Smart power grid monitoring data storage system

Abstract

The invention belongs to the technical field of intelligent power grids, and particularly relates to an intelligent power grid monitoring data storage system which comprises a data acquisition module, a data preprocessing module, a hierarchical data storage module, an intelligent data retrieval and management module and a data analysis and visualization module. Power grid operation data are collected in real time through various sensors; the data preprocessing module is used for cleaning, denoising and formatting the collected data and identifying data abnormal values by adopting a dynamic threshold detection method; the hierarchical data storage module adopts a hierarchical storage strategy of hot data, temperature data and cold data, stores the data in different media respectively, and introduces a time sequence database; the intelligent data retrieval and management module retrieves the stored data by using a multi-condition complex query algorithm, and improves the query efficiency through an intelligent data indexing and retrieval mechanism; and the data analysis and visualization module generates a report and a visualization chart through an advanced data analysis algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a smart grid monitoring and data storage system. Background Art

[0002] With the rapid development of smart grids, the amount of data generated by smart grids has exploded, covering multi-source data such as voltage, current, frequency, temperature, humidity, etc. These data provide rich information for the operation, maintenance, and decision-making of the power grid. However, traditional power grid monitoring data storage and management systems have many deficiencies and are difficult to cope with the increasingly complex operation requirements of smart grids. The following are the five main shortcomings faced by traditional systems:

[0003] 1. Insufficient real-time performance: Traditional power grid data storage systems are inefficient in processing large-scale real-time data, especially when facing high-frequency data collection and transmission. Processing delays are prone to occur, resulting in untimely data storage and response. This delay not only affects the real-time monitoring of the power grid, but may also lead to the failure to discover and respond to key issues in a timely manner;

[0004] 2. Rigid data storage structure: Traditional storage systems usually adopt a unified storage solution and cannot flexibly adjust data storage strategies based on factors such as data access frequency and time characteristics. This results in high-frequency access data and historical data being stored together, wasting high-performance storage resources. At the same time, the access efficiency of historical data is low, affecting the overall performance of the system.

[0005] 3. High complexity of data management and retrieval: Traditional data management and retrieval functions are relatively simple, and it is impossible to perform efficient combined queries based on multiple conditions (such as time, geographic location, data type, etc.). Due to the lack of optimized retrieval mechanism, facing large-scale power grid data, the query speed is slow and inefficient, and it is difficult to meet the needs of modern smart grids for fast data access;

[0006] 4. Unable to adapt to data growth and dynamic changes: With the increase in power grid equipment and data sources, traditional systems cannot efficiently handle the dynamic changes and growth of data. In particular, when processing new data streams, the system lacks flexible processing capabilities, resulting in a slow response to new data and an inability to effectively expand to cope with massive historical and real-time data.

[0007] 5. Lack of intelligent prediction and analysis functions: Traditional storage systems are often limited to data storage and basic query functions, and lack intelligent analysis, prediction, and trend identification functions. This makes it impossible for the system to fully utilize the large amount of stored data to provide more valuable insights and early warnings for power grid management, and it is difficult to support future power grid operation optimization and fault prevention decisions.

[0008] Therefore, it is particularly important to design a power grid monitoring data storage system based on intelligent algorithms and complex functions.

[0009] In view of the above-mentioned technical defects, a smart grid monitoring data storage system solution is proposed. Summary of the invention

[0010] To solve the above problems, the present invention provides the following technical solutions:

[0011] A smart grid monitoring and data storage system comprises a data acquisition module, a data preprocessing module, a hierarchical data storage module, an intelligent data retrieval and management module and a data analysis and visualization module, wherein the data acquisition module comprises a sensor, and real-time grid operation data is collected through a variety of sensors; the data preprocessing module cleans, denoises and formats the collected data and adopts a dynamic threshold detection method to identify data anomalies; the hierarchical data storage module adopts a hierarchical storage strategy for hot data, warm data and cold data, stores the data in different media respectively, and introduces a time series database to support efficient time series data storage; the intelligent data retrieval and management module uses a multi-condition complex query algorithm to retrieve the stored data, and improves the query efficiency through an intelligent data index and retrieval mechanism; the data analysis and visualization module generates reports and visualization charts through advanced data analysis algorithms to help users understand the grid operation status.

