A mapping method and system based on remote sensing big data analysis

Abstract

This invention relates to a mapping method and system based on remote sensing big data analysis, comprising: acquiring multimodal data of the mapping area; verifying images through a dual-channel pollutant detection mechanism to obtain pollution source characteristic indicators; the multimodal data includes three-dimensional environmental data, real-time sensor data, and biological behavior characteristic data; determining whether pollution exists in the mapping area based on the pollution source characteristic indicators; if pollution exists, initializing dust diffusion parameters of the pollution diffusion model based on the pollution source characteristic indicators; constructing a biological feature library based on the multimodal data; correcting the pollution diffusion model using the motion trajectory characteristics of the biological feature library; identifying biological interference types; and outputting surface mapping data; extracting vegetation adsorption characteristics based on the multimodal data; optimizing dust diffusion parameters based on the vegetation adsorption characteristics; outputting predictive control strategies; and updating the biological feature library.

Description

Technical Field

[0001] This invention relates to the field of remote sensing mapping technology, and in particular to a mapping method and system based on remote sensing big data analysis. Background Technology

[0002] Remote sensing mapping technology, as a core means of Earth observation, has been widely applied in fields such as resource surveys, environmental monitoring, urban planning, and disaster assessment. With the deployment of remote sensing constellations both domestically and internationally, we have entered the era of remote sensing big data, and data acquisition capabilities have seen a leap forward in both spatiotemporal resolution and spectral dimensions. This provides an unprecedented data foundation for acquiring high-precision, dynamic geographic information.

[0003] Traditional remote sensing mapping methods primarily rely on the spectral, texture, and shape features of optical or radar imagery, using pixel-level or object-level classification algorithms to identify land features and generate mapping products. However, these traditional methods have gradually revealed their inherent limitations when faced with complex natural environments and dynamic changes: traditional mapping methods typically treat the land surface as a relatively static target, failing to fully consider the significant impact of dynamic biological factors such as animal activity on the mapping results. For example, dust raised by large-scale animal migrations and activities, or changes in surface vegetation caused by bird colonies, can create artifacts or interference noise on remote sensing images that are highly similar to real geographical features or polluted areas. Existing technologies lack the ability to effectively distinguish between instantaneous, dynamic changes caused by biological behavior and real, persistent changes in the land surface, leading to decreased mapping accuracy and even erroneous conclusions.

[0004] Therefore, there is an urgent need for a new and intelligent remote sensing mapping method that can break through the framework of traditional static analysis, deeply integrate multimodal remote sensing big data with biological behavioral characteristics, dynamically correct physical models, accurately remove interference information, and ultimately form predictive and closed-loop decision support capabilities to solve the problems of high-precision mapping and precise prevention and control in complex environments. Summary of the Invention

[0005] This invention addresses the technical problems existing in the prior art by providing a mapping method and system based on remote sensing big data analysis. It constructs a collaborative analysis model of pollution diffusion and biological interference through multimodal data fusion, enabling accurate prediction of pollution range and dynamic removal of biological artifacts. Combined with vegetation adsorption regulation and sound-dust coupling inversion technology, it generates predictive hierarchical prevention and control strategies, ultimately forming a closed-loop optimization mechanism that significantly improves mapping accuracy and operational efficiency in complex environments.

[0006] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A mapping method based on remote sensing big data analysis, characterized in that it includes:

[0007] S1: Acquire multimodal data of the surveyed area, verify the images through a dual-channel pollutant detection mechanism, and obtain pollution source characteristic indicators; the multimodal data includes three-dimensional environmental data, real-time sensor data, and biological behavior characteristic data;

[0008] S2: Determine whether there is pollution in the surveyed area based on the pollution source characteristic indicators. If there is pollution, initialize the dust diffusion parameters of the pollution diffusion model based on the pollution source characteristic indicators. Predict the impact range through the pollution diffusion model, then execute the drone targeted cleaning strategy and output the decision verification effect.

[0009] S3: Construct a biometric database based on multimodal data, use the motion trajectory features of the biometric database to correct the pollution diffusion model, identify the type of biological disturbance, and output surface mapping.

[0010] S4: Extract vegetation adsorption characteristics based on multimodal data, optimize dust diffusion parameters according to vegetation adsorption characteristics, predict the intensity of biological disturbance and locate artifact areas through sound-dust coupling inversion, perform biological disturbance stripping and purification surface mapping, output predictive prevention and control strategies and update the biological feature database.

[0011] Furthermore, the pollution source characteristic indicators include: dust concentration distribution indicators, biological disturbance intensity indicators, vegetation adsorption efficiency indicators, and pollution diffusion kinetic parameters;

[0012] The dust concentration distribution index is a dust concentration heat map generated from three-dimensional environmental data and real-time sensor data:

[0013] ,

[0014] in, This represents the dust concentration value. The number of dust pixels per unit area. The area of ​​the scanned region. The average density of aerosols is represented by the concentration gradient on the thermogram based on the dust concentration values.

[0015] The bio-disturbance intensity index is the bio-interference coefficient calculated from animal activity trajectory density and voiceprint energy:

[0016] ,

[0017] in, The biological interference coefficient. The area of ​​the heat source region. This represents the total area of ​​the surveyed region. This represents the peak energy level of the voiceprint. This is the maximum energy reference value. , These are the corresponding weighting coefficients;

[0018] The vegetation adsorption efficiency index is the dust adsorption capacity level calculated based on vegetation type and canopy density.

[0019] The pollution diffusion dynamics parameters include the baseline value of dust diffusion radius and the diffusion rate coefficient:

[0020] ,

[0021] in, This is the baseline value for the dust dispersion radius. The wind speed is three-dimensional, with an exponent of 1.2 reflecting the nonlinear enhancement effect of wind speed on diffusion. t represents the duration of diffusion in hours. This represents the initial concentration of the aerosol. It reflects the marginal diminishing law of concentration decay;

[0022] ,

[0023] in, The diffusion rate coefficient is... For the standard deviation of dust particle size, The median particle size of the dust. This represents the rate of change in concentration.

[0024] Furthermore, the pollution diffusion model includes: a dust diffusion calculation engine, a biological interference correction module, a vegetation adsorption optimizer, and a multi-source coupling prediction interface;

[0025] The dust diffusion calculation engine is used to calculate the spatiotemporal distribution of dust based on the dust diffusion radius benchmark value and the diffusion rate coefficient, and output the diffusion trajectory shape.

