Measurement data acquisition method based on ecological detection platform construction

Abstract

The invention relates to the technical field of environmental monitoring, in particular to a measurement data acquisition method based on ecological detection platform construction, which comprises the following steps: monitoring surface change based on remote sensing and patrol data, identifying ground feature features and dividing sections, analyzing image resolution fluctuation and marking low-efficiency nodes, and optimizing monitoring point location and resource allocation. And scheduling the acquisition equipment to complement the measurement data, generating monitoring output and synchronously labeling the control table. According to the method, the surface change is identified by combining the remote sensing image and patrol data, the dynamic sensing capability of ground feature distribution is improved, node efficiency sorting is realized through path and equipment state analysis, patrol coverage balance is guaranteed, high-frequency data supplementary collection is performed on key areas by using spatial positioning and a layer tool, and the continuous recording capability of the ground feature change is enhanced; and a period monitoring result is integrated to identify a missing section and mark abnormity, so that linkage of data synchronous correction and mark switching is realized, and the real-time tracking and abnormity response capability of ecological changes is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental monitoring, and in particular to a measurement data collection method based on ecological detection platform construction. BACKGROUND

[0002] The technical field of environmental monitoring involves the quantitative and visual monitoring of elements such as air, water, soil, and organisms in the natural environment, and is one of the important basic technologies for ecological environment management and protection. The core items involved in this technical field mainly include real-time collection, long-term tracking and recording, data management and transmission, layout of the monitoring network, and analysis and evaluation methods based on collected data. By constructing a unified data platform and collection system, environmental monitoring technology can systematically support ecological environment status assessment, trend judgment, and management decision support, and is an important basis for green development, resource regulation, and ecological protection policy making.

[0003] Among them, the traditional measurement data collection method refers to a field information acquisition method mainly based on manual investigation, point sampling, and portable instrument reading. This method mainly involves sending personnel to carry out parameter reading in the target area by using single-function detection equipment, and manually recording or locally storing the results.

[0004] The existing technology relies on manual investigation and fixed-point sampling to obtain environmental data. In the face of large geographical span and frequent changes in environmental elements, there are problems of data update lag and inconsistent collection granularity. In particular, in dynamic ecological system areas, it is difficult to achieve real-time continuous monitoring. Due to the limitations of fixed routes and single-function equipment in manual patrol, monitoring blind spots and incomplete data coverage are easily encountered. Some environmental events are delayed in identification or even missed due to insufficient monitoring. Moreover, the traditional method relies on manual data recording, which has the problems of information error accumulation and low data summarization efficiency, and has obvious limitations in building a data system for spatial distribution characteristics and long-term trend analysis. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art and to provide a measurement data collection method based on ecological detection platform construction.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical solution: a measurement data collection method based on ecological detection platform construction, comprising the following steps: S1: Based on the multi-temporal remote sensing image and unmanned aerial vehicle patrol data integrated by the ecological monitoring platform, carry out the ground cover change monitoring, identify the ground object type distribution characteristics and change law in combination with the green core area planning data, and generate the ground dynamic monitoring section division result; S2: According to the ground dynamic monitoring section division result, extract the satellite cloud remote sensing image definition index and the monitoring coverage range of the tower video, analyze the fluctuation range of the image resolution in the section, mark the low-efficiency monitoring node, and generate the image optimization adjustment point; S3: According to the image optimization adjustment point, extract the unmanned aerial vehicle patrol path data and the tower video equipment running state information, identify the patrol coverage area and the monitoring blind area proportion in unit time, sort the equipment efficiency, and generate the monitoring resource allocation priority list; S4: Based on the monitoring resource allocation priority list, extract the map layer tool and spatial positioning component in the ecological detection platform, schedule the unmanned aerial vehicle and the ground measurement terminal to carry out high-frequency data collection operation, record the spatial distribution and change trend of the ground object type in the key section, complete the original measurement data gap, and generate the ground measurement data integration result.

[0007] As a further scheme of the application, the ground dynamic monitoring section division result includes ground object type distribution density, ground cover change frequency, regional planning fit degree, monitoring blind area dense section, the image optimization adjustment point includes image resolution threshold, monitoring coverage range deviation, low-efficiency monitoring node position, optimization trigger condition, the monitoring resource allocation priority list includes device patrol coverage intensity, monitoring blind area proportion, coverage priority order, blind area priority order, and the ground measurement data integration result includes ground object type annotation integrity, annotation conflict area coordinates, calibration priority, and annotation correction identifier.

[0008] As a further scheme of the application, the acquisition step of the ground dynamic monitoring section division result is specifically: S111: Based on the multi-temporal remote sensing image and unmanned aerial vehicle patrol data integrated by the ecological monitoring platform, carry out the ground cover change monitoring, combine the green core area planning data, divide the monitoring area into multiple sections according to the ground object type change frequency, analyze the spatial characteristics of the ground object type distribution in each section, and obtain the ground object type and change frequency mapping value; S112: According to the ground object type and change frequency mapping value, extract the adjacent extreme points of the ground cover change data, identify the ground object type change trend sequence, compare the change frequency and the spatial distribution reference value, screen the section number of abnormal change, and obtain the abnormal change section number sequence; S113: Based on the abnormal change section number sequence, extract the feature type distribution density data in the section, calculate the time difference of covering change and stable section, combine the change speed and frequency to divide the dynamic monitoring section, and generate the ground surface dynamic monitoring section division result.

