Big data fused physical environment remote sensing monitoring data processing method and system

Abstract

The invention discloses a big-data-fused physical environment remote sensing monitoring data processing method and system, and relates to the technical field of environment monitoring, and the method comprises the following steps: constructing a spectrum response baseline based on the time continuous sampling result of a multi-band remote sensing image, extracting the energy transition track of each band under the conditions of cloud layer movement and surface thermal disturbance, and calculating the energy transition track of each band; forming a corresponding radiation fingerprint sequence; on the basis of the radiation fingerprint sequence, an energy coupling channel is established in a spectrum crossing area, an instantaneous energy imbalance point is identified along an energy transition track, and a local spectrum regulation window is generated by taking a radiation difference value at two ends of the imbalance point as a starting point. According to the invention, continuous fusion of multi-band energy is realized by constructing a spectrum response baseline and a radiation fingerprint sequence and combining an energy coupling and regulation mechanism; energy diffusion is dynamically adjusted through rhythm energy receiving and releasing and the breathing type buffer layer, radiation stability and spectrum continuity are kept, and space-time consistency and inversion precision of remote sensing data in a complex environment are improved.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and more specifically to a method and system for processing physical environment remote sensing monitoring data that integrates big data. Background Technology

[0002] Data processing for remote sensing monitoring of the physical environment refers to the comprehensive process of preprocessing, registration, denoising, feature extraction, and model analysis of large-scale physical environment information acquired by multi-source remote sensing platforms (such as satellites, UAVs, and ground-based sensor arrays) through the integration of big data technologies. This involves preprocessing, registering, denoising, extracting features, and performing model analysis on multi-temporal, multi-scale, and multi-sensor observation data. Its core objective is to integrate spatiotemporal processing of data from different observation angles, bands, and resolutions, enabling accurate reconstruction and correlation analysis of physical quantities such as temperature, humidity, surface albedo, atmospheric aerosols, topographic deformation, and water spectral characteristics within a unified spatiotemporal coordinate system. The introduction of big data integration technologies allows the system to identify complex environmental evolution patterns within massive amounts of remote sensing information, achieving dynamic coupling and in-depth mining of multi-dimensional data. This provides precise data support and decision-making basis for climate change assessment, ecological monitoring, disaster early warning, and resource management.

[0003] The existing technology has the following shortcomings:

[0004] During the fusion of multi-band remote sensing data, transient collapses may occur in spectral intersection regions due to short-term nonlinear cloud drift or abrupt changes in local radiation characteristics caused by surface thermal disturbances. This collapse disrupts the continuity of the original bands, causing discontinuities or anomalous jumps in the reflectance spectrum in both time and space. This makes it difficult to accurately recover the true surface reflectance characteristics during spectral interpretation and energy inversion. Especially in dynamic monitoring scenarios such as vegetation cover and water body distribution, this phenomenon can cause abrupt drops or structural distortions in key parameters such as vegetation index and water reflectance, affecting ecological status assessment, environmental change determination, and the stability of long-term monitoring sequences. In severe cases, it can even cause deviations in model training and trend prediction results.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for processing physical environment remote sensing monitoring data that integrates big data, so as to solve the problems in the background art mentioned above.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for processing physical environment remote sensing monitoring data integrating big data, comprising the following steps:

[0008] Step 1: Based on the temporal continuous sampling results of multi-band remote sensing images, construct the spectral response baseline, extract the energy transition trajectory of each band under the conditions of cloud movement and surface thermal disturbance, and form the corresponding radiation fingerprint sequence to characterize the energy change characteristics of each band under dynamic environment.

[0009] Step 2: Based on the radiation fingerprint sequence, an energy coupling channel is established in the spectral intersection region. Instantaneous energy imbalance points are identified along the energy transition trajectory. A local spectral control window is generated starting from the radiation difference between the two ends of the imbalance point to constrain the energy fluctuation range of the spectral collapse region.

[0010] Step 3: Based on the local spectrum adjustment window, the energy distribution of multi-band remote sensing images is rearranged in frames, and the broken spectral lines are reconnected according to the temporal continuity, so that the processed radiation fingerprint sequence forms a smooth transition band in the spatial dimension, in order to restore the radiation continuity of the spectral intersection region.

[0011] Step 4: Based on the smooth transition band, a rhythmic energy release mechanism is introduced to dynamically adjust the energy diffusion amplitude of the next frame according to the energy balance state of the previous frame, so as to stabilize the energy transfer rhythm between bands and maintain spectral continuity.

[0012] Step 5: Set up a breathing-type spectrum buffer layer around the rhythmic energy release and release mechanism. When the cloud cover is dense or the surface thermal disturbance is enhanced, the buffer layer range is automatically compressed, and when the disturbance is weakened, the buffer layer range is expanded. This maintains the continuous stability of radiation changes in the spectrum intersection area and achieves the spatiotemporal continuity and consistency of multi-band remote sensing fusion data.