[0026] The biological disturbance correction module receives the biological disturbance intensity index and dynamically calculates the trajectory offset with a period of 0.5 seconds.

[0027] ,

[0028] in, The trajectory offset angle, Based on the offset angle;

[0029] The vegetation adsorption optimizer is used to compress the diffusion range and correct the concentration decay curve based on the vegetation adsorption efficiency index.

[0030] The multi-source coupling prediction interface is used to output a pollution impact range map that integrates biological disturbance and vegetation regulation.

[0031] Furthermore, the biometric database includes: a motion trajectory feature database, a voiceprint spectrum feature database, a species interaction relationship database, and a behavioral cycle pattern database;

[0032] The motion trajectory feature library stores the motion trajectory patterns of organisms and their spatiotemporal distribution parameters;

[0033] The voiceprint spectrum feature library records the voiceprint energy spectrum characteristics and frequency band distribution patterns of different species;

[0034] The species interaction database quantifies the coefficients of synergistic and repulsive interactions among species:

[0035] ,

[0036] in, For the synergy coefficient, The degree of overlap in trajectory space. , For the corresponding weighting coefficients, This refers to the voiceprint interlock index.

[0037] ,

[0038] in, The repulsion coefficient, This represents the difference in dust concentration. This represents the peak dust concentration. This is a species-specific constant;

[0039] The behavioral cycle pattern library marks the seasonal and diurnal periodic activity patterns of biological activities.

[0040] Furthermore, the types of biological interference include: motion trajectory interference, acoustic frequency band interference, species interaction interference, and periodic activity interference;

[0041] The motion trajectory interference is based on the spatiotemporal distribution parameters of the motion trajectory feature library to identify the animal activity trajectory pattern;

[0042] The voiceprint frequency band interference is based on the species voiceprint characteristics identified by the frequency band distribution pattern of the voiceprint spectrum feature library.

[0043] The species interaction interference is a multi-species interaction pattern identified based on the action coefficients of a species interaction database. By calling the species interaction database, pre-stored cooperation coefficients and repulsion coefficients are obtained. When a high level of cooperation or high level of repulsion is detected, a biological cooperation offset vector is obtained.

[0044] ,

[0045] in, This is the biological collaborative offset vector. Let be the concentration gradient vector. Weights for behavioral cycles;

[0046] The periodic activity disturbance is based on the peak periods of biological activity identified from the activity patterns in the behavioral cycle pattern library.

[0047] Furthermore, the pollution diffusion model predicts the impact range by: analyzing the morphology of dust diffusion trajectories, associating linear diffusion with the movement of ungulates, sheet-like diffusion with the activity of gregarious birds, and vortex-like diffusion with flight take-off and landing behavior; calculating the animal population base based on the positive correlation between diffusion rate and peak dust concentration; correcting the animal population base by comparing the frequency band distribution patterns of the voiceprint spectrum feature library, and outputting a species list and interaction level.

[0048] Furthermore, the biological interference type also includes species cooperative interference decoupling: obtaining the biological cooperative offset vector by calling the action coefficient of the species interaction relation library based on the species list; separating the composite artifact layer generated by species interaction in the three-dimensional spatiotemporal cube through the biological cooperative offset vector; and driving the dust pollution compensation model to perform local surface texture restoration on the separated artifact area based on the concentration decay curve output by the pollution diffusion model.

[0049] Furthermore, the optimization of dust dispersion parameters includes: identifying the vegetation type of the surveyed area using a hyperspectral imager, and allocating an adsorption efficiency coefficient η based on the vegetation canopy density; optimizing dust dispersion parameters based on the η value includes: compressing the dust dispersion radius.

[0050] ,

[0051] in, This refers to the radius of dust dispersion after compression. The original diffusion radius;

[0052] Correcting the concentration decay curve:

[0053] ,

[0054] in, For concentration decay rate, This refers to the aerosol settling rate;

[0055] The peak energy of the acoustic signature and the dust concentration were collected, and the number of animals was calibrated using the acoustic-dust coupling inversion formula.

[0056] ,

[0057] in, For the number of animals, The baseline number of animals corresponding to the peak voiceprint energy. This refers to the dust concentration.

[0058] Furthermore, the predictive control strategy includes: calculating the scope of impact based on the number and morphological type of animals.

[0059] ,

[0060] in, For the scope of influence, The affected area was determined by different animal types, and corrected using the adsorption efficiency coefficient η; the affected area was divided into red, yellow, and green zones, and predictive tiered prevention and control measures were implemented; The mapping relationship between the value, η value and the scope of influence is stored in the behavior cycle pattern library as a prevention benchmark for the next cycle of mapping.

[0061] A mapping system based on remote sensing big data analysis includes:

[0062] Multi-source sensing module: used to acquire three-dimensional environmental data, real-time sensor data and biological behavior characteristic data of the surveyed area, verify images through a dual-channel pollutant detection mechanism, and output pollution source characteristic indicators;

[0063] Pollution prevention and control decision module: connected to the multi-source sensing module, including a pollution diffusion model engine and a drone-targeted actuator. The pollution diffusion model engine is used to initialize dust diffusion parameters and predict the impact range, and the drone-targeted actuator is used to execute cleaning strategies and output decision verification results.

[0064] Biometrics processing module: includes a biometrics database construction unit and an interference type identifier. The biometrics database construction unit constructs a motion trajectory feature database, a voiceprint spectrum feature database, a species interaction relationship database, and a behavior cycle pattern database based on multimodal data. The interference type identifier uses the biometrics database to correct the pollution diffusion model and identify motion trajectory interference, voiceprint frequency band interference, species interaction interference, and periodic activity interference.

[0065] Prediction and optimization execution module: includes a vegetation adsorption optimizer, an acoustic-dust coupling inversion unit and an artifact stripping engine. The vegetation adsorption optimizer extracts vegetation adsorption characteristics and optimizes dust diffusion parameters. The acoustic-dust coupling inversion unit predicts the intensity of biological interference and locates artifact areas. The artifact stripping engine performs biological interference stripping and purification of the surface mapping.

