Smart power grid data management method and device, storage medium and computer equipment

Abstract

According to the smart power grid data management method and device, the storage medium and the computer equipment provided by the invention, when data management is carried out on a smart power grid, data preprocessing is carried out in situ after the operation data of the power grid equipment is acquired in a distributed manner by adopting the sensor nodes; then the processed data is encrypted and transmitted to the edge computing node by adopting a multi-stage wireless network, so that the pressure of data transmission can be reduced, and the reliability and the real-time performance of data transmission are improved; the edge computing nodes are constructed by adopting a distributed computing framework and an edge intelligent model, so that the edge computing nodes are used for analyzing and processing the received data and then storing the data into a database by adopting a hierarchical data storage strategy, distributed computing and storage of the data can be realized, dependence on a central control room is reduced, and the data storage efficiency is improved. The high efficiency and the stability of the intelligent power grid are improved; therefore, related data can be efficiently acquired from the database for visualization and application, and efficient operation of the smart power grid is ensured.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a smart grid data management method, device, storage medium and computer equipment. Background Art

[0002] The power system is a key infrastructure of modern society, covering four links: power generation, transmission, distribution and consumption. In order to cope with the continuous growth of power demand and the increasing complexity of power grids in recent years, the power system is gradually transforming into a smart grid, which can realize the automation, visualization and intelligence of the power system by integrating advanced sensing, communication, computing and control technologies.

[0003] However, the management method adopted by smart grids relies on a centralized architecture and uses wired communication technology to transmit data collected from power equipment to a central control room for centralized processing and storage. In this context, the current smart grid management method is limited by the centralized architecture and complex wiring, resulting in poor reliability and real-time performance of data transmission, which in turn affects the efficient operation of smart grids. Summary of the invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defect that the smart grid management method in the prior art is limited by the centralized architecture and complex wiring, resulting in poor reliability and real-time performance of data transmission, which in turn affects the efficient operation of the smart grid.

[0005] The present application provides a smart grid data management method, the method comprising:

[0006] The sensor nodes are used to collect the original operation data of the power grid equipment in a distributed manner, and the sensor nodes are used to perform data preprocessing on the original operation data to obtain target operation data;

[0007] The target operation data is encrypted and transmitted to the edge computing node using a multi-level wireless network; wherein the edge computing node is constructed using a distributed computing framework and an edge intelligence model;

[0008] Utilize the edge computing node to analyze and process the target operation data to obtain real-time analysis data, and adopt a hierarchical data storage strategy to store the target operation data and the real-time analysis data in a database;

[0009] The business requirements of the smart grid are determined, and target operation data and / or real-time operation data corresponding to the business requirements are obtained from the database for visualization to obtain a visualization chart.

[0010] Optionally, the using the sensor node to perform data preprocessing on the original operation data to obtain target operation data includes:

[0011] The sensor node is used to filter and compress the original operation data to obtain intermediate operation data, and abnormality detection is performed on the intermediate operation data, and data with abnormal detection results in the intermediate operation data are marked to form target operation data.

[0012] Optionally, the adopting a multi-level wireless network to encrypt and transmit the target operation data to the edge computing node includes:

[0013] Determine a multi-level wireless network consisting of at least one wireless communication network according to the distribution characteristics of the sensor nodes and the edge computing nodes; the wireless communication network includes a wireless sensor network, a cellular communication network and a low power wide area network;

[0014] Establishing a network connection channel between the sensor node and the edge computing node based on the multi-level wireless network, and configuring a communication protocol corresponding to the network connection channel;

[0015] Based on the network connection channel, the target operation data is encrypted and transmitted to the edge computing node using the communication protocol.

[0016] Optionally, the using the edge computing node to analyze and process the target operation data to obtain real-time analysis data includes:

[0017] The target operation data is aggregated and processed by utilizing the edge computing node to generate a comprehensive data report, and trend analysis and prediction are performed on the comprehensive data report to obtain real-time analysis data.

[0018] Optionally, the database includes a local repository and a cloud repository;

[0019] The adopting of a hierarchical data storage strategy to store the target operation data and the real-time analysis data in a database includes:

[0020] storing the target operation data and the real-time analysis data in the local storage repository and the cloud storage repository respectively;

[0021] The local repository is a short-term storage database; the cloud repository is a long-term storage database and is provided with a data backup mechanism.

[0022] Optionally, acquiring target operation data and / or real-time operation data corresponding to the business requirement from the database for visualization to obtain visualization data includes:

[0023] Acquire target operation data and / or real-time operation data corresponding to the business requirement from the database as visualization data;

[0024] A chart format corresponding to the business requirement is determined, and data aggregation analysis is performed on the visualization data based on the icon format, so as to generate visualization data corresponding to the icon format according to the analysis result.