[0009] As a further scheme of the present application, the acquisition of the image optimization adjustment point is specifically: S211: According to the ground surface dynamic monitoring section division result, extract satellite cloud remote image clarity index and iron tower video monitoring coverage range data, identify image resolution, coverage period and equipment state feedback data of the section, identify image resolution change and coverage range difference in unit time, and generate section image resolution sequence; S212: According to the section image resolution sequence, collect image brightness distribution, coverage range offset and equipment position angle coefficient in each monitoring node, calculate the change amplitude value of the low efficiency node by comparing the difference ratio between the parameters and the resolution fluctuation threshold, and compare with the coverage range reference fluctuation amplitude value point by point, and get the image optimization adjustment point.

[0010] As a further scheme of the present application, the acquisition of the monitoring resource allocation priority list is specifically: S311: According to the image optimization adjustment point, extract unmanned aerial vehicle patrol path data and iron tower video equipment running state information, identify the proportion of monitoring blind area, compare the continuous coverage interval in the equipment patrol path data with the proportion of blind area, analyze the relationship between the patrol coverage area and the proportion of blind area in unit time, and get the unit coverage blind area relationship interval; S312: Call the unit coverage blind area relationship interval, calculate the coverage efficiency index value through the relationship interval value of the differential equipment in the self running state, compare the index value, generate the monitoring resource allocation priority list.

[0011] As a further scheme of the present application, the acquisition of the ground measurement data integration result is specifically: S411: Based on the monitoring resource allocation priority list, extract the map layer tool and spatial positioning component configured in the ecological detection platform, analyze the boundary coordinate group and layer level parameter of the monitoring area, combine the coordinate calibration result of the spatial positioning component, carry out spatial fitting and calibration of the monitoring area layer partition boundary, and obtain the measurement area layer boundary coordinate set; S412: Call the measurement area layer boundary coordinate set, match the current schedulable resource list and task configuration parameter of unmanned aerial vehicle and ground measurement terminal, filter the measurement equipment combination with operation condition according to the equipment operation radius and response time window, sequence dispatch each combination according to the priority list, and obtain the measurement equipment dispatch sequence list; S413: Call the measurement device scheduling sequence table, instruct the device to carry out high-frequency surface data collection, identify the time sequence image sample group and spatial positioning log under each measurement area layer, calculate the attribute change ratio value of the measurement point attribute change through the multi-period pixel attribute change of the same positioning point in the image sample group, complete the attribute missing section combined with the spatial distribution trend of the change ratio, superimpose the layers and positioning log, and generate the surface measurement data integration result.

[0012] As a further scheme of the present application, the method further comprises a step S5: S5: Based on the surface measurement data integration result, extract the monitoring output values of the unmanned aerial vehicle patrol, satellite cloud remote and tower video equipment, analyze the number of missing sections in the continuous period, compare with the standard labeling interval, identify the out-of-limit nodes and abnormal labeling marks and partition switching signals, and obtain the labeling and abnormal synchronization control table of the ecological detection platform. The labeling and abnormal synchronization control table of the ecological detection platform comprises the number of missing labeling periods, the out-of-limit labeling nodes, the abnormal labeling state, and the partition switching identifier.

[0013] As a further scheme of the present application, the step of obtaining the labeling and abnormal synchronization control table of the ecological detection platform is specifically: S511: Based on the surface measurement data integration result, extract the monitoring output values of the unmanned aerial vehicle patrol, satellite cloud remote and tower video equipment, divide the time period according to the continuous period, judge whether the node labeling value is missing in each section, count the number of time periods in which the node is missing in the period, and obtain the number of missing sections of the node period labeling. S512: Call the number of missing sections of the node period labeling, judge whether the node is out of the normal range according to the number of missing sections of the node labeling and the set standard labeling interval difference value, bind the out-of-limit node index and the corresponding period number, filter the abnormal nodes, record the period performance of the out-of-limit node, and obtain the positioning value of the out-of-limit labeling node. S513: Based on the positioning value of the out-of-limit labeling node, call the partition switching threshold value, compare the number of continuous abnormal periods with the threshold value, mark the partition state to which the node belonging to the switching condition, and obtain the labeling and abnormal synchronization control table of the ecological detection platform by summarizing the current labeling value and switching state parameter of the node.

[0014] Compared with the prior art, the present application has the advantages and positive effects that: In the present application, the change of the ground cover state is identified by combining remote sensing image and patrol data, the dynamic perception ability of the ground object distribution in the region is improved, the identification and optimization of the image monitoring resource allocation efficiency are realized based on the index quantization of the definition and coverage range, the real-time sorting of the monitoring node efficiency is formed through the joint analysis of the patrol path and the equipment state, the coverage balance of the patrol operation and data collection is ensured, the high-frequency data supplement of the key area is realized combined with the spatial positioning tool and the map layer tool, the continuous recording ability of the ground object type change is enhanced, the missing section identification and abnormal annotation classification are completed through the integration of the periodic monitoring data output results, the linkage mechanism of the monitoring data synchronous correction and annotation switching is realized, and the real-time tracking of the change trend and the response ability of the abnormal management in the ecological monitoring task are strengthened. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 It is a main step schematic diagram of the present application; Figure 2 It is a flowchart of obtaining the ground dynamic monitoring section division result in the present application; Figure 3 It is a flowchart of obtaining the image optimization adjustment point in the present application; Figure 4 It is a flowchart of obtaining the monitoring resource allocation priority list in the present application; Figure 5 It is a flowchart of obtaining the ground measurement data integration result in the present application; Figure 6 It is a flowchart of obtaining the ecological detection platform annotation and abnormal synchronous control table in the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0017] In the description of the present application, it should be understood that the orientation or position relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, in the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0018] Example one, please refer to Figure 1The application provides a technical solution: a measurement data acquisition method based on ecological detection platform construction, comprising the following steps:

