Sky-air-ground three-dimensional cooperative monitoring network system construction method and system

Abstract

The invention discloses a method and system for constructing a space-air-ground three-dimensional cooperative monitoring network system, and relates to the technical field of environment monitoring, and the method specifically comprises the following steps: dividing a monitoring grid aggregation task coordinate calculation density interpolation assignment generation block, correcting a coverage radius to normalize a node attribute generation capability list, and generating a monitoring grid aggregation task coordinate calculation density interpolation assignment generation capability list; screening efficient nodes to construct cost matrix bilateral matching, extracting feature vectors, calculating distances, identifying redundant nodes, removing redundancy, and supplementing candidate output space-ground-air collaborative configuration tables. According to the method, the monitoring block is dynamically generated based on the geographic grid, the coverage radius is corrected in combination with the node direction and the hot area distance, the normalization ability list is constructed, the task grid and the node are matched through the Hungary algorithm, the cost matrix is constructed to recognize the redundant node, self-adaptive matching of the node block is achieved, the resource allocation efficiency is improved, and parameter dependence is reduced; the monitoring blind area is reduced, the coverage density and the energy consumption level are balanced, and the emergency response sensitivity is enhanced.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method and system for constructing a three-dimensional collaborative monitoring network system integrating space, air, and ground. Background Technology

[0002] The field of environmental monitoring technology involves the real-time, continuous, and multi-scale information collection and analysis of natural environmental elements such as the atmosphere, water bodies, soil, and ecosystems. Its core aspects include the acquisition of multi-source data on environmental elements, the deployment and linkage of monitoring equipment, the transmission and processing of environmental data, and the construction and operation management of monitoring networks. This technological field integrates remote sensing technology, sensor networks, mobile measurement platforms, and communication transmission technologies to achieve a comprehensive understanding of regional environmental conditions and timely response to dynamic changes. It possesses comprehensive characteristics such as wide spatial coverage, high temporal frequency, and multi-dimensional data, serving as a crucial foundation for supporting environmental management and decision-making.

[0003] The method for constructing a space-air-ground collaborative monitoring system refers to integrating multi-source information such as satellite remote sensing observation, aerial observation, and ground monitoring stations to achieve joint monitoring of regional environmental conditions. The technical issues addressed in this topic are the collaborative organization and network layout optimization of multi-source heterogeneous monitoring data. Traditional approaches to this topic employ a multi-platform hierarchical deployment method to address these technical issues. Specific methods include configuring observation time windows based on the orbital coverage of remote sensing data, delineating supplementary monitoring areas based on the distribution density of ground stations, utilizing UAV platforms to set flight paths according to mission requirements for local dynamic supplementary monitoring, pre-setting monitoring point layouts and platform selection based on existing geographic information data, and defining task execution logic and trigger relationships between platforms through static rules to achieve joint acquisition of monitoring data and collaborative task execution.

[0004] The collaborative monitoring system relies on the remote sensing orbit coverage area to preset the observation time window. The density of ground station deployment and UAV path planning are based on the static configuration of the original geographic information. It lacks the ability to dynamically perceive the spatial heterogeneity of real-time task hotspots. The triggering relationship between nodes is limited by manually preset rules. There is no quantitative evaluation system for node computing power, communication frequency and energy consumption level. This results in insufficient allocation of monitoring resources in high-value areas and redundant resources in inefficient areas. The collaborative efficiency of multiple platforms is constrained by the rigid constraints of the initial configuration parameters. When dealing with nonlinear environmental changes, it is prone to response delays and coverage blind spots. The overall effectiveness of the monitoring network is limited by the spatiotemporal adaptability defects of the preset rules. Summary of the Invention

[0005] To address the technical problems of collaborative monitoring systems relying on pre-defined observation time windows based on remote sensing orbit coverage, ground station deployment density and UAV path planning being statically configured based on raw geographic information, lacking dynamic perception capabilities of the spatial heterogeneity of real-time task hotspots, and having inter-node triggering relationships constrained by manually preset rules, the absence of a quantitative evaluation system for node computing power, communication frequency, and energy consumption levels, resulting in both insufficient allocation of monitoring resources in high-value areas and redundant resources in inefficient areas, multi-platform collaborative efficiency limited by rigid constraints of initial configuration parameters, and prone to response delays and coverage blind spots when dealing with nonlinear environmental changes, the overall effectiveness of the monitoring network is limited by the spatiotemporal adaptability defects of preset rules, this invention provides a method and system for constructing a three-dimensional collaborative monitoring network system based on space, air, and ground. The technical solution is as follows:

[0006] On the one hand, a method for constructing a three-dimensional collaborative monitoring network system integrating space, air, and ground is provided, which includes: S1: Divide the monitoring area into grids using a geographic information system, obtain the boundary coordinate set, spatially aggregate the task point coordinate set, calculate the task frequency density per unit time, assign values ​​to the grid using the Kriging interpolation method, and output the monitoring block division results. S2: Based on the monitoring block division results, obtain the node position and moving speed, correct the coverage radius according to the node moving direction and hot zone distance, and perform Min-Max normalization processing in combination with node computing power, communication frequency, and energy consumption level attributes to generate a node capability list. S3: Call the coverage radius and response latency parameters in the node capability list, sort the node priority queue in descending order, filter the nodes whose coverage radius is greater than the grid side length and whose response latency is lower than the average value, construct the response performance cost matrix by combining the grid heat value, use the Hungarian algorithm to perform bilateral matching between the task grid and nodes, and output the node-block matching table. S4: Based on the node-block matching table, extract the task access frequency, communication success rate and runtime of the matching node, perform Z-score standardization, construct a three-dimensional feature vector, calculate the Euclidean distance of the feature vector, mark combinations below the threshold, and obtain redundant node identifiers.

[0007] As a further embodiment of the present invention, the monitoring block division result is specifically spatial grid coding, spatial density classification, and geofence coordinates; the node capability list includes coverage radius, response delay standard deviation, and energy efficiency coefficient; the node-block matching table includes grid node association matrix, priority sorting index, and matching cost weight; and the redundant node identifier is specifically inefficient device identification code, feature vector Euclidean distance threshold, and node health status code. The Euclidean distance threshold of the feature vector is calculated by standardizing the three-dimensional feature vector to determine the critical distance value in the node feature space and to determine the statistical distance standard of redundant nodes. The node health status code is based on a status evaluation system constructed from task access success rate and runtime, and uses binary encoding to identify the node availability status.

[0008] As a further aspect of the present invention, the specific steps of S1 include: S101: Divide the monitoring area into grids using a geographic information system, obtain the boundary coordinate set and task point location data, combine the boundary coordinates to perform spatial boundary determination, determine whether the grid center point is located within the boundary, filter out grid numbers that do not fall within the boundary, and generate a grid number set within the monitoring area. S102: Based on the grid number set and task point coordinate set in the monitoring area, perform spatial matching operation, classify task points to corresponding grid numbers, count the number of task points in each unit, calculate the task frequency density based on the ratio of the total number of tasks to the grid area, and generate a task frequency density value. The task frequency density value is a numerical index describing the intensity of task spatial distribution. S103: Based on the task frequency density value, input the Kriging interpolation method with the grid number as the index to construct a continuous frequency distribution layer, extract the layer value interval change sequence and divide the distribution boundary to generate the monitoring block division result.

[0009] As a further aspect of the present invention, the task frequency density value is calculated using the following formula: ; in, Representing the The task frequency density value for each grid, in units of times per square kilometer. Representing the The frequency of task points within each grid, expressed in times. Representing the The area of ​​each grid, in square kilometers. This represents the total frequency of task points within the monitoring area, expressed in times. This represents the total number of grids within the monitoring area. Representing the The grid pair of the first The spatial weight influence factor for each grid cell ranges from 0 to 1 and is calculated based on a weighted average of the distance to the grid center point and the heat index. Representing the The frequency of task points within each grid, expressed in times. Representing the The area of ​​each grid, in square kilometers.