[0025] Optionally, the method further comprises:

[0026] The alarm logic mechanism is started, and the target operation data is compared with the threshold value based on the preset performance threshold table, and when there is data exceeding the threshold value in the comparison result, an alarm notification is initiated.

[0027] The present application also provides a smart grid data management device, comprising:

[0028] A data acquisition module, used to perform distributed acquisition of raw operation data of power grid equipment using sensor nodes, and perform data preprocessing on the raw operation data using the sensor nodes to obtain target operation data;

[0029] A data transmission module, used to encrypt and transmit the target operation data to an edge computing node using a multi-level wireless network; wherein the edge computing node is constructed using a distributed computing framework and an edge intelligence model;

[0030] A data storage module, used to analyze and process the target operation data using the edge computing node to obtain real-time analysis data, and store the target operation data and the real-time analysis data in a database using a hierarchical data storage strategy;

[0031] The data visualization module is used to determine the business needs of the smart grid, and obtain the target operation data and / or real-time operation data corresponding to the business needs from the database for visualization to obtain a visualization chart.

[0032] The present application also provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the smart grid data management method as described in any one of the above embodiments.

[0033] The present application also provides a computer device, comprising: one or more processors, and a memory;

[0034] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the smart grid data management method as described in any one of the above embodiments are performed.

[0035] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0036] The smart grid data management method, device, storage medium and computer equipment provided by the present application can first use sensor nodes to distribute the original operation data of the power grid equipment when managing the smart grid data, and use the sensor nodes to pre-process the original operation data to obtain the target operation data. Here, the collected data is processed in situ in a distributed manner, and then a multi-level wireless network is used to encrypt and transmit the target operation data to the edge computing node, which can reduce the pressure of data transmission. The reliability and real-time performance of data transmission can be further improved by the multi-level wireless network. Since the edge computing node is constructed by adopting a distributed computing framework and an edge intelligent model, the target operation data is analyzed and processed by the edge computing node. After obtaining the real-time analysis data, a hierarchical data storage strategy is used to store the target operation data and the real-time analysis data in the database, which can realize distributed computing and storage of data, reduce dependence on the central control room, and thus improve the efficiency and stability of the smart grid. Therefore, the present application can efficiently obtain data corresponding to the business needs of the smart grid from the database for visualization and application, thereby ensuring the efficient operation of the smart grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0038] Figure 1 A flowchart of a smart grid data management method provided in an embodiment of the present application;

[0039] Figure 2 A schematic diagram of a data encryption transmission process provided in an embodiment of the present application;

[0040] Figure 3 A schematic diagram of the structure of a smart grid data management device provided in an embodiment of the present application;

[0041] Figure 4 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0043] The management method adopted by smart grid relies on centralized architecture and uses wired communication technology to transmit the data collected from power equipment to the central control room for centralized processing and storage. In this context, the current smart grid management method is limited by the centralized architecture and complex wiring, resulting in poor reliability and real-time performance of data transmission, which in turn affects the efficient operation of smart grid.

[0044] Based on this, this application proposes the following technical solutions, see below for details:

[0045] In one embodiment, Figure 1 As shown, Figure 1 A flow chart of a smart grid data management method provided in an embodiment of the present application; the present application provides a smart grid data management method, which specifically includes the following:

[0046] S110: using sensor nodes to perform distributed collection of original operation data of power grid equipment, and using the sensor nodes to perform data preprocessing on the original operation data to obtain target operation data.

[0047] In this step, when it is necessary to manage the equipment operation data generated in real time in the smart grid, the computer equipment can use sensor nodes to perform distributed collection of the original operation data of each grid equipment in the smart grid, and then use the sensor nodes to perform data preprocessing on the collected original operation data, so as to obtain target operation data with regular data. Here, the collected data is processed on-site in a distributed manner, which can reduce the pressure of data transmission and thus improve the real-time performance of data transmission.

[0048] It is understandable that the grid equipment in the smart grid, such as substations, distribution transformers, power equipment and other equipment, are equipped with monitoring sensors according to their own monitoring needs, such as voltage sensors, current sensors, power sensors, frequency sensors or temperature sensors, etc., which are used to collect relevant data generated by the grid equipment during operation, such as voltage, current, power, frequency and temperature, etc. In this context, the present application can configure multiple sensor nodes in the smart grid to use each sensor node to read the relevant data of the grid equipment from each monitoring sensor, and perform data preprocessing on the spot before transmission, thereby improving the real-time performance of data transmission.