[0019] S1: Based on the multi-temporal remote sensing image and unmanned aerial vehicle patrol data integrated by the ecological monitoring platform, carry out ground cover change monitoring, identify the ground object type distribution characteristics and change law combined with the green core area planning data, and generate ground dynamic monitoring section division results; S2: According to the ground dynamic monitoring section division results, extract the satellite cloud remote image definition index and the monitoring coverage range of the tower video, analyze the image resolution fluctuation range in the section, mark the low-efficiency monitoring node, and generate the image optimization adjustment point; S3: According to the image optimization adjustment point, extract the unmanned aerial vehicle patrol path data and tower video equipment running state information, identify the patrol coverage area and monitoring blind area proportion in unit time, sort the equipment efficiency, 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 in the ecological detection platform, dispatch the unmanned aerial vehicle and the ground measurement terminal to carry out high-frequency data acquisition operation, record the spatial distribution and change trend of the ground object type in the key section, complete the original measurement data gap, and generate ground measurement data integration results; S5: Based on the ground measurement data integration results, extract the monitoring output value of the unmanned aerial vehicle patrol, satellite cloud remote and tower video equipment, analyze the number of missing sections in the continuous period, compare with the standard marking interval, identify the out-of-limit node and abnormal marking and partition switching signal, and obtain the ecological detection platform marking and abnormal synchronous control table.

[0020] The ground dynamic monitoring section division results include ground object type distribution density, ground cover change frequency, regional planning fit degree, and monitoring blind area dense section. The image optimization adjustment point includes image resolution threshold, monitoring coverage range deviation, low-efficiency monitoring node position and optimization trigger condition. The monitoring resource allocation priority list includes device patrol coverage intensity, monitoring blind area proportion, coverage priority order and blind area priority order. The ground measurement data integration results include ground object type marking integrity, marking conflict area coordinates, calibration priority and marking correction mark. The ecological detection platform marking and abnormal synchronous control table includes the number of missing marking periods, out-of-limit marking nodes, abnormal marking state and partition switching mark.

[0021] Please refer to Figure 2 The acquisition steps of the ground dynamic monitoring section division results are as follows: S111: Based on the multi-temporal remote sensing images and unmanned aerial vehicle patrol data integrated by the ecological monitoring platform, carry out the monitoring of the change of land cover, combine the green core area planning data, divide the monitoring area into multiple sections according to the change frequency of ground object type, analyze the spatial characteristics of the distribution of ground object type in each section, and obtain the mapping value of ground object type and change frequency; Based on the multi-temporal remote sensing images and unmanned aerial vehicle patrol data integrated by the ecological monitoring platform, for monitoring the change of land cover in the green core area, the ecological monitoring platform integrates multi-temporal Sentinel-2 remote sensing images (resolution 10 meters) and unmanned aerial vehicle patrol data (0.1 meter resolution), collects images in January, April, July and October, and carries out atmospheric correction and geometric correction, and classifies and identifies vegetation, water, bare land, buildings, roads and other ground object types. Combined with the green core area planning, the ground object change of each functional area is counted, and the conversion frequency of the ground object in one year is evaluated, such as the number of times of monitoring unit changing from "bare land" to "building" can be used to calculate the change frequency, and is divided into stable section (<0.01), moderate change section (0.01~0.1) and high change section (>0.1) according to the frequency. For example, the change frequency of section A (ecological protection area) is 0.005, which is a stable section; the change frequency of section B (agricultural demonstration area) is 0.03, which is a moderate change section; the change frequency of section C (urban-rural junction) is 0.15, which is a high change section. Further analyze the spatial characteristics of the ground object in each section, such as section A is mainly continuous distribution of arbor forest, and section C has high building density and concentrated distribution, and then extract the mapping value of ground object type and change frequency, such as "arbor forest-0.005" and "building-0.15", to obtain the mapping value of ground object type and change frequency.

[0022] S112: According to the mapping value of ground object type and change frequency, extract the adjacent extreme points of the land cover change data, identify the change trend sequence of ground object type, compare the change frequency with the spatial distribution reference value, screen the section number of change anomaly, and obtain the sequence of abnormal change section number; From the above ground feature type and change frequency mapping value, the adjacent extreme value points of the ground cover change data are extracted, the adjacent extreme value points refer to the local highest points and local lowest points on the ground feature type change frequency curve, for example, in a certain section of the historical change frequency data sequence [0.01, 0.02, 0.05, 0.03, 0.08, 0.06, 0.04], the adjacent extreme value points are 0.01 (local low point), 0.05 (local high point), 0.03 (local low point), 0.08 (local high point), identify the ground feature type change trend sequence, by analyzing the change direction and amplitude between adjacent extreme value points, determine whether the ground feature type is in a continuous growth, continuous decline, fluctuating rise or fluctuating decline trend, for example, from 0.01 to 0.05, and then to 0.03, it is identified as a "fluctuating rise" trend, compare the change frequency with the spatial distribution reference value, the spatial distribution reference value is set according to the historical monitoring data and regional planning, and is used to measure the typical value of the spatial distribution of the ground feature type, for example, for vegetation coverage, the reference value is set to 0.8 (i.e. 80%), and the change frequency reference value is set to 0.05 (i.e. 5%), if the vegetation cover change frequency of a certain section is 0.1, which is higher than the reference value 0.05, it is identified as a change anomaly, by comparison, the section numbers of the change anomaly are screened out, the judgment standard of the abnormal section is: if the ground feature type change frequency of a certain section is higher than the change frequency reference value set by the functional area where the ground feature type is located, it is determined as an abnormal section, for example, the building change frequency of section C (urban-rural junction) is 0.15, which is higher than the set change frequency reference value of 0.10, then the number "C003" of section C is screened out, and the abnormal change section number sequence is obtained, for example, the screened abnormal change section number sequence is ["C003", "E005"].

