Distributed power resource information aggregation method and system

Abstract

The invention relates to the technical field of power information aggregation, and particularly discloses a distributed power resource information aggregation method and system, and the method comprises the steps: obtaining the installation parameters of wind power equipment, and constructing a distributed power diagram according to the installation parameters; clustering electric power point locations in the distributed electric power graph, and setting edge nodes based on a clustering result; obtaining power parameters based on the edge nodes, performing aggregation sorting on the power parameters, and constructing an aggregation database; and traversing the aggregation database according to preset feature data, positioning the abnormal data and the wind power equipment thereof, and generating prompt information. The construction information of the wind power equipment is abstracted to be high, the installation positions of the wind power equipment are combined, a mapping point set corresponding to the installation positions of the wind power equipment is determined, then the distributed power diagram is constructed, the wind power equipment is aggregated in the distributed power diagram, the power equipment with extremely high similarity is quickly classified into one class, and then edge nodes are configured. And the existing information acquisition architecture is optimized.

Description

Technical Field

[0001] This invention relates to the field of power information aggregation technology, specifically a method and system for distributed power resource information aggregation. Background Technology

[0002] Distributed power resource information aggregation involves collecting, organizing, and standardizing information from various resources scattered across the power grid, such as power generation, transmission, distribution, and consumption, using distributed technology to form a unified and readily accessible information resource pool, supporting the optimized operation of the power system. With the development of wind power equipment, wind turbines, as independent power generation devices, have sufficiently low construction costs and are deployed in vast quantities, making them a typical example of distributed power resources. The operation of wind turbines requires monitoring. Current monitoring methods rely on data forwarding devices built into the wind turbines for data collection. Each wind turbine operates independently, but in practice, the operating status and environment of most wind turbines are similar, allowing for centralized data uploading. Therefore, how to aggregate wind turbine data and optimize the existing information collection architecture is the technical problem this invention aims to solve. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for aggregating distributed power resource information to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for aggregating distributed power resource information, the method comprising:

[0006] Obtain the installation parameters of the wind power equipment, and construct a power distribution map based on the installation parameters; wherein, the power distribution map contains power points determined by the installation parameters;

[0007] Cluster the power points in the distributed power map and set edge nodes based on the clustering results;

[0008] Power parameters are obtained from edge nodes, and the power parameters are aggregated, sorted, and then an aggregated database is constructed.

[0009] The system traverses the aggregated database based on preset feature data, locates abnormal data and its associated wind power equipment, and generates alert information; the feature data is used to characterize the data situation under abnormal conditions.

[0010] As a further aspect of the present invention: the step of obtaining the installation parameters of the wind power equipment and constructing a distributed power map based on the installation parameters includes:

[0011] Locate the installation location of the wind power equipment and determine the equipment inspection area based on the installation location;

[0012] Obtain the road segment status in the equipment inspection area, and determine the maintenance accessibility of the wind power equipment based on the road segment status;

[0013] Acquire the signal strength in the equipment detection area, and determine the signal reachability of the wind power equipment based on the signal strength;

[0014] Acquire weather information changes in the equipment testing area, and determine the environmental risk value based on the changes;

[0015] The fitted height range is determined based on the maintenance accessibility, signal accessibility, and environmental risk value.

[0016] Based on a preset step size, a height gradient is set within the fitting height range. The height gradient is used as the vertical coordinate to randomly determine a set of points within the device detection area. The number of points corresponding to each height gradient is a preset value.

[0017] Collect statistics from all point sets and construct a distributed power map.

[0018] As a further aspect of the present invention: the step of clustering the power points in the distributed power map and setting edge nodes based on the clustering results includes:

[0019] The distance to each power point is calculated based on the preset planar scaling factor and height scaling factor;

[0020] Cluster the power points based on the distance;

[0021] For each type of power point, query its corresponding power equipment;

[0022] Count the number of points corresponding to each power device in this type of power point, and mark the power device when the number of points reaches a preset first threshold.

[0023] When the number of marked power devices for each type of power point reaches the preset second threshold, an edge node is set, and a connection channel between the edge node and the marked power device is established simultaneously; power devices that have not established a connection channel with the edge node are marked as edge nodes.

[0024] As a further aspect of the present invention: the step of acquiring power parameters based on edge nodes, aggregating and sorting the power parameters, and constructing an aggregated database includes:

[0025] For any edge node, query the power equipment corresponding to the edge node;

[0026] Real-time acquisition of power equipment data based on edge nodes;

[0027] Query the total number of edge nodes corresponding to each power device, and determine the data upload frequency based on the total number of devices in the edge nodes and the total number of edge nodes for each device.

