A measurement data collection method based on ecological detection platform construction

Abstract

The present application relates to the field of environmental monitoring technology, specifically to a kind of measurement data acquisition method based on ecological detection platform construction, comprising the following steps: based on remote sensing and patrol data monitoring surface change, identify ground feature and divide section, analyze image resolution fluctuation and mark inefficient node, optimize monitoring point and resource allocation, dispatch acquisition equipment to complete measurement data, generate monitoring output and synchronize mark control table.In the present application, the surface change is identified by combining remote sensing image and patrol data, the dynamic perception ability of ground feature distribution is improved, the node efficiency is sorted by path and equipment state analysis, the patrol coverage is balanced, the high-frequency data is supplemented in key areas using spatial positioning and layer tools, the continuous recording ability of ground feature change is enhanced, the missing section is identified and the anomaly is marked by integrating periodic monitoring results, the data synchronous correction and marking switching linkage are realized, and the real-time tracking and abnormal response ability to ecological change is improved.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method for acquiring measurement data based on the construction of an ecological monitoring platform. Background Technology

[0002] Environmental monitoring technology involves the quantitative and visual monitoring of elements in the natural environment, such as air, water, soil, and organisms. It is one of the crucial foundational technologies for ecological environment management and protection. The core aspects of this technology include real-time acquisition of environmental parameter data, long-term tracking and recording, data management and transmission, the layout of monitoring networks, and analytical and evaluation methods based on the collected data. By constructing a unified data platform and acquisition system, environmental monitoring technology can systematically support the assessment of the current state of the ecological environment, the judgment of changing trends, and the support for management decision-making. It serves as an important basis for the formulation of green development, resource regulation, and ecological protection policies.

[0003] Traditional measurement data acquisition methods refer to on-site information acquisition primarily through manual surveys, point sampling, and portable instrument readings. These methods mainly involve periodically dispatching personnel with single-function detection equipment to read parameters in the target area, and then summarizing the results through manual recording or local data storage. For technical matters concerning the multi-dimensional, continuous, and geographically dispersed nature of the ecological environment, this approach leverages an ecological monitoring platform for data acquisition. This involves deploying sensor terminals, establishing unified collection parameter standards, and uploading data via network communication to create a sustainable data acquisition process, enabling unified collection and management of multiple monitoring indicators in the ecological environment.

[0004] Existing technologies rely on manual surveys and fixed-point sampling to obtain environmental data. In scenarios with large geographical spans and frequent changes in environmental factors, there are problems such as data update lag and inconsistent collection granularity. In particular, it is difficult to achieve real-time continuous monitoring in areas with dynamic changes in ecosystems. Due to the limitations of fixed routes and limited equipment functions, manual patrols are prone to monitoring blind spots and incomplete data coverage. Some environmental events are delayed in identification or even missed due to insufficient monitoring. Moreover, traditional methods rely on manual data recording, which leads to the accumulation of information errors and low data aggregation efficiency. They show obvious limitations in building a data system oriented towards spatial distribution characteristics and long-term trend analysis. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a measurement data acquisition method based on the construction of an ecological monitoring platform.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a measurement data acquisition method based on the construction of an ecological monitoring platform, comprising the following steps:

[0007] S1: Based on multi-temporal remote sensing images and UAV patrol data integrated by the ecological monitoring platform, conduct land cover change monitoring, combine green heart area planning data, identify the distribution characteristics and change patterns of land cover types, and generate land surface dynamic monitoring segment division results;

[0008] S2: Based on the results of the dynamic monitoring section division of the ground surface, extract the image clarity index of satellite cloud remote sensing and the monitoring coverage of the tower video, analyze the image resolution fluctuation range within the section, mark inefficient monitoring nodes, and generate image optimization and adjustment points;

[0009] S3: Optimize and adjust the locations based on the images, extract drone patrol path data and tower video equipment operation status information, identify the patrol coverage area and monitoring blind spot ratio per unit time, sort equipment performance, and generate a monitoring resource allocation priority list.

[0010] S4: Based on the monitoring resource allocation priority list, extract the map layer tool and spatial positioning component from the ecological monitoring platform, schedule UAVs and ground measurement terminals to carry out high-frequency data collection operations, record the spatial distribution and change trend of land cover types in key sections, fill in the gaps in the original measurement data, and generate integrated results of surface measurement data.

[0011] As a further aspect of the present invention, the results of the dynamic monitoring zone division include the distribution density of land cover types, the frequency of land cover change, the degree of regional planning fit, and densely populated monitoring blind spots. The image optimization and adjustment points include image resolution thresholds, monitoring coverage deviations, inefficient monitoring node locations, and optimization triggering conditions. The monitoring resource allocation priority list includes equipment inspection coverage intensity, monitoring blind spot ratio, coverage priority order, and blind spot priority order. The integrated results of the surface measurement data include the completeness of land cover type labeling, coordinates of labeled conflict areas, calibration priority, and label correction markers.

[0012] As a further aspect of the present invention, the steps for obtaining the results of the dynamic monitoring zone division of the land surface are as follows:

[0013] S111: Based on multi-temporal remote sensing images and UAV patrol data integrated by the ecological monitoring platform, land cover change monitoring is carried out. Combined with green heart area planning data, the monitoring area is divided into multiple segments according to the frequency of land cover type change. The spatial characteristics of land cover type distribution in each segment are analyzed to obtain the mapping value between land cover type and change frequency.

[0014] S112: Based on the mapping value between the land cover type and the change frequency, extract the adjacent extreme points of the land cover change data, identify the land cover type change trend sequence, compare the change frequency with the spatial distribution benchmark value, filter the segment numbers of the change anomalies, and obtain the sequence of anomalous change segment numbers.

[0015] S113: Based on the number sequence of the abnormal change section, extract the distribution density data of the land cover type in the section, calculate the time difference between the coverage change and the stable section, and divide the dynamic monitoring section by combining the change rate and frequency to generate the land surface dynamic monitoring section division result.

[0016] As a further aspect of the present invention, the step of obtaining the image optimization adjustment points specifically includes:

[0017] S211: Based on the results of the dynamic monitoring of the ground surface, extract the satellite cloud remote image clarity index and the coverage data of the tower video surveillance, identify the image resolution, coverage period and equipment status feedback data of the section, identify the changes in image resolution and coverage differences per unit time, and generate a section image resolution sequence.

[0018] S212: Based on the segment image resolution sequence, collect the image brightness distribution, coverage offset and device position angle coefficient within each monitoring node. By comparing the difference ratio between the parameters and the resolution fluctuation threshold, calculate the change amplitude value of inefficient nodes, and compare it point by point with the coverage baseline fluctuation amplitude to obtain the image optimization adjustment points.

[0019] As a further aspect of the present invention, the step of obtaining the monitoring resource allocation priority list specifically includes:

[0020] S311: Optimize and adjust the points according to the image, extract the drone patrol path data and the tower video equipment operation status information, identify the proportion of monitoring blind spots, and analyze the relationship between the patrol coverage area and the proportion of blind spots per unit time by comparing the continuous coverage interval and the proportion of blind spots in the equipment patrol path data, and obtain the unit coverage blind spot relationship interval.