[0010] As a further aspect of the present invention, the specific steps of S2 include: S201: Based on the monitoring block division results, obtain the geographic coordinates and timestamp information of the nodes, calculate the displacement distance of the nodes according to the change in node coordinates within consecutive timestamps, determine the movement direction of the nodes according to the coordinate offset, calculate the movement speed value of the nodes by combining the node displacement distance and time offset, and generate the node dynamic feature value. S202: Based on the node's dynamic characteristic value, call the spatial distance value between the node's current position and the center of the monitoring block's hot zone, combine it with the initial value of the node's coverage radius, perform offset correction through the vector relationship between the node's movement direction and the hot zone distance, calculate the node's coverage radius correction amount, update the node's coverage radius value, and obtain the node coverage adjustment value; S203: Call the node coverage adjustment value, combine the node's computing power, communication frequency and energy consumption rate per unit time, normalize the three performance data using the Min-Max normalization method, and combine the normalized data in sequence to construct the node capability vector and obtain the node capability list value. The node computing power, communication frequency, and energy consumption level attributes are normalized to a unified unit of W, and then subjected to Min-Max normalization.

[0011] As a further aspect of the present invention, the specific steps of S3 include: S301: Call the coverage radius and response delay parameters in the node capability list, calculate the overall average of the node response delay, filter the nodes whose coverage radius is greater than the grid side length and whose response delay is lower than the average, and obtain the set of nodes with compliant coverage efficiency. S302: Based on the coverage efficiency compliant node set, calculate the spatial distance from the node to the grid, and proportionally allocate the corresponding response rate value to the grid heat, construct a bidirectional response cost matrix between the node and the grid, and generate a node-grid cost matrix value set; S303: Call the node-grid cost matrix value group, use the Hungarian algorithm to perform weighted path matching, match the node number with the grid number, and obtain the node-block matching table.

[0012] As a further aspect of the present invention, the specific steps of S4 include: S401: Based on the node-block matching table, extract the task access frequency field value, communication success rate field value, and runtime field value, and use Z-score standardization to process the three indicators to generate a standardized communication indicator set; The task access frequency is measured in times per hour, the communication success rate is measured in percentage, and the runtime is measured in hours. All of these parameters are standardized using Z-score and converted to dimensionless parameters. S402: Call the standardized communication index set, combine the standardized frequency value, success rate value and runtime value of each node into three-dimensional coordinate points, calculate the spatial distance between node vectors using the Euclidean distance formula, and form a node feature distance matrix; S403: Traverse the node feature distance matrix, compare it with the preset redundancy judgment threshold, mark the node combination with the distance value below the threshold, and generate redundant node identifiers; The redundancy determination threshold is calculated based on the mean and standard deviation of the Euclidean distance according to the distribution of the original node running data, and is set as μ-σ, where μ is the mean distance and σ is the standard deviation.

[0013] As a further aspect of the present invention, the method includes step S5: S5: Call the redundant node identifier, combine the node load rate in the current network with the feature vector offset of the candidate nodes, remove some nodes whose load rate and redundancy occupy the edge position, and add candidate nodes with large feature vector offsets, and output the sky-air-ground collaborative monitoring network configuration table. The configuration table of the space-air-ground collaborative monitoring network includes load balancing factors, node redundancy evaluation values, and candidate node registry. The load balancing factor is a load assessment parameter constructed based on the node's computing resource utilization and communication frequency, used to quantify the node's workload level.

[0014] As a further aspect of the present invention, the specific steps of S5 include: S501: Call the redundant node identifier, match the node's resource type, connection relationship and task status parameters, calculate the node's task processing intensity and interaction rate, and based on the threshold standards of average task processing intensity and interaction rate, filter the node number and index data that meet the dual standards to generate redundant load node index values. S502: Based on the redundant load node index value, combined with the node function type and response delay record, calculate the number of overlapping function types between the node and the candidate node and the response delay offset range, filter the candidate node numbers that meet the conditions of overlapping number and response delay offset, and generate the function replacement node configuration rate. S503: Based on the configuration rate of the functional replacement nodes, extract the feature vector and the original mean data, calculate and sort the feature offset distance, filter the node numbers whose offset distance exceeds the corresponding interval of the feature offset median value between nodes for supplementation, delete redundant node numbers, and output the configuration table of the sky-space-ground collaborative monitoring network.

[0015] On the other hand, a system for constructing a three-dimensional collaborative monitoring network based on space, air, and ground is provided. This system is used to execute the aforementioned method for constructing a three-dimensional collaborative monitoring network based on space, air, and ground. The system includes: The grid division module is used to obtain the boundary coordinate set of the monitoring area through the geographic information system, spatially aggregate the coordinate set of task points, assign the task frequency density per unit time to the grid cell using the Kriging interpolation method, output the monitoring block division result, and pass it to the node evaluation module. The node evaluation module is used to receive the node movement speed and hot zone distance from the monitoring block division results, process the node computing power, communication frequency and energy consumption level through the Min-Max normalization method, generate a node capability list and transmit it to the node matching module. The node matching module is used to call the coverage radius and response delay parameters in the node capability list, filter nodes with a coverage radius greater than the grid side length and a response delay lower than the average value, construct a two-dimensional cost matrix of response performance and grid heat, use the Hungarian algorithm to perform bilateral matching between nodes and grids, output a node-block matching table, and pass it to the redundancy identification module. The redundancy identification module is used to extract the task access frequency, communication success rate and runtime from the node-block matching table, perform Z-score normalization to construct a three-dimensional feature space, calculate the Euclidean distance between feature vectors, generate redundant node identifiers, and pass them to the network optimization module. The network optimization module is used to call the redundant node identifier, analyze the node load rate, obtain the feature vector offset of the candidate node, perform the low load node elimination and high offset candidate node addition operations, and output the sky-space-ground collaborative monitoring network configuration table.

[0016] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this embodiment of the invention, monitoring blocks are dynamically generated based on geographic grid division and spatial aggregation. The coverage radius is dynamically corrected by combining the node movement direction and hot zone distance. A normalized capability list integrating node computing power, communication frequency, and energy consumption level is constructed. Bilateral matching between task grids and nodes is achieved through the Hungarian algorithm. A cost matrix based on response performance and grid heat is established. Redundant nodes are identified using three-dimensional feature vector Euclidean distance. A dynamic and adaptive node-block matching mechanism is formed, which breaks through the limitations of static rules in traditional hierarchical deployment in adapting to spatiotemporal heterogeneity. This improves the resource allocation efficiency of multi-source heterogeneous nodes in complex environments, reduces the dependence of manually preset parameters on network topology, reduces monitoring blind spots caused by fixed deployment patterns, achieves a dynamic balance between coverage density and energy consumption level, and enhances the response sensitivity of the monitoring network to sudden environmental events. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0020] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0021] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] Please see Figure 1 This invention provides a method for constructing a three-dimensional collaborative monitoring network system integrating space, air, and ground. The processing flow of this method may include the following steps: S1: Divide the monitoring area into grids using a geographic information system, obtain the boundary coordinate set, spatially aggregate the task point coordinate set, calculate the task frequency density per unit time, assign values ​​to the grid using the Kriging interpolation method, and output the monitoring block division results. S2: Based on the monitoring block division results, obtain the node position and movement speed, correct the coverage radius according to the node movement direction and hot zone distance, and perform Min-Max normalization processing in combination with node computing power, communication frequency, and energy consumption level attributes to generate a node capability list. S3: Call the coverage radius and response latency parameters in the node capability list, sort the node priority queue in descending order, filter the nodes whose coverage radius is greater than the grid side length and whose response latency is lower than the average value, construct the response performance cost matrix by combining the grid heat value, use the Hungarian algorithm to perform bilateral matching between the task grid and nodes, and output the node-block matching table. S4: Based on the node-block matching table, extract the task access frequency, communication success rate and runtime of the matching node, perform Z-score standardization, construct a three-dimensional feature vector, calculate the Euclidean distance of the feature vector, mark combinations below the threshold, and obtain redundant node identifiers. S5: Call the redundant node identifier, combine the node load rate in the current network with the feature vector offset of the candidate nodes, remove some nodes whose load rate and redundancy occupy the edge position, and add candidate nodes with large feature vector offsets, and output the configuration table of the sky-air-ground collaborative monitoring network. The node capability list includes coverage radius, response latency standard deviation, and energy efficiency coefficient; the node-block matching table includes grid node association matrix, priority sorting index, and matching cost weight; the redundant node identifier specifically includes inefficient device identification code, feature vector Euclidean distance threshold, and node health status code; and the sky-space-ground collaborative monitoring network configuration table includes load balancing factor, node redundancy evaluation value, and candidate node registry. The Euclidean distance threshold for feature vectors calculates the critical distance value in the node feature space through the standardization of three-dimensional feature vectors, and determines the statistical distance standard for redundant nodes. The node health status code is based on a status evaluation system built on task access success rate and runtime, and uses binary encoding to identify the node availability status. Load balancing factor: A load assessment parameter constructed based on node computing resource utilization and communication frequency, used to quantify the node's workload level.