[0013] Preferably, the steps of constructing a spectral response baseline and forming a radiation fingerprint sequence include:

[0014] After acquiring the temporally continuous sampling results of multi-band remote sensing images, the image data from satellites, drones, or ground observation platforms are organized in the order of shooting time. The radiance values ​​of the observation images at each moment are extracted according to the spectral response range and spatially gridded, so that each grid point corresponds to a set of multi-band radiance responses at a fixed surface location.

[0015] After obtaining complete time-series radiation variation data, the energy variation amplitude and direction of each band are used as references to extract energy variation feature points as reference nodes, and a continuous spectral response baseline is formed on the time axis through smoothing, so that the radiation variation patterns of different bands are unified in the same time reference system.

[0016] Along the time extension direction of the spectral response baseline, the changes in energy response of each band are refined, the energy surge point, energy drop point and energy equilibrium point are calibrated, and the transition interval is determined according to the energy change amplitude, forming an energy transition trajectory that reflects the energy evolution path of the band.

[0017] After acquiring the energy transition trajectory, the energy change information of different bands is reorganized based on the time series characteristics of each trajectory, and a radiation fingerprint sequence is formed with the spectral response baseline as a reference, so that the energy response of multiple bands remains continuous and consistent in the time and space dimensions.

[0018] Preferably, the process of calibrating the energy surge point, energy drop point and energy equilibrium point includes continuously tracking the energy change trend of adjacent time nodes along the time extension direction of the spectral response baseline, and taking the nodes where the energy change amplitude exceeds the preset threshold as the start and end points of the energy transition interval, so as to ensure that the energy transition trajectory remains continuous in the time dimension and realizes the energy response correspondence in the spatial dimension.

[0019] Preferably, the steps of establishing an energy coupling channel and generating a local spectral modulation window in the spectral cross region based on the radiation fingerprint sequence include:

[0020] After the radiation fingerprint sequence is formed, the energy distribution of adjacent bands in the spectral intersection region is compared point by point to identify the energy response overlap region and the intersection part is regarded as the boundary region of mutual energy influence, based on the response differences between different bands in the same surface area.

[0021] After identifying the spectral crossover region, an energy coupling channel is established within the crossover region along the energy transition trajectory of each band, corresponding the energy change paths of different bands in the time and space dimensions, so that energy can be continuously transferred within the crossover region to achieve a balanced connection.

[0022] After the energy coupling channel is established, the energy transition trajectory is analyzed segment by segment along the extension direction of the channel to identify the instantaneous energy imbalance point. The radiation difference between the two ends of the imbalance point is tracked along the energy transition trajectory to obtain the energy transition amplitude before and after the energy imbalance.

[0023] After identifying the instantaneous energy imbalance point, a local spectrum control window is generated starting from the radiation difference between the two ends of the imbalance point. The energy fluctuation range is limited around the instantaneous energy imbalance point, and a continuous energy transition connection is established between adjacent control windows through an energy coupling channel, thereby forming an energy control band in the spectrum intersection region.

[0024] Preferably, when the local spectrum control window is generated, the radiation difference between the two ends of the instantaneous energy imbalance point in the energy coupling channel is used as the constraint benchmark, and the energy boundaries of adjacent control windows are kept smoothly connected through continuous energy transition in the time dimension, so that the energy fluctuations in the spectrum intersection area form a continuous and stable energy control band in the spatial range.

[0025] Preferably, the steps of rearranging the energy distribution of multi-band remote sensing images by frame and restoring the radiative continuity of spectral intersection regions based on local spectral adjustment windows include:

[0026] After generating the local spectrum adjustment window, the multi-band remote sensing images are sorted into frames according to the time sampling order for the energy distribution range within each window, and the pixel energy values ​​are filtered and reassigned to the energy distribution range of adjacent frames based on the energy fluctuation range of the local spectrum adjustment window.

[0027] After completing the energy framing, the energy weights of different bands are rearranged one by one based on the constraints of the local spectrum control window, so that the energy difference between adjacent time frames is controlled within the control range, thereby establishing a continuous energy correlation.

[0028] After the frame rearrangement is completed, the energy change trend between adjacent frames is compared and analyzed along the time dimension to identify the temporal position and spatial range of the broken spectral lines. The broken spectral lines are then reconnected according to the principle of temporal continuity to form a continuous transition band for energy changes.

[0029] After the broken spectral lines are reconnected, a smooth transition band is generated in the spatial dimension based on the reconnected frame sequence. By spatially superimposing and registering the rearranged energy distribution, the radiation fingerprint sequence forms a continuous radiation distribution structure in the spectral intersection region.