[0021] S312: Call the unit coverage blind zone relationship interval, calculate the coverage performance index value through the relationship interval value of the differentiated device in the self-running state, compare and arrange the index values, and generate a monitoring resource allocation priority list.

[0022] As a further aspect of the present invention, the steps for obtaining the integrated results of the surface measurement data are specifically as follows:

[0023] S411: Based on the monitoring resource allocation priority list, extract the map layer tool and spatial positioning component configured in the ecological monitoring platform, parse the boundary coordinate group and layer level parameters of the monitoring area, and combine the coordinate calibration results of the spatial positioning component to perform spatial fitting and calibration of the monitoring area layer partition boundary to obtain the monitoring area layer boundary coordinate set;

[0024] S412: Call the boundary coordinate set of the survey area layer, match the current schedulable resource list and task configuration parameters of the UAV and the ground measurement terminal, filter the measurement equipment combination with the operation conditions according to the equipment operation radius and response time window, perform sequential scheduling of each combination according to the priority list, and obtain the measurement equipment scheduling sequence table;

[0025] S413: Call the measurement equipment scheduling sequence list, instruct the equipment to carry out high-frequency surface data acquisition, identify the time-series image sample groups and spatial positioning logs under each survey area layer, calculate the measurement point attribute change ratio value through the multi-time period pixel attribute changes of the same positioning point in the image sample group, combine the spatial distribution trend of the change ratio, fill in the missing attribute segments, overlay the layers and positioning logs, and generate the integrated surface measurement data results.

[0026] As a further aspect of the present invention, the method further includes step S5:

[0027] S5: Based on the integrated results of the surface measurement data, extract the monitoring output values ​​of UAV patrol, satellite cloud remote sensing and tower video equipment, analyze the number of missing segments in continuous period, compare with the standard annotation interval, identify the over-limit nodes and annotation anomaly markers and partition switching signals, and obtain the ecological monitoring platform annotation and anomaly synchronization control table.

[0028] The ecological monitoring platform's annotation and anomaly synchronization control table includes the number of missing annotation cycles, excessive annotation nodes, annotation anomaly status, and partition switching identifiers.

[0029] As a further aspect of the present invention, the steps for obtaining the ecological monitoring platform annotation and anomaly synchronization control table are as follows:

[0030] S511: Based on the integrated results of the surface measurement data, extract the monitoring output values ​​of UAV patrol, satellite cloud remote sensing and tower video equipment, divide the time period according to the continuous cycle, determine whether the node label value is missing in each segment, count the number of time periods in which the node is missing in the cycle, and obtain the number of missing segments of the node period label.

[0031] S512: Call the number of missing segments in the node periodic annotation, determine whether the node exceeds the normal range based on the difference between the number of missing segments in the node annotation and the set standard annotation interval, bind the index of the out-of-limit node with the corresponding period number, filter abnormal nodes, record the periodic performance of the out-of-limit node, and obtain the location value of the out-of-limit annotation node.

[0032] S513: Based on the location value of the over-limit labeled node, call the partition switching threshold, compare the number of consecutive abnormal cycles with the threshold, mark the partition status of the node that meets the switching conditions, summarize the current label value of the node and the switching status parameters, and obtain the labeling and abnormal synchronization control table of the ecological monitoring platform.

[0033] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0034] This invention enhances the dynamic perception of land cover distribution within a region by combining remote sensing imagery and patrol data to identify changes in land cover status. Based on the quantification of clarity and coverage indicators, it enables the identification and optimization of image monitoring resource allocation efficiency. Through joint analysis of patrol paths and equipment status, it establishes a real-time ranking of monitoring node effectiveness, ensuring balanced coverage between patrol operations and data collection. By combining spatial positioning tools and map layer tools, it enables high-frequency data supplementation in key areas, enhancing the continuous recording capability of land cover type changes. By integrating the output results of periodic monitoring data, it completes the identification of missing segments and the classification of anomaly annotations, realizing a linkage mechanism for synchronous correction of monitoring data and annotation switching. This strengthens the real-time tracking of changing trends and the responsiveness to anomaly management in ecological monitoring tasks. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the main steps of the present invention;

[0036] Figure 2 This is a flowchart illustrating the process of obtaining the results of the dynamic monitoring zone division of the land surface in this invention.

[0037] Figure 3 This is a flowchart illustrating the process of obtaining image optimization and adjustment points in this invention.

[0038] Figure 4 This is a flowchart illustrating the process of obtaining the monitoring resource allocation priority list in this invention.

[0039] Figure 5 This is a flowchart illustrating the process of obtaining integrated surface measurement data in this invention.

[0040] Figure 6 This is a flowchart illustrating the process of obtaining the annotation and anomaly synchronization control table for the ecological monitoring platform in this invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0042] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0043] Example 1, please refer to Figure 1 This invention provides a technical solution: a measurement data acquisition method based on an ecological monitoring platform, comprising the following steps:

[0044] S1: Based on multi-temporal remote sensing images and UAV patrol data integrated by the ecological monitoring platform, conduct land cover change monitoring, combine green heart area planning data, identify the distribution characteristics and change patterns of land cover types, and generate land surface dynamic monitoring segment division results;

[0045] S2: Based on the results of the dynamic monitoring of the ground surface, extract the image clarity index of satellite cloud remote sensing and the monitoring coverage of the tower video, analyze the image resolution fluctuation range within the section, mark inefficient monitoring nodes, and generate image optimization and adjustment points;

[0046] S3: Optimize and adjust the locations based on the images, extract drone patrol path data and tower video equipment operation status information, identify the patrol coverage area and the proportion of blind spots per unit time, sort the equipment performance, and generate a priority list for monitoring resource allocation.

[0047] S4: Based on the priority list of monitoring resource allocation, extract the map layer tools and spatial positioning components in the ecological monitoring platform, schedule drones and ground measurement terminals to carry out high-frequency data collection operations, record the spatial distribution and change trend of land cover types in key sections, fill in the gaps in the original measurement data, and generate integrated results of surface measurement data.

[0048] S5: Based on the integrated results of surface measurement data, extract the monitoring output values ​​of UAV patrol, satellite cloud remote sensing and tower video equipment, analyze the number of missing segments in continuous periods, compare with the standard annotation interval, identify over-limit nodes and annotation anomaly markers and partition switching signals, and obtain the annotation and anomaly synchronization control table of the ecological monitoring platform.

[0049] The results of the dynamic surface monitoring zone division include the distribution density of land cover types, the frequency of land cover change, the degree of regional planning fit, and densely populated monitoring blind spots. The image optimization and adjustment points include image resolution thresholds, monitoring coverage deviations, inefficient monitoring node locations, and optimization trigger conditions. The monitoring resource allocation priority list includes equipment inspection coverage intensity, monitoring blind spot ratio, coverage priority order, and blind spot priority order. The integrated results of surface measurement data include the completeness of land cover type labeling, coordinates of label conflict areas, calibration priority, and label correction indicators. The ecological monitoring platform labeling and anomaly synchronization control table includes the number of missing labeling cycles, excessive labeling nodes, labeling anomaly status, and zone switching indicators.