[0066] Closed-loop prevention and control management module: includes a hierarchical strategy generator and a biometric database update interface. The hierarchical strategy generator outputs predictive prevention and control strategies for red, yellow, and green zones, and the biometric database update interface writes the prevention and control mapping relationship into the behavior cycle pattern library to form a predictive closed loop. Attached Figure Description

[0067] Figure 1 This is a flowchart of a surveying and mapping method based on remote sensing big data analysis according to the present invention;

[0068] Figure 2This is a diagram illustrating the architecture of a surveying and mapping system based on remote sensing big data analysis, as described in this invention. Detailed Implementation

[0069] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0070] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0071] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0072] Example 1, as Figure 1 As shown, a mapping method based on remote sensing big data analysis:

[0073] S1: Acquire multimodal data of the surveyed area, verify the images through a dual-channel pollutant detection mechanism, and obtain pollution source characteristic indicators; the multimodal data includes three-dimensional environmental data, real-time sensor data, and biological behavior characteristic data;

[0074] Specifically, before the remote sensing equipment arrives at the mapping point, a swarm of low-altitude drones is dispatched to pre-scan a 50-meter radius around the mapping point. The drones, equipped with lidar, acquire three-dimensional wind field data, capturing subtle changes in wind speed and direction. Miniature meteorological sensors collect cloud thickness and aerosol concentration data to construct a three-dimensional environmental dataset. Image verification is performed using a dual-channel pollutant detection mechanism. Channel 1 (optical detection) uses a hyperspectral camera to acquire visible and near-infrared images of the ground surface, identifying dust-covered areas and pollution diffusion boundaries, generating a dust radiation distribution map. Channel 2 (radiation detection) uses an infrared sensor to capture biological heat source signals, analyzing the characteristics of dust heat radiation generated by animal activity and marking the coordinates of the heat sources. A ring-shaped acoustic array is deployed to capture the animal acoustic spectrum in the 50Hz-20kHz frequency band, combining it with the heat source coordinates to invert animal activity trajectories and generate a biological behavior feature dataset. Spatial registration is performed on the optical and radiation data; verification is considered valid when the overlap of the pollution-covered areas between the two channels is ≥85%.

[0075] The pollution source characteristic indicators include: dust concentration distribution indicators and pollution diffusion kinetic parameters;

[0076] Specifically, the spatial distribution of dust particles is obtained through lidar scanning, and the reflectance of the surface in the 0.4-2.5μm band is analyzed using hyperspectral imagery to generate dust concentration distribution indicators.

[0077] ,

[0078] in, The value represents the dust concentration (μg / m³). The number of dust pixels per unit area. The area of ​​the scanned region is (㎡). The average density of the aerosol is shown; the output thermogram indicates the concentration gradient: red zone: >500 μg / m³, yellow zone: 200-500 μg / m³, green zone: <200 μg / m³.

[0079] The three-dimensional wind speed was calculated using a lidar three-dimensional wind field model, and the diffusion radius baseline was calculated by real-time sensor network monitoring of diffusion duration and inversion of initial aerosol concentration from hyperspectral imagery.

[0080] ,

[0081] in, This is the baseline value for the dust dispersion radius. The wind speed is three-dimensional, with an exponent of 1.2 reflecting the nonlinear enhancement effect of wind speed on diffusion. t represents the duration of diffusion in hours. This represents the initial concentration of the aerosol. It reflects the marginal diminishing law of concentration decay.

[0082] The diffusion rate coefficient was calculated by inverting the standard deviation of dust particle size from lidar point cloud, calculating the median particle size using hyperspectral co-liquid lidar, and using the concentration change rate monitored by a real-time sensor network at high frequency.

[0083] ,

[0084] in, The diffusion rate coefficient is... For the standard deviation of dust particle size, The median particle size of the dust. This represents the rate of change in concentration.

[0085] S2: Determine whether there is pollution in the surveyed area based on the pollution source characteristic indicators. If there is pollution, initialize the dust diffusion parameters of the pollution diffusion model based on the pollution source characteristic indicators. Predict the impact range through the pollution diffusion model, then execute the drone targeted cleaning strategy and output the decision verification effect.

[0086] Specifically, if dust concentration distribution index If the concentration is >200 μg / m³, the surveyed area is considered contaminated; otherwise, skip the subsequent steps. This process is only executed when contamination is detected, and the initialized parameter set is output. , as input for pollution diffusion model prediction.

[0087] The pollution diffusion model includes: a dust diffusion calculation engine;

[0088] Specifically, the pollution diffusion model is driven by initial parameters, and the spatiotemporal distribution of dust is calculated using a dust diffusion calculation engine, based on... and Generate diffusion trajectory morphology (linear, sheet-like, or vortex-like). Predict the pollution impact range Ry (meters), and only execute the drone-targeted cleaning strategy when the predicted impact range Ry > 500 meters: the drone swarm is deployed in a targeted manner according to the impact range map; priority is given to red zones > yellow zones, focusing on high-concentration areas; the cleaning path is updated based on real-time sensor data to ensure coverage of diffusion hotspots; a cleaning coverage report is output, with a cleaning ratio of ≥90% in the target area. Compare the changes in pollution source characteristic indicators before and after cleaning, and calculate the cleaning efficiency using a dual-channel pollutant detection mechanism.

[0089] ,

[0090] in, For cleaning efficiency, The dust concentration before cleaning. This represents the dust concentration after cleaning.

[0091] Finally, a decision verification report is output, including the cleaning effect ( (≥85% is considered valid), impact range correction data and recommended surveying and mapping adjustments for the next cycle.

[0092] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0093] This application significantly improves the accuracy and efficiency of pollution detection through multimodal data collaborative acquisition and a dual-channel verification mechanism. Real-time three-dimensional environmental data captures the spatial distribution and diffusion dynamics of dust particles, while a real-time sensor network monitors the concentration change rate at high frequencies. Combined with hyperspectral imagery, the reflectance of the 0.4-2.5μm band of the Earth's surface is retrieved to generate a dust concentration heat map, quantifying the regional dust load. Channel 1 (optical detection) identifies dust coverage boundaries through hyperspectral imagery, while Channel 2 (radiation detection) utilizes an infrared sensor to eliminate interference from biological heat sources. Validity is determined when the overlap of polluted areas between the two channels is ≥85%, mitigating the risk of misjudgment from a single data source. Based on the dust particle size parameters retrieved from lidar point clouds and the hyperspectral concentration retrieval results, diffusion dynamics parameters and diffusion rate coefficients are directly calculated, driving the dust diffusion calculation engine to output the diffusion trajectory morphology (linear / sheet / vortex) and predict the impact range. Only when the predicted impact range Ry > 500 meters is a drone cleaning strategy triggered to focus on highly polluted areas, and subsequent mapping cycles are dynamically optimized through a closed-loop decision verification process.