[0012] Furthermore, the data acquisition module includes a data acquisition unit, which includes a voltage sensor, a current sensor, a frequency sensor and a temperature sensor. Based on the multiple sensors, the voltage, current, frequency and temperature deployed at each node of the power grid are collected, and the collected data are recorded as V(t), I(t), f(t), T(t) respectively, and the data parameters of V(t), I(t), f(t), T(t) are formed into a characteristic vector X(t) = [V(t), I(t), f(t), T(t)], and the collected data are transmitted to the data preprocessing module.

[0013] Furthermore, the data preprocessing module includes a data processing unit and a dynamic threshold anomaly detection unit. The data processing unit uses a sliding window function to smooth the data to eliminate the volatility and noise of the data. The sliding window function is as follows:

[0014]

[0015] in represents the smoothed feature vector obtained by sliding window processing, N represents the size of the sliding window, and X(t) = [V(t), I(t), f(t), T(t)] represents the unified data of the voltage sensor, current sensor, frequency sensor and temperature sensor.

[0016] Furthermore, the dynamic threshold anomaly detection unit includes a dynamic threshold function. The system uses the dynamic threshold function to detect and mark outliers to ensure the accuracy of the data. The specific analysis is as follows:

[0017] If|X(t)-μ(t)|>k·σ(t),then mark as anomaly

[0018] Where μ(t) and σ(t) represent the mean and standard deviation of the data at the current moment, respectively; k represents the adjustment factor; and X(t) represents the characteristic vector, which includes the voltage, current, frequency and temperature grid operation data at time t.

[0019] Furthermore, the hierarchical data storage module includes a hierarchical storage mechanism unit and a data migration mechanism unit. The hierarchical storage mechanism is based on the frequency of use and time characteristics of the data. The hierarchical storage mechanism unit divides the data into hot data, warm data and cold data according to the frequency of use based on the data processed by the data preprocessing module, and stores them in different types of storage media respectively. Hot data is frequently accessed data and is stored in a solid state drive, warm data is stored in a traditional hard drive, and cold data is historical data and is stored in a tape with a lower cost. The hierarchical storage of data is based on the function f hot (X(t)) and f warm (X(t)) is used to judge, as follows:

[0020] where f hot (X(t)) represents the data function of access frequency, which is used to determine whether the data is hot data, f warm (X(t)) represents the data function of access frequency, which is used to determine whether the data is warm data. α and β represent the classification threshold of hot data and warm data, respectively.

[0021] Furthermore, the data migration mechanism unit dynamically adjusts the frequency of data storage based on the hierarchical storage mechanism unit. As time goes by and the frequency of data use may decrease, the migration of data between different storage levels is dynamically adjusted through a function. The function is specifically as follows:

[0022]

[0023] where f move(X(t)) represents the data migration score, which reflects the priority of data migration between different storage tiers. last access Indicates the time when the data was last accessed, t created Indicates the time when the data was created, t current Represents the time point when the function is evaluated.

[0024] Furthermore, the data management and intelligent retrieval module includes a multi-condition retrieval mechanism unit and a retrieval acceleration unit. Based on the hierarchical data storage module, the multi-condition retrieval mechanism unit allows users to perform multi-condition queries based on time, data type and geographical location conditions, and scores the query results based on functions. The functions are as follows:

[0025]

[0026] Where Q represents the score of the query result, ω i represents the weights of different conditions, f query (X i ,t,type,location) represents the matching function between data points and query conditions.

[0027] Furthermore, the retrieval acceleration unit includes a retrieval acceleration function. Based on improving the retrieval speed, the system introduces a retrieval acceleration function based on a time interval. The specific analysis is as follows:

[0028]

[0029] where f speed (t start ,t end ) represents the retrieval acceleration function, which is used to optimize the efficiency of time interval query, t start ,t end Respectively represent the start and end points of time.