[0049] Specifically, the computer device can select the corresponding data acquisition device according to the data processing requirements of the smart grid, such as a data collector, RTU (Remote Terminal Unit), PLC (Programmable Logic Controller), etc. Then determine the installation location of the monitoring sensor in the smart grid, which is usually installed at the substation, distribution transformer, transmission line and key power equipment to comprehensively monitor the operation status of the smart grid. After determining the installation location of the monitoring sensor, the computer device can configure the parameters of the data acquisition device, such as parameter setting, data acquisition frequency, data storage and transmission mode, etc., and select the corresponding interface according to the configuration parameters to interface the data acquisition device with each monitoring sensor and calibrate the data to form multiple sensor nodes, so as to ensure that the data acquisition device can accurately read the data from the monitoring sensor. Among them, the interface between the monitoring sensor and the data acquisition device can select an analog signal interface or a digital signal interface, which is not limited here.

[0050] Furthermore, during the data collection process, the power grid equipment may be interfered by electromagnetic radiation from other equipment, resulting in noise, null values, abnormal values ​​or data in different formats in the collected data. These data, as redundant data, will not only increase the pressure of data transmission, but also affect the accuracy and reliability of subsequent data analysis. Therefore, after collecting the original operation data, the sensor node can also use the sensor node to preprocess the original operation data to reduce the pressure of data transmission.

[0051] S120: Use a multi-level wireless network to encrypt and transmit the target operation data to the edge computing node.

[0052] In this step, after obtaining the target operation data through step S110, the computer device can use a multi-level wireless network to encrypt and transmit the target operation data to the edge computing node, thereby further improving the reliability and real-time performance of data transmission through the multi-level wireless network, while also ensuring the security of data transmission through data encryption, access control and privacy protection mechanisms.

[0053] It can be understood that through a multi-level wireless network, the present application can flexibly select a communication network with corresponding bandwidth and power consumption for data transmission according to the actual application scenario of data transmission. While ensuring efficient data transmission, it can also avoid the limitations of complex wiring, thereby improving the real-time and reliability of data transmission.

[0054] Specifically, the edge computing node of the present application is obtained by constructing the edge computing device using a distributed computing framework and an edge intelligence model. The edge computing device here can select edge servers, edge gateways and other devices with certain computing power and storage capacity, so that the distributed computing framework and edge intelligence model can be deployed on the device to further enhance the computing power of the edge computing device. Among them, the distributed computing framework refers to a system architecture that allows data or computing tasks to be distributed among multiple computing nodes; and the edge intelligence model refers to a lightweight artificial intelligence model that can directly process and analyze the data at the source of data generation, thereby reducing the delay and bandwidth consumption of data transmission, and improving the response speed and efficiency of the system. Therefore, the computer device can encrypt and transmit the target operation data to the edge computing node, so that the edge computing node can perform in-depth data analysis and processing on the target operation data to further improve the real-time and reliability of data transmission.

[0055] S130: Analyze and process the target operation data using the edge computing node to obtain real-time analysis data, and use a hierarchical data storage strategy to store the target operation data and the real-time analysis data in a database.

[0056] In this step, after the target operation data is transmitted to the edge computing node through step S120, the computer device can use the edge computing node to perform in-depth analysis and processing on the target operation data to obtain real-time analysis data, and then use a hierarchical data storage strategy to store the target operation data and real-time analysis data in the database, thereby realizing distributed computing and storage of data, reducing dependence on the central control room, and thereby improving the efficiency and stability of the smart grid.

[0057] It is understandable that since edge computing nodes are constructed by using distributed computing frameworks and edge intelligence models to build edge computing devices, computer devices can use the distributed framework in the edge computing nodes to perform distributed data processing and analysis on the target operation data, and then use the edge intelligence model to realize real-time analysis and prediction of the target operation data, such as load forecasting, power quality monitoring, fault diagnosis, etc., which can provide instant feedback and decision support for subsequent data applications.

[0058] Specifically, through the hierarchical data storage strategy, computer equipment can store target operation data and real-time analysis data in different storage media in the database according to data characteristics and access frequency, so as to meet the needs of real-time analysis and long-term preservation, optimize the use of storage resources, and improve the access speed of data. In this way, when the application data is needed later, the computer equipment can quickly obtain the corresponding data from the corresponding storage medium to ensure the efficient operation of the smart grid.

[0059] S140: Determine the business needs of the smart grid, and obtain target operation data and / or real-time operation data corresponding to the business needs from a database for visualization to obtain a visualization chart.

[0060] In this step, after storing the data in the database through step S130, the business needs of the smart grid can be determined first, and then the target operation data and / or real-time operation data corresponding to the business needs can be obtained from the database for visualization to obtain a visualization chart, thereby realizing efficient application of operation data in the smart grid.