[0028] Based on the data upload frequency, the power parameters uploaded at the edge nodes are obtained, and the power parameters are aggregated and sorted to build an aggregated database;

[0029] The process of aggregating and sorting the power parameters is as follows:

[0030] Receive data containing time and type uploaded by various sensors in the wind power equipment, normalize the data, use type as column index and time as row index to statistically analyze the data, and obtain a data matrix;

[0031] The data matrix of all wind power equipment is statistically analyzed to obtain an aggregated database.

[0032] As a further aspect of the present invention: the step of traversing the aggregated database based on preset feature data, locating abnormal data and its wind power equipment, and generating prompt information includes:

[0033] Query feature data in a preset feature database; the feature database includes feature data items and anomaly description items, the feature data is a matrix of preset size, and the elements in the matrix are data ranges;

[0034] Based on feature data, the aggregated database is traversed. When a match is found, the matching power parameters and their corresponding wind power equipment are queried.

[0035] Read the anomaly description of the feature data when a match is successful, compile statistics on the anomaly description and wind power equipment, and generate a prompt message.

[0036] As a further aspect of the present invention, the method further includes:

[0037] Read the data matrix of different power devices from the aggregated database;

[0038] Data matrices of power equipment are extracted according to a preset time range, the data matrices are compared, and the matrix similarity is calculated as the data similarity of power equipment.

[0039] Read the weather information of the power equipment, compare them pairwise, and determine the environmental similarity of the power equipment based on the comparison results of the weather information;

[0040] When the environmental similarity reaches the preset first similarity threshold, the corresponding data similarity is read. When the data similarity is less than the preset second similarity threshold, a weather information update instruction is generated for the power equipment.

[0041] The present invention also provides a distributed power resource information aggregation system, the system comprising:

[0042] The power map construction module is used to obtain the installation parameters of wind power equipment and construct a distributed power map based on the installation parameters; wherein, the distributed power map contains power points determined by the installation parameters;

[0043] The edge node setting module is used to cluster the power points in the distributed power map and set edge nodes based on the clustering results.

[0044] The database construction module is used to acquire power parameters based on edge nodes, aggregate and sort the power parameters, and build an aggregated database.

[0045] The anomaly localization module is used to traverse the aggregated database based on preset feature data, locate abnormal data and its wind power equipment, and generate prompt information; among which, the feature data is used to characterize the data situation under abnormal conditions.

[0046] As a further aspect of the present invention: the power diagram construction module includes:

[0047] The detection zone setting unit is used to query the installation location of wind power equipment and determine the equipment detection zone based on the installation location.

[0048] The road segment analysis unit is used to acquire the road segment status in the equipment detection area and determine the maintenance accessibility of the wind power equipment based on the road segment status.

[0049] The signal analysis unit is used to acquire the signal strength of the equipment detection area and determine the signal reachability of the wind power equipment based on the signal strength.

[0050] The environmental analysis unit is used to acquire weather information changes in the equipment testing area and determine the environmental risk value based on the changes.

[0051] A high-fitting unit is used to determine the fitting height range based on the maintenance accessibility, signal accessibility, and environmental risk value.

[0052] The point set construction unit is used to set a height gradient within the fitting height range based on a preset step size, and to randomly determine a point set within the device detection area using the height gradient as the vertical coordinate; the number of points corresponding to each height gradient is a preset value.

[0053] Point set statistics unit, used to count all point sets and construct a distributed power map.

[0054] As a further aspect of the present invention: the edge node setting module includes:

[0055] The distance calculation unit is used to calculate the distance to each power point based on the preset planar scaling factor and height scaling factor;

[0056] A point clustering unit is used to cluster power points based on the distance;

[0057] The first query unit is used to query the corresponding power equipment for each type of power point.

[0058] The equipment marking unit is used to count the number of points corresponding to each power equipment in this type of power point. When the number of points reaches a preset first quantity threshold, the power equipment is marked.

[0059] The channel establishment unit is used to set edge nodes and synchronously establish connection channels between edge nodes and marked power equipment when the number of marked power equipment for each type of power point reaches a preset second threshold; and to mark power equipment that has not established a connection channel with edge nodes as edge nodes.

[0060] As a further aspect of the present invention: the database construction module includes:

[0061] The second query unit is used to query the power equipment corresponding to any edge node;

[0062] The data acquisition unit is used to acquire data from power equipment in real time based on edge nodes;

[0063] The frequency determination unit is used to query the total number of edge nodes corresponding to each power device and determine the data upload frequency based on the total number of devices in the edge nodes and the total number of edge nodes for each device.