[0024] Furthermore, the specific steps of S1 are as follows: S101: Divide the monitoring area into grids using a geographic information system, obtain the boundary coordinate set and task point location data, combine the boundary coordinates to perform spatial boundary determination, determine whether the grid center point is located within the boundary, filter out grid numbers that do not fall within the boundary, and generate a grid number set within the monitoring area. By dividing the monitoring area into grids using a geographic information system, the spatial boundary coordinate dataset of the area is first read. The boundary is represented as a sequence of ordered two-dimensional coordinate points, such as the boundary point set as follows: ((117.120, 36.655), (117.145, 36.655), (117.145, 36.675), (117.120, 36.675)); This boundary is used to construct a spatial polygon object for subsequent spatial determination operations. The entire monitoring area is then divided into grids using an equidistant grid method, with multiple cells spaced at 0.005° latitude and longitude intervals. The center point coordinates of each cell are calculated using the following method: center longitude = top-left corner longitude + 0.0025°, center latitude = top-left corner latitude - 0.0025°. For example, a grid with a top-left corner coordinate of (117.120, 36.675) has a center point of (117.1225, 36.6725). Subsequently, the ray casting method is used to spatially determine the coordinates of each center point. Boundary determination involves checking whether each point falls within the aforementioned boundary polygon. The specific determination logic is as follows: if a ray extending outward from the point intersects the boundary polygon at an odd number of points, then the point is inside the boundary; otherwise, the point is outside the boundary. The determination result is stored in a Boolean list. Then, the grid number corresponding to the center point that is "no" is removed, and only the set of numbers that fall within the boundary is retained. For example, if the determination result for the center point (117.135, 36.665) is "no", then the grid corresponding to number G003 will not be included in the subsequent calculation. Finally, the grid set such as number G001, G002, and G004 is obtained as the grid number set within the monitoring area.

[0025] S102: Based on the grid number set and task point coordinate set in the monitoring area, perform spatial matching operation, classify task points to corresponding grid numbers, count the number of task points in each unit, calculate the task frequency density based on the ratio of the total number of tasks to the grid area, and generate a task frequency density value. Task frequency density is a numerical index that describes the intensity of task spatial distribution. Based on the grid number set and geographical coordinate information of the task points obtained in the previous section, the input data for the spatial matching process is first constructed by listing the latitude and longitude coordinates of the task points one by one. For example, the task point coordinate set is set as follows: [(117.1235,36.6585),(117.1275,36.6615),(117.1295,36.6620),(117.1282,36.6610),(117.1270,36.6625),(117.1400,36.6700),(117.1220,36.6580),(117.1240,36.6590),(117.1285,36.6630),(117.1405,36.6705)]; Then, spatial attribution is determined for each task point by comparing its coordinates with the boundary coordinates of the grid. Each grid consists of a 0.005° side. The boundary range can be calculated by reversing the center point to obtain the four corner coordinates. For example, for grid G002 with a center point of (117.128, 36.662), its top-left corner is (117.1255, 36.6645) and its bottom-right corner is (117.1305, 36.6595). The conditions for determining whether a task point is located within this rectangular area are: longitude greater than the top-left longitude and less than the bottom-right longitude, and latitude less than the top-left latitude and greater than the bottom-right latitude. The task point (117.1282, 36.6610) has longitude 117.1282 located between 117.1255 and 117.1305, and latitude 36.6610 located between 36.6645 and 36.6595. Meeting these conditions, it is assigned to G002. After executing the above judgment process, a one-to-one correspondence table between task points and grid numbers is established, and the number of task points in each grid is counted accordingly. For example, G002 is assigned 5 points, G001 3 points, G004 2 points, and G003 has 0 points because it is not within the boundary. Next, the task frequency density value is calculated using the formula: ; in, Representing the The task frequency density value for each grid, in units of times per square kilometer. Representing the The frequency of task points within each grid, expressed in times. Representing the The area of ​​each grid, in square kilometers. This represents the total frequency of task points within the monitoring area, expressed in times. This represents the total number of grids within the monitoring area. Representing the The grid pair of the first The spatial weight influence factor for each grid cell ranges from 0 to 1 and is calculated based on a weighted average of the distance to the grid center point and the heat index. Representing the The frequency of task points within each grid, expressed in times. Representing the The area of ​​each grid, in square kilometers.

[0026] The direct density value is calculated first here. Then, take the absolute value of the difference between this and the weighted average of the surrounding grid density, and multiply it by an adjustment factor. This adjustment factor is a dimensionless value, used to proportionally amplify the density difference based on the total task volume. The specific calculation is as follows: For G001, the number of task points is 3, and the area is 0.25 km², so its initial density is 3 / 0.25 = 12.0 tasks / km². 2 Centered on G001, consider its neighboring grids G002 and G004, with densities of 20.0 and 8.0 respectively. Assume that the distance weighting is calculated as follows: , ; The weighted average density is then 20.0 × 0.6 + 80 × 0.4 / 0.6 + 0.4 = 12.0 + 32 / 1.0 = 15.2, the density difference is |12.0 - 15.2| = 3.2, the total number of task points is T = 3 + 5 + 2 = 10, and the adjustment factor is... The final frequency density is: ; Similar to calculating the density of G002, its initial density is 5 / 0.25 = 20.0 times / km². 2 Let the weights of neighbors G001 and G004 be 0.5 and 0.5 respectively. Then the weighted density is 12.0×0.5 + 80×0.5 / 1.0 = 10.0, the difference is 10.0, and the adjustment factor is also 1.3015, so the final value is 10.0×1.3015 = 13.015. Repeat the above process for the remaining grids to obtain the complete set of frequency densities, as shown below: Table 1: Calculation Results of Task Frequency Density As shown in Table 1, the task frequency density calculation for the three grids has taken into account spatial correlation and total adjustment terms. No density calculation was performed when G003 did not fall within the boundary. After the task frequency density values ​​were calculated, they were classified and judged according to the set density range: low frequency is a density value less than 5, medium frequency is 5~15, and high frequency is greater than 15. The calculated value of G001 is 4.165, which belongs to the low frequency range; G002 is 13.015, which belongs to the medium frequency range; and G004 is 10.412, which also belongs to the medium frequency range. This result is the generated task frequency density value shown at the end of the paragraph.

