Power data management method and system based on smart grid

Abstract

The application relates to a power data management method and system based on a smart power grid, which comprises the following steps: collecting external node working state information and maintenance behavior information of the smart power grid to obtain basic data; the basic data is transmitted and collected to the root of a tree structure type internet of things through an internet of things access node of the tree structure type internet of things; and a big data analysis method is used to classify, screen, classify and update the data at the root. Through the internet of things system based on the smart power grid, independent network and data collection of the smart power grid can obtain more data, thereby guaranteeing the stereoscopic nature of overall power grid monitoring; through the independent internet of things, tree-shaped integration and distortion tracing of the data are realized, the accuracy of data obtained by each node is ensured, and the data is quickly processed under the cloud computing assistance of a main controller, so that the overall workload is reduced.

Description

Technical Field

[0001] This invention relates to the field of smart grid data management technology, specifically to a power data management method and system based on smart grids. Background Technology

[0002] With technological upgrades and the construction of power engineering projects, smart grids have become an inevitable trend. Their rapid response, fault location display, and power consumption fluctuation statistics make them a necessary choice to reduce manpower burden. However, after the deployment of smart grids, the need for regular maintenance of individual nodes, data collection, and overall power consumption data collection remains. Currently, data processing is generally based on feedback from the power grid itself, resulting in significant overall fluctuations. Furthermore, while data management involves relatively simple data types, the total data volume is large, requiring substantial work. Meanwhile, node maintenance, due to human intervention, is difficult to automate through automatic feedback from the power grid.

[0003] Therefore, it is essential to design practical power data management methods and systems based on smart grids. Summary of the Invention

[0004] The purpose of this invention is to provide a power data management method and system based on smart grids to solve the technical problems of poor timeliness and high labor costs in existing smart grid data acquisition and management.

[0005] The technical solution of this invention is: A power data management method based on smart grids includes the following steps: S11. Collect the working status information and maintenance behavior information of the external nodes of the smart grid to obtain basic data; S12. The basic data is transmitted and aggregated to the root of the tree-structured Internet of Things via the Internet of Things access points. S13. Use big data analysis methods to classify, filter, categorize, and update the data at the root.

[0006] Preferably, in step S11, the working status information includes power consumption, peak power consumption curve, current pulse and fault feedback information, and the maintenance behavior information includes fault maintenance time, fault maintenance result and maintenance equipment information.

[0007] Further preferred options include: electricity consumption is the total electricity consumption of users currently connected to the smart meter; the peak electricity consumption curve is a daily curve plotted with electricity consumption per hour; the current pulse is the parameter of the current peak change under the fitted peak electricity consumption curve; the fault feedback information is whether a fault has occurred at the current node and the cause of the fault; the fault maintenance time includes the time from the occurrence of the fault to the start of maintenance and the time when the overall maintenance ends; the fault maintenance result is whether the node equipment is working normally; and the maintenance equipment information includes information on the equipment used by the personnel performing the maintenance and information on the replacement of the backup equipment.

[0008] Preferably, in step S12, each external node of the smart grid is equipped with an Internet of Things (IoT) access point; in the tree-structured IoT, the branches where the IoT access points are located are connected using data exchanges.

[0009] Preferably, in step S12, in the tree-structured Internet of Things (IoT), the data transmission process is based on the tree-structured IoT, and relay nodes are set up in combination with the attenuation characteristics of data transmission to enhance the attenuated signal; when a distorted signal occurs, the distorted signal is back-called for confirmation and distortion repair is performed.

[0010] Preferably, in step S12, in the tree-structured Internet of Things, the grid integration is based on the data exchanger to integrate data, sort the basic data obtained from different external nodes by time queue, and integrate and package the queue information of the current data exchanger. The data packet hierarchy is set based on the data exchanger hierarchy of the tree-structured Internet of Things so as to trace the data transmission link according to the data packet hierarchy.

[0011] More preferably, in step S12, in the tree-structured Internet of Things, the method for sorting the basic data obtained from different external nodes by time queue is as follows: a root pointer is set according to the time when the data is sent, a time pointer is set according to the time when the data is transmitted to the exchange, the time pointer is used as the first sorting basis, and the root pointer is used as the second sorting basis to sort the basic data obtained from different external nodes by time queue; a standard time period is set as the unit packaging time.