[0061] Specifically, computer equipment can use data visualization tools to visualize data, such as using Tableau and Power BI to create interactive charts and dashboards to intuitively display relevant data to management and users, such as displaying equipment power consumption trend charts, equipment load forecast charts, abnormal threshold alarm charts, etc., without limitation here.

[0062] Among them, Tableau refers to a data visualization tool that supports access to multiple data sources and rich visualization functions. Users can create interactive charts and dashboards by dragging and dropping. Therefore, computer equipment can use Tableau to intuitively display the analysis results to users, such as creating a real-time monitoring dashboard to display the real-time power consumption of each device, power consumption trend charts, etc.; and Power BI can support data integration, data analysis and visualization, and provide rich chart types and powerful data analysis functions. Therefore, computer equipment can use Power BI to create various data reports and visualization charts to help users make data-driven decisions, such as generating equipment power consumption reports, charts to predict future load demand, etc.

[0063] Furthermore, when determining the business needs of the smart grid, the computer device can first determine whether the user has pre-set specific business needs. If yes, relevant data can be obtained and visualized from the database according to the business needs set by the user. If no, relevant data can be obtained and visualized from the database according to the default business needs in the smart grid, thereby improving the flexibility of data visualization.

[0064] In the above embodiment, when managing the smart grid data, the original operation data of the power grid equipment can be first distributedly collected by the sensor nodes, and the original operation data can be preprocessed by the sensor nodes to obtain the target operation data. Here, the collected data is processed in situ in a distributed manner, and then a multi-level wireless network is used to encrypt and transmit the target operation data to the edge computing node, which can reduce the pressure of data transmission. The reliability and real-time performance of data transmission can be further improved by the multi-level wireless network. Since the edge computing node is constructed by adopting a distributed computing framework and an edge intelligent model, the target operation data is analyzed and processed by the edge computing node. After obtaining the real-time analysis data, a hierarchical data storage strategy is used to store the target operation data and the real-time analysis data in the database, which can realize distributed computing and storage of data, reduce dependence on the central control room, and thus improve the efficiency and stability of the smart grid. Therefore, the present application can efficiently obtain data corresponding to the business needs of the smart grid from the database for visualization and application, thereby ensuring the efficient operation of the smart grid.

[0065] In one embodiment, in step S110, using the sensor node to perform data preprocessing on the original operation data to obtain the target operation data may include:

[0066] S111: Filter and compress the original operation data using the sensor nodes to obtain intermediate operation data, perform abnormality detection on the intermediate operation data, and mark the data with abnormal detection results in the intermediate operation data to form target operation data.

[0067] In this embodiment, when the computer device uses the sensor node to perform data preprocessing on the original operation data, it can first filter the original operation data to filter out the noise in the data and ensure the accuracy of the data, and then compress the filtered data to reduce the amount of data transmission and improve the transmission efficiency; then, the computer device can also perform anomaly detection on the intermediate operation data formed after filtering and compression, and mark the data with abnormal detection results in the intermediate operation data to form target operation data, laying the foundation for in-depth analysis and processing of subsequent data.

[0068] Specifically, the computer device may use a filtering algorithm to filter the original operation data, and the filtering algorithm here may use moving average filtering, weighted moving average filtering and Kalman filtering. The calculation expression of the moving average filtering may be as follows:

[0069]

[0070] In the formula, Represents the data signal input at the nth moment; represents the filtered data signal; Indicates the filter window size

[0071] The calculation expression of weighted moving average filtering can be shown as follows:

[0072]

[0073] In the formula, Represents weights, and the weights here need to be normalized.

[0074] Kalman filtering includes two steps: prediction step and update step. The calculation expression of the prediction step can be shown as follows:

[0075]

[0076] The calculation expression of the update step can be as follows:

[0077]

[0078] In the calculation expressions of the above two steps, k represents the kth moment; represents state estimation; P represents covariance matrix; A represents state transfer matrix; B represents control input matrix; represents the control input; Q represents the process noise covariance; H represents the observation matrix; R represents the observation noise covariance; K represents the Kalman gain; and z represents the output data.

[0079] Next, the computer device can use discrete wavelet transform to compress the filtered data. Here, the calculation expression of discrete wavelet transform can be shown as follows:

[0080]

[0081] In the formula, Represents compressed data; represents the wavelet basis function; represents the wavelet coefficients.

[0082] Furthermore, the computer device may also use a Z-Score method or a statistical model to perform anomaly detection on the intermediate operation data. The calculation expression of the Z-Score method may be as follows:

[0083]

[0084] In the formula, represents the input data point; represents the data mean; represents the data standard deviation; Z represents the detection result; if Z exceeds the set threshold, the computer device can determine that the data point is abnormal data.

