and safe and stable operation of the system. For example, data processing involving transaction settlement, customer service requests, system security audits, and monitoring data with strict real-time requirements are all considered high priority. These needs may be closely related to the company's core revenue, user satisfaction, or regulatory compliance, so they need to be given priority in resource allocation to ensure that they can be processed and responded to in a timely manner even when energy supply is tight or system load is high.

[0134] The judgment of urgent data processing needs focuses on responding to emergencies and instantaneous high load conditions. Such needs are usually triggered by abnormal conditions detected by the system, such as sudden user surges, system failure alarms, network security incidents, or natural disasters, requiring the system to respond quickly and prioritize processing to mitigate potential negative impacts. For example, when unstable power supply is detected in a certain area of ​​the data center, the system will automatically transfer key data processing tasks to areas with more stable energy supply, while increasing the power supply priority in that area to ensure that data processing is not affected. In addition, when encountering unforeseen large-scale data access requests, the emergency demand identification mechanism will automatically adjust resource allocation, prioritize data service quality and system stability in high-concurrency scenarios, and prevent system crashes or service delays.

[0135] In summary, the present invention can accurately construct an energy supply and demand forecasting model through real-time collection and preprocessing of multi-dimensional data, and effectively predict future energy supply and demand conditions in combination with environmental factors. Based on these predictions, the system can dynamically adjust data storage strategies, optimize energy utilization and data storage efficiency, achieve energy conservation and emission reduction goals, and ensure high availability and security of data. This series of steps demonstrates an integrated, intelligent energy management system designed to improve the utilization efficiency of clean energy, reduce operating costs, and enhance the reliability and security of data storage.

[0136] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of a cross-regional clean energy Internet data storage system is provided.

[0137] Four cross-regional clean energy sites were selected as research objects: Site A (wind energy), Site B (solar energy), Site C (water conservancy), and Site D (geothermal energy). Intelligent terminals were deployed at each site, including but not limited to wind speed sensors, light intensity sensors, water quality flow meters, and geothermal thermometers. These devices can collect multi-dimensional data in real time. The data includes but is not limited to the production status of clean energy, environmental conditions (temperature, humidity, air pressure, weather changes), and equipment operation status.

[0138] First, smart terminals were deployed at four cross-regional clean energy sites to collect multi-dimensional data including wind energy, solar energy, water conservancy and geothermal energy in real time, providing detailed basic data for subsequent energy supply and demand forecasts and data storage strategies.

[0139] Secondly, the collected data were preprocessed, including filling missing values, identifying outliers, time series data decomposition and standardization, which ensured the quality and consistency of the data and created favorable conditions for building a prediction model.

[0140] Next, based on the preprocessed data, an LSTM model was built and trained to predict energy supply and demand conditions. By combining seasonal fluctuations, weather forecasts, and historical data, an accurate prediction of energy supply and demand was achieved, and the data storage strategy was dynamically adjusted to optimize energy use.

[0141] Finally, an encryption and consensus mechanism was designed and implemented to enhance data integrity and security. At the same time, energy supply and demand conditions were monitored in real time during data storage, cross-regional data scheduling and energy allocation were optimized, and efficient operation of the system and reliable storage of data were ensured.

[0142] The details are shown in Table 1:

[0143] Table 1 Dynamic supply, demand and energy consumption analysis of cross-regional clean energy systems

[0144]

[0145] Through the analysis of the above data tables, it can be clearly seen that the effectiveness and superiority of the method of the present invention. Compared with the traditional static energy supply and demand matching method, the present invention significantly improves the accuracy of energy allocation and the overall operating efficiency of the system by introducing a dynamic data storage strategy and a real-time supply and demand prediction model. For example, at time point t2, although the predicted energy demand is slightly higher than the actual demand, by adjusting the data storage strategy in real time, it can quickly respond to changes in supply and demand, reducing energy consumption by 100kWh, reflecting the flexibility and economic advantages of this method in dealing with fluctuating energy supply and demand.