[0094] Example 2 is an optimization of Example 1, which only uses dual-channel verification to avoid false positives and does not actively model and analyze the dynamic impact of biological behavior on diffusion paths.

[0095] S3: Construct a biometric database based on multimodal data, use the motion trajectory features of the biometric database to correct the pollution diffusion model, identify the type of biological disturbance, and output surface mapping.

[0096] The biometric database includes: a motion trajectory feature database, a voiceprint spectrum feature database, and a behavioral cycle pattern database;

[0097] Specifically, historical remote sensing images of the target area were collected, and typical movement patterns of individual species were labeled. For flying species, straight migration trajectories and circling take-off and landing patterns were recorded; for terrestrial species, foraging paths in grasslands and routes to and from water sources were recorded. A directional microphone array was deployed to collect species-specific voiceprints, recording the dominant frequency band of bird calls in the 2000-8000Hz range and the low-frequency vibrations of ungulates' treading in the 20-200Hz range. A voiceprint behavior mapping was established: high-frequency voiceprints (≥5000Hz) were associated with flight status, and low-frequency voiceprints (≤100Hz) were associated with ground movement. Observational records were integrated, such as daily cycles of waterfowl dawn flocks (05:00-07:00) and deer herds moving at dusk (17:00-19:00), seasonal cycles of migratory birds in spring (March-May), and mammals migrating to water sources during the dry season (November-February).

[0098] The pollution source characteristic indicators also include: biological disturbance intensity indicators;

[0099] Specifically, based on the infrared thermal source coordinates to locate animal activity hotspots and the acoustic energy peaks captured by the acoustic array, a biological disturbance intensity index is generated:

[0100] ,

[0101] in, This is the biological interference coefficient, with a value range of [0, 1]. The higher the value, the stronger the interference. The area of ​​the heat source region. This represents the total area of ​​the surveyed region. This represents the peak energy level of the voiceprint. This is the maximum energy reference value. , For the corresponding weighting coefficients, when the organism is an ungulate, =0.6, =0.4; when the species is a bird, =0.4, =0.6.

[0102] The pollution diffusion model also includes: a biological interference correction module;

[0103] Specifically, the biological interference correction module uses... Dynamically adjust the trajectory offset of the diffusion path:

[0104] ,

[0105] in, This is the trajectory offset angle, used to adjust the diffusion direction; Based on offset angle, linear diffusion =0.5rad, sheet-like diffusion =1.2rad, vortex diffusion =2.0 rad. Recalculated every 0.5 seconds. ,when rate of change An emergency correction is triggered when the value is greater than 0.1.

[0106] Matching the behavioral cycle template for the current time period, if it is during the species' active period (such as bird dawn flocks), will... ×1.5, during dormancy (e.g., noon) then ×0.3.

[0107] The types of biological interference include: motion trajectory interference, acoustic frequency band interference, and periodic activity interference;

[0108] Specifically, motion trajectory interference refers to the disturbance caused by the specific trajectory patterns formed by species activities. Based on the spatiotemporal distribution parameters of the motion trajectory feature database, this interference includes bird and mammal activities. Birds migrate in straight lines or take off and land in circles, creating vortex-like dust diffusion artifacts in remote sensing images, which are easily misidentified as clouds or air pollution. Mammal activities move along the ground in zigzag patterns of foraging in grasslands or in straight lines to and from water sources, producing linear diffusion trajectories that resemble real roads or rivers in images. The dust stirred up by animal footprints obscures the true surface texture, resulting in a 15%-25% distortion rate in topographic features in the mapping data.

[0109] Voiceprint frequency interference is spectral interference caused by animal-specific voiceprint frequency bands. Based on the frequency distribution patterns of the voiceprint spectral feature database, bird characteristics include calls in the 2000-8000Hz main frequency band, with high-frequency voiceprints (≥5000Hz) associated with flight patterns, creating patchy thermal radiation noise in the image. Mammal characteristics include low-frequency vibrations in the 20-200Hz stepping range, with continuous low-frequency voiceprints (≤100Hz) associated with ground movement, leading to a decrease in local image contrast. Voiceprint resonance causes abnormal thermal radiation in dusty areas, reducing the overlap of dual-channel pollutant detection to below 70%.

[0110] Periodic activity disturbances are temporal disturbances caused by the periodic patterns of biological behavior. Based on the identification of activity patterns in a behavioral periodicity pattern library, the periodicity intensity index is calculated:

[0111] ,

[0112] in, It is a periodic intensity index. For real-time activity density, For the same period in history, when An emergency response is triggered when the value is >1.2.

[0113] Real-time acquired 3D environmental data, heat source trajectories, and acoustic signature spectrum data of the surveyed area are verified through a dual-channel pollutant detection mechanism and then input into the biometric processing module. This module calls upon a motion trajectory feature library to match animal path morphology, an acoustic signature spectrum feature library to compare species-specific frequency bands, and a behavioral cycle pattern library to verify active period patterns. The interference intensity is quantified based on the bio-disturbance intensity index, driving the pollution diffusion model to correct trajectory deviations, and active period interference is weighted based on a periodic intensity index. The identification results are classified into motion trajectory interference, acoustic signature frequency band interference, and periodic activity interference, and an interference heat map is output, annotating the interference red area (…). >0.7), Interference yellow zone (0.3≤ ≤0.7), interference green zone ( <0.3). Subsequently, interference stripping is performed, the dynamic trajectory layer is separated by a three-dimensional spatiotemporal cube, the missing surface texture is filled by the neighboring frame repair technique, and the noise is suppressed by the acoustic frequency domain filtering. Finally, a cleaned surface survey map and biological activity marker map are generated.

[0114] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0115] This application overcomes the limitations of traditional surveying and mapping's passive response to environmental disturbances by employing a dynamic modeling mechanism based on biological behavioral characteristics. In the dimension of biological disturbance identification, it integrates motion trajectory analysis, acoustic signature frequency band analysis, and verification of periodic activity patterns to construct a three-level collaborative judgment system. This significantly improves the accuracy and timeliness of disturbance type identification in complex environments, effectively solving the problem of misjudgment such as bird vortex artifacts and linear diffusion of mammals. A real-time offset compensation mechanism driven by biological disturbance intensity indicators is introduced, combined with an adaptive weighting strategy based on periodic activity, enabling the pollution diffusion model to possess dynamic optimization capabilities against biological disturbances, greatly improving the consistency between predicted trajectories and the real environment. Through spatiotemporal cube stripping, neighboring frame restoration, and frequency domain noise reduction techniques, dynamic artifacts are efficiently suppressed while ensuring the authenticity of surface texture restoration, outputting high-fidelity surveying and mapping products that combine geometric accuracy and ecological value.

