Electromagnetic pollution three-dimensional mapping system based on unmanned aerial vehicle cluster

Abstract

The application relates to the technical field of surveying and mapping, in particular to a three-dimensional electromagnetic pollution surveying and mapping system based on a UAV cluster, which comprises a processor and a memory, the processor executes a computer program stored in the memory to realize the following steps: acquiring a cluster, obtaining a first abnormality characteristic value of the cluster according to the position distance between suspected electromagnetic pollution data points in the cluster and the cluster center distance between the cluster and other clusters, obtaining a second abnormality characteristic value of the cluster according to the minimum circumscribed sphere of the cluster and the average electric field intensity of all suspected electromagnetic pollution data points located in the quadrant of the three-dimensional coordinate system of the cluster, and obtaining the electromagnetic pollution abnormality degree of the cluster according to the first abnormality characteristic value and the second abnormality characteristic value of the cluster; and obtaining target electromagnetic pollution data points according to the electromagnetic pollution abnormality degree. The application can improve the accuracy of detection and identification of electromagnetic pollution data points.

Description

Technical Field

[0001] This invention relates to the field of surveying and mapping technology, specifically to a three-dimensional electromagnetic pollution surveying and mapping system based on a swarm of unmanned aerial vehicles (UAVs). Background Technology

[0002] Currently, to safeguard public health and environmental safety, and to ensure the normal operation and compliant management of electronic equipment, electromagnetic pollution detection is necessary. Therefore, 3D electromagnetic pollution mapping systems have emerged. These systems primarily utilize 3D mapping technology to acquire the electric field information of various data points within a point cloud of the area to be detected. Based on this acquired electric field information, electromagnetic pollution is detected and identified. In other words, currently, 3D laser scanning is typically used to obtain a point cloud of the area to be detected, and a swarm of drones is used to collect the electric field information of this area, or in other words, a swarm of drones collects the electric field information of each data point within the point cloud. Then, based on the acquired electric field information of each data point in the point cloud, electromagnetic pollution is identified and detected. Electromagnetic pollution (EMI) detection relies on data points, specifically the electric field information from these data points, to identify and detect EMI in areas requiring EMI detection. However, the current method of collecting this electric field information can be affected by external environmental interference, resulting in noise in the collected data. This noise can lead to inaccurate identification of EMI data points or low accuracy in EMI detection. Low accuracy in EMI detection, whether for specific data points or the overall detection of EMI, negatively impacts public health and environmental safety. Therefore, improving the accuracy of EMI detection, particularly the accuracy of identifying EMI data points, is a pressing issue that needs to be addressed. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides a three-dimensional electromagnetic pollution mapping system based on unmanned aerial vehicle (UAV) swarms, the specific technical solution of which is as follows:

[0004] One embodiment of the present invention provides a three-dimensional mapping system for electromagnetic pollution based on a drone swarm, including a processor and a memory, wherein the processor executes a computer program stored in the memory to perform the following steps:

[0005] A three-dimensional point cloud of the target area is acquired, and the electric field intensity of each data point in the three-dimensional point cloud is obtained using a UAV.

[0006] Based on the discrete characterization values ​​of the electric field intensity of all data points in the three-dimensional point cloud, the electromagnetic pollution probability characterization value corresponding to the target area is obtained.

[0007] If the electromagnetic pollution probability characterization value is greater than a preset electromagnetic pollution probability threshold, then based on the mean electric field strength of all data points in the three-dimensional point cloud, suspected electromagnetic pollution data points in the three-dimensional point cloud are obtained. Then, based on the positional distance between the suspected electromagnetic pollution data points, all suspected electromagnetic pollution data points in the three-dimensional point cloud are clustered to obtain a cluster. Based on the positional distance between the suspected electromagnetic pollution data points in the cluster and the cluster center distance between the cluster and other clusters, a first anomaly characterization value of the cluster is obtained. Based on the minimum circumsphere of the cluster and the mean electric field strength of all suspected electromagnetic pollution data points located in the quadrant of the three-dimensional coordinate system of the cluster, a second anomaly characterization value of the cluster is obtained. Based on the first and second anomaly characterization values ​​of the cluster, the electromagnetic pollution anomaly degree of the cluster is obtained.

[0008] The target electromagnetic pollution data points are obtained based on the degree of electromagnetic pollution anomaly.

[0009] Beneficial Effects: This invention first acquires a three-dimensional point cloud of the target area and uses a drone to acquire the electric field intensity of each data point in the three-dimensional point cloud. Then, based on the discrete representation values ​​of the electric field intensity of all data points in the three-dimensional point cloud, the electromagnetic pollution probability representation value corresponding to the target area is obtained. Next, it is determined whether the electromagnetic pollution probability representation value is greater than a preset electromagnetic pollution probability threshold. If so, based on the mean electric field intensity of all data points in the three-dimensional point cloud, suspected electromagnetic pollution data points in the three-dimensional point cloud are obtained. Based on the positional distance between the suspected electromagnetic pollution data points, all suspected electromagnetic pollution data points in the three-dimensional point cloud are clustered to obtain clusters. Based on the positional distance between the suspected electromagnetic pollution data points in the clusters and the cluster center distance between the clusters and other clusters, the first anomaly representation value of the cluster is obtained. Based on the minimum circumsphere of the cluster and the mean electric field intensity of all suspected electromagnetic pollution data points located in the quadrant of the three-dimensional coordinate system of the cluster, the second anomaly representation value of the cluster is obtained. Based on the first and second anomaly representation values ​​of the cluster, the electromagnetic pollution anomaly degree of the cluster is obtained. Finally, the target electromagnetic pollution data points are obtained based on the electromagnetic pollution anomaly degree. Furthermore, based on the first and second anomaly characterization values, this invention can minimize the impact of interference encountered by the UAV when collecting electric field information of the target area on the subsequent electromagnetic pollution detection results. In other words, based on the first and second anomaly characterization values, this invention can improve the accuracy of electromagnetic pollution data point detection and identification. Attached Figure Description