[0012] Preferably, in step S13, the big data analysis cloud performs reverse source analysis on the root data based on the data packet level setting rules, and simultaneously performs rapid screening of the data within the data packet to remove distorted and noisy information. The remaining valid data is clustered, integrated, and filled with tracking pointers. Data with the same tracking pointers are updated and stored. Through manual intervention, the already entered data information is effectively called, viewed, and manually modified.

[0013] Preferably, based on functional areas, the data information is set to include a pointer segment, a key segment, a function segment, and an end segment. When the function segment is the main distortion segment, the function segment is divided into multiple sub-segments. The sub-segments of the function segment are read sequentially. If there is distortion information in a certain sub-segment, the entire data chain is back-tracked for confirmation. This back-tracking is performed three times in sequence, and the distortion information is supplemented with redundant data. The back-tracking confirmation method for distortion information is as follows: after the distortion information is removed, the corresponding time pointer and root pointer are read, the corresponding external node and data collector are traced back, the corresponding basic data is re-uploaded, and the distortion information is supplemented by combining the re-acquired basic data.

[0014] A power data management system for a smart grid includes a data collector, a data exchanger, and a main controller. The data collectors are configured at external nodes of the smart grid, with one data collector at each external node to collect the corresponding node's operational status and maintenance information. The data exchangers are arranged hierarchically based on a tree network, constructing a five-layer hierarchical arrangement of backbone, secondary backbone, branches, sub-branches, and node ends. Data exchangers at the same level can interact to build a backup network. The main controller is built on big data analytics and cloud computing, performing high-speed processing and calculations on the aggregated data at each level. The main controller is also connected to input and output devices to facilitate the export and manual modification of the processed data.

[0015] Preferably, the tree-structured Internet of Things (IoT) between the data collector, data exchanger, and main controller is constructed synchronously with the smart grid, and is independent of the smart grid.

[0016] The beneficial effects of this invention are: 1. By using an IoT system that is synchronously deployed with the smart grid, independent of the smart grid network and data acquisition, more data can be obtained, thereby ensuring the three-dimensionality of the overall power grid monitoring. At the same time, through independent IoT, data is integrated in a tree structure and distortion traced, ensuring the accuracy of data acquired by each node. With the assistance of cloud computing from the main controller, the data is processed quickly, reducing the overall workload. Implementation

[0017] The present invention will now be described with reference to embodiments to assist those skilled in the art in understanding and implementing the invention. Unless otherwise stated, the following embodiments and the technical terms therein should not be understood without a background of technical knowledge in this field.

[0018] Example 1: A power data management method based on smart grid, comprising the following steps: S11. Collect the working status information and maintenance behavior information of the external nodes of the smart grid to obtain basic data; S12. The basic data is transmitted and aggregated to the root of the tree-structured Internet of Things via the Internet of Things access points. S13. Use big data analysis methods to classify, filter, categorize, and update the data at the root.

[0019] Specifically, in step S11, the operating status information includes power consumption, peak power consumption curve, current pulse, and fault feedback information; the maintenance behavior information includes fault maintenance time, fault maintenance result, and maintenance equipment information. The electricity consumption is the total electricity consumption of the users connected to the current smart meter. The peak electricity consumption curve is a daily curve of electricity consumption plotted in hourly units. The current pulse is the parameter of the current peak change under the fitted peak electricity consumption curve. The fault feedback information is whether a fault has occurred at the current node and the cause of the fault. The fault maintenance time includes the time from the occurrence of the fault to the start of maintenance and the time when the overall maintenance ends. The fault maintenance result is whether the node equipment is working normally. The maintenance equipment information includes the information of the equipment used by the personnel performing the maintenance and the information of the replacement of the backup equipment.