[0146] Through the cross-regional clean energy Internet data storage system of the present invention, not only can the difference between energy supply and demand be effectively reduced and energy utilization be improved, but also the cost increase caused by excessive energy consumption can be significantly reduced under the premise of ensuring data security. Especially during peak energy demand periods, the present invention can quickly call storage resources, smooth the supply and demand curve, and avoid the energy waste and supply shortage problems common in traditional methods. This reflects the significant advantages of the present invention in promoting the efficient use of clean energy, enhancing the stability of the power grid, and improving economic benefits.

[0147] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A cross-regional clean energy Internet data storage system, characterized by: include, Data collection module: deploy intelligent terminals to collect multi-dimensional data in real time; Data preprocessing module: preprocess multi-dimensional data; Energy supply and demand forecasting module: Based on the pre-processed multi-dimensional data, an energy supply and demand forecasting model is constructed, and the energy supply and demand status is predicted in combination with environmental factors; Storage strategy generation module: dynamically adjusts data storage strategies based on predicted energy supply and demand conditions; Distributed storage and consensus module: Based on the adjusted data storage strategy, data encryption and consensus mechanism are used to perform distributed data storage; Data scheduling and energy optimization module: During data storage, it monitors energy supply and demand in real time, and performs cross-regional data scheduling and energy allocation optimization based on energy supply and demand. The data storage strategy is dynamically adjusted according to the predicted energy supply and demand. The specific steps are as follows: When energy supply is in excess, the physical storage space of data is reduced through advanced data compression algorithms, while intelligent caching technology is used to store hot data; Distribute data on storage media with different energy efficiencies based on energy costs and data access frequency to reduce energy consumption; Enable the intelligent hibernation mechanism to hibernate storage devices that contain cold data that has not been accessed for a long time; When energy supply is insufficient, increase the number of data copies and store them in different geographical locations. At the same time, establish cloud backup as a disaster recovery point. Use authentication mechanisms to strengthen data encryption and access control; Use load balancing technology, automatic failover mechanism and redundant system design to distribute traffic, seamlessly switch and prepare backup resources; Based on the adjusted data storage strategy, data encryption and consensus mechanism are used to perform distributed data storage. The specific steps are as follows: According to the adjusted data storage strategy, the system uses the hash ring algorithm to disperse and store data in various nodes in the network, and at the same time, encrypts the data through the AES encryption algorithm; Based on the dynamic characteristics and security requirements of data in distributed networks, a data integrity assessment algorithm is designed, which is expressed as: Among them, T ( t ) represents the data integrity check coefficient at time point t, X ( D t) Represents the data D at time point t t The hash value of X ( D t-1) Represents the data D at the previous time point t-1 t-1 The hash value of A ( D t) represents the data encryption strength at time point t, V(D t ) 2 Represents the data verification result V(D t ) squared; Through statistical analysis of the historical records of data integrity transmitted and stored in the distributed network, it is determined that under normal operating conditions, T ( t ) The mean and standard deviation of the data to identify the normal range of data integrity; Based on the normal range of data integrity, a data integrity security threshold DISB is defined. By comparing DISB with T ( t ) size, optimizing data security and integrity; When T ( t ) When ≥DISB, the data integrity is considered to be threatened, and the system automatically enhances the encryption strength and the execution of the consensus mechanism; When T ( t ) When <DISB, the data integrity is considered to be within a safe range and the system maintains the current encryption and consensus mechanism configuration.

2. The cross-regional clean energy Internet data storage system according to claim 1, characterized in that: The deployed intelligent terminals include wind energy monitoring devices, solar energy monitoring devices, bioenergy detection devices, water conservancy detection devices and geothermal detection devices; The multi-dimensional data includes clean energy production status, environmental conditions, and equipment operating status.