[0010] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of a three-dimensional mapping method for electromagnetic pollution based on a drone swarm, according to the present invention. Detailed Implementation

[0012] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the protection scope of the embodiments of the present invention.

[0013] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0014] This embodiment provides a three-dimensional electromagnetic pollution mapping system based on UAV swarms, including a processor and a memory. The processor executes a computer program stored in the memory to implement a three-dimensional electromagnetic pollution mapping method based on UAV swarms, such as... Figure 1 As shown, this method for three-dimensional mapping of electromagnetic pollution based on UAV swarms includes the following steps:

[0015] Step S001: Obtain the three-dimensional point cloud of the target area, and use the UAV to obtain the electric field intensity of each data point in the three-dimensional point cloud.

[0016] When using drones to measure the electric field information of areas requiring electromagnetic pollution detection, the measurement environment is highly variable, and the measurement results are easily affected by these environmental changes. This can lead to inaccurate electric field information, which in turn can result in inaccurate 3D electromagnetic pollution mapping results. For example, abnormal electric field data caused by environmental interference may be mistaken for genuine electromagnetic pollution data. Furthermore, inaccurate detection and identification of electromagnetic pollution can negatively impact public health and environmental safety. Therefore, minimizing the interference encountered by drones when collecting electric field information in areas requiring electromagnetic pollution detection is crucial for improving the accuracy of electromagnetic pollution data point detection and identification. In other words, the main objective of this embodiment is to minimize the impact of interference encountered by drones when collecting electric field information in areas requiring electromagnetic pollution detection on the electromagnetic pollution detection results.

[0017] This embodiment first acquires any area that needs to be detected for electromagnetic pollution and marks it as the target area. Then, it uses a 3D lidar to obtain a 3D point cloud of the target area. After that, it uses a swarm of drones to measure the electric field intensity at each data point in the 3D point cloud of the target area, thereby obtaining the electric field intensity of each data point in the 3D point cloud of the target area. The data points in the point cloud are 3D data. Subsequently, the real electromagnetic pollution data points will be identified based on the characteristics of abnormal data or noise caused by environmental interference and electromagnetic pollution characteristics.

[0018] Step S002: Based on the discrete characterization values ​​of the electric field intensity of all data points in the three-dimensional point cloud, obtain the electromagnetic pollution probability characterization value corresponding to the target area.

[0019] Because electromagnetic pollution typically has a small distribution range and a highly uncertain location—meaning the polluted area won't cover the entire surveyed area—the electromagnetic pollution data will differ significantly from normal electric field data if it exists within the surveyed area. The surveyed area refers to the target region. Since there's no gradual transition between electromagnetic pollution and the normal electric field, the normal electric field data and electromagnetic pollution data will be located near the extremes of the target region's electric field strength range, respectively. Therefore, when electromagnetic pollution is present in the target region, the measured electric field strength distribution will be relatively dispersed; conversely, if there is no electromagnetic pollution in the surveyed area, the overall electric field strength distribution in the target region will be relatively concentrated. To maintain a field strength near the normal field strength within the survey space, even if the measured electric field strength data itself fluctuates, the distribution of electric field strength in the survey area is relatively dense compared to the presence of electromagnetic pollution in the survey space. Based on the above analysis, this embodiment will next analyze the possibility of electromagnetic pollution in the target area based on the discreteness or density of the electric field strength distribution. Specifically, this embodiment will obtain the electromagnetic pollution probability characterization value corresponding to the target area based on the discrete characterization values ​​of the electric field strength of all data points in the 3D point cloud of the target area. This electromagnetic pollution probability characterization value can reflect the possibility of electromagnetic pollution in the target area. The specific process for obtaining the electromagnetic pollution probability characterization value corresponding to the target area is as follows:

[0020] First, the variance of the electric field intensity of all data points in the 3D point cloud is calculated, and the calculated variance is normalized using the normalization function Norm(). The result of the normalization is recorded as the first discrete representation value. Then, the electric field intensities of all data points in the 3D point cloud are sorted in ascending order, and the sorting result is recorded as the electric field intensity sequence. Next, based on the squared differences between all adjacent electric field intensities in the electric field intensity sequence, a characteristic adjacent difference sequence of the electric field intensity sequence is constructed. That is, the a-th characteristic adjacent difference in the characteristic adjacent difference sequence of the electric field intensity sequence is the electric field intensity sequence value. The squared difference between the a-th and (a+1)-th electric field intensities in the sequence is calculated. Next, the range of the electric field intensity sequence and the mean of the characteristic adjacent difference sequence are calculated. The range of the sequence is the difference between the maximum and minimum values ​​in the corresponding sequence. The product of the range of the electric field intensity sequence and the mean of the characteristic adjacent difference sequence is calculated, and the product is normalized using the normalization function Norm(). The result of the normalization is used as the second discrete characterization value. Finally, the mean of the first and second discrete characterization values ​​is calculated and used as the electromagnetic pollution probability characterization value corresponding to the target area. The specific calculation expression for the electromagnetic pollution probability characterization value corresponding to the target area is as follows:

[0021]

[0022] Where W is the electromagnetic pollution probability characterization value corresponding to the target area, and the value ranges from 0 to 1. Norm() is the minimum-maximum normalization function. Let V be the variance of the electric field intensity of all data points in the 3D point cloud. The maximum electric field intensity in the electric field intensity sequence. Let A be the minimum electric field intensity in the electric field intensity sequence, and let A be the total number of electric field intensities in the sequence, which is also the total number of data points in the 3D point cloud. That is, one data point corresponds to one electric field intensity. Let be the a-th electric field intensity in the electric field intensity sequence. It represents the (a+1)th electric field intensity in the electric field intensity sequence.

[0023] And when the variance of the electric field intensity of all data points in the 3D point cloud A larger range indicates a more discrete distribution of electric field intensity among data points in the 3D point cloud, or a stronger dispersion of electric field intensity in the target region. A larger range also indicates a greater mean of the squared differences between all adjacent electric field intensities in the electric field intensity sequence. A larger value indicates a more discrete distribution of the electric field intensity among the data points in the 3D point cloud, or a stronger dispersion of the electric field intensity in the target region; while and The larger the value of W, the more discrete the electric field intensity distribution, or the stronger the dispersion. A more discrete or stronger dispersion indicates a higher probability of electromagnetic pollution in the target area. Conversely, a smaller value of W indicates a lower probability of electromagnetic pollution in the target area. Furthermore, the minimum and maximum values ​​used in the formula for normalizing the electromagnetic pollution probability characterization are based on historical cumulative monitoring. This is because electromagnetic pollution will inevitably interfere with the monitoring data during long-term monitoring or detection. Therefore, after normalizing using historical monitoring data, suspected electromagnetic pollution data points and normal data points will be divided at opposite ends of the value range. For example, in long-term detection, historical data from multiple electromagnetic pollution detection tasks will be accumulated. For each historical detection task, its corresponding historical variance (i.e., the variance of the electric field intensity of all data points in that task) will be calculated. From the historical variance calculation results of all historical detection tasks, the global maximum and global minimum values ​​are found, and these global maximum and global minimum values ​​are used to adjust the above formula. Normalization, i.e. The maximum and minimum values ​​used in the calculation are the extreme values ​​of the historical variance. Similarly.

[0024] Therefore, this embodiment can obtain the electromagnetic pollution probability characterization value corresponding to the target area through the above process.

[0025] Step S003: Determine whether the electromagnetic pollution probability characterization value is greater than a preset electromagnetic pollution probability threshold. If so, obtain suspected electromagnetic pollution data points in the three-dimensional point cloud based on the average electric field strength of all data points in the three-dimensional point cloud. Cluster all suspected electromagnetic pollution data points in the three-dimensional point cloud based on the positional distance between the suspected electromagnetic pollution data points to obtain clusters. Obtain a first anomaly characterization value of the cluster based on the positional distance between the suspected electromagnetic pollution data points in the cluster and the cluster center distance between the cluster and other clusters. Obtain a second anomaly characterization value of the cluster based on the minimum circumsphere of the cluster and the average electric field strength of all suspected electromagnetic pollution data points located in the quadrant of the three-dimensional coordinate system of the cluster. Obtain the electromagnetic pollution anomaly degree of the cluster based on the first and second anomaly characterization values. Obtain the target electromagnetic pollution data point based on the electromagnetic pollution anomaly degree.

[0026] Since the magnitude of the electromagnetic pollution probability characterization value reflects the likelihood of electromagnetic pollution in the target area, this embodiment sets a preset electromagnetic pollution probability threshold after obtaining the electromagnetic pollution probability characterization value. Then, it determines whether the electromagnetic pollution probability characterization value corresponding to the target area is greater than the preset threshold. If it is not greater, it is determined that there is no electromagnetic pollution in the target area, and subsequent detection and identification of electromagnetic pollution data points are unnecessary. However, if the electromagnetic pollution probability characterization value corresponding to the target area is greater than the preset threshold, it is determined that electromagnetic pollution exists in the target area, and subsequent detection and identification of electromagnetic pollution data points are necessary. That is, when the electromagnetic pollution probability characterization value corresponding to the target area is greater than the preset threshold, the electric field intensity of the data points in the 3D point cloud of the target area is analyzed to obtain the actual electromagnetic pollution data points in the target area, i.e., the target electromagnetic pollution data points. Furthermore, in specific applications, the implementer needs to set the preset electromagnetic pollution probability threshold based on the value range of the electromagnetic pollution probability characterization value, experimental statistics, and other actual conditions. For example, in this embodiment, the preset electromagnetic pollution probability threshold can be set to 0.6.