[0064] The aggregation execution unit is used to obtain the power parameters uploaded at the edge nodes based on the data upload frequency, aggregate and sort the power parameters, and build an aggregate database.

[0065] The process of aggregating and sorting the power parameters is as follows:

[0066] Receive data containing time and type uploaded by various sensors in the wind power equipment, normalize the data, use type as column index and time as row index to statistically analyze the data, and obtain a data matrix;

[0067] The data matrix of all wind power equipment is statistically analyzed to obtain aggregated data.

[0068] Compared with the prior art, the beneficial effects of the present invention are: the present invention abstracts the construction information of wind power equipment into height, combines the installation location of wind power equipment to determine the corresponding mapping point set, and then constructs a distributed power map. In the distributed power map, wind power equipment is aggregated, and power equipment with high similarity is quickly classified into one category. Then, edge nodes are equipped, which optimizes the existing information collection architecture. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0070] Figure 1 This is a flowchart of a distributed power resource information aggregation method.

[0071] Figure 2 This is the first sub-flow flowchart of the distributed power resource information aggregation method.

[0072] Figure 3 This is the second sub-flow flowchart of the distributed power resource information aggregation method.

[0073] Figure 4 This is the third sub-process flowchart of the distributed power resource information aggregation method.

[0074] Figure 5 This is the fourth sub-flow flowchart of the distributed power resource information aggregation method.

[0075] Figure 6 This is a block diagram of the structure of a distributed power resource information aggregation system. Detailed Implementation

[0076] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0077] Figure 1 This is a flowchart of a distributed power resource information aggregation method. In this embodiment of the invention, a distributed power resource information aggregation method includes:

[0078] Step S100: Obtain the installation parameters of the wind power equipment, and construct a power distribution map based on the installation parameters; wherein, the power distribution map contains power points determined by the installation parameters;

[0079] Wind power equipment refers to wind power generation equipment. Its installation process requires registration. For the entity implementing this method, the installation parameters are known data and can be directly read. The installation parameters are the installation location and model of the wind power equipment. The installation parameters are converted, and points are created based on the map to obtain the power distribution map. The power distribution map is actually a three-dimensional map. More figuratively, it is somewhat similar to a 2.5D model map. Based on the three-dimensional map (including altitude), some points are determined based on different altitudes, which are called power points. One wind power equipment corresponds to multiple points, that is, one wind power equipment corresponds to one set of points.

[0080] Step S200: Cluster the power points in the distributed power map and set edge nodes based on the clustering results;

[0081] In the technical solution of this invention, the height of the point corresponding to the wind power equipment is not the actual height, because the wind power equipment itself is a large physical entity. When it is abstracted into a point, the height is a very important parameter. This invention updates the height to reflect the operating status of the power equipment, and thus sets the power points; the power points are clustered, thereby clustering the power equipment, and then a data forwarding device, called an edge node, is set up for each type of power equipment.

[0082] Step S300: Obtain power parameters based on edge nodes, aggregate and sort the power parameters, and construct an aggregate database;

[0083] Power parameters are obtained from data forwarding devices at edge nodes, and then interact with the execution body of this method to aggregate and sort the obtained power parameters to obtain a clustered database. The aggregation and sorting process is a data statistics process and a time-based data sorting process. The aggregated database contains the power parameters of all power devices.

[0084] Step S400: Traverse the aggregated database according to the preset feature data, locate abnormal data and its wind power equipment, and generate prompt information; wherein, the feature data is used to characterize the data situation under abnormal conditions;

[0085] Staff members pre-calculate the range of equipment data corresponding to abnormal situations and construct feature data. The feature data is then traversed and matched in the aggregated database to locate the abnormal data and simultaneously determine the wind power equipment corresponding to the abnormal data. Based on this, a prompt message pointing to the wind power equipment is generated and notified to the management. A maintenance form will then be generated subsequently.

[0086] Figure 2 This is a flowchart of the first sub-process of the distributed power resource information aggregation method. The step of obtaining the installation parameters of wind power equipment and constructing a distributed power map based on the installation parameters includes:

[0087] Step S101: Locate the installation location of the wind power equipment and determine the equipment testing area based on the installation location;

[0088] Step S102: Obtain the road segment status in the equipment detection area, and determine the maintenance accessibility of the wind power equipment based on the road segment status;

[0089] Step S103: Obtain the signal strength of the equipment detection area, and determine the signal reachability of the wind power equipment based on the signal strength;

[0090] Step S104: Obtain the weather information changes in the equipment detection area, and determine the environmental risk value based on the changes;

[0091] Step S105: Determine the fitting height range based on the maintenance accessibility, signal accessibility, and environmental risk value;

[0092] Step S106: Set a height gradient within the fitting height range based on a preset step size, use the height gradient as the vertical coordinate, and randomly determine a set of points within the device detection area; the number of points corresponding to each height gradient is a preset value.