[0030] Preferably, during the process of reconnecting broken spectral lines after frame rearrangement, the energy fluctuation range provided by the local spectrum control window is used as a constraint to smoothly extend the energy difference between adjacent frames in the time axis direction and continuously connect the energy of adjacent pixels in the spatial dimension, so that the energy change forms a consistent transition structure in time and space, thereby ensuring the overall continuity of the radiation fingerprint sequence.

[0031] Preferably, the steps of introducing a rhythmic energy release and harvesting mechanism on the basis of a smooth transition band and maintaining spectral continuity include:

[0032] After the smooth transition zone is formed, for the rearranged and connected multi-band remote sensing image frame sequence, the energy distribution range in the spatial dimension and the energy change trend in the temporal dimension of each band are extracted frame by frame. By comparing the energy differences between adjacent frames, the dynamic characteristics of energy accumulation and energy dissipation are determined.

[0033] After obtaining the energy distribution and diffusion trend of each frame, the energy adjustment benchmark of the rhythmic energy release mechanism is determined based on the smooth transition band. The energy balance state of the previous frame is used as a reference standard, and the initial amplitude range of energy release is determined according to the degree of energy distribution balance and gradient amplitude.

[0034] After determining the energy expansion and contraction reference range, the energy diffusion amplitude of the next frame is dynamically adjusted along the time sequence according to the energy distribution state of the current frame, so that the energy forms a rhythmic fluctuation with appropriate expansion and contraction between consecutive frames and constitutes a time-continuous energy transfer chain.

[0035] After completing the dynamic adjustment of the energy diffusion amplitude, the rhythmically adjusted energy frame sequence is superimposed with the smooth transition band, so that the energy release region and the energy absorption region are spatially matched and form a continuous energy propagation chain, thereby achieving the overall continuity and stability of the spectral response.

[0036] Preferably, the steps of setting up a breathing-type spectral buffer layer around the rhythmic energy release and harvesting mechanism and maintaining continuous and stable radiation changes include:

[0037] Based on the stable operation of the rhythmic energy release mechanism, for the multi-band remote sensing image frame sequence after dynamic energy adjustment, the distribution characteristics of energy in the spatial and temporal dimensions are extracted frame by frame. With the energy diffusion boundary of the rhythmic energy release mechanism as a reference, the target area that needs to be set with a breathing-type spectral buffer layer is determined and the initial buffer layer structure is formed.

[0038] After the initial buffer layer is formed, the expansion and contraction response rules of the breathing-type spectral buffer layer are determined based on the energy dynamic state of the rhythmic energy release and contraction mechanism. The periodic changes in energy release and absorption are used as a reference to define the expansion and contraction threshold of the buffer layer, so as to achieve adaptive adjustment to the strength of external disturbances.

[0039] After determining the scaling response rules, the dynamic adjustment of the breathing-type spectral buffer layer is implemented in the continuous time frame sequence around the operation state of the rhythmic energy release mechanism. The energy diffusion range is divided into the core control area, the transition buffer zone and the peripheral stable area, and the energy breathing effect is formed by the periodic expansion and contraction of the buffer layer.

[0040] After the breathing-type spectrum buffer layer achieves dynamic expansion and contraction adjustment, the energy distribution after compression and expansion adjustment is recoupled with the rhythmic energy release and contraction mechanism, so that the energy remains coordinated in the time and space dimensions, thereby maintaining the continuous stability of radiation changes in the spectrum intersection region.

[0041] The physical environment remote sensing monitoring data processing system integrating big data includes a radiation fingerprint construction module, an energy coupling regulation module, a spectral line rearrangement and connection module, a rhythmic energy regulation module, and a breathing-type spectrum buffer module;

[0042] Radiation fingerprint construction module: Based on the temporal continuous sampling results of multi-band remote sensing images, a spectral response baseline is constructed, and the energy transition trajectory of each band under the conditions of cloud movement and surface thermal disturbance is extracted to form the corresponding radiation fingerprint sequence.

[0043] Energy Coupling Control Module: Based on the radiation fingerprint sequence, an energy coupling channel is established in the spectrum intersection region, instantaneous energy imbalance points are identified along the energy transition trajectory, and a local spectrum control window is generated starting from the radiation difference between the two ends of the imbalance point.

[0044] Spectral line rearrangement and connection module: Based on the local spectrum control window, the energy distribution of multi-band remote sensing images is rearranged in frames, and the broken spectral lines are reconnected according to the temporal continuity, so that the processed radiation fingerprint sequence forms a smooth transition band in the spatial dimension.

[0045] Rhythmic energy regulation module: Based on the smooth transition band, a rhythmic energy release mechanism is introduced to dynamically adjust the energy diffusion amplitude of the next frame according to the energy balance state of the previous frame.