[0020] Specifically, in step S12, each external node of the smart grid is equipped with an Internet of Things (IoT) access point; in the tree-structured IoT, the branches where the IoT access points are located are connected using data exchanges. The specific details of data transmission, grid integration, and time-series arrangement within the tree-structured Internet of Things are as follows: The data transmission process is based on the Internet of Things (IoT) simultaneously deployed on the power grid, and relay nodes are set up based on the attenuation of data transmission. Distorted signals are back-called for confirmation, distortion is repaired, and attenuated signals are enhanced. Grid integration is based on data exchange for data integration. Basic data obtained from different external nodes is queued and sorted by time. The queue information of the current data exchange is integrated and packaged to build a tree-like hierarchical exchange layout. Data packet hierarchical division is constructed, and rapid source tracing is based on the hierarchical division of data packets.

[0021] The data exchange uses a time queue for sorting. A standard time period is set as the unit packaging time. A root pointer is set based on the time the data is sent, and a time pointer is set based on the time the data is transmitted to the exchange. The time pointer is used as the first sorting criterion, and the root pointer is used as the second sorting criterion to sort the basic data obtained from different external nodes.

[0022] Specifically, in step S13, the big data analysis cloud performs reverse source analysis on the root data based on the hierarchical packaging rules in the data transmission process, and at the same time performs rapid screening of the data in the data packet, in which distorted information and noise information are removed, the remaining valid data is clustered and integrated, and tracking pointers are filled in. Based on the tracking pointers, the data under the same pointer is updated, and the data is stored effectively. Through manual intervention, the entered data can be effectively accessed, viewed, and modified manually.

[0023] The data signal is divided based on functional areas, including pointer, key, function, and end. The function part is the main distortion segment, which is divided into three segments. Each segment is read sequentially. After distortion, the entire data chain is back-called for confirmation. This back-call is performed three times in sequence, and the data is correctly filled by filling in redundant data.

[0024] For the callback confirmation of distorted information: After the distorted information is removed, the corresponding time pointer and root pointer are read, the corresponding external nodes and data collectors are traced back, and the corresponding basic data is re-uploaded. Combined with the re-acquired basic data, the data is correctly populated.

[0025] Example 2: A power data management system for a smart grid, comprising a data acquisition unit, a data exchange unit, and a main controller: The data acquisition device is configured at the external nodes of the smart grid. One data acquisition device is configured at each external node to collect the working status information and maintenance behavior information of the corresponding external node. The data exchange is arranged hierarchically based on a tree network, constructing a five-layer hierarchical arrangement of trunk, secondary trunk, branches, secondary branches and node ends, and data exchangers of the same level can interact to build a backup network. The main controller is built on big data analytics and cloud computing, and performs high-speed processing and calculation on the data aggregated at all levels. At the same time, the main controller is connected to input and output devices, which allows the processed data to be exported and manually configured and modified. The input and output devices form a human-computer interaction interface, which facilitates the implementation of manual intervention measures.

[0026] The tree-structured Internet of Things (IoT) system, consisting of data collectors, data exchanges, and main controllers, is built synchronously with the smart grid, yet operates independently of it.

[0027] Example 3: A power data management method based on a smart grid, comprising a data collection point component, a tree-structured data network component, and an information filtering and management component, including the following steps: S100. The data acquisition point is composed of several independent data acquisition devices. Each data acquisition device works with each power grid working node to collect the working status information and maintenance behavior information of the power grid working node. S200. The tree-like data network is based on the Internet of Things and has several access nodes. Each access node corresponds to a data collector to effectively transmit the collected data. The branches where each access node is located are effectively connected to the network based on the data exchanger and the data is arranged and transmitted in time sequence. S300. The target information management part is built on a big data analysis cloud, which classifies, filters, categorizes, and updates the working status information and maintenance behavior information collected by the data collection points.

[0028] Specifically, the operating status information of the power grid working node includes electricity consumption, peak electricity consumption curve, current pulse and fault feedback information; the maintenance behavior information of the power grid working node includes fault maintenance time, fault maintenance result and maintenance equipment information. The electricity consumption is the total electricity consumption of the users connected to the current smart meter. The peak electricity consumption curve is a daily curve of electricity consumption plotted in hourly units. The current pulse is the parameter of the current peak change under the fitted peak electricity consumption curve. The fault feedback information is whether a fault has occurred at the current node and the cause of the fault. The fault maintenance time includes the time from the occurrence of the fault to the start of maintenance and the time when the overall maintenance ends. The fault maintenance result is whether the node equipment is working normally. The maintenance equipment information includes the information of the equipment used by the personnel performing the maintenance and the information of the replacement of the backup equipment.