[0023] S113: Based on the abnormal change section number sequence, extract the ground feature type distribution density data in the section, calculate the time difference between the cover change and the stable section, combine the change speed and frequency to divide the dynamic monitoring section, and generate the ground dynamic monitoring section division result; From the abnormal change section number sequence, for example, from the sequence ["C003", "E005"], extract the feature type distribution density data within the section, calculate the time difference between the change section and the stable section, the change section refers to the section with high frequency of feature type change, the stable section refers to the section with low frequency of feature type change, the time difference refers to the length of time from the significant change of feature type to its stability, for example, in the monitoring period, section C003 has a significant increase in building area from January, and the building area tends to be stable in July, the time difference is 6 months, while the stable section A001 has been stable, the time difference is 0, combine the change speed and frequency to divide the dynamic monitoring section, the change speed refers to the amount of change of feature type per unit time, the frequency refers to the number of times of feature type change per unit time, for example, section C003 has an increase of 5 hectares of building area in 6 months, the change speed is 0.83 hectares / month, and the change frequency is 10 times, the section with change speed higher than 0.5 hectares / month and change frequency higher than 8 times is divided into high dynamic monitoring section, the section with change speed between 0.1 hectares / month and 0.5 hectares / month and change frequency between 3 and 8 times is divided into medium dynamic monitoring section, the section with change speed lower than 0.1 hectares / month and change frequency lower than 3 times is divided into low dynamic monitoring section, for example, section C003 meets the division standard of high dynamic monitoring section, and finally generates the surface dynamic monitoring section division result.

[0024] Please refer to Figure 3 The acquisition steps of the image optimization adjustment point are specifically: S211: According to the surface dynamic monitoring section division result, extract satellite cloud remote image clarity index and iron tower video monitoring coverage range data, identify image resolution, coverage period and equipment state feedback data of the section, identify image resolution change and coverage range difference per unit time, and generate section image resolution sequence; According to the ground dynamic monitoring section division result, the satellite cloud remote image clarity index is extracted from the high dynamic monitoring section (such as HD001, HD002), the index is calculated based on edge sharpness and contrast, the range is 0-100, the higher the value represents the clearer the image, such as HD001 is 85, HD002 is 78. At the same time, the coverage and state of the tower video monitoring equipment are extracted, such as tower No. 1 coverage radius 500 meters, state normal, resolution 0.5 meters, coverage period 1 hour. Further, the satellite image resolution (such as 10 meters / pixel) and update period (such as 3 days) of the section are compared and analyzed, whether the monitoring resolution fluctuates, and combined with the theoretical and actual coverage range of the tower monitoring, the coverage difference is calculated. For example, the theoretical coverage area of tower No. 1 is 78.5 hectares, and the actual coverage area is only 70 hectares due to shielding, the difference is 8.5 hectares, and finally the image resolution sequence of the section is generated, for example, the satellite image resolution sequence of HD001 section is [10 meters, 10 meters, 10 meters, …], and the tower video resolution sequence is [0.5 meters, 0.5 meters, 0.5 meters, …].