[0050] Please see Figure 2 The specific steps for obtaining the results of the surface dynamic monitoring zone division are as follows:

[0051] S111: Based on multi-temporal remote sensing images and UAV patrol data integrated by the ecological monitoring platform, land cover change monitoring is carried out. Combined with green heart area planning data, the monitoring area is divided into multiple segments according to the frequency of land cover type change. The spatial characteristics of land cover type distribution in each segment are analyzed to obtain the mapping value between land cover type and change frequency.

[0052] Based on multi-temporal remote sensing imagery and UAV patrol data integrated by the ecological monitoring platform, in order to monitor land cover changes in the green heart area, the ecological monitoring platform integrates multi-temporal Sentinel-2 remote sensing imagery (10-meter resolution) and UAV patrol data (0.1-meter resolution). Images were collected in January, April, July, and October, and atmospheric and geometric corrections were performed to classify and identify land cover types such as vegetation, water bodies, bare land, buildings, and roads. In conjunction with the green heart area plan, the changes in land cover in each functional area were statistically analyzed to assess the frequency of land cover transformation within a year. For example, the number of times a monitoring unit changes from "bare land" to "building" can be used to calculate the frequency of change, which is divided into stable segments (<0.01), moderate change segments (0.01~0.1), and height change segments (>0.1). For example, the frequency of change in section A (ecological protection zone) is 0.005, indicating a stable zone; section B (agricultural demonstration zone) is 0.03, indicating a moderately changing zone; and section C (urban-rural fringe area) is 0.15, indicating a highly changing zone. Further analysis of the spatial characteristics of land features within each section reveals that section A is mainly composed of continuously distributed arbor forests, while section C has a high density and concentrated distribution of buildings. This allows for the extraction of mapping values ​​between land feature types and their frequency of change, such as "arbor forest - 0.005" and "buildings - 0.15," thus obtaining the mapping values ​​between land feature types and their frequency of change.

[0053] S112: Based on the mapping value between land cover type and change frequency, extract adjacent extreme points of land cover change data, identify the land cover type change trend sequence, compare the change frequency with the spatial distribution benchmark value, filter the segment numbers of change anomalies, and obtain the sequence of anomalous change segment numbers.

[0054] From the above mapping values ​​of land cover types and their frequency of change, adjacent extreme points of land cover change data are extracted. Adjacent extreme points refer to the local highest and lowest points on the land cover type frequency of change curve. For example, in the historical frequency of change data sequence [0.01, 0.02, 0.05, 0.03, 0.08, 0.06, 0.04] of a certain segment, the adjacent extreme points are 0.01 (local low), 0.05 (local high), 0.03 (local low), and 0.08 (local high). The trend sequence of land cover type change is identified. By analyzing the direction and magnitude of change between adjacent extreme points, it is determined whether the land cover type shows a trend of continuous growth, continuous decline, fluctuating increase, or fluctuating decrease. For example, an increase from 0.01 to 0.05 and then a decrease to 0.03 is identified as a "fluctuating increase" trend. The frequency of change is compared with the spatial distribution benchmark value, which is based on historical data. The monitoring data and regional planning set typical values ​​to measure the spatial distribution of land cover types. For example, for vegetation cover, the benchmark value is set at 0.8 (i.e., 80%), and the benchmark value for change frequency is set at 0.05 (i.e., 5%). If the change frequency of vegetation cover in a certain section is 0.1, which is higher than the benchmark value of 0.05, it is identified as an abnormal change. By comparison, the section number of the abnormal change is selected. The judgment criterion for the abnormal section is: if the change frequency of a land cover type in a certain section is higher than the change frequency benchmark value set for the functional area where the land cover type is located, it is judged as an abnormal section. For example, the change frequency of buildings in section C (urban-rural fringe) is 0.15, which is higher than the set change frequency benchmark value of 0.10. Then the number "C003" of section C is selected to obtain the abnormal change section number sequence. For example, the selected abnormal change section number sequence is ["C003", "E005"].

[0055] S113: Based on the number sequence of anomalous change sections, extract the distribution density data of land cover types in the sections, calculate the time difference between cover change and stable sections, and divide the dynamic monitoring sections by combining the change rate and frequency to generate the land surface dynamic monitoring section division results.

[0056] From the sequence of abnormal change zones, such as ["C003", "E005"], extract the distribution density data of vegetation types within the zones. Calculate the time difference between change zones and stable zones. Change zones refer to zones with a high frequency of vegetation type changes, while stable zones refer to zones with a low frequency of changes. The time difference refers to the length of time from the onset of significant changes in vegetation type to its stabilization. For example, during the monitoring period, zone C003 showed a significant increase in built-up land area starting in January, which stabilized by July; this time difference is 6 months. Meanwhile, the vegetation cover in stable zone A001 remained stable, with a time difference of 0. Dynamic monitoring zones are then divided based on the rate and frequency of change. Velocity refers to the amount of change in land cover type per unit time, while frequency refers to the number of times land cover type changes per unit time. For example, in segment C003, the built-up land area increased by 5 hectares in 6 months, with a rate of change of 0.83 hectares / month and a frequency of change of 10 times. Segments with a rate of change higher than 0.5 hectares / month and a frequency of change higher than 8 times are classified as high dynamic monitoring segments. Segments with a rate of change between 0.1 hectares / month and 0.5 hectares / month and a frequency of change between 3 and 8 times are classified as medium dynamic monitoring segments. Segments with a rate of change lower than 0.1 hectares / month and a frequency of change lower than 3 times are classified as low dynamic monitoring segments. For example, segment C003 meets the classification criteria for high dynamic monitoring segments, and the final result of the land surface dynamic monitoring segment classification is generated.

[0057] Please see Figure 3 The specific steps for obtaining image optimization and adjustment points are as follows:

[0058] S211: Based on the results of the dynamic monitoring of the ground surface, extract the clarity index of satellite cloud remote image and the coverage data of tower video surveillance, identify the image resolution, coverage period and equipment status feedback data of the section, identify the changes in image resolution and coverage differences per unit time, and generate the section image resolution sequence.