[0030] Furthermore, the data analysis and visualization module includes a data analysis and key performance indicator unit, a trend prediction function unit and a visualization report generation unit. The data analysis and visualization module performs in-depth analysis on the stored data through advanced data analysis algorithms. The specific analysis is as follows:

[0031] The data analysis and key performance indicator unit analyzes the key performance indicators of the power grid through complex functions, analyzes the load balance of each node in the power grid, and ensures the reasonable distribution of the load of each node. The specific function is as follows:

[0032]

[0033] where f balance(t) represents the load balance calculation function, which evaluates the uniformity of the system load, L i (t) represents the load of the i-th node at time t, L total (t) represents the total load of the system;

[0034] The trend prediction unit predicts the future operation trend of the power grid through a time series model. The time series model uses the function

[0035] f predict (t+Δt)=φ·X(t)+(1-φ)·f previous (t)

[0036] where f predict (t+Δt) represents the time series prediction function, φ represents the smoothing factor, which affects the weight of the prediction, and Δt represents the prediction time interval;

[0037] The visualization report generation unit generates a chart report through a visualization function based on the analysis and prediction results. The visualization report can help managers monitor the operation status of the power grid in real time and make reasonable decisions.

[0038] Further, according to the smart grid monitoring data storage method of claim 9, the steps of using the smart grid monitoring data storage system of any one of claims 1 to 9 are as follows:

[0039] Step 1: First, the power grid operation data is collected in real time through various sensors in the data acquisition module. The sensors are distributed at key nodes of the power grid and can collect various types of data at a high frequency (such as every second) to ensure the timeliness and accuracy of the data;

[0040] Step 2: After data collection is completed, the data preprocessing module cleans, denoises and formats the raw data. Through the dynamic threshold detection method, the system can identify and eliminate outliers;

[0041] Step 3: The pre-processed data is input into the hierarchical data storage module. According to the frequency of data use, the data is divided into hot data, warm data and cold data for hierarchical storage, and the data is stored in different media respectively;

[0042] Step 4: After the data is stored, the intelligent data retrieval and management module provides efficient data retrieval services through multi-condition complex query algorithms;

[0043] Step 5: Finally, the data analysis and visualization module conducts in-depth analysis of the stored data through advanced data analysis algorithms. The analysis results are presented in the form of reports and visual charts to help users quickly understand the operating status of the power grid.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. A smart grid monitoring data storage system of the present invention includes a data acquisition module, a data preprocessing module, a hierarchical data storage module, an intelligent data retrieval and management module, and a data analysis and visualization module. The data acquisition module includes sensors, and real-time grid operation data is collected through a variety of sensors; the data preprocessing module cleans, denoises and formats the collected data and adopts a dynamic threshold detection method to identify data abnormal values; the hierarchical data storage module adopts a hierarchical storage strategy for hot data, warm data and cold data, stores the data in different media respectively, and introduces a time series database; the intelligent data retrieval and management module uses a multi-condition complex query algorithm to retrieve the stored data, and improves the query efficiency through an intelligent data index and retrieval mechanism. The hierarchical data storage module optimizes the utilization efficiency of storage resources by classifying and storing the data according to the frequency of use;

[0046] 2. The present invention provides a smart grid monitoring data storage system, which generates reports and visualization charts through advanced data analysis algorithms through a data analysis and visualization module. The data analysis and visualization module uses advanced analysis algorithms and visualization tools to make the grid operation status intuitive and easy to understand. Through real-time monitoring and historical data analysis, the system can identify potential problems and issue early warnings in a timely manner, thereby supporting data-driven intelligent decision-making and improving the safety and reliability of grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0048] Figure 1 This is a flow chart of a smart grid monitoring data storage system of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] like Figure 1As shown, a smart grid monitoring and data storage system includes a data acquisition module, a data preprocessing module, a hierarchical data storage module, an intelligent data retrieval and management module, and a data analysis and visualization module. The data acquisition module includes sensors, which collect grid operation data in real time through multiple sensors; the data preprocessing module cleans, denoises and formats the collected data and uses a dynamic threshold detection method to identify data anomalies; the hierarchical data storage module adopts a hierarchical storage strategy for hot data, warm data and cold data, stores data in different media respectively, and introduces a time series database to support efficient time series data storage; the intelligent data retrieval and management module uses a multi-condition complex query algorithm to retrieve the stored data, and improves the query efficiency through an intelligent data index and retrieval mechanism; the data analysis and visualization module generates reports and visualization charts through advanced data analysis algorithms to help users understand the operation status of the grid.