[0116] Example 3, while Example 2 solved the problem of single-species biological interference, only dealt with the activities of a single species and could not cope with the complex interference caused by the synergy of multiple species. It also lacked the ability to decouple the superimposed artifacts generated by the interaction of multiple species. This example is a further optimization based on Example 2.

[0117] The pollution diffusion model predicts the impact range by: analyzing the morphology of dust diffusion trajectories, linking linear diffusion to ungulate movement, sheet-like diffusion to gregarious bird activity, and vortex-like diffusion to flight take-off and landing behavior; calculating the animal population base based on the positive correlation between diffusion rate and peak dust concentration; correcting the animal population base by comparing the frequency band distribution patterns of the voiceprint spectrum feature library, and outputting a species list and interaction level.

[0118] Specifically, by analyzing the continuous linear distribution characteristics of dust in space (aspect ratio > 5:1), we can correlate it with the directional behavior of ungulates; when these animals move, their hooves continuously raise dust, forming a strip-shaped pollution zone that closely matches their migration path. Identifying the diffuse distribution of dust in a two-dimensional plane (dispersion > 60%), we can correlate it with the flocking activities of gregarious birds; the superposition effect of airflow generated by bird wing flapping causes dust to diffuse in irregular sheets. Detecting the spiral spatial distribution of dust (vortex curvature radius < 10 meters), we can correlate it with the take-off and landing behavior of flying organisms; the wingtip vortices of flying organisms trigger local cyclones, forming characteristic vortex pollution clouds.

[0119] Real-time monitoring of dust dispersion rate using a pollution dispersion model. and concentration peak Establish a formula for calculating the base number of animals:

[0120] ,

[0121] in, As the base number of animals, The species-specific coefficient is 0.05 for ungulates and 0.03 for birds.

[0122] The real-time acquired speaker spectrum data is matched with the frequency band distribution patterns of the speaker spectrum feature database to calculate the speaker energy peak offset:

[0123] ,

[0124] in, This represents the peak offset of the voiceprint energy. The peak value of the acoustic signature is collected by the sensor in real time. This is the baseline energy value for the corresponding species in the acoustic signature spectral feature database. If the real-time energy peak value is higher than the baseline value in the database, the baseline value needs to be adjusted upwards; conversely, it should be adjusted downwards during the dormant period.

[0125] The animal population size is dynamically adjusted based on frequency band comparison results. For high-frequency band (≥5000Hz) detection, the population size is increased by 1.2-1.5 times. For low-frequency band (≤100Hz) detection, the population size is decreased by 0.3-0.7 times. In case of frequency band mismatch, an emergency correction mechanism is triggered, using interpolation compensation based on historical frequency band distribution patterns.

[0126] The corrected animal population base is used to generate a species list and interaction levels. The species list includes the identified species and their key attributes, including species type, population estimate, and activity status.

[0127] The species interaction database also includes: a species interaction database;

[0128] Specifically, the enhanced effect of interspecies mutualistic behavior on pollution diffusion is quantified using a species interaction database:

[0129] ,

[0130] in, For the synergy coefficient, The degree of overlap in trajectory space. , For the corresponding weights, the default is... =0.6、 =0.4, when birds are in flocks, the influence of voiceprints is enhanced. =0.4、 =0.6, This refers to the voiceprint interlock index. ,in, The overlapping frequency band voiceprint energy is the sum of the energy within the intersection of the active frequency bands of the two species' voiceprints. The baseline energy for the strongest species is determined by taking the baseline value of the species with the highest peak voiceprint energy among those participating in the interlocking process. The time-series correlation coefficient is the Pearson correlation coefficient used to calculate the fluctuation of voiceprint energy between two species over time, with a value range of [0, 1]. The higher the value, the more likely the voiceprints of the two species fluctuate with the same frequency and time and the same trend, indicating a strong synergy and a high probability of causing composite dust artifacts.

[0131] Quantifying the inhibitory effect of interspecies conflict behavior on diffusion:

[0132] ,

[0133] in, The repulsion coefficient, This represents the difference in dust concentration. This represents the peak dust concentration. This is a species-specific constant, set by historical observations.

[0134] Interaction level quantifies the intensity of synergistic or repulsive interactions between species, and is divided into three levels: low, medium, and high. Low synergistic level (…) <0.3: Corresponds to weak mutualistic behavior, low interference intensity, and the interaction level is judged as low interaction; medium cooperation level (0.3≤ ≤0.7): Corresponds to moderate symbiotic behavior, moderate interference intensity, and a medium level of interaction synchronization; high level of synergy ( >0.7): Corresponds to strong cooperative behavior, with significant interference intensity, and the interaction level is upgraded to high interaction. Low repulsion level ( <0.3: Corresponds to weak conflict behavior, interference is negligible, and the interaction level is low interaction; medium rejection level (0.3≤ ≤0.7): Corresponds to moderate adversarial behavior; interference requires dynamic correction; interaction level is synchronous with moderate interaction; high rejection level ( >0.7): This corresponds to strong rejection behavior, intense interference requiring an urgent response, and the interaction level is set as high interaction.

[0135] The types of biological interference also include species interaction interference and species synergistic interference decoupling: the biological synergistic offset vector is obtained by calling the action coefficient of the species interaction relation library based on the species list; the composite artifact layer generated by species interaction is separated in the three-dimensional spatiotemporal cube by the biological synergistic offset vector; based on the concentration decay curve output by the pollution diffusion model, the dust pollution compensation model is driven to perform local surface texture restoration on the separated artifact area.

[0136] Specifically, species interaction interference is a multi-species interaction pattern identified based on the role coefficients of the species interaction relation database. It manifests as a composite pollution diffusion artifact caused by synergistic or repulsive behaviors. By calling the species interaction relation database, the pre-stored synergistic and repulsive coefficients are obtained, and the species interaction interference is output as either "high synergistic" or "high repulsive", and then labeled on the biological activity heatmap.