[0027] S103: Based on the task frequency density value, input the Kriging interpolation method with the grid number as the index to construct a continuous frequency distribution layer, extract the layer value interval change sequence and divide the distribution boundary to generate the monitoring block division result; Using the task frequency density value as input, the spatial index information of the effective grids must first be extracted. This involves matching the center point coordinates and frequency density values ​​of the three grid numbers (G001, G002, and G004) that fall within the boundary, forming a spatial point density set. During execution, each record consists of: point coordinates + density value. For example, G001 is (117.123, 36.658, 4.165), G002 is (117.128, 36.662, 13.015), and G004 is (117.140, 36.670, 10.412). Then, a continuous layer construction step is performed on this point set. The construction logic involves dividing the entire monitoring area into a higher-density interpolation node network, generating grid nodes at 0.001° intervals. For each interpolation node, the following operations are performed: Calculate the Euclidean distance between node P and known density points, and then set a weight factor based on the inverse ratio of the distances. Let the distances of node P to G001, G002, and G004 be d1, d2, and d3 respectively, and calculate the weight as follows: , , Then, normalize the weights to make the total weight 1, obtain the standard weights, and then perform a weighted average on the density values, that is, the density value of the node is: If node P is located at (117.130, 36.665), and the distances are d1=0.0086, d2=0.0036, and d3=0.0114 respectively, then the initial values ​​of the multi-term weights are... , , After normalization, it becomes: , , Substituting the corresponding density into the calculation, we get: ; Repeat this operation to calculate the density values ​​of the interpolation nodes throughout the monitoring area, forming a continuous spatial distribution layer. Then, divide the layer into intervals according to the density values, using pre-defined interval rules: density values ​​less than 5 are low-frequency areas, 5 to 15 are mid-frequency areas, and above 15 are high-frequency areas. Traverse the nodes in the layer, assign them to the corresponding intervals based on their density values, and construct contour lines in adjacent areas. Extract the boundary lines based on the density boundary values ​​of 5 and 15 to form a set of spatial boundary curves. For example, a certain line in the interpolation node starts from a density value of 4. The line segment that crosses from 9 to 5.1 is marked as the 5.0 contour line area. Finally, spatial clustering is performed on the closed area formed by the contour lines, and the points within the boundary are divided into the corresponding task density segments. If the interpolation points in the continuous area all fall within the range of 5 to 15, the whole area is defined as the mid-frequency task area. If most points in another area have values ​​above 15, it is defined as the high-frequency area. Combining the interpolation value near the center point of G002 listed above, which is about 11.587, it belongs to the mid-frequency area. The continuous layer and task density block division results of the overall monitoring area are completed.

[0028] Furthermore, the specific steps of S2 are as follows: S201: Based on the monitoring block division results, obtain the geographic coordinates and timestamp information of the nodes, calculate the displacement distance of the nodes based on the change in node coordinates within consecutive timestamps, determine the direction of node movement based on the coordinate offset, and calculate the node movement speed value by combining the node displacement distance and time offset to generate the node dynamic characteristic value. Based on the monitoring block division results, the center point coordinates of each block are first extracted from the density layer, along with the corresponding timestamp information, to construct a set of triplets in the form of (longitude, latitude, time). Then, each record in the set is sorted according to the timestamp to achieve the arrangement of coordinate data at continuous time nodes. For example, the coordinates of a node at t1=10:00 are (117.1280, 36.6620), and the coordinates at t2=10:05 are (117.1287, 36.6624). The longitude and latitude offset of the node are calculated based on two sets of coordinates between consecutive timestamps. The steps are as follows: Subtract the longitude of the second time point from the longitude of the first time point to obtain the longitude offset Δlon = 117.1287 – 117.1280 = 0.0007°. Similarly, the latitude difference is obtained as Δlat = 0.0004°. Then, the longitude and latitude offsets are converted into actual distances. Since 1° latitude is approximately equal to 111.32km, and 1° longitude at latitude 36.662° is approximately 88.7km, the displacement distance is: Km; This represents the displacement distance of the node within 5 minutes. Substituting the time interval 5 minutes = 300 seconds, we obtain the average moving speed as V = 0.0764 / 300 = 0.0002547 km / s = 0.2547 m / s. This speed is recorded as one of the node's dynamic characteristic values. The above process is repeated to traverse the node's coordinate records under each pair of consecutive timestamps to form a complete set of dynamic characteristics. The offset direction is further classified and identified based on the positive and negative combinations of longitude and latitude offset values: Δlon > 0 and Δlat > 0 indicates a northeast direction, and Δlon < 0 and Δlat > 0 indicates a northwest direction. Δlon < 0 and Δlat < 0 indicates the southwest direction, and Δlon > 0 and Δlat < 0 indicates the southeast direction. If Δlon or Δlat is 0, it is considered an axial offset. Let the offset direction of node A at two times be Δlon = +0.0005 and Δlat = -0.0006, then the direction is identified as "southeast". After determining the direction, the direction and velocity value are combined to form a dynamic feature vector, for example, the vector form is [southeast, 0.2547m / s]. This is added to the node's dynamic attribute list. This process is repeated to form the dynamic feature data of the node in different time periods, and finally the following data structure is formed: Table 2: Node Dynamic Feature Value Table As shown in Table 2, the dynamic feature values ​​of the nodes include the coordinate changes at continuous time points, the calculated direction and velocity parameters. The direction is determined by the combination of latitude and longitude offset signs, and the velocity is calculated by displacement distance and time difference. The direction of node N001 is northeast and the velocity is 0.2547 m / s, the direction of node N002 is southwest and the velocity is 0.3510 m / s, and node N003 has no displacement at the two time points, so it is judged to be stationary. The node data is included in the dynamic feature list for subsequent steps.

[0029] S202: Based on the node's dynamic characteristic value, call the spatial distance value between the node's current position and the center of the hot zone of the monitoring block, combine it with the initial value of the node's coverage radius, perform offset correction through the vector relationship between the node's movement direction and the hot zone distance, calculate the node coverage radius correction amount, update the node coverage radius value, and obtain the node coverage adjustment value; Based on the recorded dynamic characteristic values ​​of the nodes, the movement direction vector of each node at the current moment is first extracted from its velocity and direction attributes. This vector is then used to determine the distance and offset angle relative to the center point of the hot zone in the target monitoring block. For example, suppose node B's current coordinates are (117.1287, 36.6624), the corresponding direction is "northeast," and the velocity is 0.2547 m / s. The center coordinates of the target hot zone are (117.1350, 36.6680). First, the current coordinates of the node and the center coordinates of the hot zone are converted into two-dimensional planar vectors, and the Euclidean distance between them is calculated. ; Using the current latitude of 36.6624°, the unit distance is approximately 88.7 km for 1° of longitude and 111.32 km for 1° of latitude. The converted distance is approximately: ; Further, based on the node's current movement direction being "Northeast," its corresponding unit direction vector is (1, 1). This vector is then normalized to the direction vector pointing from the node towards the center of the hot zone (117.1350-117.1287, 36.6680-36.6624), and the angle between them is determined. The direction offset angle is calculated, and then combined with the velocity value and the cosine of the direction angle to determine the approach trend of the hot zone. If the angle is less than 45° and the cosine value is greater than 0.7, the node's movement direction is determined to be towards the center of the hot zone. Based on this, a coverage radius adjustment operation is performed, with the initial node coverage radius set at 100 meters. If the direction offset is less than 45° and the velocity is higher than 0.3 m / s, a coverage radius expansion operation is performed, with the adjustment amount set to 20% of the radius, i.e., the new radius is 100∙(1+0.2)=120m. If the directional offset is greater than 135°, it is considered to be moving away from the hot zone. The radius is reduced by 20% to 80 meters. The other angle and velocity combinations are set to remain unchanged. Node C has a velocity of 0.351 m / s and a direction of "southwest". Its target hot zone is located at (117.1450, 36.6750). The current coordinates of the node are (117.1400, 36.6700). The calculated directional angle is 145°, and the cosine value is approximately -0.8. Therefore, it is determined to be moving away from the hot zone. Based on the conditions, the coverage radius is adjusted to 80% of the original value, that is, the final coverage radius is 100∙0.8=80m. The adjusted radius, node number and time label are recorded as new coverage attribute values, forming a complete coverage adjustment value sequence. Through this adjustment rule, the coverage status of the node in the current time period is updated.