[0051] The data acquisition module includes a data acquisition unit, which includes a voltage sensor, a current sensor, a frequency sensor and a temperature sensor. The voltage, current, frequency and temperature deployed at each node of the power grid are collected based on multiple sensors, and the collected data are recorded as V(t), I(t), f(t), T(t) respectively, and the data parameters of V(t), I(t), f(t), T(t) are formed into a feature vector X(t) = [V(t), I(t), f(t), T(t)], and the collected data are transmitted to the data preprocessing module.

[0052] The data acquisition module is responsible for collecting grid operation data in real time, monitoring it through a variety of sensors, and using Internet of Things technology to securely transmit the data to the central processing system. This module ensures the timeliness and accuracy of the data, providing a reliable foundation for subsequent data processing and analysis.

[0053] The data preprocessing module includes a data processing unit and a dynamic threshold anomaly detection unit. The data processing unit uses a sliding window function to smooth the data and eliminate the volatility and noise of the data. The sliding window function is as follows:

[0054]

[0055] in represents the smoothed feature vector obtained by sliding window processing, N represents the size of the sliding window, and X(t) = [V(t), I(t), f(t), T(t)] represents the unified data of the voltage sensor, current sensor, frequency sensor and temperature sensor.

[0056] The dynamic threshold anomaly detection unit includes a dynamic threshold function. The system uses the dynamic threshold function to detect and mark outliers to ensure the accuracy of the data. The specific analysis is as follows:

[0057] If|X(t)-μ(t)|>k·σ(t),then mark as anomaly

[0058] Where μ(t) and σ(t) represent the mean and standard deviation of the data at the current moment, respectively; k represents the adjustment factor; and X(t) represents the characteristic vector, which includes the voltage, current, frequency and temperature grid operation data at time t.

[0059] The data preprocessing module cleans, denoises and formats the collected data to improve data quality to ensure the effectiveness of the analysis. By adopting a dynamic threshold detection method, the module can effectively identify outliers, thereby ensuring the accuracy and reliability of subsequent analysis results.

[0060] The hierarchical data storage module includes a hierarchical storage mechanism unit and a data migration mechanism unit. The hierarchical storage mechanism is based on the frequency of use and time characteristics of data. The hierarchical storage mechanism unit divides the data into hot data, warm data and cold data according to the frequency of use based on the data processed by the data preprocessing module, and stores them in different types of storage media. Hot data is frequently accessed data and is stored in solid-state drives, warm data is stored in traditional hard drives, and cold data is historical data and is stored in low-cost tapes. The hierarchical storage of data is based on the function f hot (X(t)) and f warm (X(t)) is used to judge, as follows:

[0061]

[0062] where f hot (X(t)) represents the data function of access frequency, which is used to determine whether the data is hot data, f warm (X(t)) represents the data function of access frequency, which is used to determine whether the data is warm data. α and β represent the classification threshold of hot data and warm data, respectively.

[0063] The hierarchical data storage module adopts a hierarchical storage strategy, which divides data into hot data, warm data and cold data according to the access frequency, and stores them in different media respectively. By using the time series database, this module optimizes the storage efficiency and data access speed to support efficient time series data storage and management.