[0137] When a high level of cooperation or high level of repulsion is detected, the biological cooperation offset vector is obtained by calling the action coefficients from the species interaction database based on the species list:

[0138] ,

[0139] in, This is the biological collaborative offset vector. This is the concentration gradient vector output by the pollution diffusion model. The weights for the behavior cycle are: dry season = 1.2, nighttime = 1.5. If the value is >0, then collaboration dominates and the diffusion path expands outward; If the value is less than 0, the diffusion path will shrink, indicating a rejection of the dominant pathway.

[0140] The composite artifact layer is located in a three-dimensional coordinate system. The interference layer is separated according to the vector direction. The cooperating artifacts expand along the ∇C direction to form an artifact layer with an expanded diffusion range. The repulsive artifacts are compressed in the opposite ∇C direction to form a local high-concentration interference layer. The output is a pure surface layer and a stripped biological interference layer.

[0141] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0142] This application analyzes the morphology of dust diffusion trajectories using a pollution diffusion model. Linear diffusion is associated with the movement of ungulates, sheet-like diffusion with the activity of gregarious birds, and vortex-like diffusion with the take-off and landing behavior of flying organisms. Based on the positive correlation between diffusion rate and peak dust concentration, an initial animal population is calculated, and then the population is corrected by comparing it with the frequency band distribution pattern of the voiceprint spectrum feature library: the trigger population for high-frequency voiceprints is increased by 1.2-1.5 times, and the trigger population for low-frequency voiceprints is decreased by 0.3-0.7 times. The final output is a list of species and interaction levels. The species interaction database supports interference decoupling through quantified synergy and repulsion coefficients. The synergy coefficient is calculated from the trajectory space overlap degree and the voiceprint interlocking index, while the repulsion coefficient is generated based on the dust concentration difference and species-specific constants. The interaction levels are divided into low, medium, and high. When a high synergy or high repulsion level is detected, the system generates a biological synergy offset vector. This vector separates the composite artifact layer in a three-dimensional spatiotemporal cube. The synergy artifact extends along the ∇C direction, and the repulsion artifact is compressed inversely, outputting a clean surface layer and a stripped biological interference layer.

[0143] Example 4 is an optimization of Example 3, which only relied on biological characteristics to correct pollution diffusion and did not consider the natural inhibition of dust by vegetation adsorption.

[0144] The pollution source characteristic indicators also include: vegetation adsorption efficiency indicators;

[0145] Specifically, vegetation types are identified based on hyperspectral imaging, and are divided into broad-leaved forests, coniferous forests, and shrubs. Canopy density is analyzed using laser point cloud analysis. A point cloud density ≥ 800 points / m² is considered high density, 400 ≤ point cloud density < 800 points / m² is considered medium density, and point cloud density < 400 points / m² is considered low to medium density. The adsorption efficiency of vegetation is determined by the combined effect of vegetation type and canopy density, forming a three-level adsorption system with a corresponding dynamic efficiency coefficient η. Broadleaf forests are rated as Level 1 adsorption under high-density canopy conditions, with an efficiency coefficient η ranging from [0.85 to 0.95]. Their large leaf structure and dense canopy can efficiently intercept dust particles and significantly reduce the pollution diffusion radius. Coniferous forests are rated as Level 2 adsorption under medium-density canopy conditions, with an η ranging from [0.65 to 0.75]. Their needle-like leaves and medium canopy density provide a balanced adsorption capacity. Shrubs are rated as Level 3 adsorption under low-density canopy conditions, with an η ranging from [0.35 to 0.45]. Their sparse canopy and small plants result in limited adsorption capacity.

[0146] S4: Extract vegetation adsorption characteristics based on multimodal data, optimize dust diffusion parameters according to vegetation adsorption characteristics, predict the intensity of biological disturbance and locate artifact areas through sound-dust coupling inversion, perform biological disturbance stripping and purification surface mapping, output predictive prevention and control strategies and update the biological feature database.

[0147] The pollution diffusion model also includes: a vegetation adsorption optimizer;

[0148] Specifically, the vegetation type of the surveyed area is identified using a hyperspectral imager, and dust dispersion parameters are optimized based on the η value, including: reducing the dust dispersion radius.

[0149] ,

[0150] in, This refers to the radius of dust dispersion after compression. The original diffusion radius;

[0151] Correcting the concentration decay curve:

[0152] ,

[0153] in, For concentration decay rate, This refers to the aerosol settling rate;

[0154] The peak energy of the acoustic signature and the dust concentration were collected, and the number of animals was calibrated using the acoustic-dust coupling inversion formula.

[0155] ,

[0156] in, For the number of animals, As the base number of animals, This refers to the dust concentration.

[0157] The predictive control strategy includes: based on animal numbers And the scope of influence of morphological type calculation:

[0158] ,

[0159] in, For the scope of influence, The unit area of ​​influence is set according to the species' movement patterns to determine the area of ​​influence for different types of animals: ungulates are characterized by linear movement and long-distance dispersal. =50 (m² / individual); the movement characteristics of gregarious birds are sheet-like clusters and short-distance bursts of dispersal. =30 (m² / individual); the motion characteristics of flight-type aircraft are vortex takeoff and landing, and localized high-intensity diffusion. =20 (m² / individual). The actual influence range is corrected based on the adsorption efficiency coefficient η.

[0160] .

[0161] The pollution diffusion model also includes: a multi-source coupling prediction interface;

[0162] Specifically, according to Values ​​are used to classify prevention and control levels and implement differentiated responses. Areas ≥1000m² are designated as red zones, requiring immediate targeted cleaning using drones, focusing on areas with dust concentrations >500μg / m³; areas ≤500μg / m³ are also addressed. Areas smaller than 1000m² are designated as yellow zones; real-time sensors track the spread trend and prepare cleaning resources. Areas smaller than 500m² are designated as green zones and marked as natural purification zones, subject to periodic remote sensing verification.

[0163] Based on the biological interference layer, artifact regions are located using acoustic-dust coupling inversion. A biological interference heatmap is generated through acoustic signature frequency band analysis and dust concentration inversion, marking the interference red areas. In a three-dimensional spatiotemporal cube, cooperative artifacts are extended and stripped along the concentration gradient ∇C direction, while repulsive artifacts are compressed and stripped against the ∇C direction.

[0164] The clean surface layer and artifact layer are restored using a neighboring frame inpainting technique to fill in texture loss caused by stripping. Fourier transform analysis of the acoustic spectrum filters out high-frequency noise (such as bird acoustic signatures ≥5000Hz) to ensure the clean surface layer is free of interference. The final output is a clean surface mapping map and an updated biomarker map.