[0093] Step S107: Statistically analyze all point sets and construct a distributed power map.

[0094] In one example of the technical solution of this invention, the process of constructing a distributed power map is described. The installation location of the wind turbine is queried, and an equipment inspection area is determined based on the installation location. This process generally involves setting a radius, defining a circular area centered on the installation location, obtaining the road conditions within the equipment inspection area, and determining the maintenance accessibility of the wind turbine based on the road conditions. This process can involve querying the nearest maintenance station within the equipment inspection area, obtaining the travel time from the maintenance station to the installation location, and determining the maintenance accessibility by the inverse ratio of the travel time, representing the ease of maintenance. If there is no maintenance station within the equipment inspection area... If there are maintenance sites, then the minimum maintenance accessibility value is directly taken. Regarding the passage time, it is generally calculated by statistically analyzing the passage time within a time period and taking the average as the final passage time. Of course, time segmentation can also be performed to divide the time period into segments, determine the power generation information (peak and valley) for different time periods, determine the weight corresponding to different time periods based on the power generation information, and then sum the passage times to obtain a passage time that better reflects the actual situation. Generally, during peak power generation periods, equipment is more likely to require maintenance, and correspondingly, the weight of the passage time during this period should be greater.

[0095] Furthermore, the signal strength of the equipment detection area is acquired, and the signal accessibility of the wind power equipment is determined based on the signal strength. Signal strength describes the ease of data transmission for the wind power equipment; higher signal strength indicates easier signal transmission and greater signal accessibility. Weather information changes in the equipment detection area are also acquired, and an environmental risk value is determined based on these changes. Greater weather information changes result in a higher environmental risk value. Weather information changes can be measured using two indicators: the average wind speed within a preset time period and the maximum temperature difference within the preset time period. Finally, the maintenance accessibility, signal accessibility, and environmental factors are considered together. The risk value determines the fitting height range. This is actually based on the maintenance accessibility, signal accessibility, and environmental risk value to determine a minimum height and a maximum height, resulting in a range. A height gradient is set within the fitting height range based on a preset step size. This process involves creating an arithmetic sequence by combining the minimum height, maximum height, and step size. Then, the height gradient is used as the vertical coordinate to randomly determine a set of points within the equipment detection area. The number of points corresponding to each height gradient is a preset value. At this point, the static state of the power equipment is described by a set of points. The above point set creation process is actually determining some points in a small three-dimensional space.

[0096] It should be noted that both the minimum and maximum heights are inversely proportional to maintenance accessibility and signal accessibility, and directly proportional to environmental risk values. The difference lies in the different coefficients of each parameter, which determines the two height values ​​as the minimum and maximum heights. The practical significance is that the more difficult the power equipment is to maintain, the more difficult the power equipment is to obtain signals, and the more severe the environmental conditions of the power equipment, the higher the average height of the point set.

[0097] Figure 3 This is the second sub-flow flowchart of the distributed power resource information aggregation method. The step of clustering power points in the distributed power map and setting edge nodes based on the clustering results includes:

[0098] Step S201: Calculate the distance between each power point based on the preset planar scaling factor and height scaling factor;

[0099] Step S202: Cluster the power points based on the distance;

[0100] Step S203: For each type of power point, query its corresponding power equipment;

[0101] Step S204: Count the number of points corresponding to each power device in this type of power point. When the number of points reaches the preset first number threshold, mark the power device.

[0102] Step S205: When the number of marked power devices for each type of power point reaches the preset second quantity threshold, set an edge node and synchronously establish a connection channel between the edge node and the marked power device; mark the power device that has not established a connection channel with the edge node as an edge node.