[0064] The data migration mechanism unit dynamically adjusts the frequency of data storage based on the tiered storage mechanism unit. As time goes by and the frequency of data use may decrease, the migration of data between different storage levels is dynamically adjusted through functions. The functions are as follows:

[0065]

[0066] where f move (X(t)) represents the data migration score, which reflects the priority of data migration between different storage tiers. last access Indicates the time when the data was last accessed, t created Indicates the time when the data was created, t current Represents the time point when the function is evaluated.

[0067] The data management and intelligent retrieval module includes a multi-condition retrieval mechanism unit and a retrieval acceleration unit. Based on the hierarchical data storage module, the multi-condition retrieval mechanism unit allows users to perform multi-condition queries based on time, data type and geographical location, and score the query results based on functions. The functions are as follows:

[0068]

[0069] Where Q represents the score of the query result, ω i represents the weights of different conditions, f query (X i ,t,type,location) represents the matching function between data points and query conditions.

[0070] The retrieval acceleration unit includes a retrieval acceleration function. In order to improve the retrieval speed, the system introduces a retrieval acceleration function based on time intervals. The specific analysis is as follows:

[0071]

[0072] where f speed (t start ,t end ) represents the retrieval acceleration function, which is used to optimize the efficiency of time interval query, t start ,t end Respectively represent the start and end points of time.

[0073] The intelligent data retrieval and management module uses a multi-condition complex query algorithm to achieve flexible retrieval of stored data. Through intelligent data indexing and caching mechanisms, the module significantly improves query efficiency, allowing users to quickly obtain required information, thereby improving the timeliness and accuracy of decision-making.

[0074] The data analysis and visualization module includes a data analysis and key performance indicator unit, a trend prediction function unit, and a visualization report generation unit. The data analysis and visualization module uses advanced data analysis algorithms to conduct in-depth analysis of the stored data. The specific analysis is as follows:

[0075] The data analysis and key performance indicator unit analyzes the key performance indicators of the power grid through complex functions, analyzes the load balance of each node in the power grid, and ensures the reasonable distribution of the load of each node. The specific functions are as follows:

[0076]

[0077] where f balance (t) represents the load balance calculation function, which evaluates the uniformity of the system load, L i (t) represents the load of the i-th node at time t, L total (t) represents the total load of the system;

[0078] The trend prediction unit predicts the future operation trend of the power grid through the time series model. The time series model uses the function

[0079] f predict (t+Δt)=φ·X(t)+(1-φ)·f previous (t)

[0080] where f predict (t+Δt) represents the time series prediction function, φ represents the smoothing factor, which affects the weight of the prediction, and Δt represents the prediction time interval;

[0081] The visualization report generation unit generates chart reports through visualization functions based on the analysis and prediction results. The visualization report can help managers monitor the operating status of the power grid in real time and make reasonable decisions.

[0082] The data analysis and visualization module generates in-depth analysis reports and interactive visualization charts through advanced data analysis algorithms to help users understand the operation status of the power grid. This module not only provides real-time monitoring information, but also uses predictive models to reveal potential problems, support data-driven decision-making processes, and improve the level of intelligence in power grid management.

[0083] According to a smart grid monitoring data storage method according to claim 9, the steps of using a smart grid monitoring data storage system according to any one of claims 1 to 9 are as follows:

[0084] Step 1: First, the power grid operation data is collected in real time through various sensors in the data acquisition module. The sensors are distributed at key nodes of the power grid and can collect various types of data at a high frequency (such as every second) to ensure the timeliness and accuracy of the data;

[0085] Step 2: After data collection is completed, the data preprocessing module cleans, denoises and formats the raw data. Through the dynamic threshold detection method, the system can identify and eliminate outliers;

[0086] Step 3: The pre-processed data is input into the hierarchical data storage module. According to the frequency of data use, the data is divided into hot data, warm data and cold data for hierarchical storage, and the data is stored in different media respectively;

[0087] Step 4: After the data is stored, the intelligent data retrieval and management module provides efficient data retrieval services through multi-condition complex query algorithms;

[0088] Step 5: Finally, the data analysis and visualization module conducts in-depth analysis of the stored data through advanced data analysis algorithms. The analysis results are presented in the form of reports and visual charts to help users quickly understand the operating status of the power grid.