[0059] Based on the results of the surface dynamic monitoring segment division, satellite cloud remote image clarity index is extracted from high dynamic monitoring segments (such as HD001 and HD002). The index is calculated based on edge sharpness and contrast, ranging from 0 to 100. The higher the value, the clearer the image; for example, HD001 is 85 and HD002 is 78. Simultaneously, the coverage range and status of the tower video surveillance equipment are extracted. For example, Tower 1 has a coverage radius of 500 meters, is in normal status, has a resolution of 0.5 meters, and a coverage cycle of 1 hour. Further comparative analysis is performed on the satellite image resolution (e.g., 10 meters / pixel) and update cycle (e.g., 3 days) of the segments to monitor for resolution fluctuations. Coverage differences are calculated by combining the theoretical and actual coverage range of tower monitoring. For example, the theoretical coverage area of ​​Tower 1 is 78.5 hectares, but due to obstruction, the actual coverage area is only 70 hectares, a difference of 8.5 hectares. The final generated segment image resolution sequence is as follows: for example, the satellite image resolution sequence of segment HD001 is [10 meters, 10 meters, 10 meters, ...], and the Tower video resolution sequence is [0.5 meters, 0.5 meters, 0.5 meters, ...].

[0060] S212: Based on the segment image resolution sequence, collect the image brightness distribution, coverage offset, and device position angle coefficient within each monitoring node. By comparing the ratio of the difference between these parameters and the resolution fluctuation threshold, the formula is used:

[0061] ;

[0062] Calculate the change amplitude of inefficient nodes and compare it point by point with the baseline fluctuation amplitude of the coverage area to determine the image optimization and adjustment points;

[0063] in, This represents the magnitude of change at inefficient nodes. This represents the actual installation angle of the current monitoring node equipment. The theoretically optimal installation angle for the device representing the node. The ground coverage direction offset angle coefficient representing the monitoring node. This represents the image resolution acquisition deviation value for the k-th segment. This represents the average image resolution deviation across all acquired areas. This represents the set threshold for image resolution fluctuation. This represents the historical average brightness offset of the current monitored node. This represents the number of image segments sampled;

[0064] Based on the segment image resolution sequence, the image brightness distribution, coverage offset, and device position angle coefficient are collected for each monitoring node. Image brightness distribution refers to the statistical characteristics of pixel brightness values ​​in the image, such as average brightness and brightness standard deviation. Coverage offset refers to the geographical deviation between the actual coverage area and the planned coverage area of ​​the monitoring device. The device position angle coefficient refers to the deviation angle of the monitoring device relative to its optimal installation position; this coefficient is obtained through the device's built-in gyroscope. For example, for a certain monitoring node, its average brightness value is 120 (range 0-255), its coverage offset is 5 meters, and its device position angle coefficient is 0.95. By comparing the difference ratio between these parameters and the resolution fluctuation threshold, the resolution fluctuation threshold is determined. This threshold is set as the upper limit for the rate of change in image resolution. If this threshold is exceeded, it indicates abnormal resolution fluctuation. This threshold is obtained based on historical data analysis. For example, through statistical analysis of data from 1000 monitoring nodes over the past year, nodes with resolution fluctuations exceeding 5% are considered abnormal. Therefore, a resolution fluctuation threshold is set. The threshold value was set based on statistical analysis of the resolution changes in surveillance images over the past year. Experiments verified that when... Setting the threshold to 0.05 effectively identifies resolution anomalies caused by factors such as equipment aging and environmental interference. Based on experience, this threshold is set to 0.05, representing a 5% resolution fluctuation. The difference ratio between the node's image brightness distribution, coverage offset, and device position angle coefficient and the set resolution fluctuation threshold is calculated. For example, if the resolution fluctuation threshold is set to 0.05, and the image resolution change rate is 0.08, then the difference ratio is 0.08 / 0.05 = 1.6. This step also involves the calculation of a formula:

[0065] This formula is used to calculate the variation magnitude G of inefficient nodes, where, This represents the actual installation angle of the current monitoring node device (unit: degrees). The theoretically optimal installation angle of the device at this node (unit: degrees). The ground coverage direction offset angle coefficient represents the monitoring node and is used to quantify the impact of the actual installation angle on the ground coverage direction. Its value ranges from 0 to 1. The image resolution acquisition deviation value (unit: pixels) represents the image resolution acquisition deviation between the actual acquisition resolution and the ideal resolution for the k-th segment. This represents the average image resolution deviation (in pixels) across all acquired areas. This represents the set image resolution fluctuation threshold, which is set to 0.05. This threshold is based on historical data statistical analysis; when the resolution fluctuation exceeds 5%, a problem is considered to exist. This represents the historical average brightness offset of the current monitoring node, used to measure the impact of changes in ambient light on image quality. For example, by collecting the image brightness values ​​of this node at noon every day over the past month, the average deviation from the standard brightness value is calculated and set to 10. m represents the number of image segments sampled, for example, m=3 here.

[0066] Now, we will assign and calculate specific parameters: Assume a monitoring node with θ = 65 degrees, the actual installation angle, which is obtained in real time through the device's built-in sensor.

[0067] The optimal installation angle, theoretically, is provided by the equipment manufacturer or determined based on on-site surveys. The ground cover orientation offset angle coefficient is obtained through regression analysis of the installation angle and the actual shape of the covered area. For example, when the angle deviates by 5 degrees, the ground cover shape deviates by 20%. ; The image resolution acquisition deviation value of the first segment indicates that the actual resolution of the segment differs from the ideal resolution by 2 pixels. The image resolution acquisition deviation value of the second segment; The image resolution acquisition deviation value of the third segment; The average image resolution deviation value of all acquisition sections; The set image resolution fluctuation threshold; The historical average brightness offset of the current monitoring node; The number of image segments sampled;

[0068] Substitute the value into the formula: First, calculate the molecule: ; The sum of the numerators is ;

[0069] Calculate the denominator again: ;

[0070] Final calculation : ;

[0071] The advantage of this formula is that by considering multiple parameters such as the deviation between the actual installation angle and the optimal angle of the equipment, the offset of the ground coverage direction, the actual acquisition deviation of the image resolution, and the historical brightness offset, it comprehensively evaluates the inefficiency of the monitoring equipment, making the evaluation of image quality more comprehensive and accurate.

[0072] Calculate the change magnitude of inefficient nodes This value is then compared point-by-point with the coverage range benchmark fluctuation amplitude, which is set at 0.5. This benchmark value is determined through statistical analysis of historical operational data from a large number of monitoring nodes, combined with expert experience. When the fluctuation amplitude of inefficient nodes... A value greater than 0.5 indicates that the node requires image optimization and adjustment. A value less than or equal to 0.5 indicates that the node is operating normally. For example, the change value of an inefficient node in a monitoring node. If the fluctuation amplitude is greater than the baseline value of the coverage area by 0.5, the node is determined to require image optimization and adjustment. The image optimization and adjustment points are obtained. This result shows that the image quality of the monitoring node has significant problems and needs to be optimized and adjusted.

[0073] Please see Figure 4 The specific steps for obtaining the monitoring resource allocation priority list are as follows:

[0074] S311: Optimize and adjust the locations based on the images, extract the drone patrol path data and the tower video equipment operation status information, identify the proportion of monitoring blind spots, and analyze the relationship between the patrol coverage area and the proportion of blind spots per unit time by comparing the continuous coverage interval and the proportion of blind spots in the equipment patrol path data, and obtain the unit coverage blind spot relationship interval.

