CN-121995538-A - Multi-source sensor fused wind field monitoring data error compensation method and system

CN121995538ACN 121995538 ACN121995538 ACN 121995538ACN-121995538-A

Abstract

The invention discloses a multisource sensor fusion wind field monitoring data error compensation method and system, which belong to the technical field of meteorological monitoring and comprise the following steps of throwing sensor nodes into various points of a detection area, and collecting wind field monitoring data and communication signal intensity data; the method comprises the steps of determining spatial position information of each sensor node according to communication signal intensity data, extracting terrain shielding coefficients of each sensor node, calculating wind field monitoring data of each sensor node according to error correction coefficients, outputting compensated wind field monitoring data, continuously carrying out drift detection on the wind field monitoring data of each sensor node in the process of outputting the compensated wind field monitoring data, and carrying out replacement processing on the error correction coefficients of abnormal nodes when drift abnormality is detected. The invention changes the defects that the traditional compensation method only looks at data and does not look at the environment by establishing a three-dimensional coordinate system and introducing the terrain shielding coefficient on the basis.

Inventors

PENG BAILIN
LIANG CHENGJIE

Assignees

安赛尔（长沙）机电科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260408

Claims (10)

1. A multi-source sensor fused wind field monitoring data error compensation method is characterized by comprising the following steps: the sensor nodes are put in all the points of the detection area, and wind field monitoring data and communication signal intensity data are collected; Determining the spatial position information of each sensor node according to the communication signal intensity data, and extracting the terrain shielding coefficient of each sensor node; calculating error correction coefficients of all sensor nodes based on the spatial position information and the terrain shielding coefficients; Calculating wind field monitoring data of each sensor node according to the error correction coefficient, and outputting compensated wind field monitoring data; and in the process of outputting the compensated wind field monitoring data, continuously carrying out drift detection on the wind field monitoring data of each sensor node, and carrying out replacement processing on error correction coefficients of abnormal nodes when drift abnormality is detected.
2. The method for compensating error of multi-source sensor fusion wind farm monitoring data of claim 1, wherein determining spatial location information of each sensor node comprises: constructing a multi-source sensor data fusion system, wherein the multi-source sensor data fusion system is in wireless communication connection with each sensor node, and receives wind field monitoring data and communication signal intensity data uploaded by each sensor node; The multi-source sensor data fusion system calculates signal transmission attenuation coefficients from each sensor node to a core center monitoring node according to a logarithmic path loss model according to communication signal intensity data of each sensor node; Screening sensor nodes with communication signal strength data meeting a first preset threshold as core center monitoring nodes, taking the core center monitoring nodes as coordinate origins, and recursively calculating three-dimensional coordinate distances of the sensor nodes relative to the core center monitoring nodes according to a difference relation between signal transmission attenuation coefficients and reference path losses corresponding to the reference distances; And determining three-dimensional relative coordinates of each sensor node by combining the communication signal arrival azimuth information.
3. The method for compensating error of multi-source sensor integrated wind field monitoring data according to claim 2, wherein the formula for calculating the signal transmission attenuation coefficient is expressed as: ; Wherein, the For the signal transmission attenuation coefficient, For the reference signal strength at the reference distance, The measured communication signal intensity data of the ith sensor node is represented by n which is a path loss index and is determined by the terrain environment calibration of the detection area, For the three-dimensional coordinate distance of the ith sensor node to the core center monitoring node, Is the reference distance.
4. The method for compensating for error of multi-source sensor integrated wind field monitoring data of claim 3, wherein the step of calculating the three-dimensional coordinate distance and the three-dimensional relative coordinates comprises the steps of: Deforming the logarithmic path loss model, and inversely solving the three-dimensional coordinate distance by using the difference relation between the signal transmission attenuation coefficient and the reference path loss, wherein the deformation formula is as follows: ; recursively calculating three-dimensional coordinate distances of each sensor node relative to the core center monitoring node according to a deformation formula; Acquiring azimuth angles and pitch angles of communication signals of all sensor nodes reaching a core center monitoring node, combining three-dimensional coordinate distances, and decomposing and calculating three-dimensional relative coordinates of all sensor nodes The method is specifically expressed as follows: ; ; ; in the formula, For the azimuth angle, Is the pitch angle.