[0027] In this embodiment, the specific process for obtaining the target electromagnetic pollution data points is as follows:

[0028] First, based on the average electric field intensity of all data points in the 3D point cloud of the target area, suspected electromagnetic pollution data points are obtained. These suspected electromagnetic pollution data points are the basis for subsequently obtaining target electromagnetic pollution data points. The specific process for obtaining suspected electromagnetic pollution data points in the 3D point cloud of the target area is as follows: First, the average electric field intensity of all data points in the 3D point cloud of the target area is calculated and used as the threshold for judging suspected electromagnetic pollution in the target area. Then, all data points in the 3D cloud with an electric field intensity greater than the suspected electromagnetic pollution threshold are recorded as suspected electromagnetic pollution data points, i.e., the electric field intensity of suspected electromagnetic pollution data points is greater than the suspected electromagnetic pollution threshold. Furthermore, when electromagnetic pollution may exist in the target area, the normal electric field intensity and the electromagnetic pollution intensity in the area are considered. The electric field strength values ​​will be located near the two ends of the target area's electric field strength range, and the electromagnetic pollution intensity will be close to the maximum value. Therefore, the normal electric field strength is generally lower than the average value, which is lower than the suspected electromagnetic pollution judgment threshold. The electromagnetic pollution electric field strength is generally higher than the average value, which is higher than the suspected electromagnetic pollution judgment threshold. However, environmental interference and data acquisition equipment during monitoring and measurement can cause noise. Noise may also be higher than the suspected electromagnetic pollution judgment threshold. Therefore, data points higher than the suspected electromagnetic pollution judgment threshold are not necessarily all electromagnetic pollution data points, but may also be noise. That is, data points higher than the suspected electromagnetic pollution judgment threshold cannot be directly regarded as real electromagnetic pollution data points. Therefore, data points higher than the suspected electromagnetic pollution judgment threshold are recorded as suspected electromagnetic pollution data points for subsequent analysis and identification.

[0029] After obtaining suspected electromagnetic pollution data points, this embodiment requires further screening and analysis to obtain the actual electromagnetic pollution data points, i.e., the target electromagnetic pollution data points. Therefore, the specific process of analyzing suspected electromagnetic pollution data points to obtain target electromagnetic pollution data points in this embodiment is as follows:

[0030] Since electromagnetic pollution typically manifests as abnormal electric field data in a specific area, real electromagnetic pollution usually causes multiple consecutive points within that area to exhibit anomalies and spatial clustering. In contrast, noise points are usually randomly and discretely distributed, generally appearing as isolated local points or abrupt transitions in space, without significant spatial correlation. Therefore, based on the above analysis, this embodiment first performs clustering based on the positional distance between suspected electromagnetic pollution data points, and then analyzes the degree of electromagnetic pollution anomaly based on the clustering results. The degree of electromagnetic pollution anomaly reflects the likelihood that a data point is a real electromagnetic pollution data point. The specific clustering process is as follows: First, the positional distance between suspected electromagnetic pollution data points in the 3D point cloud of the target area is obtained. The distance between any two data points is the Euclidean distance between them, calculated based on their coordinates. Then, based on the distance between suspected electromagnetic pollution data points in the 3D point cloud of the target region, K-means clustering is performed on all suspected electromagnetic pollution data points in the 3D point cloud to obtain individual clusters. The number of cluster centers for K-means clustering of suspected electromagnetic pollution data points is obtained using the elbow method. The metric distance during clustering is the distance between the suspected electromagnetic pollution data points. Since the process of K-means clustering of suspected electromagnetic pollution data points is well-known given the number of cluster centers and the metric distance, it will not be described in detail in this embodiment.

[0031] After obtaining the clusters, the first anomaly characterization value of each cluster is obtained based on the characteristics that the electromagnetic pollution clusters have strong spatial clustering and the noise distribution is usually random and discrete. That is, in this embodiment, the first anomaly characterization value of each cluster will be obtained based on the positional distance between data points in each cluster and the cluster center distance between each cluster and other clusters. The first anomaly characterization value is a key parameter for subsequently obtaining the electromagnetic pollution anomaly degree of the clusters. The electromagnetic pollution anomaly degree can reflect the probability that the data points in the cluster are the target electromagnetic pollution data points. The specific process of obtaining the first anomaly characterization value of each cluster is as follows:

[0032] For any cluster S: First, based on the positional distances between each suspected electromagnetic pollution data point in cluster S and other suspected electromagnetic pollution data points in cluster S excluding the corresponding suspected electromagnetic pollution data point, the distance feature value of each suspected electromagnetic pollution data point in cluster S is obtained. Then, for any suspected electromagnetic pollution data point g in cluster S, the set consisting of all other suspected electromagnetic pollution data points in cluster S excluding g is obtained and denoted as the set to be calculated for suspected electromagnetic pollution data point g. The positional distances between suspected electromagnetic pollution data point g and each suspected electromagnetic pollution data point in the set to be calculated for g are calculated. The average of the positional distances between suspected electromagnetic pollution data point g and all suspected electromagnetic pollution data points in the set to be calculated for g is taken as the distance feature value of suspected electromagnetic pollution data point g. That is, the distance feature value of suspected electromagnetic pollution data point g is... , Let g be the total number of suspected electromagnetic pollution data points in the set to be calculated for suspected electromagnetic pollution data points. Let g be the location distance between the suspected electromagnetic pollution data point and the j-th suspected electromagnetic pollution data point in the set to be calculated. Then, calculate the mean of the distance feature values ​​of all suspected electromagnetic pollution data points in cluster S, and perform a reverse mapping to normalization on the mean of the distance feature values ​​of all suspected electromagnetic pollution data points in cluster S, and use the processing result as the first distribution characterization value. Then, obtain the set constructed by all other clusters besides cluster S, and denote it as the set to be processed corresponding to cluster S. Next, calculate the location distance between the cluster center of cluster S and the cluster centers of each cluster in the set to be processed, and denote it as the cluster center between cluster S and each cluster in the set to be processed. The distance is defined as follows: The set of cluster center distances between cluster S and each cluster in the set to be processed is denoted as the cluster center distance set corresponding to cluster S. The v-th cluster center distance in this set is the distance between cluster S and the v-th cluster in the set to be processed. Then, the mean of the cluster center distance set corresponding to cluster S is calculated, and this mean is inversely mapped to a normalized value. The result is used as the second distribution representation value. This inverse mapping involves taking the reciprocal of the mean and normalizing it to a minimum-maximum value. Finally, the mean of the first and second distribution representation values ​​is calculated and used as the first outlier representation value of cluster S. The specific expression for calculating the first outlier representation value of cluster S is as follows:

[0033]

[0034] in, Let Z0 be the first outlier characteristic value of cluster S, Norm() be the minimum-maximum normalization function, and Z0 be the total number of suspected electromagnetic pollution data points in cluster S. Let be the distance feature value of the t-th suspected electromagnetic pollution data point in cluster S. The larger the value, the greater the distance between the t-th suspected electromagnetic pollution data point and other suspected electromagnetic pollution data points in cluster S. Z1 represents the total number of clusters in the set to be processed corresponding to cluster S. This represents the distance to the v-th cluster center in the set of cluster center distances corresponding to cluster S. The larger the value, the farther away cluster S is from other clusters.

[0035] Furthermore, the denser the data points within a cluster and the closer the cluster is to other clusters, the more likely the data points within that cluster are spatially clustered electromagnetic pollution data points. Conversely, the more dispersed the data points within a cluster and the farther the cluster is from other clusters, the more likely the data points within that cluster are interference noise points generated during UAV data collection. Also, because when The larger or The smaller the value, the greater the distance between the suspected electromagnetic pollution data points in cluster S and other suspected electromagnetic pollution data points within the cluster. This also indicates a more dispersed distribution of data points within cluster S. The larger or The smaller the value, the farther away cluster S is from other clusters. Therefore, when smaller and The smaller, that is The smaller the value, the greater the likelihood that the data points within cluster S are noise points, while when... The larger and When it is larger, that is The larger the value, the greater the likelihood that the data points within cluster S are either actual electromagnetic pollution data points or target electromagnetic pollution data points.

[0036] In addition to noise with discrete distribution characteristics, there are also noise with clustered distributions, such as the noise generated by wind fields during electromagnetic data acquisition and monitoring by UAVs. Specifically, UAVs are highly susceptible to wind field influences during electromagnetic data acquisition and monitoring, causing unstable vibrations in the fuselage and resulting in deviations between the acquisition location and the preset location, thus leading to noise. Furthermore, wind fields and electromagnetic pollution fields share a common regional concentration. This regional concentration means that relying solely on the spatial distribution clustering characteristics analyzed above is insufficient to accurately obtain the true electromagnetic pollution data points. In other words, the regional concentration of wind fields and electromagnetic pollution fields leads to limitations in accurately obtaining true electromagnetic pollution data points based solely on... The electromagnetic pollution data points obtained from the first anomaly characterization value may also contain noise points caused by the wind field environment. In order to further improve the accuracy of electromagnetic pollution data point detection and identification, this embodiment will further distinguish between real electromagnetic pollution data points and noise points caused by wind field based on the spatial distribution characteristics of data points caused by electromagnetic pollution and data points caused by wind field. The spatial distribution characteristics of data points caused by electromagnetic pollution are: the direction of the electric field will cause large differences in field strength in different directions around the same electromagnetic source, and will form ellipsoidal or fan-shaped regions. That is, the clusters formed by real electromagnetic pollution data points have field strength around the center point. The data points exhibit several characteristics: significant differences in their distribution, a relatively clustered spherical shape in the clusters formed by real electromagnetic pollution data points, and a relatively large proportion of suspected electromagnetic pollution data points within the smallest circumsphere of these clusters. The spatial distribution of data points caused by wind fields is characterized by: the fluidity of wind fields and their direction (from one end to the other) leading to random but relatively stable turbulence of drones within the wind field. Therefore, the field strength around the center point of the clusters formed by wind field interference does not show significant differences, and the data points within these clusters exhibit smaller differences and more uniform shapes. It will not exhibit a clustered spherical shape, and the proportion of suspected electromagnetic pollution data points within the smallest circumsphere of the clusters formed by wind field interference is relatively small. Based on the spatial distribution characteristics of data points caused by electromagnetic pollution and those caused by wind field interference described above, this embodiment will next obtain the second anomaly characterization value of each cluster based on the smallest circumsphere of each cluster and the average electric field intensity of all suspected electromagnetic pollution data points located in the quadrants of the three-dimensional coordinate system of each cluster. The second anomaly characterization value is also a key parameter for subsequently obtaining the degree of electromagnetic pollution anomaly of the clusters. The specific process for obtaining the second anomaly characterization value of each cluster is as follows:

[0037] For any cluster S: First, obtain the minimum bounding sphere of cluster S and calculate its volume; then, construct a three-dimensional coordinate system for cluster S, with the cluster center point as the origin. The positive direction of the horizontal axis of the three-dimensional coordinate system is parallel to the positive direction of the horizontal axis of the three-dimensional space where the three-dimensional point cloud is located; the positive direction of the vertical axis of the three-dimensional coordinate system is parallel to the positive direction of the vertical axis of the three-dimensional space where the three-dimensional point cloud is located; and the positive direction of the vertical axis of the three-dimensional coordinate system is parallel to the positive direction of the horizontal axis of the three-dimensional space where the three-dimensional point cloud is located. The positive direction of the vertical axis of the 3D point cloud is parallel to the coordinate axis. The positive direction of the coordinate axis refers to the direction in which the value increases along the coordinate axis in the coordinate system. Then, a set is obtained comprising all suspected electromagnetic pollution data points belonging to cluster S and located within the same quadrant of the 3D coordinate system of cluster S. This set is denoted as the set of suspected electromagnetic pollution data points corresponding to each quadrant of the 3D coordinate system of cluster S. That is, the set of suspected electromagnetic pollution data points corresponding to the r-th quadrant of the 3D coordinate system of cluster S is composed of data points belonging to cluster S and located within the r-th quadrant of the 3D coordinate system of cluster S. All suspected electromagnetic pollution data points constitute a three-dimensional coordinate system with eight quadrants. The mean electric field intensity of all suspected electromagnetic pollution data points in the set corresponding to each quadrant of the three-dimensional coordinate system of cluster S is calculated and denoted as the mean electric field intensity of the corresponding quadrant. The set of the mean electric field intensity of all quadrants corresponding to the three-dimensional coordinate system of cluster S is denoted as the set of mean electric field intensity of the quadrants corresponding to cluster S. That is, the mean electric field intensity of the f-th quadrant in the set of mean electric field intensity of the quadrants corresponding to cluster S is the electric field intensity of the f-th quadrant corresponding to the three-dimensional coordinate system of cluster S. The average intensity is calculated first; then, the ratio of the total number of suspected electromagnetic pollution data points in cluster S to the volume of the smallest circumsphere of cluster S is calculated and recorded as the proportion characteristic value corresponding to cluster S. Next, the standard deviation of the set of quadrant electric field intensity means corresponding to cluster S is calculated, and the product of the proportion characteristic value and the standard deviation of the set of quadrant electric field intensity means corresponding to cluster S is calculated. Then, the product of the proportion characteristic value and the standard deviation of the set of quadrant electric field intensity means corresponding to cluster S is normalized, and the normalized result is used as the second anomaly characteristic value of cluster S. The specific calculation expression for the second anomaly characteristic value of cluster S is as follows:

[0038]

[0039] in, Z0 is the second anomaly characteristic value of cluster S, Z0 is the total number of suspected electromagnetic pollution data points in cluster S, and V0 is the volume of the minimum circumscribed sphere of cluster S. This represents the proportion of cluster S. Let be the standard deviation of the set of mean electric field intensities in the quadrants corresponding to cluster S. And when... The larger the value, the more likely the cluster S is to exhibit a relatively clustered spherical distribution, or the larger the proportion of suspected electromagnetic pollution data points within the smallest circumscribed sphere of cluster S. Therefore, it tends to exhibit spatial distribution characteristics of electromagnetic pollution. The larger the value, the more pronounced the difference in field strength around the center point of cluster S. Therefore, it exhibits stronger spatial distribution characteristics of electromagnetic pollution. and When it is larger, that is A larger value indicates that the suspected electromagnetic pollution data points within cluster S exhibit more typical distribution patterns characteristic of electromagnetic pollution, meaning that the likelihood of these suspected electromagnetic pollution data points being actual electromagnetic pollution data points is higher. Conversely, a smaller value indicates a lower likelihood of these suspected electromagnetic pollution data points being actual electromagnetic pollution data points. and The smaller, that is The smaller the value, the more the suspected electromagnetic pollution data points within cluster S exhibit wind field distribution patterns, indicating a higher probability that the suspected electromagnetic pollution data points within cluster S are noise points.

[0040] The minimum and maximum values ​​used in the minimum-maximum normalization process during the calculation of the first and second anomaly characterization values ​​are also determined based on historical cumulative monitoring. This normalization not only unifies the computational scale of all clusters but also divides the real-time monitoring cluster data points with genuine anomalies into the extreme range of maximum values, thus initially eliminating discrete noise interference. For example, in long-term monitoring, multiple electromagnetic pollution detection tasks are accumulated. For each historical detection task, the average intra-cluster distance of each cluster obtained from that task is calculated (the mean of the distance feature values ​​of all suspected electromagnetic pollution data points in any cluster is the average intra-cluster distance). From the inverse mapping results of the average intra-cluster distances of all clusters obtained from all historical detection tasks, the global maximum and global minimum values ​​are found, and these global maximum and global minimum values ​​are used to... Normalization, i.e. The maximum and minimum values ​​used in the calculation are the extreme values ​​obtained by inverse mapping of the historical average intra-cluster distance. and Similarly.