[0075] Based on the image optimization and adjustment points, the proportion of monitoring blind spots is identified. From the image optimization and adjustment point data, drone patrol path data and tower video equipment operation status information are extracted. The drone patrol path data includes the drone's flight path, patrol time, and geographic coordinates of the photos taken for each patrol. The tower video equipment operation status information includes the online status, fault codes, and last maintenance time of each tower video device. For example, the drone patrol path corresponding to optimization and adjustment point A is extracted as [P1, P2, P3], the patrol time is June 1st, the corresponding tower video device ID is T001, and its operation status is normal. The proportion of monitoring blind spots is identified. Monitoring blind spots refer to areas that are not effectively covered by both drone patrols and tower video surveillance. By analyzing the drone patrol path data and tower video surveillance coverage data, the coverage area of ​​both is superimposed. The method calculates the proportion of uncovered areas to the total monitored area. By comparing the proportion of continuous coverage intervals and blind spots in the equipment patrol path data, the method analyzes the relationship between patrol coverage area and blind spot ratio per unit time. Continuous coverage intervals refer to the geographical range that drones or tower video surveillance equipment can continuously cover during a single patrol or operation. For example, if a drone continuously covers an area of ​​5 kilometers along a path, the blind spot ratio is 0.1. The method also analyzes the relationship between patrol coverage area and blind spot ratio per unit time. By statistically analyzing patrol data over a period of time (e.g., one month), the total patrol coverage area of ​​drones and tower video surveillance per unit time is calculated. Combined with the blind spot ratio data of the same period, the correlation between the two is analyzed to obtain the unit coverage blind spot relationship interval. This relationship interval describes the correspondence between patrol coverage area and monitoring blind spot ratio per unit time under a specific patrol strategy.

[0076] S312: The unit covers the blind zone relationship interval, using the relationship interval value of the differentiated device in self-operating state, employing the formula:

[0077] ;

[0078] Calculate coverage performance index values, compare and rank the index values, and generate a priority list for monitoring resource allocation;

[0079] in, Represents the coverage effectiveness indicator value. The value of the relation interval corresponding to the unit with the number u in the d-th differentiated device operating state. This represents the arithmetic mean of the values ​​in all unit relation intervals under the operating state of the d-th differentiated device. The signal interference correction parameter represents the unit with the number u and the corresponding operating state of the d-th differentiated device. The distance measurement uncertainty correction parameter represents the unit with the number u in the d-th differentiated device operating state. This represents the total number of differentiated devices in self-operating mode. For differentiated devices, use the index number. Index number for units;

[0080] This step retrieves the relationship interval values ​​of differentiated devices in auto-operation mode from the unit coverage blind zone relationship interval. Differentiated devices refer to different types and models of monitoring equipment, such as drones, fixed tower video cameras, and mobile vehicle-mounted monitoring devices. These devices have different coverage capabilities and efficiencies in auto-operation mode. For example, a drone in auto-operation mode has a relationship interval value of [0.8, 0.9], indicating high coverage efficiency, while a fixed tower video camera has a relationship interval value of [0.6, 0.7], indicating moderate coverage efficiency. This step involves formula calculation, the formula is:

[0081] This formula is used to calculate the coverage effectiveness index value. ,in, This represents the relational interval value corresponding to the d-th differentiated device operating state of the unit with the number u. For example, when (Representing a high-dynamic monitoring area) and (When representing drone equipment) This represents the range of values ​​indicating the relationship between the high dynamic monitoring area and the operating state of the UAV. Representing the The arithmetic mean of the values ​​of all unit relation intervals under the different operating conditions of each device. For example, when hour, It is the average value of the relationship interval values ​​across all monitoring areas under the drone's operating status. Representative number is The unit in the first Signal interference correction parameters are assigned to different equipment operating conditions to quantify the impact of signal interference on coverage performance. For example, in urban high-rise areas, signal interference causes these parameters to be higher. Representative number is The unit in the first Ranging uncertainty correction parameters corresponding to the operating states of different devices are used to quantify the impact of ranging errors on coverage. This represents the total number of differentiated devices in self-operating state, for example, here. (Drones and fixed iron towers). For differentiated devices, use the index number. The index number is a unit; for example, the index numbers for high dynamic monitoring area, medium dynamic monitoring area, and low dynamic monitoring area are 1, 2, and 3, respectively.

[0082] Now let's proceed with the specific parameter assignment and calculation: Assume there are two different types of equipment: drones. and fixed iron tower and three monitoring units (sections): high dynamic monitoring area Medium dynamic monitoring area Low dynamic monitoring area , .

[0083] Table 1: Values ​​and Correction Parameters of Relationships between Monitoring Units under Different Equipment Operating States

[0084]

[0085] Table 1 lists the relationship interval values, signal interference correction parameters, and ranging uncertainty correction parameters for different monitoring units under the operating conditions of UAVs and fixed tower equipment.

[0086] First, calculate the arithmetic mean of the values ​​for all unit relation intervals under each differentiated device: ;

[0087] ;

[0088] Now, taking unit number 1 (high dynamic monitoring area) as an example, calculate the sum of its numerators under both types of equipment:

[0089] for (Drone):

[0090] ;

[0091] for (Fixed iron tower):

[0092] ;

[0093] Sum of numerators:

[0094] ;

[0095] Now calculate the denominator:

[0096] ;

[0097] The denominator is:

[0098] ;

[0099] for calculate : ;

[0100] Calculate using the same method and ;

[0101] for (Mid-Dynamic Monitoring Zone): Molecular:

[0102] ; ;

[0103] Sum of numerators:

[0104] ;

[0105] Denominator:

[0106] The denominator is ; ;

[0107] for (Low dynamic monitoring area):

[0108] molecular: ; ;

[0109] Sum of numerators: ;

[0110] Denominator:

[0111] The denominator is ; ;

[0112] The advantage of this formula is that by considering the deviation between the relational interval values ​​and the average value of each unit under different equipment operating conditions, and by introducing signal interference correction parameters and ranging uncertainty correction parameters, it can more comprehensively evaluate the coverage effectiveness of each monitoring unit, thereby providing an accurate quantitative basis for the optimal allocation of monitoring resources.

[0113] The calculated coverage effectiveness index values ​​are as follows: High Dynamic Monitoring Area Dynamic monitoring area Low dynamic monitoring area The indicator values ​​are compared and arranged to cover performance indicator values. The smaller the value, the higher the coverage effectiveness and the higher the priority of the unit. Therefore, the comparison result is: Medium dynamic monitoring area Low dynamic monitoring area High dynamic monitoring area Generate a priority list for monitoring resource allocation. For example, the priority list is: medium dynamic monitoring area (priority 1), low dynamic monitoring area (priority 2), and high dynamic monitoring area (priority 3).

[0114] Please see Figure 5 The specific steps for obtaining the integrated results of surface measurement data are as follows:

[0115] S411: Based on the priority list of monitoring resource allocation, extract the map layer tools and spatial positioning components configured in the ecological monitoring platform, parse the boundary coordinate group and layer level parameters of the monitoring area, combine the coordinate calibration results of the spatial positioning component, perform spatial fitting and calibration of the monitoring area layer partition boundary, and obtain the boundary coordinate set of the monitoring area layer.