[0085] The calculation expression of the statistical model can be shown as follows:

[0086]

[0087] It should be noted that in statistical models, outliers are usually defined as those that exceed Range of data points.

[0088] In one embodiment, Figure 2 As shown, Figure 2 A schematic diagram of a data encryption transmission process provided in an embodiment of the present application; Figure 2 In step S120, the target operation data is encrypted and transmitted to the edge computing node using a multi-level wireless network, which may include:

[0089] S121: Determine a multi-level wireless network consisting of at least one wireless communication network according to distribution characteristics of sensor nodes and edge computing nodes.

[0090] S122: Establish a network connection channel between the sensor node and the edge computing node based on the multi-level wireless network, and configure a communication protocol corresponding to the network connection channel.

[0091] S123: Based on the network connection channel, the target operation data is encrypted and transmitted to the edge computing node using the communication protocol.

[0092] In this embodiment, when the computer device transmits the target operation data to the edge computing node, it can first determine a multi-level wireless network composed of at least one wireless communication network based on the distribution characteristics of the sensor nodes and the edge computing nodes, and then establish a network connection channel between the sensor nodes and the edge computing nodes based on the multi-level wireless network, and configure a communication protocol corresponding to the network connection channel. Finally, based on the network connection channel, the computer device can use the communication protocol to encrypt and transmit the target operation data to the edge computing node.

[0093] It is understandable that the wireless communication network used in this application may include wireless sensor networks, cellular communication networks and low-power wide area networks. Among them, by deploying wireless sensor networks, computer equipment can achieve low-power data transmission; by deploying cellular communication networks, computer equipment can achieve high-bandwidth and low-latency data transmission; by deploying low-power wide area networks, computer equipment can achieve long-distance, low-power data transmission, which can generally be deployed using technologies such as LoRa and NB-IoT.

[0094] Specifically, after the network connection channel is established, the computer device can also configure the communication protocol corresponding to the network connection channel, and the communication protocol here may include Modbus, DNP3, IEC 61850 and other protocols. Among them, Modbus is mainly configured in the master-slave communication scenario with a relatively simple layout, DNP3 is mainly configured in the power system communication with a relatively complex layout, and IEC 61850 is mainly configured in the communication scenario with high bandwidth and real-time transmission.

[0095] Furthermore, when encrypting and transmitting the target operation data, the computer device can use symmetric encryption technology or asymmetric encryption technology, which is not limited here. To elaborate, in symmetric encryption technology, the computer device can first generate a symmetric key, such as a 28-bit, 192-bit or 256-bit key, and then use the AES algorithm and the generated key to encrypt the data to generate ciphertext, and finally transmit the encrypted ciphertext to the edge computing node, and then the edge computing node can use the same key as the encryption key to decrypt the ciphertext; while in asymmetric encryption technology, the computer device can first generate a pair of keys, including a public key and a private key, and then use the public key to encrypt the data to generate a ciphertext, and finally transmit the encrypted ciphertext to the edge computing node, and then the edge computing node can use the private key to decrypt the ciphertext.

[0096] In one embodiment, in step S130, the target operation data is analyzed and processed by the edge computing node to obtain real-time analysis data, which may include:

[0097] S131: Use edge computing nodes to aggregate target operation data, generate comprehensive data reports, and perform trend analysis and prediction on the comprehensive data reports to obtain real-time analysis data.

[0098] In this embodiment, after the edge computing node receives and decrypts the target operation data, the computer device can use the edge computing node to aggregate the target operation data, generate a comprehensive data report, and perform trend analysis and prediction on the comprehensive data report to obtain real-time analysis data as the basic data for subsequent data visualization.

[0099] Specifically, the edge computing node can use an aggregation algorithm or a weighted aggregation algorithm to aggregate the target operation data. The calculation expression of the aggregation algorithm can be as follows:

[0100]

[0101] In the formula, represents the data of each sensor node before aggregation; n represents the total number of sensor nodes; Represents aggregated data.

[0102] The calculation expression of the weighted aggregation algorithm can be shown as follows:

[0103]

[0104] In the formula, represents the data of each sensor node before aggregation; n represents the total number of sensor nodes; Represents aggregated data; Represents weight.