[0025] S212: According to the image resolution sequence of the section, the image brightness distribution, coverage range offset and device position angle coefficient in each monitoring node are collected, and by comparing the difference between the parameters and the resolution fluctuation threshold, the formula is used: ; The change amplitude value of the inefficient node is calculated, and compared with the coverage range reference fluctuation amplitude point by point to judge the image optimization adjustment point; Among them, represents the change amplitude value of the inefficient node, represents the actual installation angle of the current monitoring node device, represents the theoretical optimal installation angle of the device of the node, represents the ground coverage direction offset angle coefficient of the monitoring node, represents the image resolution collection deviation value of the kth section, represents the average value of the image resolution deviation value of all collected sections, represents the set image resolution fluctuation threshold, represents the historical brightness offset average value of the current monitoring node, represents the number of sampled image sections; According to the resolution sequence of the image segments, the image brightness distribution, coverage range offset and device position angle coefficient in each monitoring node are collected. The image brightness distribution refers to the statistical characteristics of the pixel brightness value in the image, such as the average brightness and brightness standard deviation. The coverage range offset refers to the geographical position deviation between the actual coverage area of the monitoring device and the planned coverage area. The device position angle coefficient refers to the deviation angle of the monitoring device relative to its optimal installation position, which is obtained through the built-in gyroscope of the device. For example, for a certain monitoring node, the average brightness value of the image brightness distribution is 120 (range 0-255), the coverage range offset is 5 meters, and the device position angle coefficient is 0.95. By comparing the difference ratio between the parameters and the resolution fluctuation threshold, the resolution fluctuation threshold is set as the upper limit of the image resolution change rate. If this threshold is exceeded, it indicates that the resolution fluctuation is abnormal. This threshold is obtained through statistical analysis of historical data. For example, through statistical analysis of the data of 1000 monitoring nodes in the past year, nodes with a resolution fluctuation exceeding 5% are identified as abnormal. Therefore, the resolution fluctuation threshold is set to 0.05, i.e. 5% of the resolution fluctuation. The threshold value is derived from statistical analysis of the monitoring image resolution changes in the past year. Through experimental verification, when the threshold value is set to 0.05, the resolution abnormality caused by device aging, environmental interference and other factors can be effectively identified. According to experience, this threshold is set to 0.05, i.e. 5% of the resolution fluctuation. The image brightness distribution, coverage range offset and device position angle coefficient of the node are calculated with the set resolution fluctuation threshold. For example, if the image resolution change rate is 0.08, the difference ratio is 0.08 / 0.05=1.6. At the same time, this step also involves formula calculation. The formula is: This formula is used to calculate the low-efficiency node change amplitude value G, where represents the actual installation angle of the monitoring node device (unit: degree), represents the theoretical optimal installation angle of the device of the node (unit: degree), represents the ground coverage direction offset angle coefficient of the monitoring node, which quantifies the influence of the actual installation angle on the ground coverage direction, and its value ranges from 0 to 1, represents the image resolution collection deviation value of the kth segment (unit: pixel), which represents the deviation between the actual collection resolution and the ideal resolution, represents the average value of the image resolution deviation values of all collection segments (unit: pixel), represents the set image resolution fluctuation threshold, which is set to 0.05. This threshold is obtained through statistical analysis of historical data. When the resolution fluctuation exceeds 5%, it is considered to be a problem, The historical brightness offset average value representing the current monitoring node, used to measure the impact of ambient light changes on image quality, for example, by collecting the image brightness values of this node at noon every day in the past month, calculating the average deviation from the standard brightness value, the value is set to 10, m represents the number of sampled image sections, for example, here m=3; Now make specific parameter assignment and calculation: assume that a monitoring node: θ=65 degrees, actual installation angle, real-time acquisition through built-in sensor of the device; θ0=60 degrees, the theoretical optimal installation angle, provided by the device manufacturer or determined according to on-site survey; , ground coverage direction offset angle coefficient, obtained by regression analysis of installation angle and actual coverage area shape, for example, when the angle deviates by 5 degrees, the ground coverage shape deviates by 20%, then ; , image resolution collection deviation value of the first section, indicating that the actual resolution of this section deviates from the ideal resolution by 2 pixels; , image resolution collection deviation value of the second section; , image resolution collection deviation value of the third section; , average value of image resolution deviation values of all collected sections; , set image resolution fluctuation threshold value; , historical brightness offset average value of the current monitoring node; , number of sampled image sections; Substitute the values into the formula: First, calculate the numerator: ; ; the sum of the numerator is ; Then calculate the denominator: ; Finally, calculate : ; The benefits of this formula are that by considering the deviation of the actual installation angle of the device from the optimal angle, the ground coverage direction offset, and the actual collection deviation of the image resolution and the historical brightness offset, etc., the inefficiency of the monitoring device is comprehensively evaluated, making the evaluation of image quality more comprehensive and accurate; Calculate the change amplitude value of the inefficient node , and compare this value with the coverage range reference fluctuation amplitude point by point, the coverage range reference fluctuation amplitude is set to 0.5, this reference value is determined by statistical analysis of a large amount of historical operation data of monitoring nodes and combined with expert experience, when the change amplitude value of the inefficient node is greater than 0.5, it indicates that the node needs image optimization adjustment, when Less than or equal to 0.5, indicating that the node is running normally, for example, the low node change range value of a certain monitoring node , greater than the coverage range reference fluctuation range value 0.5, the node is determined to need to be adjusted, and the image optimization adjustment point is obtained, which indicates that the image quality of the monitoring node has significant problems and needs to be optimized.

[0026] Please refer to Figure 4 , the acquisition step of the monitoring resource allocation priority list is specifically: S311: According to the image optimization adjustment point, extract the unmanned aerial vehicle patrol path data and tower video equipment running state information, identify the monitoring blind area proportion, compare the continuous coverage interval in the device patrol path data and the blind area proportion, analyze the relationship between the patrol coverage area and the blind area proportion in unit time, and obtain the unit coverage blind area relationship interval; According to the image optimization adjustment point, identify the monitoring blind area proportion, extract the unmanned aerial vehicle patrol path data and tower video equipment running state information from the image optimization adjustment point data, the unmanned aerial vehicle patrol path data contains the flight trajectory, patrol time, and geographical coordinates of the photographed photo of each patrol of the unmanned aerial vehicle, and the tower video equipment running state information contains the online state, fault code, and last maintenance time of each tower video equipment, for example, the unmanned aerial vehicle patrol path corresponding to the optimization adjustment point A is [P1, P2, P3], the patrol time is June 1, and the corresponding tower video equipment ID is T001, its running state is normal, identify the monitoring blind area proportion, the monitoring blind area refers to the area that is not effectively covered by the unmanned aerial vehicle patrol and the tower video monitoring, by analyzing the unmanned aerial vehicle patrol path data and the tower video monitoring coverage range data, superimposing the coverage areas of the two, calculating the proportion of the area of the area not covered in the total monitoring area, comparing the continuous coverage interval in the device patrol path data and the blind area proportion, the continuous coverage interval refers to the geographical range that can be continuously and uninterruptedly covered by the unmanned aerial vehicle or the tower video monitoring device in one patrol or operation, for example, the unmanned aerial vehicle continuously covers a 5-kilometer area on a path, and the blind area proportion is 0.1, analyze the relationship between the patrol coverage area and the blind area proportion in unit time, by statistically analyzing the patrol data in a period of time (for example, one month), calculate the total patrol coverage area of the unmanned aerial vehicle and the tower video monitoring in unit time, and combine the blind area proportion data in the same period to analyze the correlation between the two, and obtain the unit coverage blind area relationship interval, which describes the corresponding relationship between the patrol coverage area and the monitoring blind area proportion in unit time under a certain patrol strategy.