[0089] Working principle: First, the power grid operation data is collected in real time through various sensors in the data acquisition module. The sensors are distributed at the key nodes of the power grid and can collect various types of data at a high frequency (such as every second) to ensure the timeliness and accuracy of the data. After the data collection is completed, the data preprocessing module cleans, denoises and formats the original data. Through the dynamic threshold detection method, the system can identify and eliminate outliers. The preprocessed data is input into the hierarchical data storage module. According to the frequency of data use, the data is divided into hot data, warm data and cold data for hierarchical storage, and the data is stored in different media respectively. After the data is stored, the intelligent data retrieval and management module provides efficient data retrieval services through multi-condition complex query algorithms. Finally, the data analysis and visualization module uses advanced data analysis algorithms to conduct in-depth analysis of the stored data. The analysis results are presented in the form of reports and visual charts to help users quickly understand the operation status of the power grid.

[0090] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A smart grid monitoring data storage system, characterized in that: It includes a data acquisition module, a data preprocessing module, a hierarchical data storage module, an intelligent data retrieval and management module, and a data analysis and visualization module. The data acquisition module includes sensors, which collect power grid operation data in real time through multiple sensors; the data preprocessing module cleans, denoises and formats the collected data and adopts a dynamic threshold detection method to identify data anomalies; the hierarchical data storage module adopts a hierarchical storage strategy for hot data, warm data and cold data, stores data in different media respectively, and introduces a time series database to support efficient time series data storage; the intelligent data retrieval and management module uses a multi-condition complex query algorithm to retrieve the stored data, and improves the query efficiency through an intelligent data index and retrieval mechanism; the data analysis and visualization module generates reports and visualization charts through advanced data analysis algorithms to help users understand the operation status of the power grid.

2. A smart grid monitoring data storage system according to claim 1, characterized in that: The data acquisition module includes a data acquisition unit, which includes a voltage sensor, a current sensor, a frequency sensor and a temperature sensor. The voltage, current, frequency and temperature deployed at each node of the power grid are collected based on multiple sensors, and the collected data are recorded as V(t), I(t), f(t), T(t) respectively, and the data parameters of V(t), I(t), f(t), T(t) are formed into a characteristic vector X(t)=[V(t), I(t), f(t), T(t)], and the collected data are transmitted to the data preprocessing module.

3. A smart grid monitoring data storage system according to claim 2, characterized in that: The data preprocessing module includes a data processing unit and a dynamic threshold anomaly detection unit. The data processing unit uses a sliding window function to smooth the data to eliminate the volatility and noise of the data. The sliding window function is as follows: in represents the smoothed feature vector obtained by sliding window processing, N represents the size of the sliding window, and X(t) = [V(t), I(t), f(t), T(t)] represents the unified data of the voltage sensor, current sensor, frequency sensor and temperature sensor.

4. A smart grid monitoring data storage system according to claim 3, characterized in that: The dynamic threshold anomaly detection unit includes a dynamic threshold function. The system uses the dynamic threshold function to detect and mark outliers to ensure the accuracy of the data. The specific analysis is as follows: If|X(t)-μ(t)|>k·σ(t),then mark as anomaly Where μ(t) and σ(t) represent the mean and standard deviation of the data at the current moment, respectively; k represents the adjustment factor; and X(t) represents the characteristic vector, which includes the voltage, current, frequency and temperature grid operation data at time t.

5. A smart grid monitoring data storage system according to claim 1, characterized in that: The hierarchical data storage module includes a hierarchical storage mechanism unit and a data migration mechanism unit. The hierarchical storage mechanism is based on the frequency of use and time characteristics of data. The hierarchical storage mechanism unit divides the data into hot data, warm data and cold data according to the frequency of use based on the data processed by the data preprocessing module, and stores them in different types of storage media respectively. Hot data is frequently accessed data and is stored in a solid state drive, warm data is stored in a traditional hard drive, and cold data is historical data and is stored in a low-cost magnetic tape. The hierarchical storage of data is based on the function f hot (X(t)) and f warm (X(t)) is used to judge, as follows: where f hot (X(t)) represents the data function of access frequency, which is used to determine whether the data is hot data, f warm (X(t)) represents the data function of access frequency, which is used to determine whether the data is warm data. α and β represent the classification threshold of hot data and warm data, respectively.