[0116] Based on the monitoring resource allocation priority list, and according to the configuration of "Medium Dynamic Monitoring Area (Priority 1)," the ecological monitoring platform needs to call the map layer tool and spatial positioning component to complete the regional monitoring modeling. The map layer tool includes a basic topographic map, remote sensing image map, and administrative division map, used to manage the display and overlay of geographic data. The spatial positioning component relies on GPS / BeiDou high-precision positioning capabilities to parse the boundary coordinate group and layer level parameters, ensuring that the map layer accurately covers the monitoring range. For example, the boundary coordinates of monitoring area A are [(113.88, 22.58), (113.90, 22.60), (113.92, 22.58), (113.90, 22.56)], and the layer display order is: topographic map at the bottom, remote sensing image map next, and administrative division map at the top. The spatial positioning component uses differential GPS or RTK technology to calibrate the original coordinates (113.89, 22.59) to (113.8901, 22.5902), achieving sub-meter accuracy. Based on this, a B-spline curve algorithm is used to spatially fit the boundary coordinates, forming a smooth closed boundary. High-resolution satellite imagery is then overlaid for manual or automatic calibration, ultimately generating a set of boundary coordinates for the survey area, enabling precise management and positioning of the region.

[0117] S412: Call the boundary coordinate set of the survey area layer, match the current schedulable resource list and task configuration parameters of the UAV and ground measurement terminal, filter the measurement equipment combination with the operation conditions according to the equipment operation radius and response time window, perform sequential scheduling of each combination according to the priority list, and obtain the measurement equipment scheduling sequence table;

[0118] After accessing the boundary coordinate set of the survey area layer and the boundary coordinate set of monitoring area A, the system needs to match the schedulable resource list and task parameters to perform intelligent screening and scheduling of measurement equipment. The resource list lists the models, quantities, battery levels, and payload capacities of all available UAVs and ground terminals. The task parameters specify requirements for resolution, endurance, and operational response, such as resolution better than 0.1 meters and endurance exceeding 4 hours. Combining the equipment's operating radius and response time, for example, if survey area A is 5 kilometers from the take-off and landing point, the UAV's operating radius is 10 kilometers and its response time is 15 minutes, and the RTK terminal's radius is 2 kilometers and its response time is 5 minutes, the system selects 2 UAVs and 3 RTK terminals that meet the requirement of being reachable within 30 minutes. These are then scheduled and prioritized according to the "Priority 1 principle of the medium dynamic monitoring area," with the optimal performance combination being selected first. For example, UAV A is scheduled to perform aerial surveying tasks in the north of survey area A, while ground RTK terminal B is responsible for collecting data from the southern ground control points, ensuring priority coverage of the core area and completion of the task configuration objectives, ultimately forming a measurement equipment scheduling sequence list.

[0119] S413: Invoke the measurement equipment scheduling sequence list, instruct the equipment to conduct high-frequency surface data acquisition, identify the time-series image sample groups and spatial positioning logs under each survey area layer, and use the formula to analyze the multi-time period pixel attribute changes of the same positioning point in the image sample group: ;

[0120] Calculate the change rate of the attribute of the measurement point, combine the spatial distribution trend of the change rate, fill in the missing attribute sections, overlay the layers and location logs, and generate the integrated result of the surface measurement data.

[0121] in, This represents the rate of change of the attribute at the measurement point. , Let be the image pixel values ​​of the i-th measurement point at times t and t+1, respectively. This indicates the spatial accuracy level of the measurement at the i-th measurement point. Let be the cell length value of the layer corresponding to the i-th measurement point. The number of times the equipment in the measurement area where the i-th measurement point is located is recorded. The number of times the location of the i-th measurement point is missing in the location log, where n represents the number of measurement points collected;

[0122] The surveying equipment scheduling sequence list is invoked. From the scheduling information of UAV A (responsible for the northern part of survey area A), the equipment is instructed to conduct high-frequency surface data acquisition. High-frequency acquisition means multiple data acquisitions within a short period. For example, UAV A is instructed to acquire orthophotos of the northern area of ​​survey area A at a frequency of once per minute. Simultaneously, the ground surveying terminal is instructed to record RTK positioning data every 5 seconds. The temporal image sample groups and spatial positioning logs under each survey area layer are identified. The temporal image sample group is a collection of images acquired at different times within the same survey area. The spatial positioning log records the precise geographic coordinates and timestamps of each image sample acquisition. For example, the temporal image data of the northern part of survey area A is identified. The sample group includes images at times such as 8:00 AM, 8:01 AM, and 8:02 AM, along with corresponding spatial positioning logs. The logs record the center coordinates and GPS time for each image. By analyzing the changes in pixel attributes at the same location point across multiple time periods within the image sample group, the integrated results of the surface measurement data are calculated, completed, and generated. For example, selecting a location point in the northern part of survey area A, whose RGB pixel values ​​in images from different time periods are [120, 130, 110] (time t) and [140, 150, 120] (time t+1), the changes in pixel values ​​are compared to determine whether the land cover type at that point has changed. This step involves the calculation of a formula, which is:

[0123] This formula is used to calculate the rate of change of the attribute of the measurement point. ,in, , The first Measurement point at and The image pixel value at any given time, taking the pixel brightness value as an example, ranges from 0 to 255. Indicates the first The spatial accuracy level of the measurement point acquisition ranges from 1 to 5, where 1 represents high accuracy (error < 0.1 meters) and 5 represents low accuracy (error > 1 meter). The aim is to assign higher weights to high-precision measurement points. For the first The pixel length value of the layer corresponding to the measurement point, that is, the actual ground distance represented by a single pixel (unit: meters / pixel). For the first The number of times the measurement point is recorded by equipment in the measurement area. For example, a certain measurement point was recorded a total of 100 times by drones and ground RTK in the past 24 hours. For the first The number of times a measurement point is missing in the positioning log, i.e., the number of times the data for that point was not successfully recorded due to reasons such as obstruction or signal loss. This indicates the number of measurement points collected, for example, here. Now, let's assign and calculate the specific parameters: Assume data was collected from 3 measurement points: Table 2 shows the attribute data of the measurement points.

[0124] Table 2: Measurement Point Attribute Data

[0125]

[0126] Table 2 lists the pixel values, spatial accuracy level, pixel length value, number of device records, and number of missing values ​​in the positioning log for the three measurement points at different times.