[0165] Key parameters in the prevention and control process, such as species type and season, , , Write it into the behavior cycle pattern library, and in the next cycle under the same scenario, call it directly. , , The mapping relationship is triggered when the actual error exceeds 10%. Values ​​recalibrated.

[0166] Based on the aforementioned patented method, this application also provides a mapping system based on remote sensing big data analysis, such as... Figure 2 As shown, the system includes:

[0167] Multi-source sensing module: used to acquire three-dimensional environmental data, real-time sensor data and biological behavior characteristic data of the surveyed area, verify images through a dual-channel pollutant detection mechanism, and output pollution source characteristic indicators;

[0168] Pollution prevention and control decision module: connected to the multi-source sensing module, including a pollution diffusion model engine and a drone-targeted actuator. The pollution diffusion model engine is used to initialize dust diffusion parameters and predict the impact range, and the drone-targeted actuator is used to execute cleaning strategies and output decision verification results.

[0169] Biometrics processing module: includes a biometrics database construction unit and an interference type identifier. The biometrics database construction unit constructs a motion trajectory feature database, a voiceprint spectrum feature database, a species interaction relationship database, and a behavior cycle pattern database based on multimodal data. The interference type identifier uses the biometrics database to correct the pollution diffusion model and identify motion trajectory interference, voiceprint frequency band interference, species interaction interference, and periodic activity interference.

[0170] Prediction and optimization execution module: includes a vegetation adsorption optimizer, an acoustic-dust coupling inversion unit and an artifact stripping engine. The vegetation adsorption optimizer extracts vegetation adsorption characteristics and optimizes dust diffusion parameters. The acoustic-dust coupling inversion unit predicts the intensity of biological interference and locates artifact areas. The artifact stripping engine performs biological interference stripping and purification of the surface mapping.

[0171] Closed-loop prevention and control management module: includes a hierarchical strategy generator and a biometric database update interface. The hierarchical strategy generator outputs predictive prevention and control strategies for red, yellow, and green zones, and the biometric database update interface writes the prevention and control mapping relationship into the behavior cycle pattern library to form a predictive closed loop.

[0172] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0173] This application enhances the accuracy and anti-interference capability of remote sensing mapping in complex environments by combining vegetation adsorption regulation mechanisms with acoustic-dust coupling inversion technology. The system quantifies vegetation adsorption efficiency through hyperspectral imaging and canopy density analysis, dynamically integrating it into a pollution diffusion model to intelligently correct dust diffusion range and concentration decay curves. The strong adsorption effect in densely vegetated areas actively compresses the pollution diffusion radius, reduces dust settling rate, and weakens artifact intensity caused by biological activity at its source. Based on the coupled inversion of acoustic signature spectrum and dust concentration, the system further achieves quantitative prediction of biological interference intensity and precise location of artifact areas: by analyzing the correlation between acoustic signature frequency band characteristics and dust concentration changes, the animal population base is dynamically calibrated, and combined with cooperative offset vectors to decouple complex interference caused by multi-species interactions, dynamic artifact layers are efficiently separated in a three-dimensional spatiotemporal cube. Then, neighboring frame restoration and frequency domain noise reduction techniques are used to restore the true surface texture, improving the geometric fidelity and ecological reliability of the mapping products.

[0174] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0175] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0176] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0177] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0178] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0179] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0180] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A surveying and mapping method based on remote sensing big data analysis, characterized in that, include: S1: Acquire multimodal data of the surveyed area, verify the images through a dual-channel pollutant detection mechanism, and obtain pollution source characteristic indicators; the multimodal data includes three-dimensional environmental data, real-time sensor data, and biological behavior characteristic data; S2: Determine whether there is pollution in the surveyed area based on the pollution source characteristic indicators. If there is pollution, initialize the dust diffusion parameters of the pollution diffusion model based on the pollution source characteristic indicators. Predict the impact range through the pollution diffusion model, then execute the drone targeted cleaning strategy and output the decision verification effect. S3: Construct a biometric database based on multimodal data, use the motion trajectory features of the biometric database to correct the pollution diffusion model, identify the type of biological disturbance, and output surface mapping. S4: Extract vegetation adsorption characteristics based on multimodal data, optimize dust diffusion parameters based on vegetation adsorption characteristics, predict the intensity of biological disturbance and locate artifact areas through sound-dust coupling inversion, perform biological disturbance stripping and purification surface mapping, output predictive prevention and control strategies and update the biological feature database. The optimized dust dispersion parameters include: identifying vegetation types in the surveyed area using a hyperspectral imager, and allocating adsorption efficiency coefficients η based on vegetation canopy density; optimizing dust dispersion parameters based on the η value includes: compressing the dust dispersion radius. ， wherein, is the diffusion radius of the dust after compression, is the original diffusion radius; Correcting the concentration decay curve: ， wherein, is the concentration decay rate, is the aerosol settling rate; The peak energy of the acoustic signature and the dust concentration were collected, and the number of animals was calibrated using the acoustic-dust coupling inversion formula. ， in, For the number of animals, The baseline number of animals corresponding to the peak voiceprint energy. This refers to the dust concentration.

2. The mapping method based on remote sensing big data analysis according to claim 1, characterized in that, The pollution source characteristic indicators include: dust concentration distribution indicators, biological disturbance intensity indicators, vegetation adsorption efficiency indicators, and pollution diffusion kinetic parameters; The dust concentration distribution index is a dust concentration heat map generated from three-dimensional environmental data and real-time sensor data: ， in, This represents the dust concentration value. This refers to the number of dust pixels per unit area. The area of ​​the scanned region. The average density of aerosols is represented by the concentration gradient on the thermogram based on the dust concentration values. The bio-disturbance intensity index is the bio-interference coefficient calculated from animal activity trajectory density and voiceprint energy: ， in, The biological interference coefficient. The area of ​​the heat source region. This represents the total area of ​​the surveyed region. This represents the peak energy level of the voiceprint. This is the maximum energy reference value. , These are the corresponding weighting coefficients; The vegetation adsorption efficiency index is the dust adsorption capacity level calculated based on vegetation type and canopy density. The pollution diffusion dynamics parameters include the baseline value of dust diffusion radius and the diffusion rate coefficient: ， in, This is the baseline value for the dust dispersion radius. The wind speed is three-dimensional, with an exponent of 1.2 reflecting the nonlinear enhancement effect of wind speed on diffusion. t represents the duration of diffusion in hours. This represents the initial concentration of the aerosol. It reflects the marginal diminishing law of concentration decay; ， in, The diffusion rate coefficient is... For the standard deviation of dust particle size, The median particle size of the dust. This represents the rate of change in concentration.