[0046] Breathing-type spectrum buffer module: A breathing-type spectrum buffer layer is set up around the rhythmic energy release mechanism. When the cloud layer is dense or the surface thermal disturbance is enhanced, the buffer layer range is automatically compressed and the buffer layer range is expanded when the disturbance is weakened.

[0047] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0048] This invention constructs a spectral response baseline and forms a radiation fingerprint sequence based on continuous temporal sampling of multi-band remote sensing images, enabling the integrated correlation of multi-band energy change processes in time and space. Combined with the establishment of energy coupling channels and local spectral control windows, the spectral intersection regions affected by cloud drift and surface thermal disturbances are dynamically constrained, thereby effectively eliminating spectral line breaks caused by instantaneous energy imbalances. This ensures that radiation information remains continuous and stable during the fusion process, improving the radiation reconstruction accuracy of remote sensing images in complex environments.

[0049] This invention introduces a rhythmic energy release and take-off mechanism on the basis of a smooth transition zone and sets up a breathing-type spectrum buffer layer, so that energy forms an adaptive rhythmic transfer relationship in multi-frame time sequence. It can automatically adjust the energy diffusion range according to the strength of environmental disturbances, thereby keeping the multi-band energy transfer process coordinated and stable, avoiding abnormal radiation fluctuations caused by local energy mutations, ensuring the continuity and consistency of remote sensing data in spatiotemporal fusion, and providing higher reliability and accuracy for environmental monitoring and surface feature inversion. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0051] Figure 1This is a flowchart of the physical environment remote sensing monitoring data processing method that integrates big data according to the present invention.

[0052] Figure 2 This is a schematic diagram of the modules of the physical environment remote sensing monitoring data processing system that integrates big data according to the present invention. Detailed Implementation

[0053] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0054] This invention provides, for example Figure 1 The physical environment remote sensing monitoring data processing method shown includes the following steps:

[0055] Step 1: Based on the temporal continuous sampling results of multi-band remote sensing images, construct the spectral response baseline, extract the energy transition trajectory of each band under the conditions of cloud movement and surface thermal disturbance, and form the corresponding radiation fingerprint sequence to characterize the energy change characteristics of each band under dynamic environment.

[0056] The specific implementation method for this step is as follows:

[0057] After acquiring the temporally continuous sampling results of multi-band remote sensing images, the image data from satellites, UAVs, or ground observation platforms are organized according to the order of capture time, ensuring that the time interval is within the range of minutes or seconds to capture energy changes caused by cloud drift and surface thermal disturbance. For each moment of the observed image, radiance values ​​are extracted for each of its bands according to the spectral response range, and these radiance values ​​are spatially gridded so that each grid point corresponds to a set of multi-band radiance responses at a fixed surface location. In the time series, the radiance responses at the same surface location at adjacent time points are compared to calculate the amplitude and direction of radiance changes in each band over time. Through continuous comparison, the energy change trajectory of each band throughout the entire time sampling period can be obtained. To ensure the continuity of the change process, the energy changes at abrupt change points are smoothed, and the multi-band radiance change sequence formed by continuous time sampling is used as the original energy change data, providing reliable input for constructing the spectral response baseline.

[0058] After obtaining complete time-series radiation variation data, using the energy variation amplitude and direction of each band as a reference, energy variation feature points are extracted as reference nodes for each band during the periods of cloud drift and surface thermal disturbance. These feature points are generally located in cloud edge regions or areas with significant surface temperature changes, and their radiation variation curves exhibit nonlinear trends. These feature points are arranged chronologically to form a continuous energy variation path on the time axis. To eliminate differences in energy response between different bands, interpolation and smoothing methods are used to map the energy variation trends of all bands onto a unified reference frame on the time axis, thus forming a spectral response baseline. This spectral response baseline spans all observation time points and reflects the energy evolution under the influence of cloud movement and surface thermal disturbance under multi-band conditions. Each band has a continuous energy variation curve within this baseline, and the relative positional relationship between the curves represents the coordination of energy responses between bands. Through this process, the radiation variation patterns of different bands are unified under the same time reference system, facilitating subsequent extraction of energy transition trajectories.

[0059] After establishing the spectral response baseline, the energy response changes of each band are refined along the time extension direction of the baseline. For each band, energy response change nodes are marked on the baseline, including energy surge points, energy drop points, and energy equilibrium points, and their specific time and spatial location are recorded. For the energy change trend between adjacent nodes, the transition interval is determined based on the continuity of the energy change amplitude. Then, these transition intervals are continuously tracked along the time direction, connecting the energy change trends of each band at different time points into a continuous curve. This curve is the energy transition trajectory, used to reflect the evolution path of the energy response of each band under dynamic environmental disturbance conditions. In this way, the energy migration direction and change law of each band during cloud nonlinear drift and surface thermal disturbance can be clearly identified. In the process of forming the energy transition trajectory, the trajectories of different bands are also time-registered, so that the energy changes at the same moment can correspond between different bands, thereby realizing cross-band energy response correlation. The energy transition trajectory obtained in this way not only reflects the continuous changes in the time dimension, but also reveals the mutual influence of energy responses in different bands in the spatial dimension, providing a quantifiable basis for subsequent reconstruction of radiation characteristics.