[0030] S203: Call the node coverage adjustment value, combine the node's computing power, communication frequency and energy consumption rate per unit time, normalize the three performance data using the Min-Max normalization method, and combine the normalized data in sequence to construct the node capability vector and obtain the node capability list value. After unifying the unit of measurement to W, the node computing power, communication frequency, and energy consumption level attributes are normalized using Min-Max. The updated node coverage radius value is retrieved and combined with the computing power, communication frequency, and energy consumption per unit time recorded in the previous steps to construct a performance vector before normalization. For example, if node D has a computing power of 12W, a communication frequency of 800MHz, and energy consumption per unit time of 0.5W / s, its physical dimensions need to be unified before normalization to ensure the rationality of the comparison.

[0031] Specifically, computing power itself is represented by W, which has power implications and requires no conversion. However, communication frequency and energy consumption per unit time need to be mapped to equivalent power values ​​through a model. For communication frequency, a linear mapping relationship from frequency to power can be established based on a preset chip power consumption curve. For example, assuming the typical power of a communication module is 1.5W at 400MHz and 4.5W at 1200MHz, it can be represented by a linear function: Convert the communication frequency value (MHz) into the corresponding equivalent power value. (Unit: W).

[0032] Regarding energy consumption per unit time, the energy consumption of node D is 0.5W / s, which remains stable on average during this round of time, and is therefore recorded as 0.5W.

[0033] After the above processing, each node will obtain three performance index values ​​with unified physical meaning as "watts (W): computing power, communication equivalent power consumption, and average energy consumption." Then, Min-Max normalization is performed to map the indexes to the standard interval [0, 1]. Within the node, the minimum computing power is 5W and the maximum is 20W; the minimum communication frequency is 400MHz and the maximum is 1200MHz (equivalent power of 1.5W and 4.5W respectively); and the minimum energy consumption per unit is 0.2W and the maximum is 1.0W. The normalization results are as follows:

[0034] Normalized computational power: ; Normalized communication frequency (equivalent power 3.0W): ; Normalized energy consumption per unit time: ; Therefore, the standard capability vector for node D is constructed as [0.4667, 0.5, 0.375]. This capability vector is then combined with the node number to form a capability data record. For example, if the node number is N004, it is represented as N004: [0.4667, 0.5, 0.375]. Nodes are processed uniformly in this way to ensure that their capability vectors are comparable and normalized. The final set of capability vectors will serve as the basis for node sorting, filtering, and resource allocation in the task scheduling algorithm. Before each round of scheduling, the normalization interval is dynamically updated based on the maximum and minimum values ​​of the current node set to ensure adaptability and fairness.

[0035] Furthermore, the specific steps of S3 are as follows: S301: Call the coverage radius and response latency parameters in the node capability list, calculate the overall average response latency of the nodes, filter the nodes whose coverage radius is greater than the grid side length and whose response latency is lower than the average, and obtain the set of nodes with compliant coverage efficiency. The process calls the coverage radius and response latency parameters from the node capability list. First, it reads the list of all node numbers in the node capability list, sequentially obtaining the coverage radius value of each node and comparing it with the grid edge length. The current grid edge length parameter is set to 50 meters. The criterion is whether the node's coverage radius is greater than this edge length value. Taking node A as an example, its coverage radius is 60 meters. Since 60 > 50, it meets the screening condition and is temporarily included in the candidate node set. After repeating this initial screening process, the next stage of processing begins: calculating the average response latency of the nodes in the candidate set. The response latency data for each node is recorded uniformly in milliseconds (ms). Suppose the response latencies of five nodes are 18ms, 25ms, 21ms, 16ms, and 20ms, then the corresponding average is calculated as follows: (18+25+21+16+20) / 5=100 / 5=20ms; Using this average as a threshold, the response latency of each node in the initial candidate set is traversed, and it is determined whether it is lower than the average. Taking node A as an example, if its response latency is 18ms, it meets the response requirement because 18 < 20 and is retained in the compliant node set. If the response latency of a node B is 22ms, it is removed from the result set because 22 > 20. Finally, the set of nodes that simultaneously meet the requirements of a coverage radius greater than 50 meters and a response latency lower than the average of 20ms is selected. This set is the coverage efficiency compliant node set.

[0036] S302: Based on the coverage efficiency compliant node set, calculate the spatial distance from the node to the grid, and proportionally allocate the corresponding response rate value to the grid heat, construct the bidirectional response cost matrix between the node and the grid, and generate the node-grid cost matrix value set; Based on the coverage efficiency compliant node set, the spatial coordinate information of each node is read, and the Euclidean distance between it and the target grid point is calculated. In a two-dimensional coordinate system, let the coordinates of node P be (30, 40) and the coordinates of grid cell G be (50, 65), then the corresponding spatial distance is: rice; Simultaneously, the response rate data corresponding to the nodes is extracted from the capability list and recorded in the form of "response / second". For example, the response rate of node P is 4 responses / second. The node response heat contribution value is defined as the response rate divided by the distance. The corresponding contribution value of node P to grid G ​​is 4 / 32.02≈0.125. This value is mapped to the grid heat update matrix. This calculation process is performed on nodes and grid cells. After each item is calculated, a complete node-grid response heat matrix is ​​generated. The heat value is directly related to the distance of the node to the grid and the response rate. The heat value is then standardized and converted into response cost value. That is, the cost value is set as the reciprocal of the heat value and normalized to the interval [0,1]. The specific normalization process adopts the minimum-maximum standard method. Each pair of node-grid combinations forms a set of values, representing the cell item value in the response cost matrix.

[0037] S303: Call the node-grid cost matrix value group, use the Hungarian algorithm to perform weighted path matching, match the node number with the grid number, and obtain the node-block matching table; The node-grid cost matrix value group is called and path matching is performed on it. The matrix rows are defined as nodes and the columns as grids. Each cell represents the response cost of a node and a grid cell. The minimum cost path combination is identified from the matrix to ensure that each node matches only one grid and the total cost is minimized. During the matching process, the column containing the minimum value of each row in the matrix is ​​taken in turn, and the matched rows and columns are removed from the remaining matrix until the matching step is completed. For example, when there are 3 nodes and 3 grids, the cost of node 1 and grid 2 is 0.32, the cost of node 2 and grid 3 is 0.21, and the cost of node 3 and grid 1 is 0.27. Then node 1 corresponds to grid 2, node 2 corresponds to grid 3, and node 3 corresponds to grid 1. The final node-block matching table is as follows: node 1 → grid 2, node 2 → grid 3, node 3 → grid 1. After the full table is constructed, it is saved as the input and output parameters for task scheduling.

[0038] Table 3: Coverage Node Response Data Table Table 3 lists the coverage radius, response delay, spatial coordinates, and response rate of some nodes, which serve as a reference for the calculation and judgment of multiple parameters in the filtering, heat contribution calculation, and matching steps.