5. The method for compensating error of multisource sensor fusion wind field monitoring data of claim 4, wherein the step of extracting the terrain shielding coefficients of each sensor node comprises the steps of: collecting a topographic image through a visual monitoring camera carried by each sensor node; Extracting a topographic structure outline of the topographic image, classifying the topographic structure outline according to preset analysis conditions, and screening out a potential shielding area; Carrying out connected domain analysis on the potential shielding areas, extracting continuous shielding areas, and calculating the pixel area of each shielding area; and accumulating the pixel areas of all the shielding areas to obtain the total shielding pixel number, and calculating the terrain shielding coefficient by combining the total pixel number of the image.
6. The method for compensating error of multi-source sensor fusion wind farm monitoring data of claim 5, wherein the step of calculating error correction coefficients of each sensor node comprises: Calculating a height influence factor and a distance attenuation factor according to the three-dimensional relative coordinates of each sensor node; and combining the terrain shielding coefficients, constructing an error correction model to calculate error correction coefficients, wherein the error correction model is expressed as: ; in the formula, 、 For the preset weight coefficient, the weight coefficient is set, For the shading coefficient of the terrain, As a high degree of influence factor, As a distance attenuation factor, Is an error correction coefficient; And compensating the original wind field monitoring data by using the error correction coefficient to obtain compensated wind field monitoring data.
7. The method for compensating error of multi-source sensor integrated wind field monitoring data of claim 6, wherein the step of continuously performing drift detection on the wind field monitoring data of each sensor node comprises the steps of: Acquiring a historical monitoring sequence of a target sensor node in a set sliding window period; calculating the average value of the historical monitoring sequence, and introducing the data average value of the neighborhood sensor nodes as a reference standard; Judging whether the data change rate of the target sensor node exceeds a preset abnormal threshold value, and if so, judging that the sensor node is abnormal in drift.
8. The method for compensating error of multi-source sensor integrated wind field monitoring data of claim 7, wherein the step of replacing the error correction coefficient of the abnormal node comprises the steps of: Screening a plurality of neighborhood sensor nodes with normal running states in a preset communication radius by taking the sensor node with abnormal drift as the center; acquiring three-dimensional relative coordinates, terrain shielding coefficients and currently effective error correction coefficients of each neighborhood sensor node; Respectively calculating the linear distance between each neighborhood sensor node and the abnormal node, and distributing the contribution weight of each neighborhood node according to the reciprocal of the distance; comparing the terrain shielding coefficients among the neighborhood sensor nodes, removing neighborhood nodes with the terrain shielding coefficient difference exceeding a preset proportion from the abnormal nodes, and revising the contribution weight; multiplying and summing the error correction coefficient of the residual neighborhood sensor node and the corresponding contribution weight to generate a replacement correction coefficient for the abnormal node; and covering the original coefficient of the abnormal node with the replacement correction coefficient, and collecting wind field monitoring data of the abnormal node again for secondary verification.
9. The method for compensating error of multi-source sensor fusion wind farm monitoring data of claim 8, wherein the step of secondary verification comprises the steps of: extracting a real-time monitoring value of the abnormal node after the replacement correction coefficient is applied, and calculating residual errors of the abnormal node and a monitoring mean value of the neighborhood sensor node group; Judging whether the residual error is converged within a preset error allowable range or not; If the residual error is not converged, the historical health data characteristics of the point positions corresponding to the abnormal nodes are called, and gain adjustment is carried out on the replacement correction coefficients; and if the residual error is not converged after the fine adjustment of the preset times, marking the abnormal node as a hardware fault state.
10. A multisource sensor-fused wind field monitoring data error compensation system, applying a multisource sensor-fused wind field monitoring data error compensation method according to any one of claims 1 to 9, comprising: The acquisition module is used for acquiring wind field monitoring data and communication signal intensity data; The terrain shielding coefficient extraction module is used for determining the spatial position information of each sensor node according to the communication signal intensity data and extracting the terrain shielding coefficient of each sensor node; The calculation module is used for calculating error correction coefficients of all sensor nodes based on the spatial position information and the terrain shielding coefficients; And the compensation module is used for calculating the wind field monitoring data of each sensor node according to the error correction coefficient and outputting the compensated wind field monitoring data.