[0060] After acquiring the energy transition trajectories of each band, the energy change information of different bands is reorganized based on the time-series characteristics of these trajectories to form a radiation fingerprint sequence. Specifically, using the spectral response baseline as a unified reference, the energy responses of all bands at the same time point are arranged into a set of correspondences, and each set of correspondences constitutes a radiation fingerprint unit. Each radiation fingerprint unit contains the energy response distribution of each band at the same time, reflecting the overall radiation characteristics under environmental disturbances at that moment. Subsequently, the radiation fingerprint units at all time points are connected in chronological order to form a complete radiation fingerprint sequence. This sequence not only records the energy change trend of each band but also preserves the energy coupling relationship between different bands. Through this sequence, the propagation law of energy in the temporal and spatial dimensions can be clearly tracked, especially in areas where cloud drift and surface thermal disturbances frequently change, reflecting the dynamic coordination of band energy responses. The formation of the radiation fingerprint sequence enables a unified description of the energy evolution process of each band throughout the entire observation period, and the radiation response at any time can be traced and compared through this sequence. By analyzing the radiation fingerprint sequence, energy difference identification, spectral collapse recovery, and radiation continuity reconstruction can be achieved in subsequent processing stages, thereby ensuring the stable fusion and spatiotemporal consistency of multi-band remote sensing data in complex environments.

[0061] Through the sequential implementation of the above steps, the entire process from acquiring continuously sampled data over time to constructing the spectral response baseline, and then to extracting energy transition trajectories and forming radiation fingerprint sequences, is realized. The results of each step directly affect the processing accuracy of the next step, making the entire energy characterization process hierarchical and traceable. In this way, the energy changes of multi-band remote sensing images under dynamic environmental conditions can be fully restored, providing a complete physical basis and stable spatiotemporal support for subsequent energy coupling, spectral modulation, and radiation continuity restoration.

[0062] Step 2: Based on the radiation fingerprint sequence, an energy coupling channel is established in the spectral intersection region. Instantaneous energy imbalance points are identified along the energy transition trajectory. A local spectral control window is generated starting from the radiation difference between the two ends of the imbalance point to constrain the energy fluctuation range of the spectral collapse region.

[0063] The specific implementation method for this step is as follows:

[0064] After generating the radiation fingerprint sequence, the energy distribution of adjacent bands within the spectral intersection region is compared point-by-point to identify overlapping energy responses, based on the differences in response between different bands in the same surface area. By comparing the differences in energy amplitude and direction of energy change in the radiation fingerprints of each band within the same time period, the convergence range of energy responses of different bands, i.e., the spectral intersection region, is determined. To establish the basic relationship of energy coupling, each pair of band energy within the intersection region is paired accordingly at this stage, and the intersection is regarded as the boundary region of mutual energy influence. This boundary region reflects the overlap characteristics of the energy responses of two bands in physical space, providing a spatial reference for the subsequent establishment of energy coupling channels. Through this point-by-point comparison method, the regions where energy interaction occurs between different bands in the radiation fingerprint sequence can be clearly identified, giving the energy coupling process an identifiable physical range.

[0065] After identifying the spectral overlap region, an energy coupling channel is established within the overlap region along the energy transition trajectories of each band. This energy coupling channel is based on the energy distribution curves of the two bands in the overlap region, corresponding the two energy change paths in both time and space dimensions, enabling energy to be continuously transferred and balanced within the overlap region. To ensure smooth energy transfer, when establishing the energy coupling channel, the common time point of the energy curves of the two bands is used as the starting point, based on the energy overlap range determined in the previous step, and extends along the energy transition trajectory to the end point of energy change. Structurally, this channel is a continuous energy transition path, allowing the radiation responses from different bands to form a coupling relationship within the overlap region. In this way, the energy differences between different bands in the overlap region are transformed into a balanced energy transmission structure, avoiding energy discontinuities caused by independent changes and providing a continuous energy flow basis for identifying instantaneous energy imbalance points.