[0029] Specifically, the data transmission, grid integration, and timing arrangement are as follows: The data transmission process is based on the Internet of Things (IoT) simultaneously deployed on the power grid, and relay nodes are set up based on the attenuation of data transmission. Distorted signals are back-called for confirmation, distortion is repaired, and attenuated signals are enhanced. Grid integration is based on data exchangers to integrate data. The information obtained from each data collection point is sorted by time and the queue information of the current data exchange is integrated and packaged to build a tree-like hierarchical exchange layout. Data packet hierarchical division is constructed and rapid source tracing is based on the hierarchical division of data packets.

[0030] Specifically, the target information management part consists of a main controller, a human-machine interface, and input / output devices. The big data analysis cloud is the core of the main controller. Based on the hierarchical packaging during data transmission, it performs reverse source analysis and quickly filters the data in the data packets. Distorted and noisy information is removed, and the remaining valid data is clustered, integrated, and filled with tracking pointers. Based on the tracking pointers, the data under the same pointer is updated, and the data is stored effectively. The human-computer interaction interface allows for effective retrieval, viewing, and manual modification of the entered data through human intervention. This human intervention is achieved through input / output devices, which can also export the stored real-time data.

[0031] Specifically, the callback confirmation of the distorted signal involves dividing the data signal based on functional areas, including pointer, key, function, and end. The functional part is the main distorted segment, which is divided into three segments. Each segment is read sequentially, and the entire data chain is callback confirmed after distortion. The callback is performed three times in sequence, and the data is correctly filled by filling redundant data.

[0032] Specifically, the time queue sorting of the data exchanger is based on the addition of a time pointer and a root pointer when the data is transmitted to the exchanger, with the time pointer having higher priority than the root pointer, and a standard time period is set as the unit packaging time.

[0033] An implementation system for a smart grid-based power data management method includes a data acquisition unit, a data exchange unit, and a central controller. The data acquisition device consists of several basic data acquisition points that are matched with various external nodes of the smart grid to collect data on the current node's operation. The data exchange is arranged hierarchically based on a tree network, constructing a five-layer hierarchical arrangement of trunk, secondary trunk, branches, secondary branches and node ends, and data exchangers of the same level can interact to build a backup network. The main controller is built on gimbal computing and performs high-speed processing and calculation on the data aggregated at all levels. At the same time, the human-computer interaction interface and input / output devices of the controller itself make the processed data exportable and manually configured and modified. The Internet of Things (IoT) network between the data acquisition points, data exchangers, and main controllers is built synchronously with the smart grid and is independent of the smart grid.

[0034] Specifically, after the distorted information is removed, the corresponding time pointer and root pointer are read, the corresponding node and the cooperating data collector are traced back, and the collected data is uploaded again.

[0035] The present invention has been described in detail above with reference to the embodiments. It should be understood that it is impossible to exhaustively describe all possible implementation methods in practice. The inventive concept of the present invention is illustrated to the extent possible by way of examples. Without departing from the inventive concept of the present invention and without creative effort, any specific embodiments formed by selecting and combining technical features in the above embodiments, experimenting with and changing specific parameters, or by conventionally replacing the technical means disclosed in the present invention using existing technology in the present invention, shall all be considered as implied disclosures of the present invention.