[0105] Furthermore, the computer device can also perform trend analysis and prediction on the comprehensive data report generated after data aggregation to obtain real-time analysis data. The analysis process adopted here mainly includes three steps: historical data analysis, predictive analysis and decision support. To expand, historical data analysis can include descriptive statistical analysis and time series analysis; among them, descriptive statistical analysis refers to calculating the mean, variance, median, standard deviation and other descriptive statistics of historical data, so as to understand the basic characteristics of the data; time series analysis refers to analyzing the trend of parameters such as power load and power changing over time, which can use autocorrelation function (ACF) and partial autocorrelation function (PACF) to identify the periodicity and trend of data. In the predictive analysis step, the computer device can use a pre-trained prediction model for load prediction, fault prediction, etc. The initial model used for training here can use a pre-trained model such as linear regression, ARIMA model, LSTM, etc., which is not limited here. In the decision support step, the computer device can combine the results of historical data analysis and predictive analysis to form comprehensive real-time analysis data, so the computer device can formulate an optimization strategy for the operation and management of the smart grid based on the real-time analysis data, and then the optimized strategy can be implemented in the operation and management of the smart grid.

[0106] In one embodiment, the database in step S130 may include a local storage repository and a cloud storage repository; wherein the step of storing the target operation data and the real-time analysis data in the database using a hierarchical data storage strategy may include:

[0107] S132: Storing the target operation data and the real-time analysis data in a local storage repository and a cloud storage repository respectively.

[0108] In this embodiment, the computer device can store the target operation data and real-time analysis data in the local repository and the cloud repository respectively. Here, combining the short-term storage of the local repository and the long-term storage of the cloud repository, the present application can achieve fast access to data.

[0109] Specifically, the local repository can select an SQLite database, whose storage fields include timestamp, voltage, current and power, etc. The database has high reliability and fast access speed. When storing data in the local repository, the computer device can first determine the data storage format of the local repository, such as CSV, JSON, SQLite or InfluxDB, and then convert and store the data to be stored according to the timestamp and data storage format to ensure the order and traceability of the data; the computer device can also use Unix timestamp to record the data collection time. For the cloud repository, the computer device can select the corresponding cloud service provider and set the data upload interface, so that the data can be uploaded to the cloud API through HTTP POST request. The data format is usually JSON, which can ensure that the data can be uploaded to the cloud safely and reliably.

[0110] Furthermore, a data backup mechanism is also set in the cloud storage repository, which specifies the backup frequency, storage location and backup method. Therefore, based on the data backup mechanism, the computer device can perform full or incremental backup of the data in the cloud storage repository at a specified time to ensure that the data can be restored in unexpected situations, and regularly test the data recovery process to ensure the integrity and availability of the backup data.

[0111] In one embodiment, in step S140, obtaining target operation data and / or real-time operation data corresponding to the business requirements from the database for visualization to obtain visualization data may include:

[0112] S141: Obtain target operation data and / or real-time operation data corresponding to business requirements from a database as visualization data.

[0113] S142: Determine a chart format corresponding to the business requirement, and perform data aggregation analysis on the visualization data based on the icon format, so as to generate visualization data corresponding to the icon format according to the analysis result.

[0114] In this embodiment, when visualizing data in a database, the computer device can first obtain target operation data and / or real-time operation data corresponding to the business needs from the database as visualization data, and then determine the chart format corresponding to the business needs, and perform data aggregation analysis on the visualization data based on the icon format. Finally, the computer device can generate visualization data corresponding to the icon format based on the analysis results.

[0115] Specifically, the computer device can first perform preprocessing operations on the visualization data to ensure that the format of the visualization data is suitable for visualization display, and then perform data aggregation analysis on the visualization data based on the icon format to generate visualization data corresponding to the icon format according to the analysis results. The visualization data is mainly used to display key parameters such as voltage, current, and power. For example, a time series table can be used to display the trend of parameters such as voltage, current, and power changing over time; pie charts and bar charts can be used to display the proportion and distribution of different classification data.

[0116] In one embodiment, the method may further include:

[0117] S150: Start the alarm logic mechanism, and perform threshold comparison on the target operation data based on a preset performance threshold table, and initiate an alarm notification when there is data exceeding the threshold in the comparison result.

[0118] In this embodiment, in addition to visualizing the database in the database, the computer device can also activate the alarm logic mechanism, and perform threshold comparison on the target operating data based on a preset performance threshold table, and initiate an alarm notification when there is data exceeding the threshold in the comparison result, so as to further maintain the efficient and stable operation of the smart grid.

[0119] Specifically, the computer device can set the alarm threshold of the key performance of each grid device according to the actual operation requirements in the smart grid, generate a performance threshold table, and then start the alarm logic mechanism to monitor the threshold of the target operation data obtained after real-time processing in the smart grid. Once the computer device detects that the data in the target operation data exceeds the threshold corresponding to the performance threshold table, it triggers an alarm and notifies the operation and maintenance personnel of the smart grid through SMS, email or other means to eliminate the abnormality, thereby ensuring the efficient and stable operation of the smart grid.