[0127] First, calculate the numerator: ;

[0128] For measurement point 1: For measurement point 2: For measurement point 3: Total numerator: ;

[0129] Calculate the denominator term again: ;

[0130] First part of the denominator: ;

[0131] The second and third parts of the denominator: and The number of times the equipment recorded and the number of times the positioning log was missing in the measurement area where the measurement point is located. ; ;

[0132] Sum of denominators: ;

[0133] Final calculation : ;

[0134] The advantage of this formula is that it comprehensively considers the actual changes in pixel values, the weight of spatial accuracy level on changes, the impact of pixel length on the scale of changes, and the completeness and reliability of data records (through the number of records and the number of missing records). This allows for a more accurate quantification of the rate of change of surface attributes, especially by giving higher weight to high-precision areas and correcting for missing data, making the rate of change more valuable for reference.

[0135] Calculate the rate of change of the attribute of the measurement point. By combining the spatial distribution trend of the change rate, missing attribute segments can be filled in. For example, if some image data of a certain segment is missing, but the change rate of its surrounding area is not, the missing data can be filled in. The values ​​are generally high and show obvious spatial change trends (e.g., rapid transformation from vegetation to bare land). Therefore, interpolation is performed using the change trends of the surrounding areas to complete the attribute data of the missing segments. Layers and location logs are overlaid, and the completed surface attribute data is overlaid with the original map layer and spatial location logs to form a complete, multi-source fused surface measurement data integration result. The result indicates that there is moderate surface attribute change in the survey area, and further analysis of its spatial distribution trend is needed to complete the data.

[0136] Please see Figure 6 The specific steps for obtaining the ecological monitoring platform's annotation and anomaly synchronization control table are as follows:

[0137] S511: Based on the integrated results of surface measurement data, extract the monitoring output values ​​of UAV patrol, satellite cloud remote sensing and tower video equipment, divide the time period according to the continuous cycle, determine whether the node label value is missing in each segment, count the number of time periods in which the node is missing in the cycle, and obtain the number of missing segments of the node period label.

[0138] Based on the integrated results of surface measurement data, monitoring output values ​​from UAV patrols, satellite remote sensing, and tower video equipment are extracted from the integrated results, which include complete surface attribute data and location logs. These output values ​​include image data, location data, and equipment operating status data. Time periods are divided according to continuous cycles; for example, a day's monitoring data is divided into 24 one-hour periods, or a week's monitoring data is divided into 7 24-hour periods. The system then determines whether node annotation values ​​are missing within each period. Node annotation values ​​refer to the automatic or manual annotations made by the monitoring equipment to surface attributes, such as vegetation coverage, water area, and building density. If a certain time period is missing... If image data for a node within a segment fails to be successfully acquired, or if the acquired data quality is too low to be effectively labeled, then the label value for that node is considered missing. For example, in a 1-hour time period, the vegetation coverage label value of the drone patrol node numbered N001 is "N / A" (missing) in its monitoring output value. The number of time periods in which the node has missing labels within the cycle is counted. For example, for node numbered N001, in a 24-hour monitoring cycle, it is found that its label values ​​are missing in the two time periods of 2:00-3:00 AM and 10:00-11:00 AM, then the number of missing time periods is 2, and the number of missing label segments of the node in the cycle is obtained.

[0139] S512: Call the number of missing segments in the node periodic annotation. Based on the difference between the number of missing segments in the node annotation and the set standard annotation interval, determine whether the node exceeds the normal range. Bind the index of the out-of-limit node to the corresponding period number, filter abnormal nodes, record the periodic performance of the out-of-limit node, and obtain the location value of the out-of-limit annotation node.

[0140] The system retrieves the number of missing segments from the periodic annotations of node N001. From this data, where the number of missing segments is 2, it determines whether the node exceeds the normal range based on the difference between the node's missing segment count and the set standard annotation interval. The standard annotation interval difference refers to the maximum number of time periods during which node annotation values ​​are allowed to be missing under normal operating conditions. For example, if the standard annotation interval difference is set to 1, it means that the number of time periods during which node annotation values ​​are missing should not exceed 1 within a monitoring cycle. If node N001 has 2 missing segments, which is greater than the set standard annotation interval difference of 1, then the node is determined to be outside the normal range. The index of the node exceeding the limit is then bound to the corresponding cycle number. The index of the node exceeding the limit is a unique identifier. Identify the node's code. The cycle number is a unique identifier for that monitoring cycle. For example, bind the index of the out-of-limit node N001 to the monitoring cycle 20250601. Filter out abnormal nodes. An abnormal node is a node whose number of missing segments exceeds the normal range. For example, filter out N001 from all monitoring nodes and record the cycle performance of the out-of-limit node. The cycle performance includes the specific missing time period and the reason for the missing segment (such as signal interruption, equipment failure, etc.) within the out-of-limit cycle. For example, record that N001 has a missing time period of [2:00-3:00, 10:00-11:00] within the cycle 20250601, and the reason for the missing segment is unstable signal. Obtain the location value of the out-of-limit labeled node.

[0141] S513: Based on the location value of the over-limit labeled node, call the partition switching threshold, compare the number of consecutive abnormal cycles with the threshold, mark the partition status of the node that meets the switching conditions, summarize the current labeling value of the node and the switching status parameters, and obtain the labeling and anomaly synchronization control table of the ecological monitoring platform.

[0142] Based on the location value of the node exceeding the limit, from the data {"N001": "20250601", "Missing Time Period": "2:00-3:00, 10:00-11:00", "Missing Reason": "Unstable Signal"}, the partition switching threshold is retrieved. The partition switching threshold refers to the number of consecutive abnormal periods that will automatically adjust the monitoring partition or scheduling strategy to which the node belongs. For example, if the partition switching threshold is set to 3, it means that if a node has abnormalities for 3 consecutive monitoring periods, a partition switch will be triggered. The number of consecutive abnormal periods is compared with the threshold. For example, if node N001 is determined to be an over-limit node in the three consecutive monitoring periods of 20250601, 20250602, and 20250603, then its consecutive abnormal period count is 3, which reaches and exceeds the partition switching threshold of 3. The node that meets the switching conditions is marked. If the number of consecutive abnormal cycles reaches or exceeds the partition switching threshold, the partition status of the node is marked as "to be adjusted" or "high risk". For example, the partition status of node N001 is marked as "to be adjusted". The current annotation value of the node and the switching status parameters are summarized. The current annotation value of the node refers to the latest non-missing surface attribute annotation value of the node. The switching status parameters include whether a switch is needed, the reason for the switch, and the recommended partition type to switch to. For example, the current vegetation coverage of node N001 is 85%, and the switching status parameters are {"Need to switch": "Yes", "Reason for switch": "Continuous multiple cycles of abnormality", "Recommended to switch to": "Manual inspection area"}. The ecological monitoring platform annotation and abnormality synchronization control table is obtained. This table records in detail the annotation status, abnormal situation, and decision information on whether a partition switch is needed for all nodes.