6. A smart grid monitoring data storage system according to claim 5, characterized in that: The data migration mechanism unit dynamically adjusts the frequency of data storage based on the hierarchical storage mechanism unit. As time goes by and the frequency of data use may decrease, the migration of data between different storage levels is dynamically adjusted through a function. The function is as follows: where f move (X(t)) represents the data migration score, which reflects the priority of data migration between different storage tiers. lastaccess Indicates the time when the data was last accessed, t created Indicates the time when the data was created, t current Represents the time point when the function is evaluated.

7. A smart grid monitoring data storage system according to claim 1, characterized in that: The data management and intelligent retrieval module includes a multi-condition retrieval mechanism unit and a retrieval acceleration unit. Based on the hierarchical data storage module, the multi-condition retrieval mechanism unit allows users to perform multi-condition queries based on time, data type and geographical location conditions, and scores the query results based on functions. The functions are as follows: Where Q represents the score of the query result, ω i represents the weights of different conditions, f query (X i ,t,type,location) represents the matching function between data points and query conditions.

8. A smart grid monitoring data storage system according to claim 7, characterized in that: The retrieval acceleration unit includes a retrieval acceleration function. In order to improve the retrieval speed, the system introduces a retrieval acceleration function based on a time interval. The specific analysis is as follows: where f speed (t start ,t end ) represents the retrieval acceleration function, which is used to optimize the efficiency of time interval query, t start ,t end Respectively represent the start and end points of time.

9. A smart grid monitoring data storage system according to claim 7, characterized in that: The data analysis and visualization module includes a data analysis and key performance indicator unit, a trend prediction function unit and a visualization report generation unit. The data analysis and visualization module uses advanced data analysis algorithms to perform in-depth analysis on the stored data. The specific analysis is as follows: The data analysis and key performance indicator unit analyzes the key performance indicators of the power grid through complex functions, analyzes the load balance of each node in the power grid, and ensures the reasonable distribution of the load of each node. The specific function is as follows: where f balance (t) represents the load balance calculation function, which evaluates the uniformity of the system load, L i (t) represents the load of the i-th node at time t, L total (t) represents the total load of the system; The trend prediction unit predicts the future operation trend of the power grid through a time series model. The time series model uses the function f predict (t+Δt)=φ·X(t)+(1-φ)·f previous (t) where f predict (t+Δt) represents the time series prediction function, φ represents the smoothing factor, which affects the weight of the prediction, and Δt represents the prediction time interval; The visualization report generation unit generates a chart report through a visualization function based on the analysis and prediction results. The visualization report can help managers monitor the operation status of the power grid in real time and make reasonable decisions.

10. A smart grid monitoring data storage system according to claim 9, characterized in that: The steps of using a smart grid monitoring data storage system as described in any one of claims 1 to 9 are as follows: Step 1: First, the power grid operation data is collected in real time through various sensors in the data acquisition module; Step 2: After data collection is completed, the data preprocessing module cleans, denoises and formats the raw data. Through the dynamic threshold detection method, the system can identify and eliminate outliers; Step 3: The pre-processed data is input into the hierarchical data storage module. According to the frequency of data use, the data is divided into hot data, warm data and cold data for hierarchical storage, and the data is stored in different media respectively; Step 4: After the data is stored, the intelligent data retrieval and management module provides efficient data retrieval services through multi-condition complex query algorithms; Step 5: Finally, the data analysis and visualization module conducts in-depth analysis of the stored data through advanced data analysis algorithms. The analysis results are presented in the form of reports and visual charts to help users quickly understand the operating status of the power grid.

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