[0027] S312: Call the unit coverage blind area relationship interval, and use the formula: ; Calculate coverage performance index values, compare and rank the index values, and generate a priority list for monitoring resource allocation; 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; 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: 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. The unit with the number corresponds to the ranging uncertainty correction parameter of the differential equipment running state, which is used to quantify the influence of ranging error on coverage, The total number of differential equipment in self-running state, for example, here (Drones and fixed towers), is the index number of differential equipment, is the index number of the unit, for example, the index numbers of high dynamic monitoring area, medium dynamic monitoring area and low dynamic monitoring area are 1, 2 and 3 respectively; Now make specific parameter assignment and calculation: assume there are two kinds of differential equipment: drones and fixed towers , and three monitoring units (sections): high dynamic monitoring area , medium dynamic monitoring area , and low dynamic monitoring area , .

[0028] Table 1: Relationship interval values and correction parameters of monitoring units under different equipment running states

[0029] Table 1 lists the relationship interval values, signal interference correction parameters and ranging uncertainty correction parameters of different monitoring units under the running state of drones and fixed towers equipment; First, calculate the arithmetic mean of the relationship interval values of all units under each differential equipment: ; ; Now, take the unit with number 1 (high dynamic monitoring area) as an example, calculate the sum of the numerator items under the two kinds of equipment: For (drones): ; For (fixed towers): ; The sum of the numerators is: ;

[0030] Now calculate the denominator: ; The denominator is: ; For Computing : ; Computing and ; For (high dynamic monitoring area): the molecule is: ; ; The sum of the molecules is: ; The denominator is:

[0031] The denominator is ; ; For (low dynamic monitoring area): The molecule is: ; ; The sum of the molecules is: ; The denominator is:

[0032] The denominator is ; ; The advantage of the formula is that by considering the deviation of the interval value and the average value of each unit under different equipment operating conditions, and introducing signal interference correction parameters and ranging uncertainty correction parameters, the coverage efficiency of each monitoring unit can be more comprehensively evaluated, thereby providing accurate quantitative basis for the optimization of monitoring resource allocation; The calculated coverage efficiency index values are: high dynamic monitoring area , medium dynamic monitoring area , and low dynamic monitoring area . Comparing the index values, the smaller the coverage efficiency index value , the higher the coverage efficiency of the unit, and the higher the priority. Therefore, the comparison result is: medium dynamic monitoring area > low dynamic monitoring area > high dynamic monitoring area , generating a monitoring resource allocation priority list, 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).

[0033] Please refer to Figure 5 , the steps for obtaining the integrated results of surface measurement data are: S411: Based on the monitoring resource allocation priority list, extract the map layer tool and spatial positioning component configured in the ecological detection platform, parse the boundary coordinate set and layer level parameter of the monitoring area, combine the coordinate calibration result of the spatial positioning component, perform spatial fitting and calibration of the monitoring area layer partition boundary, and obtain the measurement area layer boundary coordinate set; Based on the monitoring resource allocation priority list, according to the configuration of "dynamic monitoring area (priority 1)", the ecological detection platform needs to call the map layer tool and spatial positioning component to complete the area monitoring modeling. The map layer tool includes basic topographic map, remote sensing image map and administrative division map, which is used to manage the display and superposition of geographic data. The spatial positioning component relies on the high-precision positioning capability of GPS / Beidou to parse the boundary coordinate set and layer level parameter, and ensures 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 next, and 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) with a precision of sub-millimeter. On this basis, the boundary coordinates are spatially fitted by B-spline curve algorithm to form a smooth closed boundary, and high-resolution satellite images are superimposed for manual or automatic calibration, finally generating the measurement area boundary coordinate set, realizing the accurate management and positioning of the area space.

[0034] S412: Call the measurement area layer boundary coordinate set, match the current schedulable resource list and task configuration parameter of the unmanned aerial vehicle and ground measurement terminal, filter the measurement equipment combination with operation conditions according to the equipment operation radius and response time window, sequence schedule each combination according to the priority list, and obtain the measurement equipment scheduling sequence list; The boundary coordinate set of the survey area layer is called, and after calling the boundary coordinate set of the monitoring area A, the schedulable resource list is matched with the task parameters, and the intelligent screening and scheduling of the measurement equipment are performed. The resource list lists the models, quantities, power and load capacity of all available unmanned aerial vehicles and ground terminals, and the task parameters specifically require resolution, endurance and operation response, such as resolution better than 0.1 meters, endurance more than 4 hours. Combined with the equipment operation radius and response time, such as the survey area A being 5 kilometers away from the take-off and landing point, the unmanned aerial vehicle operation radius being 10 kilometers, the response time being 15 minutes, the RTK terminal radius being 2 kilometers, and the response being 5 minutes, the system selects 2 unmanned aerial vehicles and 3 RTK terminals that meet the requirement of being reachable within 30 minutes, and schedules and sorts them according to the priority 1 principle of the “medium dynamic monitoring area”, and selects the most optimal combination first. For example, unmanned aerial vehicle A is dispatched to the north of survey area A to perform aerial surveying, and ground RTK terminal B is responsible for the south ground control point collection, to ensure that the core area is preferentially covered and the task configuration target is completed, and finally a measurement equipment scheduling sequence list is formed.