[0120] Furthermore, computer equipment can also monitor the key performance parameters of each grid equipment in the smart grid in real time. To elaborate, computer equipment can select a suitable real-time monitoring platform, such as Grafana or Kibana; the monitoring platform is equipped with a monitoring interface to display real-time data of key performance parameters, such as voltage, current, power and other key performances. In addition, the monitoring platform is also equipped with a dashboard to display real-time data and historical trends of key performance parameters; based on the monitoring platform, computer equipment can develop a data interface, and then push the key performance parameters in the target operation data to the monitoring platform for visualization.

[0121] The following describes a smart grid data management device provided in an embodiment of the present application. The smart grid data management device described below and the smart grid data management method described above can be referenced to each other.

[0122] In one embodiment, Figure 3 As shown, Figure 3 A schematic diagram of the structure of a smart grid data management device provided in an embodiment of the present application; the present application also provides a smart grid data management device, including a data acquisition module 210, a data transmission module 220, a data storage module 230 and a data visualization module 240, specifically including the following:

[0123] The data acquisition module 210 is used to perform distributed acquisition of original operation data of power grid equipment using sensor nodes, and perform data preprocessing on the original operation data using the sensor nodes to obtain target operation data.

[0124] The data transmission module 220 is used to encrypt and transmit the target operation data to the edge computing node using a multi-level wireless network; wherein the edge computing node is constructed using a distributed computing framework and an edge intelligence model.

[0125] The data storage module 230 is used to analyze and process the target operation data using the edge computing node to obtain real-time analysis data, and adopt a hierarchical data storage strategy to store the target operation data and the real-time analysis data in the database.

[0126] The data visualization module 240 is used to determine the business needs of the smart grid, and obtain target operation data and / or real-time operation data corresponding to the business needs from the database for visualization to obtain a visualization chart.

[0127] In the above embodiment, when managing the smart grid data, the original operation data of the power grid equipment can be first distributedly collected by the sensor nodes, and the original operation data can be preprocessed by the sensor nodes to obtain the target operation data. Here, the collected data is processed in situ in a distributed manner, and then a multi-level wireless network is used to encrypt and transmit the target operation data to the edge computing node, which can reduce the pressure of data transmission. The reliability and real-time performance of data transmission can be further improved by the multi-level wireless network. Since the edge computing node is constructed by adopting a distributed computing framework and an edge intelligent model, the target operation data is analyzed and processed by the edge computing node. After obtaining the real-time analysis data, a hierarchical data storage strategy is used to store the target operation data and the real-time analysis data in the database, which can realize distributed computing and storage of data, reduce dependence on the central control room, and thus improve the efficiency and stability of the smart grid. Therefore, the present application can efficiently obtain data corresponding to the business needs of the smart grid from the database for visualization and application, thereby ensuring the efficient operation of the smart grid.

[0128] In one embodiment, the data collection module 210 may include:

[0129] The data preprocessing submodule is used to filter and compress the original operation data using the sensor nodes to obtain intermediate operation data, perform anomaly detection on the intermediate operation data, and mark the data with abnormal detection results in the intermediate operation data to form target operation data.

[0130] In one embodiment, the data transmission module 220 may include:

[0131] The network determination submodule is used to determine a multi-level wireless network composed of at least one wireless communication network according to the distribution characteristics of sensor nodes and edge computing nodes; the wireless communication network includes a wireless sensor network, a cellular communication network and a low-power wide area network.

[0132] The protocol configuration submodule is used to establish a network connection channel between the sensor node and the edge computing node based on a multi-level wireless network, and configure a communication protocol corresponding to the network connection channel.

[0133] The encrypted transmission submodule is used to encrypt and transmit the target running data to the edge computing node based on the network connection channel and the communication protocol.

[0134] In one embodiment, the data storage module 230 may include:

[0135] The data processing submodule is used to use edge computing nodes to aggregate the target operation data, generate comprehensive data reports, and perform trend analysis and prediction on the comprehensive data reports to obtain real-time analysis data.

[0136] In one embodiment, the data storage module 230 may further include:

[0137] The data storage submodule is used to store the target operation data and real-time analysis data in the local repository and the cloud repository respectively.

[0138] Among them, the local repository is a short-term storage database; the cloud repository is a long-term storage database and is equipped with a data backup mechanism.

[0139] In one embodiment, the data visualization module 240 may include:

[0140] The data acquisition submodule is used to obtain target operation data and / or real-time operation data corresponding to business requirements from the database as visualization data.

[0141] The data aggregation analysis submodule is used to determine the chart format corresponding to the business needs, and perform data aggregation analysis on the visualization data based on the icon format to generate visualization data corresponding to the icon format according to the analysis results.

[0142] In one embodiment, the method may further include:

[0143] The data alarm module is used to start the alarm logic mechanism and perform threshold comparison on the target operation data based on the preset performance threshold table, and initiate an alarm notification when there is data exceeding the threshold in the comparison result.