[0041] Since both the first and second anomaly representation values ​​of each cluster reflect the probability that the suspected electromagnetic pollution data points within the corresponding cluster are actually electromagnetic pollution data points, this embodiment, after obtaining the first and second anomaly representation values ​​of each cluster, fuses the first and second anomaly representation values ​​of each cluster to obtain the electromagnetic pollution anomaly degree of each cluster. The electromagnetic pollution anomaly degree of the cluster is mainly used to determine whether the suspected electromagnetic pollution data points within the corresponding cluster are actually electromagnetic pollution data points. In this embodiment, the electromagnetic pollution anomaly degree of each cluster is the average of the first and second anomaly representation values ​​of the corresponding cluster. That is, the electromagnetic pollution anomaly degree of cluster S is the average of the first and second anomaly representation values ​​of cluster S. The greater the electromagnetic pollution anomaly degree of cluster S, the greater the probability that the suspected electromagnetic pollution data points within cluster S are actually electromagnetic pollution data points. Conversely, the smaller the electromagnetic pollution anomaly degree of cluster S, the greater the probability that the suspected electromagnetic pollution data points within cluster S are noise points.

[0042] Since the degree of electromagnetic pollution anomaly in a cluster is used to determine whether suspected electromagnetic pollution data points within the corresponding cluster are actual electromagnetic pollution data points, this embodiment will next obtain the target electromagnetic pollution data points in the target area, i.e., the actual electromagnetic pollution data points, based on the degree of electromagnetic pollution anomaly in the cluster. The specific process is as follows:

[0043] For any cluster S, determine whether the electromagnetic pollution anomaly level of cluster S is greater than a preset electromagnetic pollution anomaly threshold. If so, determine that all suspected electromagnetic pollution data points in cluster S are target electromagnetic pollution data points, i.e., real electromagnetic pollution data points. Otherwise, determine that all suspected electromagnetic pollution data points in cluster S are noise points caused by environmental interference during data collection. In addition, in specific applications, implementers need to set a preset electromagnetic pollution anomaly threshold according to the value range of electromagnetic pollution anomaly level, experimental statistics, and other actual conditions. For example, in this embodiment, the preset electromagnetic pollution anomaly threshold can be set to 0.5.

[0044] Thus, this embodiment completes the detection and identification of electromagnetic pollution data points in the target area.

[0045] In summary, this embodiment first acquires a 3D point cloud of the target area and uses a UAV to acquire the electric field intensity of each data point in the 3D point cloud. Then, based on the discrete representation values ​​of the electric field intensity of all data points in the 3D point cloud, the electromagnetic pollution probability representation value corresponding to the target area is obtained. Next, it is determined whether the electromagnetic pollution probability representation value is greater than a preset electromagnetic pollution probability threshold. If so, based on the mean electric field intensity of all data points in the 3D point cloud, suspected electromagnetic pollution data points in the 3D point cloud are obtained. Based on the positional distance between the suspected electromagnetic pollution data points, all suspected electromagnetic pollution data points in the 3D point cloud are clustered to obtain clusters. Based on the positional distance between the suspected electromagnetic pollution data points in the clusters and the cluster center distance between the clusters and other clusters, the first anomaly representation value of the clusters is obtained. Based on the minimum circumsphere of the clusters and the mean electric field intensity of all suspected electromagnetic pollution data points located in the quadrant of the 3D coordinate system of the clusters, the second anomaly representation value of the clusters is obtained. Based on the first and second anomaly representation values ​​of the clusters, the electromagnetic pollution anomaly degree of the clusters is obtained. Finally, the target electromagnetic pollution data points are obtained based on the electromagnetic pollution anomaly degree. Furthermore, based on the first and second anomaly characterization values, this embodiment can minimize the impact of interference encountered by the UAV when collecting electric field information of the target area on the subsequent electromagnetic pollution detection results. In other words, the present invention can improve the accuracy of electromagnetic pollution data point detection and identification based on the first and second anomaly characterization values.

[0046] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A three-dimensional mapping system for electromagnetic pollution based on UAV swarms, comprising a processor and a memory, characterized in that, The processor executes the computer program stored in the memory to perform the following steps: A three-dimensional point cloud of the target area is acquired, and the electric field intensity of each data point in the three-dimensional point cloud is obtained using a UAV. Based on the discrete characterization values ​​of the electric field intensity of all data points in the three-dimensional point cloud, the electromagnetic pollution probability characterization value corresponding to the target area is obtained. If the electromagnetic pollution probability characterization value is greater than a preset electromagnetic pollution probability threshold, then based on the mean electric field strength of all data points in the three-dimensional point cloud, suspected electromagnetic pollution data points in the three-dimensional point cloud are obtained. Then, based on the positional distance between the suspected electromagnetic pollution data points, all suspected electromagnetic pollution data points in the three-dimensional point cloud are clustered to obtain a cluster. Based on the positional distance between the suspected electromagnetic pollution data points in the cluster and the cluster center distance between the cluster and other clusters, a first anomaly characterization value of the cluster is obtained. Based on the minimum circumsphere of the cluster and the mean electric field strength of all suspected electromagnetic pollution data points located in the quadrant of the three-dimensional coordinate system of the cluster, a second anomaly characterization value of the cluster is obtained. Based on the first and second anomaly characterization values ​​of the cluster, the electromagnetic pollution anomaly degree of the cluster is obtained. The target electromagnetic pollution data points are obtained based on the degree of electromagnetic pollution anomaly.