[0039] Furthermore, the specific steps of S4 are as follows: S401: Extract the task access frequency field value, communication success rate field value and runtime field value based on the node-block matching table, and use Z-score to standardize the three indicators to generate a standardized communication indicator set; The task access frequency is expressed as times / hour, the communication success rate as a percentage, and the runtime as hours. All parameters are standardized using Z-score and converted to dimensionless parameters. Based on the node communication attribute information recorded in the node-block matching table, the task access frequency, communication success rate, and runtime value of each node are retrieved sequentially. The raw data for each indicator is read and summarized in a list according to the field order for standardization. The unit for the task access frequency field is times / hour. For the five nodes N001 to N005, the values ​​are 12, 8, 14, 10, and 9 times / hour, respectively. The mean of this data set is calculated to be 10.6, and the standard deviation is 2.155. The Z-score standardization result is obtained using the formula... The values ​​obtained were 0.6499, -1.2070, 1.5784, -0.2785, and -0.7428, respectively. Next, the communication success rate field data was read; the original values ​​were 97.5%, 95.0%, 99.0%, 96.3%, and 94.1%, respectively. The calculated mean was 96.38, the standard deviation was 1.735, and the Z-score standardized results were 0.6418, -0.7908, 1.5013, -0.0458, and -1.3065. Then, the runtime field data, in hours, was read; the original values ​​were 5.6, 4.8, 6.2, and 5, respectively. For 0 and 4.5 hours, the mean of this column is calculated to be 5.22, and the standard deviation is 0.606. After Z-score standardization, the values ​​are 0.6251, -0.6908, 1.6120, -0.3619, and -1.1843. The three standardized values ​​are combined as three-dimensional parameters to form a standardized vector dataset of the communication attributes of the node in the current time period. For example, the three standardized values ​​of node N001 are [0.6499, 0.6418, 0.6251], and those of node N003 are [1.5784, 1.5013, 1.6120], etc., forming the following standardized table of communication indicators.

[0040] Table 4: Standardized Values ​​of Communication Indicators As shown in Table 4, the three indicators of each node have been converted into dimensionless parameters, making them comparable and serving as the basis for subsequent distance calculations.

[0041] S402: Call the standardized communication indicator set, combine the standardized frequency value, success rate value and runtime value of each node into three-dimensional coordinate points, calculate the spatial distance between node vectors using the Euclidean distance formula, and form a node feature distance matrix; Based on the standardized communication index set obtained in Table 4, the three standardized values ​​of each node are used as its coordinates in three-dimensional space. Specifically, node N001 is set to three-dimensional coordinates (0.6499, 0.6418, 0.6251), N002 to (-1.2070, -0.7908, -0.6908), and so on for the remaining nodes. For each pair of nodes, the Euclidean distance is calculated pairwise using the Euclidean distance formula. The Euclidean distance formula between any two nodes is: ; Taking N001 and N002 as examples, their three-dimensional coordinates are (0.6499, 0.6418, 0.6251) and (-1.2070, -0.7908, -0.6908) respectively. The corresponding distance calculation process is as follows: ; Similarly, the Euclidean distances between node pairs are calculated, resulting in 10 pairs. The results form a 5×5 symmetric matrix with the main diagonal set to 0, yielding the characteristic distance matrix between nodes. For example, the distance between N003 and N005 is: ; This type of operation iterates through the list of node numbers to construct the feature distance data between node pairs, ultimately forming a complete node feature distance matrix.

[0042] S403: Traverse the node feature distance matrix, compare it with the preset redundancy judgment threshold, mark the node combination with the distance value below the threshold, and generate redundant node identifiers; The redundancy threshold is calculated based on the mean and standard deviation of the Euclidean distance from the original node's operational data distribution, and is set as follows: ,in The average distance, Standard deviation; After obtaining the complete feature distance matrix between nodes, the Euclidean distance values ​​between each pair of nodes in the matrix are traversed and analyzed to determine whether they are lower than the preset redundancy threshold. This threshold is calculated based on the overall distribution of node feature distances and is based on an outlier identification strategy derived from statistics. This strategy considers samples that are one standard deviation below the mean to be highly similar in the overall distribution. Set threshold ,in This represents the average distance between nodes. To find the standard deviation, first, calculate the Euclidean distances between all 10 node combinations. Then, list the values ​​sequentially and calculate the mean and standard deviation. For example, if the ten distances are [2.689, 1.421, 4.286, 2.793, 2.006, 3.845, 2.374, 1.917, 2.501, 3.288], then the mean is: ; Standard deviation: ; Redundancy threshold: Traverse the distance between each group of nodes in the matrix and filter out the combinations with a distance less than 1.974. That is, the two pairs of nodes with distances of 1.421 and 1.917 are marked as redundant combinations, namely N002-N005 and N004-N005, respectively. Record their node number combination as "[N002, N005]" and "[N004, N005]", and mark them as redundant node identifier combinations, which is the final result obtained in this section.

[0043] Furthermore, the steps of S5 are as follows: S501: Call the redundant node identifier, match the node's resource type, connection relationship and task status parameters, calculate the node's task processing intensity and interaction rate, and based on the threshold standards of average task processing intensity and interaction rate, filter the node number and index data that meet the dual standards to generate redundant load node index values. The process involves calling the redundant node identifier, reading the node number recorded in the current network, and obtaining the resource type parameter data recorded by the node, including values ​​for computational resource capacity (GFLOPS), storage resource capacity (GB), and communication resource bandwidth (Mbps). Then, it calls the node connection relationship parameter table to read the link records between each node and its communication neighbors, determines whether each link is active, and records the link quality level (divided into 0-3 levels, representing unavailable, low quality, medium quality, and high quality, respectively). Subsequently, based on the task status parameters, it extracts the number of tasks processed by each node per unit time (times / minute) and the average computational resource required for each task (GFLOP). The task processing intensity of the node is then calculated, specifically the sum of the total computational requirements for tasks processed by the node per unit time. If a node processes 10 tasks per minute, and each task requires an average of 4 GFLOP, then the task processing intensity is 10 × 4 = 40. GFLOPS / min; then extract the number of interactions between the node and its neighboring nodes per unit time and calculate the average to determine the interaction rate. If the node interacts with its 5 neighboring nodes 10, 12, 8, 10, and 9 times per minute respectively, the interaction rate is (10+12+8+10+9) / 5 = 9.8 interactions / minute. Calculate the average of the node's task processing intensity and interaction rate as a threshold standard. Assuming there are 100 nodes in a network, and the total node processing intensity is 5200 GFLOPS / min, then the average task processing intensity is 5200 / 100 = 52. If the total interaction rate is 1100 times / minute and the GFLOPS / min is 1100 times / minute, then the average interaction rate is 1100 / 100=11 times / minute. Then, the task processing intensity and interaction rate of each node are compared with the average value. The node number that simultaneously meets the conditions of task processing intensity greater than 52GFLOPS / min and interaction rate greater than 11 times / minute is selected, and its index data is marked and added to the candidate list. Finally, the node number, task intensity, interaction rate and other indicators are recorded in the candidate list, and the redundant load node indicator value is generated and output.

[0044] Table 5: Redundant Node Resource and Load Parameters As shown in Table 5, nodes N001 and N003 meet the criteria that both task intensity and interaction rate are higher than the average, and are therefore selected as redundant load nodes. Their index values ​​are {node number: N001, task intensity: 60, interaction rate: 12.4} and {node number: N003, task intensity: 58, interaction rate: 13.2}, respectively.