[0066] After establishing the energy coupling channel, the energy transition trajectory is analyzed segment by segment along the channel's extension direction to identify instantaneous energy imbalance points. Specifically, by observing the changing trends of the energy response of each band within the coupling channel, the instantaneous positions where the energy curve shows a sharp rise or fall are determined. When the energy difference between two bands at the same time point exceeds the average difference range of the preceding and following time periods, this position can be identified as an instantaneous energy imbalance point. Such imbalance points typically occur in areas of rapid cloud movement, sudden changes in local surface temperature, or abrupt changes in atmospheric humidity, and are the key locations in the energy transition trajectory most prone to causing spectral collapse. After identifying the instantaneous energy imbalance point, the energy change trends at both ends of the energy transition trajectory are traced to obtain the radiation difference between adjacent positions before and after the imbalance point. This radiation difference represents the energy transition amplitude between the two moments before and after the energy imbalance and serves as the basic reference for subsequently generating local spectral control windows. By marking these instantaneous energy imbalance points and their corresponding radiation differences within the coupling channel, the critical range of energy fluctuations can be clearly defined, providing a clear physical boundary for local energy control.

[0067] After identifying the instantaneous energy imbalance point, a local spectral control window is generated starting from the radiation difference between the two ends of the imbalance point. This window is used to constrain the energy fluctuation range in the spectral collapse region, thereby restoring energy continuity within the intersection region. Specifically, around each instantaneous energy imbalance point, the boundary range of the control window is determined according to the amplitude of the radiation difference between its two ends, controlling the energy fluctuation within this range within an acceptable gradient interval. By limiting the range of energy changes within the control window, the phenomenon of sudden energy drops or rises can be prevented from spreading to adjacent regions, thereby suppressing the expansion of spectral collapse. Subsequently, a continuous energy transition connection is established between each control window, allowing the energy boundaries of different windows to maintain a smooth connection in the time dimension. In this process, the energy coupling channel established in the previous step provides a path for energy conduction between the various control windows, enabling the control windows to not only locally constrain energy fluctuations but also achieve overall balance through energy coupling. Finally, an energy control band composed of multiple local spectral control windows is formed in the spectral intersection region. This control band can effectively limit the energy fluctuation range in the spectral collapse region, restoring the energy distribution of each band in the intersection region to a balanced state.

[0068] Through the aforementioned sequential implementation steps, a complete process was achieved, from extracting energy information from the radiation fingerprint sequence to establishing energy coupling channels in the spectral crossover region, and then to identifying instantaneous energy imbalance points and generating local spectral control windows. This process can effectively capture instantaneous changes in energy response under dynamic environmental conditions, accurately locate key areas of energy imbalance, and constrain the range of energy fluctuations by constructing local control windows, thereby maintaining the spatiotemporal continuity and stability of energy within the spectral crossover region.

[0069] Step 3: Based on the local spectrum adjustment window, the energy distribution of multi-band remote sensing images is rearranged in frames, and the broken spectral lines are reconnected according to the temporal continuity, so that the processed radiation fingerprint sequence forms a smooth transition band in the spatial dimension, in order to restore the radiation continuity of the spectral intersection region.

[0070] The specific implementation method for this step is as follows:

[0071] After generating local spectral control windows, the multi-band remote sensing images are framed and organized according to the temporal sampling order for the energy distribution range corresponding to each window. Specifically, the multi-band remote sensing images in the continuous time series are divided into several frames at fixed time intervals, with each frame containing the energy response images of each band at the same time. To maintain temporal continuity, the frame interval should be consistent with the time step of the energy transition trajectory in the previous step to ensure that the temporal characteristics of energy changes are completely preserved after framing. In each frame, according to the energy fluctuation range defined by the local spectral control window, the pixel energy values ​​in the intersection area are filtered and organized, and energy responses exceeding the control range are reassigned to the energy distribution interval of adjacent frames to achieve a redistribution of energy in the temporal dimension. In this way, the energy distribution within the local spectral control window is re-divided into a series of temporally ordered energy frames, providing a structured energy basis for subsequent frame rearrangement.

[0072] After energy framing and processing, the energy weights of different bands are rearranged one by one based on the constraints of the local spectral adjustment window, considering the multi-band energy distribution within each frame. Specifically, the energy of pixels at the same surface location in the previous and subsequent frames is compared to identify areas where energy abruptly changes due to cloud movement or surface thermal disturbance. For these areas, the upper and lower limits of energy defined in the local spectral adjustment window are used as constraint boundaries. By adjusting the energy distribution ratio between bands, the energy difference between adjacent time frames is controlled within the adjustment range. The energy value of each pixel after rearrangement is repositioned in its respective time frame, resulting in a smooth transition trend in energy changes over time. This framing rearrangement process essentially redistributes energy through the local spectral adjustment window, ensuring a continuous correlation in energy responses between different time frames. The rearranged frame sequence not only reflects the energy evolution law under continuous time but also eliminates energy gaps caused by local spectral collapse, creating a continuous energy channel for the reconnection of broken spectral lines.