2. The electromagnetic pollution three-dimensional mapping system based on UAV swarm as described in claim 1, characterized in that, The method for obtaining the electromagnetic pollution probability characterization value corresponding to the target area includes: The normalized result of the variance of the electric field intensity of all data points in the three-dimensional point cloud is denoted as the first discrete characterization value. The electric field intensities of all data points in the three-dimensional point cloud are sorted in ascending order to obtain an electric field intensity sequence. The characteristic adjacent difference sequence of the electric field intensity sequence is obtained. The a-th characteristic adjacent difference in the characteristic adjacent difference sequence is the squared difference between the a-th electric field intensity and the (a+1)-th electric field intensity in the electric field intensity sequence. The product of the range of the electric field intensity sequence and the mean of the characteristic adjacent difference sequence is normalized and used as the second discrete characterization value. The average of the first discrete characterization value and the second discrete characterization value is denoted as the electromagnetic pollution probability characterization value corresponding to the target area.

3. The electromagnetic pollution three-dimensional mapping system based on UAV swarm as described in claim 1, characterized in that, The method for obtaining suspected electromagnetic pollution data points in the 3D point cloud includes: The average electric field strength of all data points in the three-dimensional point cloud is used as the threshold for judging suspected electromagnetic pollution. All data points in the three-dimensional point cloud with an electric field strength greater than the threshold for judging suspected electromagnetic pollution are recorded as suspected electromagnetic pollution data points.

4. The electromagnetic pollution three-dimensional mapping system based on UAV swarm as described in claim 1, characterized in that, The method for obtaining the first anomaly representation value of the cluster includes: For any given cluster, distance feature values ​​are obtained for each suspected electromagnetic pollution data point in the cluster based on the positional distances between each suspected electromagnetic pollution data point in the cluster and other suspected electromagnetic pollution data points in the cluster. The mean of the distance feature values ​​of all suspected electromagnetic pollution data points in the cluster is inversely mapped to a normalized result and used as a first distribution characterization value. The set of cluster center distances corresponding to the cluster is obtained, and the mean of the set of cluster center distances is inversely mapped to a normalized result and used as a second distribution characterization value. The set of cluster center distances consists of the cluster center distances between the cluster and all other clusters except the cluster itself. The mean of the first distribution characterization value and the second distribution characterization value is used as the first anomaly characterization value of the cluster.

5. The electromagnetic pollution three-dimensional mapping system based on UAV swarm as described in claim 4, characterized in that, The method for obtaining the distance feature values ​​of each suspected electromagnetic pollution data point in the cluster includes: For any suspected electromagnetic pollution data point in the cluster, the set of all other suspected electromagnetic pollution data points in the cluster, excluding the suspected electromagnetic pollution data point, is denoted as the set to be calculated for the suspected electromagnetic pollution data point. The mean of the positional distance between the suspected electromagnetic pollution data point and all suspected electromagnetic pollution data points in the set to be calculated is taken as the distance feature value of the suspected electromagnetic pollution data point.

6. The electromagnetic pollution three-dimensional mapping system based on UAV swarm as described in claim 1, characterized in that, The three-dimensional coordinate system of the cluster is a three-dimensional coordinate system constructed with the cluster center point of the cluster as the origin. The positive direction of the horizontal axis of the three-dimensional coordinate system of the cluster is parallel to the positive direction of the horizontal axis of the three-dimensional space in which the three-dimensional point cloud is located. The positive direction of the vertical axis of the three-dimensional coordinate system of the cluster is parallel to the positive direction of the vertical axis of the three-dimensional space in which the three-dimensional point cloud is located. The positive direction of the vertical axis of the three-dimensional coordinate system of the cluster is parallel to the positive direction of the vertical axis of the three-dimensional space in which the three-dimensional point cloud is located.

7. The electromagnetic pollution three-dimensional mapping system based on UAV swarm as described in claim 1, characterized in that, The method for obtaining the second anomaly representation value of the cluster includes: For any cluster, obtain the volume of the minimum circumscribed sphere of the cluster, and record the ratio of the total number of suspected electromagnetic pollution data points in the cluster to the volume of the minimum circumscribed sphere of the cluster as the proportion characterization value corresponding to the cluster. Obtain the set of mean values ​​of the quadrant electric field intensity corresponding to the cluster, and multiply the standard deviation of the set of mean values ​​of the quadrant electric field intensity by the volume proportion characterization value and then normalize the result as the second anomaly characterization value of the cluster.

8. The electromagnetic pollution three-dimensional mapping system based on UAV swarm as described in claim 7, characterized in that, The mean electric field intensity in the f-th quadrant of the set of mean electric field intensity values ​​corresponding to the cluster is the mean electric field intensity of all suspected electromagnetic pollution data points belonging to the cluster and located in the f-th quadrant region of the three-dimensional coordinate system of the cluster.

9. The electromagnetic pollution three-dimensional mapping system based on UAV swarm as described in claim 1, characterized in that, The electromagnetic pollution anomaly degree of the cluster is the average of the first and second anomaly characterization values ​​of the corresponding cluster.

10. The electromagnetic pollution three-dimensional mapping system based on UAV swarm as described in claim 1, characterized in that, A method for obtaining target electromagnetic pollution data points based on the degree of electromagnetic pollution anomaly includes: For any cluster, if the electromagnetic pollution anomaly level of the cluster is greater than a preset electromagnetic pollution anomaly threshold, then all suspected electromagnetic pollution data points in the cluster are determined to be target electromagnetic pollution data points.

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