[0103] In one example of the technical solution of this invention, the process of setting edge nodes is described. The process of setting edge nodes involves a point clustering scheme. The point clustering scheme relies on distance as a parameter. However, the planar distance between different power devices is definitely greater than the height difference. During clustering, power points of the same type of power device will be grouped into a separate category, which is meaningless. Therefore, the technical solution of this invention also has another parameter, namely the planar scaling factor and the height scaling factor, which are used to reduce the distance between different power devices in the map (the distance in the top view, also known as the planar distance), so that the planar distance and the height difference are as close to the same scale as possible. A common approach is to obtain the planar distance of the nearest power device, match it with a preset step size, calculate a ratio, and use it as the scaling factor, or use it as the scaling factor after fine-tuning. At this time, the point set corresponding to all power devices is in the same space and has a high concentration. Then, the power point clustering algorithm based on distance can be applied to cluster the points. Then, for each type of power point, its corresponding power equipment is queried (each power equipment corresponds to multiple power points). The power points are classified to obtain the number of power points corresponding to each power equipment in that type of power point. When the number of power points reaches a preset first threshold, it is considered that the power equipment corresponds to more power points in that type, and at this time, the power equipment is marked. When the number of marked power equipment in each type of power point reaches a preset second threshold, it is considered that the type of power point corresponds to multiple power equipment, and these power equipment are sufficiently similar. At this time, an edge node is set, and a connection channel between the edge node and the marked power equipment is established synchronously. In fact, for a type of power point that does not meet the conditions for setting an edge node, the built-in data transmitter of each power equipment is considered to be an edge node. The purpose of the above process is only to group sufficiently similar power equipment into one type and use the same data forwarding device for data collection. For power equipment that is not sufficiently similar, the original data collection scheme is used.

[0104] Figure 4 The third sub-process flowchart of the distributed power resource information aggregation method includes the following steps: acquiring power parameters based on edge nodes, aggregating and sorting the power parameters, and constructing an aggregated database:

[0105] Step S301: For any edge node, query the power equipment corresponding to the edge node;

[0106] Step S302: Acquire data from power equipment in real time based on edge nodes;

[0107] Step S303: Query the total number of edge nodes corresponding to each power device, and determine the data upload frequency based on the total number of devices in the edge nodes and the total number of edge nodes for each device;

[0108] Step S304: Obtain the power parameters uploaded at the edge nodes based on the data upload frequency, aggregate and sort the power parameters, and construct an aggregate database.

[0109] The above describes the data acquisition and aggregation process. For any edge node, the corresponding power equipment is queried. Data from the power equipment is acquired in real time based on the data forwarding devices installed at the edge node. The data acquisition process is real-time, but the data upload process is not necessarily real-time. Real-time acquisition of power equipment data is equivalent to local storage at the edge node, while the data upload process is equivalent to periodically sending data to the execution entity of this method. The data upload frequency for each edge node is undetermined. The total number of edge nodes corresponding to each power equipment is queried, and the data upload frequency is determined based on the total number of devices at each edge node and the total number of edge nodes for each device. For the execution entity of this method, the power parameters uploaded at the edge node are acquired based on the data upload frequency, and the power parameters are aggregated, sorted, and an aggregated database is constructed.

[0110] The process of aggregating and sorting the power parameters is as follows:

[0111] Receive data containing time and type uploaded by various sensors in the wind power equipment, normalize the data, use type as column index and time as row index to statistically analyze the data, and obtain a data matrix;

[0112] The data matrix of all wind power equipment is statistically analyzed to obtain an aggregated database.

[0113] The above content defines the data structure for power parameters. When acquiring data, the data includes time and type. Type is used as the column index, and time as the row index. All acquired data can be inserted into a matrix with an ever-increasing number of rows, called a data matrix. It should be noted that the meanings of the row and column numbers can be interchanged, i.e., type is used as the row index and time as the column index. It should also be noted that different data types have different units, requiring normalization to obtain dimensionless values ​​before inserting them into the data matrix. The goal of normalization is to convert all data into positive indicators. After obtaining the corresponding data matrix for each wind turbine, the data matrices of all wind turbines are statistically analyzed to obtain an aggregated database.

[0114] Figure 5This is the fourth sub-flow flowchart of the distributed power resource information aggregation method. The step of traversing the aggregation database based on preset feature data, locating abnormal data and its wind power equipment, and generating prompt information includes:

[0115] Step S401: Query feature data in a preset feature database; the feature database includes feature data items and anomaly description items, the feature data is a matrix of preset size, and the elements in the matrix are data ranges;

[0116] Step S402: Traverse the aggregated database based on feature data. When a match is found, query the matched power parameters and their corresponding wind power equipment.

[0117] Step S403: Read the anomaly description of the feature data when the match is successful, count the anomaly description and wind power equipment, and generate a prompt message.

[0118] In one example of the technical solution of this invention, the process of generating the prompt message is described. Feature data is queried in a pre-built feature database, which includes feature data items and anomaly description items. The feature data is a matrix of a pre-defined size, where each element represents a data range. In simpler terms, the feature data is a small matrix composed of numerical ranges. Feature data is read sequentially from the feature database, and the database is traversed and compared based on the feature data. This process is equivalent to traversing and matching a small matrix within a larger matrix, which is a common comparison technique used in existing methods, such as convolutional recognition models. When a match is successful, the matched power parameters are queried, and the corresponding wind power equipment is simultaneously queried. The anomaly description of the successfully matched feature data is read from the feature database, and the anomaly description and wind power equipment are statistically analyzed to generate a prompt message, which is then sent to the management.