[0045] S502: Based on the redundant load node index value, combined with the node function type and response delay record, calculate the number of overlapping function types between the node and the candidate node and the response delay offset range, filter the candidate node numbers that meet the conditions of overlapping number and response delay offset, and generate the function replacement node configuration rate. Based on the redundant load node index values, the node numbers corresponding to the candidate nodes are first extracted, and the function type parameters of the corresponding nodes are read. These parameters can be categorized according to the node's execution capabilities into four types, such as "data acquisition," "image processing," "relay forwarding," and "edge computing." The recording method is a key-value pair format binding node number and function type. For example, node N001 is bound to the function "image processing," and node N003 is bound to the function "edge computing." Then, the response latency records of the candidate nodes during task execution are read, in milliseconds. The recording format is the statistical average of the difference between the start and end times of each response. For example, for a certain node... The response times for the five most recent responses were 95ms, 105ms, 98ms, 110ms, and 102ms, respectively. Therefore, the average response time is (95+105+98+110+102) / 5 = 102ms. Subsequently, for each redundant load node, a candidate node set is selected within its adjacency range in the network topology. For example, if the adjacent nodes of N001 are N005, N006, and N007, the number of overlapping functional types between each candidate node and the redundant node needs to be calculated. This is done by comparing the functional items in their respective functional sets. If N001 has the functional types "image processing" and "data acquisition," then... N005 has "image processing" and "relay forwarding" capabilities, so its overlap count is 1. After sequentially traversing all adjacent nodes, the overlap count is recorded, along with the average response delay of each adjacent node. The offset difference between this offset and the response delay of the redundant node is calculated, and the absolute value is assigned to an offset interval. The interval is divided according to the actual network response delay range. For example, the offset interval can be defined as: less than or equal to 10ms as "low offset", 11~30ms as "medium offset", and greater than 30ms as "high offset". If the average response delay of N005 is 115ms, then its offset from N001 is |115-102|=13m. s is classified as "medium offset". Then, the lower limit for the number of overlapping functions is set to 1, and the upper limit for the response offset interval is below "medium offset". That is, the number of overlaps must be ≥1 and the response offset interval must be "low" or "medium" offset. If the above two conditions are met, the corresponding adjacent node number is screened into the functional replacement candidate pool, and the functional replacement node configuration rate is calculated according to the ratio of the number of nodes to the total number of redundant nodes. If there are 3 adjacent nodes of N001, and 2 of them meet the conditions, then the configuration rate of N001 is 2 / 3 = 0.667. The replacement configuration rate of the redundant nodes is calculated in turn as the basis for subsequent screening and elimination.

[0046] S503: Based on the configuration rate of functional replacement nodes, extract feature vectors and original mean data, calculate and sort feature offset distances, filter node numbers whose offset distances exceed the interval corresponding to the median value of feature offsets between nodes for supplementation, delete redundant node numbers, and output the configuration table of the sky-space-ground collaborative monitoring network. Based on the configuration rate of functional replacement nodes, the original state feature vectors of the corresponding redundant nodes are extracted sequentially. These vectors can be set as three-dimensional features composed of the node's average task intensity, response latency, and interaction frequency over a past period. For example, the feature vector of N001 is set as follows: Then, extract the similar feature values ​​of all nodes and average them to obtain the original mean data. For example, assuming the average task intensity is 52, the response latency is 105ms, and the interaction rate is 11 times / minute, the original mean vector would be... Then, the feature offset distance is calculated node by node using the Euclidean distance formula: ; For N001, we have: ; The offset distances of redundant nodes are calculated, and the median value is obtained after sorting the distances. Assuming there are 5 redundant nodes with offset distances of 6.5, 7.2, 8.66, 9.1, and 10.3, the median value is 8.66. Node numbers with offset distances greater than 8.66 are filtered out. For example, the offset of N003 is 9.1, so it is identified as a feature deviation node. The node is then added in, which involves migrating its configuration to the functionally equivalent node recommended in the alternative node list and marking the mapping number. At the same time, it is deleted from the redundant node index list. Finally, based on the latest features, functions, and link status of the retained nodes, the configuration table of the space-air-ground collaborative monitoring network is compiled and output.

[0047] like Figure 2 As shown in the figure, this invention also provides a system for constructing a three-dimensional collaborative monitoring network system integrating space, air, and ground. The system includes: The grid division module is used to obtain the boundary coordinate set of the monitoring area through the geographic information system, spatially aggregate the coordinate set of task points, assign the task frequency density per unit time to the grid cell using the Kriging interpolation method, output the monitoring block division result, and pass it to the node evaluation module. The node evaluation module receives the node movement speed and hot zone distance from the monitoring block division results, processes the node computing power, communication frequency, and energy consumption level through the Min-Max normalization method, generates a node capability list, and transmits it to the node matching module. The node matching module is used to call the coverage radius and response latency parameters in the node capability list, filter nodes with a coverage radius greater than the grid side length and a response latency lower than the average, construct a two-dimensional cost matrix of response performance and grid heat, use the Hungarian algorithm to perform bilateral matching between nodes and grids, output a node-block matching table, and pass it to the redundancy identification module. The redundancy identification module is used to extract the task access frequency, communication success rate and runtime from the node-block matching table, perform Z-score normalization to construct a three-dimensional feature space, calculate the Euclidean distance between feature vectors, generate redundant node identifiers, and pass them to the network optimization module. The network optimization module is used to call redundant node identifiers, analyze node load rates, obtain the feature vector offsets of candidate nodes, perform low-load node removal and high-offset candidate node addition operations, and output the configuration table of the space-air-ground collaborative monitoring network.

[0048] For ease of explanation, Figure 2 Only the main components of the system are shown. The system of this embodiment can be used to perform... Figure 1 The technical solutions of the method embodiments shown are similar in principle and in effect, and will not be described again here.

[0049] In this embodiment of the invention, monitoring blocks are dynamically generated based on geographic grid division and spatial aggregation. The coverage radius is dynamically corrected by combining the node movement direction and hot zone distance. A normalized capability list integrating node computing power, communication frequency, and energy consumption level is constructed. Bilateral matching between task grids and nodes is achieved through the Hungarian algorithm. A cost matrix based on response performance and grid heat is established. Redundant nodes are identified using three-dimensional feature vector Euclidean distance. A dynamic and adaptive node-block matching mechanism is formed, which breaks through the limitations of static rules in traditional hierarchical deployment in adapting to spatiotemporal heterogeneity. This improves the resource allocation efficiency of multi-source heterogeneous nodes in complex environments, reduces the dependence of manually preset parameters on network topology, reduces monitoring blind spots caused by fixed deployment patterns, achieves a dynamic balance between coverage density and energy consumption level, and enhances the response sensitivity of the monitoring network to sudden environmental events.

[0050] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for constructing a three-dimensional collaborative monitoring network system integrating space, air, and ground, characterized in that: Includes the following steps: S1: Divide the monitoring area into grids using a geographic information system, obtain the boundary coordinate set, spatially aggregate the task point coordinate set, calculate the task frequency density per unit time, assign values ​​to the grid using the Kriging interpolation method, and output the monitoring block division results. S2: Based on the monitoring block division results, obtain the node position and moving speed, correct the coverage radius according to the node moving direction and hot zone distance, and perform Min-Max normalization processing in combination with node computing power, communication frequency, and energy consumption level attributes to generate a node capability list. S3: Call the coverage radius and response latency parameters in the node capability list, sort the node priority queue in descending order, filter the nodes whose coverage radius is greater than the grid side length and whose response latency is lower than the average value, construct the response performance cost matrix by combining the grid heat value, use the Hungarian algorithm to perform bilateral matching between the task grid and nodes, and output the node-block matching table. S4: Based on the node-block matching table, extract the task access frequency, communication success rate and runtime of the matching node, perform Z-score standardization, construct a three-dimensional feature vector, calculate the Euclidean distance of the feature vector, mark combinations below the threshold, and obtain redundant node identifiers.

2. The method for constructing a three-dimensional collaborative monitoring network system based on space-air-ground as described in claim 1, characterized in that, The monitoring block division results are specifically spatial grid coding, spatial density classification, and geofence coordinates. The node capability list includes coverage radius, response delay standard deviation, and energy efficiency coefficient. The node-block matching table includes grid node association matrix, priority sorting index, and matching cost weight. The redundant node identifier is specifically inefficient device identifier code, feature vector Euclidean distance threshold, and node health status code. The Euclidean distance threshold of the feature vector is calculated by standardizing the three-dimensional feature vector to determine the critical distance value in the node feature space and to determine the statistical distance standard of redundant nodes. The node health status code is based on a status evaluation system constructed from task access success rate and runtime, and uses binary encoding to identify the node availability status.