[0144] In one embodiment, the present application also provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the smart grid data management method as described in any of the above embodiments.

[0145] In one embodiment, the present application also provides a computer device having computer-readable instructions stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the smart grid data management method as described in any one of the above embodiments.

[0146] Indicatively, Figure 4 As shown, Figure 4 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 may be provided as a server. Figure 4 The computer device 300 includes a processing component 302, which further includes one or more processors, and a memory resource represented by a memory 301 for storing instructions executable by the processing component 302, such as an application. The application stored in the memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 302 is configured to execute instructions to perform the smart grid data management method of any of the above embodiments.

[0147] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0148] Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0149] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0150] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0151] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart grid data management method, characterized in that: The method comprises: The sensor nodes are used to collect the original operation data of the power grid equipment in a distributed manner, and the sensor nodes are used to perform data preprocessing on the original operation data to obtain target operation data; The target operation data is encrypted and transmitted to the edge computing node using a multi-level wireless network; wherein the edge computing node is constructed using a distributed computing framework and an edge intelligence model; Utilize the edge computing node to analyze and process the target operation data to obtain real-time analysis data, and adopt a hierarchical data storage strategy to store the target operation data and the real-time analysis data in a database; The business requirements of the smart grid are determined, and target operation data and / or real-time operation data corresponding to the business requirements are obtained from the database for visualization to obtain a visualization chart.

2. The smart grid data management method according to claim 1, characterized in that: The using the sensor node to perform data preprocessing on the original operation data to obtain target operation data includes: The sensor node is used to filter and compress the original operation data to obtain intermediate operation data, and abnormality detection is performed on the intermediate operation data, and data with abnormal detection results in the intermediate operation data are marked to form target operation data.

3. The smart grid data management method according to claim 1, characterized in that: The method of using a multi-level wireless network to encrypt and transmit the target operation data to an edge computing node includes: Determine a multi-level wireless network consisting of at least one wireless communication network according to the distribution characteristics of the sensor nodes and the edge computing nodes; the wireless communication network includes a wireless sensor network, a cellular communication network and a low power wide area network; Establishing a network connection channel between the sensor node and the edge computing node based on the multi-level wireless network, and configuring a communication protocol corresponding to the network connection channel; Based on the network connection channel, the target operation data is encrypted and transmitted to the edge computing node using the communication protocol.

4. The smart grid data management method according to claim 1, characterized in that: The using the edge computing node to analyze and process the target operation data to obtain real-time analysis data includes: The target operation data is aggregated and processed by utilizing the edge computing node to generate a comprehensive data report, and trend analysis and prediction are performed on the comprehensive data report to obtain real-time analysis data.

5. The smart grid data management method according to claim 1, characterized in that: The database includes a local repository and a cloud repository; The adopting of a hierarchical data storage strategy to store the target operation data and the real-time analysis data in a database includes: storing the target operation data and the real-time analysis data in the local storage repository and the cloud storage repository respectively; The local repository is a short-term storage database; the cloud repository is a long-term storage database and is provided with a data backup mechanism.

6. The smart grid data management method according to claim 1, characterized in that: The acquiring target operation data and / or real-time operation data corresponding to the business requirement from the database for visualization to obtain visualization data includes: Acquire target operation data and / or real-time operation data corresponding to the business requirement from the database as visualization data; A chart format corresponding to the business requirement is determined, and data aggregation analysis is performed on the visualization data based on the icon format, so as to generate visualization data corresponding to the icon format according to the analysis result.

7. The smart grid data management method according to claim 1, characterized in that: The method further comprises: The alarm logic mechanism is started, and the target operation data is compared with the threshold value based on the preset performance threshold table, and when there is data exceeding the threshold value in the comparison result, an alarm notification is initiated.

8. A smart grid data management device, characterized in that: include: A data acquisition module, used to perform distributed acquisition of raw operation data of power grid equipment using sensor nodes, and perform data preprocessing on the raw operation data using the sensor nodes to obtain target operation data; A data transmission module, used to encrypt and transmit the target operation data to an edge computing node using a multi-level wireless network; wherein the edge computing node is constructed using a distributed computing framework and an edge intelligence model; A data storage module, used to analyze and process the target operation data using the edge computing node to obtain real-time analysis data, and store the target operation data and the real-time analysis data in a database using a hierarchical data storage strategy; The data visualization module is used to determine the business needs of the smart grid, and obtain the target operation data and / or real-time operation data corresponding to the business needs from the database for visualization to obtain a visualization chart.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the smart grid data management method as described in any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the smart grid data management method according to any one of claims 1 to 7 are performed.

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