[0119] It's worth noting that the structure of feature data can be targeted. For example, specific limitations can be imposed on two parameters, limiting them to a small range, while other parameters can be limited to a very large range, such as historical minimum and maximum values. In this case, a row matrix with the same number of columns as the data matrix can be constructed. After determining multiple rows, the feature data is obtained. This typically involves first collecting data during abnormal situations, then determining the data ranges at different times, and superimposing them to obtain the data range over a period of time, which serves as the feature data.

[0120] As a preferred embodiment of the technical solution of the present invention, the method further includes:

[0121] Read the data matrix of different power devices from the aggregated database;

[0122] Data matrices of power equipment are extracted according to a preset time range, the data matrices are compared, and the matrix similarity is calculated as the data similarity of power equipment.

[0123] Read the weather information of the power equipment, compare them pairwise, and determine the environmental similarity of the power equipment based on the comparison results of the weather information;

[0124] When the environmental similarity reaches the preset first similarity threshold, the corresponding data similarity is read. When the data similarity is less than the preset second similarity threshold, a weather information update instruction is generated for the power equipment.

[0125] In one example of the technical solution of this invention, a data comparison process is introduced based on existing technical solutions. Data matrices of different power devices are read from an aggregated database. Data matrices of power devices are extracted according to a preset time range, and the data matrices are compared to calculate matrix similarity. This describes the similarity of the motion states of two power devices during operation, and is called data similarity. Based on this, weather information of the power devices is read and compared pairwise. The weather information comparison process is very simple; for example, comparing temperatures is done by simply subtracting, so it will not be elaborated further. Based on the comparison results of the weather information, the similarity of the environments in which the power devices are located is determined, and this is called environmental similarity. When the environmental similarity reaches a preset first similarity threshold, the environments in which the two power devices are located are considered sufficiently similar. At this time, the data similarity corresponding to the two power devices is read. Normally, the data similarity of the two power devices should also be high. However, if the data similarity is less than a preset second similarity threshold, indicating that it is sufficiently low, it means that their actual operating states are different. This is very likely because the weather information has changed but has not been updated. Therefore, a weather information update instruction is generated for the power devices to ensure that managers can promptly detect environmental changes in the power devices.

[0126] It should be noted that in the data matrix comparison process, the total number of data types is the same, but the collection frequency of different data may not be the same. This means that the number of columns is the same, but the number of rows may not be. In this case, the number of rows within the same time range (24 hours) may be different during the comparison. Therefore, data simulation is required during the comparison. For example, a time point is set every five minutes, such as 0:00, 0:05, 0:10, and so on, to obtain different numbers of rows. For any type of data, the two parameters closest to that time are queried, their average is calculated, and used as the filler value. At this time, the dimensions of the data matrix to be compared are the same, and the existing matrix similarity calculation process can be applied.

[0127] Figure 6This is a block diagram of the structure of a distributed power resource information aggregation system. In this embodiment of the invention, a distributed power resource information aggregation system, system 10, includes:

[0128] The power map construction module 11 is used to obtain the installation parameters of the wind power equipment and construct a distributed power map based on the installation parameters; wherein, the distributed power map contains power points determined by the installation parameters;

[0129] The edge node setting module 12 is used to cluster the power points in the distributed power map and set edge nodes based on the clustering results.

[0130] Database construction module 13 is used to obtain power parameters based on edge nodes, aggregate and sort the power parameters, and build an aggregate database;

[0131] The anomaly location module 14 is used to traverse the aggregated database according to preset feature data, locate abnormal data and its wind power equipment, and generate prompt information; wherein, the feature data is used to characterize the data situation under abnormal conditions.

[0132] Furthermore, the power map construction module 11 includes:

[0133] The detection zone setting unit is used to query the installation location of wind power equipment and determine the equipment detection zone based on the installation location.

[0134] The road segment analysis unit is used to acquire the road segment status in the equipment detection area and determine the maintenance accessibility of the wind power equipment based on the road segment status.

[0135] The signal analysis unit is used to acquire the signal strength of the equipment detection area and determine the signal reachability of the wind power equipment based on the signal strength.

[0136] The environmental analysis unit is used to acquire weather information changes in the equipment testing area and determine the environmental risk value based on the changes.

[0137] A high-fitting unit is used to determine the fitting height range based on the maintenance accessibility, signal accessibility, and environmental risk value.