3. The mapping method based on remote sensing big data analysis according to claim 1, characterized in that, The pollution diffusion model includes: a dust diffusion calculation engine, a biological interference correction module, a vegetation adsorption optimizer, and a multi-source coupling prediction interface. The dust diffusion calculation engine is used to calculate the spatiotemporal distribution of dust based on the dust diffusion radius benchmark value and the diffusion rate coefficient, and output the diffusion trajectory shape. The biological disturbance correction module receives the biological disturbance intensity index and dynamically calculates the trajectory offset with a period of 0.5 seconds. ， in, The trajectory offset angle, Based on the offset angle; The vegetation adsorption optimizer is used to compress the diffusion range and correct the concentration decay curve based on the vegetation adsorption efficiency index. The multi-source coupling prediction interface is used to output a pollution impact range map that integrates biological disturbance and vegetation regulation.

4. The mapping method based on remote sensing big data analysis according to claim 1, characterized in that, The biometric database includes: a motion trajectory feature database, a voiceprint spectrum feature database, a species interaction relationship database, and a behavioral cycle pattern database; The motion trajectory feature library stores the motion trajectory patterns of organisms and their spatiotemporal distribution parameters; The voiceprint spectrum feature library records the voiceprint energy spectrum characteristics and frequency band distribution patterns of different species; The species interaction database quantifies the coefficients of synergistic and repulsive interactions among species: ， in, For the synergy coefficient, The degree of overlap in trajectory space. , For the corresponding weighting coefficients, This refers to the voiceprint interlock index. ， in, The repulsion coefficient, This represents the difference in dust concentration. This represents the peak dust concentration. This is a species-specific constant; The behavioral cycle pattern library marks the seasonal and diurnal periodic activity patterns of biological activities.

5. A mapping method based on remote sensing big data analysis according to claim 1, characterized in that, The types of biological interference include: motion trajectory interference, acoustic frequency band interference, species interaction interference, and periodic activity interference. The motion trajectory interference is based on the spatiotemporal distribution parameters of the motion trajectory feature library to identify the animal activity trajectory pattern; The voiceprint frequency band interference is based on the species voiceprint characteristics identified by the frequency band distribution pattern of the voiceprint spectrum feature library. The species interaction interference is a multi-species interaction pattern identified based on the action coefficients of a species interaction database. By calling the species interaction database, pre-stored cooperation coefficients and repulsion coefficients are obtained. When a high level of cooperation or high level of repulsion is detected, a biological cooperation offset vector is obtained. ， in, This is the biological collaborative offset vector. Let be the concentration gradient vector. Weights for behavioral cycles; The periodic activity disturbance is based on the peak periods of biological activity identified from the activity patterns in the behavioral cycle pattern library.

6. The mapping method based on remote sensing big data analysis according to claim 1, characterized in that, The pollution diffusion model predicts the impact range by: analyzing the morphology of dust diffusion trajectories, linking linear diffusion to ungulate movement, sheet-like diffusion to gregarious bird activity, and vortex-like diffusion to flight take-off and landing behavior; calculating the animal population base based on the positive correlation between diffusion rate and peak dust concentration; correcting the animal population base by comparing the frequency band distribution patterns of the voiceprint spectrum feature library, and outputting a species list and interaction level.

7. A mapping method based on remote sensing big data analysis according to claim 6, characterized in that, The biological interference types also include species synergistic interference decoupling: obtaining the biological synergistic offset vector by calling the action coefficient of the species interaction relation library based on the species list; separating the composite artifact layer generated by species interaction in the three-dimensional spatiotemporal cube through the biological synergistic offset vector; and driving the dust pollution compensation model to perform local surface texture restoration on the separated artifact area based on the concentration decay curve output by the pollution diffusion model.

8. A mapping method based on remote sensing big data analysis according to claim 1, characterized in that, The predictive control strategy includes: calculating the impact range based on the number and morphological type of animals. ， in, For the scope of influence, The affected area was determined by different types of animals, and corrected using the adsorption efficiency coefficient η; the affected area was divided into red, yellow, and green zones, and predictive tiered prevention and control measures were implemented; The mapping relationship between the value, η value and the scope of influence is stored in the behavior cycle pattern library as a prevention benchmark for the next cycle of mapping.

9. A surveying and mapping system based on remote sensing big data analysis, applied to a surveying and mapping method based on remote sensing big data analysis as described in any one of claims 1 to 8, characterized in that, The system includes: Multi-source sensing module: used to acquire three-dimensional environmental data, real-time sensor data and biological behavior characteristic data of the surveyed area, verify images through a dual-channel pollutant detection mechanism, and output pollution source characteristic indicators; Pollution prevention and control decision module: connected to the multi-source sensing module, including a pollution diffusion model engine and a drone-targeted actuator. The pollution diffusion model engine is used to initialize dust diffusion parameters and predict the impact range, and the drone-targeted actuator is used to execute cleaning strategies and output decision verification results. Biometrics processing module: includes a biometrics database construction unit and an interference type identifier. The biometrics database construction unit constructs a motion trajectory feature database, a voiceprint spectrum feature database, a species interaction relationship database, and a behavior cycle pattern database based on multimodal data. The interference type identifier uses the biometrics database to correct the pollution diffusion model and identify motion trajectory interference, voiceprint frequency band interference, species interaction interference, and periodic activity interference. Prediction and optimization execution module: includes a vegetation adsorption optimizer, an acoustic-dust coupling inversion unit and an artifact stripping engine. The vegetation adsorption optimizer extracts vegetation adsorption characteristics and optimizes dust diffusion parameters. The acoustic-dust coupling inversion unit predicts the intensity of biological interference and locates artifact areas. The artifact stripping engine performs biological interference stripping and purification of the surface mapping. Closed-loop prevention and control management module: includes a hierarchical strategy generator and a biometric database update interface. The hierarchical strategy generator outputs predictive prevention and control strategies for red, yellow, and green zones, and the biometric database update interface writes the prevention and control mapping relationship into the behavior cycle pattern library to form a predictive closed loop.

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