Claims

1. A power data management method based on smart grids, characterized in that, Includes the following steps: S11. Collect the working status information and maintenance behavior information of the external nodes of the smart grid to obtain basic data; S12. The basic data is transmitted and aggregated to the root of the tree-structured Internet of Things via the Internet of Things access points. S13. Use big data analysis methods to classify, filter, categorize, and update the data at the root. In step S13, the big data analysis cloud performs source analysis on the root data based on the data packet level setting rules, and at the same time performs rapid screening of the data within the data packet to remove distorted and noisy information. The remaining valid data is clustered, integrated, and filled with tracking pointers. Data with the same tracking pointers are updated and stored. Through manual intervention, the entered data can be effectively retrieved, viewed, and manually modified. Based on functional areas, the data information is set to include pointer segments, key segments, function segments, and end segments. When the function segment is the main distortion segment, the function segment is divided into multiple sub-segments. The sub-segments of the function segment are read sequentially. If there is distorted information in a certain sub-segment, the entire data chain is back-updated for confirmation. This back-up is performed three times in sequence, and the distorted information is supplemented with redundant data. The method of back-up confirmation of distorted information is as follows: after the distorted information is removed, the corresponding time pointer and root pointer are read, the corresponding external nodes and data collectors are traced back, the corresponding basic data is re-uploaded, and the distorted information is supplemented by combining the newly acquired basic data. The root pointer is a pointer set according to the time when the data is sent; the time pointer is a pointer set according to the time when the data is transmitted to the switch.

2. The power data management method based on smart grid as described in claim 1, characterized in that, In step S11, the working status information includes power consumption, peak power consumption curve, current pulse and fault feedback information, and the maintenance behavior information includes fault maintenance time, fault maintenance result and maintenance equipment information.

3. The power data management method based on smart grid as described in claim 2, characterized in that, Electricity consumption refers to the total electricity consumption of users currently connected to the smart meter. The peak electricity consumption curve is a daily curve plotted with electricity consumption per hour. The current pulse is the parameter of the current peak change under the fitted peak electricity consumption curve. The fault feedback information is whether a fault has occurred at the current node and the cause of the fault. The fault maintenance time includes the time from the occurrence of the fault to the start of maintenance and the time when the overall maintenance ends. The fault maintenance result is whether the node equipment is working normally. The maintenance equipment information includes information on the equipment used by the personnel performing the maintenance and information on the replacement of the backup equipment.

4. The power data management method based on smart grid as described in claim 1, characterized in that, In step S12, each external node of the smart grid is equipped with an Internet of Things (IoT) access point; in the tree-structured IoT, the branches where the IoT access points are located are connected using data exchanges.

5. The power data management method based on smart grid as described in claim 1, characterized in that, In step S12, in the tree-structured Internet of Things (IoT), the data transmission process is based on the tree-structured IoT, and relay nodes are set up in combination with the attenuation characteristics of data transmission to enhance the attenuated signal; when a distorted signal occurs, the distorted signal is back-called for confirmation and distortion repair is performed.

6. The power data management method based on smart grid as described in claim 1, characterized in that, In step S12, in the tree-structured Internet of Things (IoT), grid integration is based on data exchange for data integration. The basic data obtained from different external nodes is sorted by time queue, and the queue information of the current data exchange is integrated and packaged. The data packet hierarchy is set based on the data exchange hierarchy of the tree-structured IoT to facilitate the tracing of the data transmission link according to the data packet hierarchy.

7. The power data management method based on smart grid as described in claim 6, characterized in that, In step S12, in the tree-structured Internet of Things, the method for sorting the basic data obtained from different external nodes by time queue is as follows: set a root pointer according to the time when the data is sent, set a time pointer according to the time when the data is transmitted to the exchange, use the time pointer as the first sorting basis, and use the root pointer as the second sorting basis to sort the basic data obtained from different external nodes by time queue. Set the standard time period as the unit packaging time.

8. A power data management system for a smart grid, characterized in that, It includes a data collector, a data exchanger, and a main controller. The data collector is configured at the external nodes of the smart grid, with one data collector at each external node to collect the working status information and maintenance behavior information of the corresponding external node. The data exchanger is arranged hierarchically based on a tree network, constructing a five-layer hierarchical arrangement of trunk, secondary trunk, branches, secondary branches, and node ends. Data exchangers at the same level can interact to build a backup network. The main controller is built on big data analytics and cloud computing, and performs high-speed processing and calculation on the data aggregated at all levels. At the same time, the main controller is connected to input and output devices to facilitate the export and manual configuration and modification of the processed data.