[0138] The point set construction unit is used to set a height gradient within the fitting height range based on a preset step size, and to randomly determine a point set within the device detection area using the height gradient as the vertical coordinate; the number of points corresponding to each height gradient is a preset value.

[0139] Point set statistics unit, used to count all point sets and construct a distributed power map.

[0140] Specifically, the edge node setting module 12 includes:

[0141] The distance calculation unit is used to calculate the distance to each power point based on the preset planar scaling factor and height scaling factor;

[0142] A point clustering unit is used to cluster power points based on the distance;

[0143] The first query unit is used to query the corresponding power equipment for each type of power point.

[0144] The equipment marking unit is used to count the number of points corresponding to each power equipment in this type of power point. When the number of points reaches a preset first quantity threshold, the power equipment is marked.

[0145] The channel establishment unit is used to set edge nodes and synchronously establish connection channels between edge nodes and marked power equipment when the number of marked power equipment for each type of power point reaches a preset second threshold; and to mark power equipment that has not established a connection channel with edge nodes as edge nodes.

[0146] Furthermore, the database construction module 13 includes:

[0147] The second query unit is used to query the power equipment corresponding to any edge node;

[0148] The data acquisition unit is used to acquire data from power equipment in real time based on edge nodes;

[0149] The frequency determination unit is used to query the total number of edge nodes corresponding to each power device and determine the data upload frequency based on the total number of devices in the edge nodes and the total number of edge nodes for each device.

[0150] The aggregation execution unit is used to obtain the power parameters uploaded at the edge nodes based on the data upload frequency, aggregate and sort the power parameters, and build an aggregate database.

[0151] The process of aggregating and sorting the power parameters is as follows:

[0152] Receive data containing time and type uploaded by various sensors in the wind power equipment, normalize the data, use type as column index and time as row index to statistically analyze the data, and obtain a data matrix;

[0153] The data matrix of all wind power equipment is statistically analyzed to obtain aggregated data.

[0154] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for distributed power resource information aggregation, characterized in that, The method includes: Obtain the installation parameters of the wind power equipment, and construct a power distribution map based on the installation parameters; wherein, the power distribution map contains power points determined by the installation parameters; Cluster the power points in the distributed power map and set edge nodes based on the clustering results; Power parameters are obtained from edge nodes, and the power parameters are aggregated, sorted, and then an aggregated database is constructed. The system traverses the aggregated database based on preset feature data, locates abnormal data and its associated wind power equipment, and generates alert information; the feature data is used to characterize the data situation under abnormal conditions.

2. The distributed power resource information aggregation method according to claim 1, characterized in that, The step of obtaining the installation parameters of the wind power equipment and constructing a distributed power map based on the installation parameters includes: Locate the installation location of the wind power equipment and determine the equipment inspection area based on the installation location; Obtain the road segment status in the equipment inspection area, and determine the maintenance accessibility of the wind power equipment based on the road segment status; Acquire the signal strength in the equipment detection area, and determine the signal reachability of the wind power equipment based on the signal strength; Acquire weather information changes in the equipment testing area, and determine the environmental risk value based on the changes; The fitted height range is determined based on the maintenance accessibility, signal accessibility, and environmental risk value. Based on a preset step size, a height gradient is set within the fitting height range. The height gradient is used as the vertical coordinate to randomly determine a set of points within the device detection area. The number of points corresponding to each height gradient is a preset value. Collect statistics from all point sets and construct a distributed power map.

3. The distributed power resource information aggregation method according to claim 1, characterized in that, The step of clustering the power points in the distributed power map and setting edge nodes based on the clustering results includes: The distance to each power point is calculated based on the preset planar scaling factor and height scaling factor; Cluster the power points based on the distance; For each type of power point, query its corresponding power equipment; Count the number of points corresponding to each power device in this type of power point, and mark the power device when the number of points reaches a preset first threshold. When the number of marked power devices for each type of power point reaches the preset second threshold, an edge node is set, and a connection channel between the edge node and the marked power device is established simultaneously; power devices that have not established a connection channel with the edge node are marked as edge nodes.

4. The distributed power resource information aggregation method according to claim 1, characterized in that, The steps of acquiring power parameters based on edge nodes, aggregating and sorting the power parameters, and constructing an aggregated database include: For any edge node, query the power equipment corresponding to the edge node; Real-time acquisition of power equipment data based on edge nodes; Query the total number of edge nodes corresponding to each power device, and determine the data upload frequency based on the total number of devices in the edge nodes and the total number of edge nodes for each device. Based on the data upload frequency, the power parameters uploaded at the edge nodes are obtained, and the power parameters are aggregated and sorted to build an aggregated database; The process of aggregating and sorting the power parameters is as follows: Receive data containing time and type uploaded by various sensors in the wind power equipment, normalize the data, use type as column index and time as row index to statistically analyze the data, and obtain a data matrix; The data matrix of all wind power equipment is statistically analyzed to obtain an aggregated database.