3. The method for constructing a three-dimensional collaborative monitoring network system based on space-air-ground as described in claim 1, characterized in that, The specific steps of S1 include: S101: Divide the monitoring area into grids using a geographic information system, obtain the boundary coordinate set and task point location data, combine the boundary coordinates to perform spatial boundary determination, determine whether the grid center point is located within the boundary, filter out grid numbers that do not fall within the boundary, and generate a grid number set within the monitoring area. S102: Based on the grid number set and task point coordinate set in the monitoring area, perform spatial matching operation, classify task points to corresponding grid numbers, count the number of task points in each unit, calculate the task frequency density based on the ratio of the total number of tasks to the grid area, and generate a task frequency density value. The task frequency density value is a numerical index describing the intensity of task spatial distribution. S103: Based on the task frequency density value, input the Kriging interpolation method with the grid number as the index to construct a continuous frequency distribution layer, extract the layer value interval change sequence and divide the distribution boundary to generate the monitoring block division result.

4. The method for constructing a three-dimensional collaborative monitoring network system based on space-air-ground as described in claim 3, characterized in that, The task frequency density value is calculated using the following formula: ； in, Representing the The task frequency density value for each grid, in units of times per square kilometer. Representing the The frequency of task points within each grid, expressed in times. Representing the The area of ​​each grid, in square kilometers. This represents the total frequency of task points within the monitoring area, expressed in times. This represents the total number of grids within the monitoring area. Representing the The grid pair of the first The spatial weight influence factor for each grid cell ranges from 0 to 1 and is calculated based on a weighted average of the distance to the grid center point and the heat index. Representing the The frequency of task points within each grid, expressed in times. Representing the The area of ​​each grid, in square kilometers.

5. The method for constructing a three-dimensional collaborative monitoring network system based on space, air, and ground as described in claim 3, is characterized in that... The specific steps of S2 include: S201: Based on the monitoring block division results, obtain the geographic coordinates and timestamp information of the nodes, calculate the displacement distance of the nodes according to the change in node coordinates within consecutive timestamps, determine the movement direction of the nodes according to the coordinate offset, calculate the movement speed value of the nodes by combining the node displacement distance and time offset, and generate the node dynamic feature value. S202: Based on the node's dynamic characteristic value, call the spatial distance value between the node's current position and the center of the monitoring block's hot zone, combine it with the initial value of the node's coverage radius, perform offset correction through the vector relationship between the node's movement direction and the hot zone distance, calculate the node's coverage radius correction amount, update the node's coverage radius value, and obtain the node coverage adjustment value; S203: Call the node coverage adjustment value, combine the node's computing power, communication frequency and energy consumption rate per unit time, normalize the three performance data using the Min-Max normalization method, and combine the normalized data in sequence to construct the node capability vector and obtain the node capability list value. The node computing power, communication frequency, and energy consumption level attributes are normalized to a unified unit of W, and then subjected to Min-Max normalization.

6. The method for constructing a three-dimensional collaborative monitoring network system based on space-air-ground as described in claim 5, characterized in that, The specific steps of S3 include: S301: Call the coverage radius and response delay parameters in the node capability list, calculate the overall average of the node response delay, filter the nodes whose coverage radius is greater than the grid side length and whose response delay is lower than the average, and obtain the set of nodes with compliant coverage efficiency. S302: Based on the coverage efficiency compliant node set, calculate the spatial distance from the node to the grid, and proportionally allocate the corresponding response rate value to the grid heat, construct a bidirectional response cost matrix between the node and the grid, and generate a node-grid cost matrix value set; S303: Call the node-grid cost matrix value group, use the Hungarian algorithm to perform weighted path matching, match the node number with the grid number, and obtain the node-block matching table.

7. The method for constructing a three-dimensional collaborative monitoring network system based on space-air-ground as described in claim 6, characterized in that, The specific steps of S4 include: S401: Based on the node-block matching table, extract the task access frequency field value, communication success rate field value, and runtime field value, and use Z-score standardization to process the three indicators to generate a standardized communication indicator set; The task access frequency is measured in times per hour, the communication success rate is measured in percentage, and the runtime is measured in hours. All of these parameters are standardized using Z-score to become dimensionless parameters. S402: Call the standardized communication index set, combine the standardized frequency value, success rate value and runtime value of each node into three-dimensional coordinate points, calculate the spatial distance between node vectors using the Euclidean distance formula, and form a node feature distance matrix; S403: Traverse the node feature distance matrix, compare it with the preset redundancy judgment threshold, mark the node combination with the distance value below the threshold, and generate redundant node identifiers; The redundancy determination threshold is calculated based on the mean and standard deviation of the Euclidean distance according to the distribution of the original node running data, and is set as μ-σ, where μ is the mean distance and σ is the standard deviation.

8. The method for constructing a three-dimensional collaborative monitoring network system based on space-air-ground as described in claim 1, characterized in that, The method includes step S5: S5: Call the redundant node identifier, combine the node load rate in the current network with the feature vector offset of the candidate nodes, remove some nodes whose load rate and redundancy occupy the edge position, and add candidate nodes with large feature vector offsets, and output the sky-air-ground collaborative monitoring network configuration table. The configuration table of the space-air-ground collaborative monitoring network includes load balancing factors, node redundancy evaluation values, and candidate node registry. The load balancing factor is a load assessment parameter constructed based on the node's computing resource utilization and communication frequency, used to quantify the node's workload level.

9. The method for constructing a three-dimensional collaborative monitoring network system based on space-air-ground as described in claim 8, characterized in that, The specific steps of S5 include: S501: Call the redundant node identifier, match the node's resource type, connection relationship and task status parameters, calculate the node's task processing intensity and interaction rate, and based on the threshold standards of average task processing intensity and interaction rate, filter the node number and index data that meet the dual standards to generate redundant load node index values. S502: Based on the redundant load node index value, combined with the node function type and response delay record, calculate the number of overlapping function types between the node and the candidate node and the response delay offset range, filter the candidate node numbers that meet the conditions of overlapping number and response delay offset, and generate the function replacement node configuration rate. S503: Based on the configuration rate of the functional replacement nodes, extract the feature vector and the original mean data, calculate and sort the feature offset distance, filter the node numbers whose offset distance exceeds the corresponding interval of the feature offset median value between nodes for supplementation, delete redundant node numbers, and output the configuration table of the sky-space-ground collaborative monitoring network.

10. A system for constructing a three-dimensional collaborative monitoring network system integrating space, air, and ground, characterized in that: The system is used to implement the method for constructing a three-dimensional collaborative monitoring network system based on any one of claims 1-9, and the system includes: The grid division module is used to obtain the boundary coordinate set of the monitoring area through the geographic information system, spatially aggregate the coordinate set of task points, assign the task frequency density per unit time to the grid cell using the Kriging interpolation method, output the monitoring block division result, and pass it to the node evaluation module. The node evaluation module is used to receive the node movement speed and hot zone distance from the monitoring block division results, process the node computing power, communication frequency and energy consumption level through the Min-Max normalization method, generate a node capability list and transmit it to the node matching module. The node matching module is used to call the coverage radius and response delay parameters in the node capability list, filter nodes with a coverage radius greater than the grid side length and a response delay lower than the average value, construct a two-dimensional cost matrix of response performance and grid heat, use the Hungarian algorithm to perform bilateral matching between nodes and grids, output a node-block matching table, and pass it to the redundancy identification module. The redundancy identification module is used to extract the task access frequency, communication success rate and runtime from the node-block matching table, perform Z-score normalization to construct a three-dimensional feature space, calculate the Euclidean distance between feature vectors, generate redundant node identifiers, and pass them to the network optimization module. The network optimization module is used to call the redundant node identifier, analyze the node load rate, obtain the feature vector offset of the candidate node, perform the low load node elimination and high offset candidate node addition operations, and output the sky-space-ground collaborative monitoring network configuration table.

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