Description

Multi-source sensor fused wind field monitoring data error compensation method and system Technical Field The invention relates to the technical field of meteorological monitoring, in particular to a method and a system for compensating wind field monitoring data errors by fusion of multisource sensors. Background In the field of meteorological monitoring, the accuracy of wind field data directly affects subsequent decision analysis. Existing wind farm monitoring approaches typically rely on a network of sensors dispersed within a target area. However, in complex geographical environments, there are often errors in the data collected by the sensors. Most of the traditional sensor compensation methods only depend on single physical quantity correction, and lack depth fusion of the spatial characteristics of the monitored environment. For example, in mountainous or densely built areas, two adjacent sensor nodes may have significant non-linear deviations in the wind speed data they collect due to subtle differences in the micro-topography (e.g., one of the nodes is behind a retaining wall and the other is on the open ground). The traditional linear compensation algorithm cannot identify the local wind field distortion caused by 'terrain shielding', and when the sensor generates electrical characteristic drift along with the increase of service time, the system can only roughly reject abnormal points, so that a data vacuum area appears in a monitoring network, and high-precision real-time complement and self-repair cannot be realized. Disclosure of Invention In view of the above problems, the present invention provides a method and a system for compensating error of wind field monitoring data by fusion of multiple source sensors. Therefore, the technical problem solved by the invention is that the traditional sensor compensation method mostly only depends on single physical quantity correction, and lacks depth fusion on the spatial characteristics of the monitored environment. The technical scheme includes that sensor nodes are put in various points of a detection area, wind field monitoring data and communication signal intensity data are collected, space position information of each sensor node is determined according to the communication signal intensity data, terrain shielding coefficients of each sensor node are extracted, error correction coefficients of each sensor node are calculated based on the space position information and the terrain shielding coefficients, wind field monitoring data of each sensor node are calculated according to the error correction coefficients, compensated wind field monitoring data are output, drift detection is continuously conducted on the wind field monitoring data of each sensor node in the process of outputting the compensated wind field monitoring data, and when drift abnormality is detected, error correction coefficients of abnormal nodes are replaced. The method comprises the steps of constructing a multi-source sensor data fusion system, receiving wind field monitoring data and communication signal intensity data uploaded by each sensor node, calculating signal transmission attenuation coefficients from each sensor node to a core center monitoring node according to a logarithmic path loss model by the multi-source sensor data fusion system according to the communication signal intensity data of each sensor node, screening the sensor nodes with the communication signal intensity data meeting a first preset threshold value as the core center monitoring node, recursively calculating three-dimensional coordinate distances of each sensor node relative to the core center monitoring node according to a difference relation between the signal transmission attenuation coefficients and reference path loss corresponding to the reference distances, and determining three-dimensional relative coordinates of each sensor node by combining communication signal arrival azimuth angle information. As a preferable scheme of the multisource sensor fusion wind field monitoring data error compensation method, the calculation formula of the signal transmission attenuation coefficient is shown as follows: ; Wherein, the For the signal transmission attenuation coefficient,For the reference signal strength at the reference distance,The measured communication signal intensity data of the ith sensor node is represented by n which is a path loss index and is determined by the terrain environment calibration of the detection area,For the three-dimensional coordinate distance of the ith sensor node to the core center monitoring node,Is the reference distance. As a preferable scheme of the multisource sensor fusion wind field monitoring data error compensation method, the method comprises the following steps of: Deforming the logarithmic path loss model, and inversely solving the three-dimensional coordinate distance by using the difference relation between the signal transmission attenuation coeffici