5. The distributed power resource information aggregation method according to claim 1, characterized in that, The step of traversing the aggregated database based on preset feature data, locating abnormal data and its wind power equipment, and generating a prompt message includes: Query feature data in a preset feature database; the feature database includes feature data items and anomaly description items, the feature data is a matrix of preset size, and the elements in the matrix are data ranges; Based on feature data, the aggregated database is traversed. When a match is found, the matching power parameters and their corresponding wind power equipment are queried. Read the anomaly description of the feature data when a match is successful, compile statistics on the anomaly description and wind power equipment, and generate a prompt message.

6. The distributed power resource information aggregation method according to claim 4, characterized in that, The method further includes: Read the data matrix of different power devices from the aggregated database; Data matrices of power equipment are extracted according to a preset time range, the data matrices are compared, and the matrix similarity is calculated as the data similarity of power equipment. Read the weather information of the power equipment, compare them pairwise, and determine the environmental similarity of the power equipment based on the comparison results of the weather information; When the environmental similarity reaches the preset first similarity threshold, the corresponding data similarity is read. When the data similarity is less than the preset second similarity threshold, a weather information update instruction is generated for the power equipment.

7. A distributed power resource information aggregation system, characterized in that, The system includes: The power map construction module is used to obtain the installation parameters of wind power equipment and construct a distributed power map based on the installation parameters; wherein, the distributed power map contains power points determined by the installation parameters; The edge node setting module is used to cluster the power points in the distributed power map and set edge nodes based on the clustering results. The database construction module is used to acquire power parameters based on edge nodes, aggregate and sort the power parameters, and build an aggregated database. The anomaly localization module is used to traverse the aggregated database based on preset feature data, locate abnormal data and its wind power equipment, and generate prompt information; among which, the feature data is used to characterize the data situation under abnormal conditions.

8. The distributed power resource information aggregation system according to claim 7, characterized in that, The power graph construction module includes: The detection zone setting unit is used to query the installation location of wind power equipment and determine the equipment detection zone based on the installation location. The road segment analysis unit is used to acquire the road segment status in the equipment detection area and determine the maintenance accessibility of the wind power equipment based on the road segment status. The signal analysis unit is used to acquire the signal strength of the equipment detection area and determine the signal reachability of the wind power equipment based on the signal strength. The environmental analysis unit is used to acquire weather information changes in the equipment testing area and determine the environmental risk value based on the changes. A high-fitting unit is used to determine the fitting height range based on the maintenance accessibility, signal accessibility, and environmental risk value. The point set construction unit is used to set a height gradient within the fitting height range based on a preset step size, and to randomly determine a point set within the device detection area using the height gradient as the vertical coordinate; the number of points corresponding to each height gradient is a preset value. Point set statistics unit, used to count all point sets and construct a distributed power map.

9. The distributed power resource information aggregation system according to claim 7, characterized in that, The edge node setting module includes: The distance calculation unit is used to calculate the distance to each power point based on the preset planar scaling factor and height scaling factor; A point clustering unit is used to cluster power points based on the distance; The first query unit is used to query the corresponding power equipment for each type of power point. The equipment marking unit is used to count the number of points corresponding to each power equipment in this type of power point. When the number of points reaches a preset first quantity threshold, the power equipment is marked. The channel establishment unit is used to set edge nodes and synchronously establish connection channels between edge nodes and marked power equipment when the number of marked power equipment for each type of power point reaches a preset second threshold; and to mark power equipment that has not established a connection channel with edge nodes as edge nodes.

10. The distributed power resource information aggregation system according to claim 7, characterized in that, The database construction module includes: The second query unit is used to query the power equipment corresponding to any edge node; The data acquisition unit is used to acquire data from power equipment in real time based on edge nodes; The frequency determination unit is used to query the total number of edge nodes corresponding to each power device and determine the data upload frequency based on the total number of devices in the edge nodes and the total number of edge nodes for each device. The aggregation execution unit is used to obtain the power parameters uploaded at the edge nodes based on the data upload frequency, aggregate and sort the power parameters, and build an aggregate database. The process of aggregating and sorting the power parameters is as follows: Receive data containing time and type uploaded by various sensors in the wind power equipment, normalize the data, use type as column index and time as row index to statistically analyze the data, and obtain a data matrix; The data matrix of all wind power equipment is statistically analyzed to obtain aggregated data.

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