[0035] S413: Call the measurement equipment scheduling sequence list, instruct the equipment to carry out high-frequency ground data collection, identify the time sequence image sample group and spatial positioning log under each survey area layer, and through the change of pixel attribute of the same positioning point in the image sample group in multiple time periods, use the formula: ; Calculate the measurement point attribute change ratio value, combine the spatial distribution trend of the change ratio, complete the attribute missing section, superimpose the layer and the positioning log, and generate the ground measurement data set integration result; wherein, represents the measurement point attribute change ratio value, , and represent the image pixel values of the ith measurement point at time t and t+1 respectively, represents the spatial accuracy level collected by the ith measurement point, is the pixel length value of the layer corresponding to the ith measurement point, is the number of equipment records of the ith measurement point in the survey area, is the number of missing positions of the ith measurement point in the positioning log, and n represents the number of collected measurement points; Call the measurement device scheduling sequence table, from the scheduling information of UAV A (responsible for the north of measurement area A), instruct the device to carry out high-frequency ground data collection, high-frequency collection means multiple data acquisition in a short time, for example, instruct UAV A to collect orthophoto images of the north of measurement area A at a frequency of once per minute, while instructing the ground measurement terminal to record RTK positioning data every 5 seconds, identify the time sequence image sample group and spatial positioning log under each measurement area layer, the time sequence image sample group is a set of images collected at different time points in the same measurement area, and the spatial positioning log records the accurate geographic coordinates and timestamps of each image sample collection, for example, identify the time sequence image sample group of the north of measurement area A, which includes images at time points of 8:00, 8:01, 8:02, etc., and the corresponding spatial positioning log, which records the center coordinates and GPS time of each image, through the change of pixel attribute of the same positioning point in the image sample group at different periods, the ground measurement data set is integrated, and the results are operated, completed and generated, for example, select a positioning point in the north of measurement area A, the RGB pixel value of this point in images at different periods is [120, 130, 110] (t time) and [140, 150, 120] (t+1 time), by comparing the change of pixel value, it is judged whether the ground object type of the point changes, this step involves formula calculation, the formula is: The formula is used to calculate the measurement point attribute change ratio value , wherein , are the image pixel values of the first measurement point at and time, here the brightness value of the pixel is taken as an example, the range is 0-255, indicates the spatial accuracy level of the first measurement point, the value range is 1 to 5, 1 represents high accuracy (error <0.1 meter), and 5 represents low accuracy (error >1 meter), the purpose is to give higher weight to high-precision measurement points, is the pixel length value of the layer corresponding to the first measurement point, that is, the actual ground distance represented by a single pixel (unit: meter / pixel), is the number of records of the first measurement point in the measurement area device, for example, a certain measurement point has been recorded 100 times in total by UAV and ground RTK in the past 24 hours, is the number of missing positions of the first measurement point in the positioning log, that is, the number of times the point data is not successfully recorded due to shielding, signal loss, etc., indicates the number of measurement points collected, for example, here Now, the specific parameter assignment and calculation are carried out: it is assumed that the data of three measuring points are collected: Table 2: Measuring point attribute data

[0036] Table 2: Measuring point attribute data

[0037] Table 2 lists the pixel values, spatial accuracy levels, pixel length values, equipment record times and missing times in the positioning log of the three measuring points at different times; First, the numerator is calculated: ; For measuring point 1: ; for measuring point 2: ; for measuring point 3: ; the sum of the numerator: ; Then, the denominator is calculated: ; The first part of the denominator: ; The second and third parts of the denominator: and are the equipment record times and the missing times in the positioning log of the measuring area where the measuring point is located, ; ; The sum of the denominator: ; Finally, the : ; The advantage of this formula is that it comprehensively considers the actual change of the pixel value, the weight of the spatial accuracy level on the change, the influence of the pixel length on the change scale, and the integrity and reliability of the data record (through the record times and the missing times), so that the change rate of the surface attribute can be more accurately quantified, especially for high-precision areas, a higher weight is given, and the data missing is corrected, so that the change rate is more valuable for reference.

[0038] Calculate the measuring point attribute change rate value , combined with the spatial distribution trend of the change rate, complete the attribute missing section, for example, the image data of a certain section exists partial missing, but the change rate If the values are generally high and show obvious spatial variation trends (e.g., rapid transition from vegetation to bare land), interpolate the variation trends of the surrounding area to complete the attribute data of the missing section, superimpose the layers and the positioning log, superimpose the completed surface attribute data on the original map layer and the spatial positioning log, and form a complete, multi-source integrated surface measurement data result. The result shows that there is moderate surface attribute change in the survey area, and further analysis of the spatial distribution trend is needed to complete the data.