[0143] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for acquiring measurement data based on an ecological monitoring platform, characterized in that, Includes the following steps: S1: Based on multi-temporal remote sensing images and UAV patrol data integrated by the ecological monitoring platform, conduct land cover change monitoring, combine green heart area planning data, identify the distribution characteristics and change patterns of land cover types, and generate land surface dynamic monitoring segment division results; S2: Based on the results of the dynamic monitoring section division of the ground surface, extract the image clarity index of satellite cloud remote sensing and the monitoring coverage of the tower video, analyze the image resolution fluctuation range within the section, mark inefficient monitoring nodes, and generate image optimization and adjustment points; S3: Optimize and adjust the locations based on the images, extract drone patrol path data and tower video equipment operation status information, identify the patrol coverage area and monitoring blind spot ratio per unit time, sort equipment performance, and generate a monitoring resource allocation priority list. S4: Based on the monitoring resource allocation priority list, extract the map layer tool and spatial positioning component from the ecological monitoring platform, schedule drones and ground measurement terminals to carry out high-frequency data collection operations, record the spatial distribution and change trend of land cover types in key sections, fill in the gaps in the original measurement data, and generate integrated results of surface measurement data. The specific steps for obtaining the integrated results of the surface measurement data are as follows: S411: Based on the monitoring resource allocation priority list, extract the map layer tool and spatial positioning component configured in the ecological monitoring platform, parse the boundary coordinate group and layer level parameters of the monitoring area, and combine the coordinate calibration results of the spatial positioning component to perform spatial fitting and calibration of the monitoring area layer partition boundary to obtain the monitoring area layer boundary coordinate set; S412: Call the boundary coordinate set of the survey area layer, match the current schedulable resource list and task configuration parameters of the UAV and the ground measurement terminal, filter the measurement equipment combination with the operation conditions according to the equipment operation radius and response time window, perform sequential scheduling of each combination according to the priority list, and obtain the measurement equipment scheduling sequence table; S413: Call the measurement equipment scheduling sequence list, instruct the equipment to carry out high-frequency surface data acquisition, identify the time-series image sample groups and spatial positioning logs under each measurement area layer, calculate the measurement point attribute change ratio value by the multi-time period pixel attribute changes of the same positioning point in the image sample group, combine the spatial distribution trend of the change ratio, fill in the missing attribute segments, overlay the layers and positioning logs, and generate the integrated surface measurement data results. S5: Based on the integrated results of the surface measurement data, extract the monitoring output values ​​of UAV patrol, satellite cloud remote sensing and tower video equipment, analyze the number of missing segments in continuous period, compare with the standard annotation interval, identify the over-limit nodes and annotation anomaly markers and partition switching signals, and obtain the ecological monitoring platform annotation and anomaly synchronization control table. The ecological monitoring platform's annotation and anomaly synchronization control table includes the number of missing annotation cycles, excessive annotation nodes, annotation anomaly status, and partition switching identifiers.

2. The measurement data acquisition method based on the construction of an ecological monitoring platform according to claim 1, characterized in that, The results of the dynamic monitoring of the land surface include the distribution density of land cover types, the frequency of land cover change, the degree of fit with regional planning, and densely populated areas of monitoring blind spots. The image optimization and adjustment points include image resolution thresholds, monitoring coverage deviations, locations of inefficient monitoring nodes, and optimization trigger conditions. The priority list for monitoring resource allocation includes equipment inspection coverage intensity, the proportion of monitoring blind spots, coverage priority order, and blind spot priority order. The integrated results of the land surface measurement data include the completeness of land cover type labeling, coordinates of labeled conflict areas, calibration priority, and label correction markers.

3. The measurement data acquisition method based on the construction of an ecological monitoring platform according to claim 1, characterized in that, The specific steps for obtaining the results of the dynamic surface monitoring zone division are as follows: S111: Based on multi-temporal remote sensing images and UAV patrol data integrated by the ecological monitoring platform, land cover change monitoring is carried out. Combined with green heart area planning data, the monitoring area is divided into multiple segments according to the frequency of land cover type change. The spatial characteristics of land cover type distribution in each segment are analyzed to obtain the mapping value between land cover type and change frequency. S112: Based on the mapping value between the land cover type and the change frequency, extract the adjacent extreme points of the land cover change data, identify the land cover type change trend sequence, compare the change frequency with the spatial distribution benchmark value, filter the segment numbers of the change anomalies, and obtain the sequence of anomalous change segment numbers. S113: Based on the number sequence of the abnormal change section, extract the distribution density data of the land cover type in the section, calculate the time difference between the coverage change and the stable section, and divide the dynamic monitoring section by combining the change rate and frequency to generate the land surface dynamic monitoring section division result.

4. The measurement data acquisition method based on the construction of an ecological monitoring platform according to claim 3, characterized in that, The specific steps for obtaining the image optimization and adjustment points are as follows: S211: Based on the results of the dynamic monitoring of the ground surface, extract the satellite cloud remote image clarity index and the coverage data of the tower video surveillance, identify the image resolution, coverage period and equipment status feedback data of the section, identify the changes in image resolution and coverage differences per unit time, and generate a section image resolution sequence. S212: Based on the segment image resolution sequence, collect the image brightness distribution, coverage offset and device position angle coefficient within each monitoring node. By comparing the difference ratio between the parameters and the resolution fluctuation threshold, calculate the change amplitude value of inefficient nodes, and compare it point by point with the coverage baseline fluctuation amplitude to obtain the image optimization adjustment points.

5. The measurement data acquisition method based on the construction of an ecological monitoring platform according to claim 4, characterized in that, The specific steps for obtaining the monitoring resource allocation priority list are as follows: S311: Optimize and adjust the points according to the image, extract the drone patrol path data and the tower video equipment operation status information, identify the proportion of monitoring blind spots, and analyze the relationship between the patrol coverage area and the proportion of blind spots per unit time by comparing the continuous coverage interval and the proportion of blind spots in the equipment patrol path data, and obtain the unit coverage blind spot relationship interval. S312: Call the unit coverage blind zone relationship interval, calculate the coverage performance index value through the relationship interval value of the differentiated device in the self-running state, compare and arrange the index values, and generate a monitoring resource allocation priority list.

6. The measurement data acquisition method based on the construction of an ecological monitoring platform according to claim 1, characterized in that, The specific steps for obtaining the ecological monitoring platform's annotation and anomaly synchronization control table are as follows: S511: Based on the integrated results of the surface measurement data, extract the monitoring output values ​​of UAV patrol, satellite cloud remote sensing and tower video equipment, divide the time period according to the continuous cycle, determine whether the node label value is missing in each segment, count the number of time periods in which the node is missing in the cycle, and obtain the number of missing segments of the node period label. S512: Call the number of missing segments in the node periodic annotation, determine whether the node exceeds the normal range based on the difference between the number of missing segments in the node annotation and the set standard annotation interval, bind the index of the out-of-limit node with the corresponding period number, filter abnormal nodes, record the periodic performance of the out-of-limit node, and obtain the location value of the out-of-limit annotation node. S513: Based on the location value of the over-limit labeled node, call the partition switching threshold, compare the number of consecutive abnormal cycles with the threshold, mark the partition status of the node that meets the switching conditions, summarize the current label value of the node and the switching status parameters, and obtain the labeling and abnormal synchronization control table of the ecological monitoring platform.

Images