[0073] After frame rearrangement, the energy change trends between adjacent frames are compared and analyzed along the time dimension to identify the temporal location and spatial range of broken spectral lines. These broken spectral lines are then reconnected according to the principle of temporal continuity. Specifically, by observing the energy change curves at the same surface location in adjacent frames, the time node where the energy response is interrupted is determined. Using the energy range provided by the local spectral adjustment window as a reference, the energy difference between the previous and subsequent frames is smoothly extended spatially. For energy interruption regions, the missing energy intervals are interpolated and connected along the time axis, guided by the energy change trends of adjacent frames, so that the broken spectral lines form a continuous transition band in the time dimension. Simultaneously, in the spatial dimension, by smoothly connecting the energy distribution of adjacent pixels, a stable gradient distribution of energy transition between bands is achieved. After this connection process, the broken spectral lines originally caused by cloud drift or thermal disturbance are reconnected into a continuous energy change path, ensuring the temporal coherence and spatial integrity of the radiation fingerprint sequence.

[0074] After the broken spectral lines are reconnected, a smooth transition band is generated in the spatial dimension based on the reconnected frame sequence, enabling the processed radiation fingerprint sequence to form a continuous radiation distribution in the spectral intersection region. Specifically, the rearranged energy distribution in each frame is superimposed according to spatial location, so that the temporally continuous energy response forms an energy gradient layer in the spatial direction. This energy gradient layer reflects the gradual change of energy from one band to another and is a key structure for restoring radiation continuity. To maintain a natural transition in energy distribution, the energy responses of different bands are spatially registered during the superposition process, ensuring that energy changes in the same surface area remain consistent across multiple bands. Through this two-way combination of spatial superposition and temporal extension, the resulting smooth transition band presents a continuous radiation distribution in space, allowing the energy responses of different bands to connect with each other in the intersection region without any breaks or abrupt changes. Ultimately, the processed radiation fingerprint sequence forms a stable energy distribution structure in the spatial dimension, providing a unified radiation basis for subsequent rhythmic energy release and radiation balance regulation.

[0075] Through the above steps, energy discontinuities caused by cloud drift and surface thermal disturbances in spectral intersection regions can be effectively repaired, ensuring the radiation fingerprint sequence remains continuous and consistent both temporally and spatially. By employing a comprehensive approach of energy framing, rearrangement, interleaving, and spatial smoothing, not only is the radiation continuity in spectral intersection regions restored, but continuous energy support is also provided for subsequent rhythmic energy stabilization and multi-band data fusion. This ensures that the entire remote sensing monitoring process maintains the integrity and spatiotemporal consistency of radiation information even under complex environmental conditions.

[0076] Step 4: Based on the smooth transition band, a rhythmic energy release mechanism is introduced to dynamically adjust the energy diffusion amplitude of the next frame according to the energy balance state of the previous frame, so as to stabilize the energy transfer rhythm between bands and maintain spectral continuity.

[0077] The specific implementation method for this step is as follows:

[0078] After the smooth transition zone is formed, for the rearranged and stitched multi-band remote sensing image frame sequence, the energy distribution range in the spatial dimension and the energy change trend in the temporal dimension of each band are extracted frame by frame. Specifically, the energy values ​​of each band in each frame are spatially mapped onto the surface grid to obtain the overall shape of the energy distribution in space, and the average energy value and local energy gradient of each band in that frame are calculated. Subsequently, the differences in energy changes at the same spatial location between adjacent frames are compared to obtain the diffusion trend and direction of energy change in time. Through this process, the dynamic characteristics of energy accumulation and dissipation in each band during continuous temporal changes can be clarified, providing a basic reference for establishing rhythmic energy contraction and release relationships. Since the smooth transition zone itself has formed a continuous gradient structure in the spatial dimension, the energy features extracted in this stage not only contain information on spatial smoothness but also reflect the energy transfer law under temporal extension.

[0079] After obtaining the energy distribution and diffusion trend of each frame, an energy adjustment benchmark for the rhythmic energy release mechanism is determined based on the smooth transition band. Specifically, the energy balance state of the previous frame is used as a reference standard, and the initial amplitude range of energy release is determined according to the balance and gradient amplitude of the energy distribution in each band within that frame. When energy accumulation occurs in the previous frame, it indicates that the energy in a local band has not diffused sufficiently, and the energy diffusion amplitude needs to be appropriately increased in the next frame. Conversely, when energy decay occurs in the previous frame, the energy diffusion amplitude is correspondingly reduced in the next frame to prevent rapid energy dissipation from disrupting radiation continuity. In this way, a rhythmic adjustment relationship is formed between frames in the time dimension, causing the energy diffusion amplitude to fluctuate periodically over time, thereby forming a stable rhythmic balance in the multi-band energy transfer process. The establishment of this rhythmic benchmark ensures that subsequent energy diffusion adjustment has a unified reference basis, making the energy transfer process between different frames continuous and controllable.