[0039] Please refer to Figure 6 The acquisition steps of the ecological detection platform label and abnormal synchronous control table are as follows: S511: Based on the integrated surface measurement data result, extract the monitoring output values of the unmanned aerial vehicle patrol, satellite cloud remote sensing and tower video equipment, divide the time period according to the continuous period, judge whether the node label value is missing in each period, count the number of time periods in which the node is missing in the period, and obtain the number of missing periods of the node in the period. Based on the integrated surface measurement data result, extract the monitoring output values of the unmanned aerial vehicle patrol, satellite cloud remote sensing and tower video equipment from the integrated result containing complete surface attribute data and positioning log. The monitoring output values include image data, positioning data, equipment running state data, etc. Divide the time period according to the continuous period, for example, divide the monitoring data of one day into 24 one-hour time periods, or divide the monitoring data of one week into 7 24-hour time periods. Judge whether the node label value is missing in each period. The node label value refers to the automatic or manual labeling of the surface attribute by the monitoring equipment, such as vegetation coverage, water area, building density, etc. If the image data of a node cannot be successfully collected or the collected data quality is too low to be effectively labeled in a certain time period, it is considered that the label value of the node is missing, for example, in a one-hour time period, the monitoring output value of the unmanned aerial vehicle patrol node N001 has a vegetation coverage label value of “N / A” (missing). Count the number of time periods in which the node is missing in the period, for example, for the node N001, in a 24-hour monitoring period, it is found that the label value is missing in the time period from 2 a.m. to 3 a.m. and from 10 a.m. to 11 a.m. The number of missing time periods is 2. Get the number of missing periods of the node in the period.

[0040] S512: Call the number of missing periods of the node in the period, and judge whether the node is out of the normal range according to the number of missing periods of the node and the set standard label interval difference. Bind the index of the out-of-limit node with the corresponding period number, filter the abnormal nodes, record the period performance of the out-of-limit node, and get the positioning value of the out-of-limit label node. The number of missing period labels of the calling node is called. From the data of the node N001 with 2 missing period labels, it is judged whether the node is out of the normal range according to the difference between the number of missing labels of the node and the standard label interval. The standard label interval difference refers to the maximum number of time periods allowed for the node to miss labels under normal operating conditions. For example, if the standard label interval difference is set to 1, it means that the number of time periods for which the node misses labels should not exceed 1 within a monitoring period. If the missing period number of node N001 is 2, which is greater than the set standard label interval difference 1, it is determined that the node is out of the normal range. The out-of-limit node index and the corresponding period number are bound. The out-of-limit node index is a code that uniquely identifies the node, and the period number is a unique identifier of the monitoring period. For example, the index of the out-of-limit node N001 is bound to the monitoring period 20250601. Abnormal nodes are screened. An abnormal node refers to a node whose missing label period number is out of the normal range. For example, N001 is selected from all monitoring nodes. The period performance of the out-of-limit node is recorded. The period performance includes the specific missing time period of the node within the out-of-limit period and the missing reason (such as signal interruption, device failure, etc.). For example, the missing time period of N001 within the period 20250601 is recorded as [2:00-3:00, 10:00-11:00], and the missing reason is signal instability. The out-of-limit label node positioning value is obtained.

[0041] S513: Based on the out-of-limit label node positioning value, the partition switching threshold is called. The number of consecutive abnormal periods is compared with the threshold value. The partition state to which the node belonging to the switching condition is marked. The current label value of the node and the switching state parameter are summarized. The ecological detection platform label and abnormal synchronous control table is obtained. Based on the ultra-limit labeling node positioning value, from the data of {“N001”:“20250601”,“missing time period”:“2:00-3:00, 10:00-11:00”,“missing reason”:“signal instability”}, the partition switching threshold is called, which refers to when the number of continuous abnormal periods reaches the threshold, the monitoring partition or scheduling strategy to which the node belongs will be automatically adjusted, for example, the partition switching threshold is set to 3, which means if a node appears abnormal for 3 consecutive monitoring periods, the partition switching will be triggered, the number of continuous abnormal periods is compared with the threshold, for example, node N001 is judged as an ultra-limit node in the consecutive three monitoring periods of 20250601, 20250602 and 20250603, and the number of continuous abnormal periods is 3, which reaches and exceeds the partition switching threshold 3, the state of the partition to which the node belongs that meets the switching condition is marked, if the number of continuous abnormal periods reaches or exceeds the partition switching threshold, the state of the partition to which the node belongs is marked as“to be adjusted”or“high risk”, for example, the state of the partition to which node N001 belongs is marked as“to be adjusted”, the current labeling value of the node and the switching state parameter are summarized, the current labeling value of the node refers to the latest, non-missing ground attribute labeling value of the node, the switching state parameter includes whether to switch, the switching reason, the recommended partition type to switch to, etc., for example, the current vegetation coverage of node N001 is 85%, the switching state parameter is {“need to switch”:“yes”,“switching reason”:“continuous multi-period abnormality”,“recommended switching to”:“manual inspection area”}, and the ecological detection platform labeling and abnormality synchronization control table is obtained, which records in detail the labeling state, abnormality, and decision information whether to switch the partition of all nodes.

[0042] The above is only a preferred embodiment of the present application, and does not limit the present application in other forms. Any skilled person in the art can modify or change the above disclosed technical content to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application still belongs to the protection scope of the present application.

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 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.

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 monitoring zone division of the land surface 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 5, characterized in that, 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 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.

7. The measurement data acquisition method based on the construction of an ecological monitoring platform according to claim 1, characterized in that, The method also includes step S5: 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.

8. The measurement data acquisition method based on the construction of an ecological monitoring platform according to claim 7, 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.

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