3. The cross-regional clean energy Internet data storage system according to claim 2, characterized in that: The specific steps of preprocessing the multi-dimensional data are as follows: Linear interpolation is used to fill missing values ​​in multidimensional data; Use the Z-score method to identify outliers in the filled multi-dimensional data; Use the STL method to extract the periodic characteristics of time series; Through standardization, multiple dimensions are adjusted to a unified scale.

4. The cross-regional clean energy Internet data storage system according to claim 3, characterized in that: The energy supply and demand prediction model is constructed based on the preprocessed multi-dimensional data. The specific steps are as follows: The energy supply and demand forecasting model is constructed based on LSTM. The input layer of the model receives preprocessed multi-dimensional data, the LSTM layer captures the long-term dependencies of the time series, the Dropout layer prevents overfitting, and the output layer outputs the clean energy supply. The energy supply and demand forecasting model expression is: Y' t =σ(W out ·h t +b out ) Among them, Y' t is the predicted value of energy demand output by the model at time point t, σ is the Sigmoid activation function, which is used to map the output of the linear combination to an appropriate value range, and W out is the weight matrix of the output layer, which connects the hidden state ht of the LSTM layer and the output layer, h t represents the hidden state of the LSTM layer at time point t. It is a vector whose dimension depends on the hidden layer size of the LSTM unit. out is the bias term of the output layer, which is a scalar used to adjust the zero point of the activation function of the output layer so that the model can fit the offset in the data; The energy supply and demand prediction model is trained using mean square error as the loss function, and the Adam optimizer is used to update the weights.

5. The cross-regional clean energy Internet data storage system according to claim 4, characterized in that: The specific steps of predicting energy supply and demand conditions in combination with environmental factors are as follows: The energy supply and demand is predicted based on the seasonal fluctuations of energy supply and demand, weather condition index and historical energy supply and demand ratio. The expression is: Among them, P ( Y' t) represents the forecast value of energy supply and demand after comprehensive adjustment at time point t, F i represents the seasonal adjustment factor at time point i, W i represents the weather condition index at time point i, H represents the historical energy supply and demand ratio at time point t, and Y' t is the predicted value of energy demand output by the model at time point t, W represents the number of past time points considered by the model, t represents the time point at which energy supply and demand forecast is currently desired, i is an index variable, and max ( H ) Indicates the maximum value of the historical supply-demand ratio, min ( H ) Indicates the minimum value of the historical supply-demand ratio; Defining energy abundance threshold T based on historical energy supply-demand ratio ES and energy shortage threshold T ST , by comparing P ( Y' t) With T ES and T ST The size of the energy supply and demand is determined by the When P ( Y' t) The value is higher than T ES When , the predicted energy supply and demand is at a sufficient level, and it is considered that the current energy supply is in excess; When P ( Y' t) The value is lower than T ST When , the predicted energy supply and demand is at a shortage level, and it is believed that the current energy supply is insufficient.

6. The cross-regional clean energy Internet data storage system according to claim 5, characterized in that: The specific steps of real-time monitoring of energy supply and demand are as follows: In the process of executing distributed data storage, multi-dimensional data including clean energy production status, environmental parameters, and equipment operation status are continuously obtained from smart terminals; The HTTPS protocol is used to transmit the collected multi-dimensional data to the data prediction processing module and the energy supply and demand prediction module to evaluate the energy supply and demand situation.

7. The cross-regional clean energy Internet data storage system according to claim 6, characterized in that: Based on data integrity, cross-regional data scheduling and energy allocation optimization are performed. The specific steps are as follows: Dynamically migrate data to cost-effective storage locations based on energy supply and demand, while taking into account fast data access and high availability, dynamically adjust power supply, and prioritize high-priority and urgent data processing needs; Increase local caching of hot data in energy-sufficient areas to reduce the need for remote access to the main storage system; In areas where energy is scarce, the storage load of non-hotspot data is reduced, access efficiency of key data is prioritized, energy consumption is reduced through intelligent scheduling, and the stability and economy of data services are ensured; During periods of high energy costs, hibernate storage devices containing inactive data.

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