[0080] After determining the baseline range for rhythmic energy expansion and contraction, the energy diffusion amplitude of the next frame is dynamically adjusted along the time sequence based on the energy distribution state of the current frame. Specifically, according to the balance of energy distribution in each band in the previous frame, the direction and amplitude of energy diffusion in the same band in the next frame are adjusted region by region. For regions with relatively high energy in the previous frame, their energy release rate in the next frame is controlled, allowing energy to diffuse slowly to the surrounding areas in a gradient manner. For regions with relatively low energy, their energy absorption capacity is enhanced, enabling them to obtain continuous energy compensation in the time dimension. Through this dynamic adjustment, energy forms a rhythmic fluctuation with controlled expansion and contraction between consecutive frames. The energy diffusion state of each frame is affected by the balance result of the previous frame, thus forming a time-continuous energy transfer chain. As the frame sequence continues to extend, the diffusion and accumulation of energy exhibit periodic balance changes throughout the smooth transition zone, making the energy flow between bands stable and sustainable. This process essentially uses time-continuous energy feedback to regulate the rhythm of spatial energy diffusion, thereby preventing nonlinear fluctuations or irregular transitions of local energy in the time series.

[0081] After dynamically adjusting the energy diffusion amplitude, the multi-band energy distribution regulated by the rhythmic energy release and absorption mechanism is spatiotemporally integrated to maintain the overall continuity of the spectral response. In this process, the rhythmically regulated energy frame sequence in the temporal dimension is superimposed with the already formed smooth transition band in the spatial dimension. This ensures that the energy release and absorption state of each frame spatially compensates for the energy transfer direction of adjacent frames, thus forming a continuous energy propagation chain. Specifically, the energy release region and the energy absorption region are spatially matched, allowing the energy released by the former to be received by the latter during temporal evolution, achieving a closed-loop energy balance. As the frame sequence progresses in time, the energy release and absorption rhythm remains synchronized throughout the smooth transition band, coordinating the energy transfer rhythm between different bands in spatial distribution. Through this spatiotemporal fusion, the energy changes in the smooth transition band exhibit flexible and continuous evolutionary characteristics. The spectral response curves of each band remain coherent in the temporal dimension and form a uniform gradient in the spatial dimension, thereby achieving stable maintenance of spectral continuity. Ultimately, the radiation fingerprint sequence, regulated by the rhythmic energy release and release mechanism, exhibits continuous and coordinated energy transfer characteristics in the spectral crossover region, providing an orderly energy basis for subsequent energy buffering and radiation stability control.

[0082] By implementing the above steps, a rhythmic energy release and contraction mechanism is introduced on the basis of a smooth transition band, and the energy diffusion amplitude is dynamically adjusted throughout the entire process based on the energy balance relationship between the previous frame and the next frame. This mechanism can form adaptive rhythmic regulation of energy in the time dimension, keeping the energy transfer process between multiple bands coordinated and stable, thereby effectively maintaining the continuity of the spectral response.

[0083] Step 5: Set up a breathing-type spectrum buffer layer around the rhythmic energy release and release mechanism. When the cloud cover is dense or the surface thermal disturbance is enhanced, the buffer layer range is automatically compressed, and when the disturbance is weakened, the buffer layer range is expanded. In this way, the radiation change is maintained in the spectrum intersection area, and the spatiotemporal continuity and consistency of multi-band remote sensing fusion data are achieved.

[0084] The specific implementation method for this step is as follows:

[0085] Based on the stable operation of the rhythmic energy release mechanism, for a multi-band remote sensing image frame sequence after dynamic energy adjustment, the energy distribution characteristics in the spatial and temporal dimensions are extracted frame by frame to determine the target areas where a breathing-style spectral buffer layer needs to be set. Specifically, using the energy diffusion boundary in the rhythmic energy release mechanism as an initial reference, the amplitude difference of energy release and absorption in each band between adjacent frames is calculated, and the spatial range with the most active energy fluctuations in the spectral intersection area is identified based on the degree of energy change equilibrium. Typically, these ranges appear in areas of rapid cloud accumulation, abrupt changes in surface temperature, or rapid changes in atmospheric humidity, where the frequency of energy changes is much higher than the overall scene average. To maintain the stability of energy transfer in these areas, the surrounding area is divided layer by layer according to the energy gradient, with the energy boundary of the rhythmic energy release mechanism as the core, forming an initial buffer layer structure. This initial buffer layer can accommodate the micro-fluctuations of energy during the rhythmic energy release and absorption process and provide a spatial framework for subsequent automatic compression and expansion.

[0086] After the initial buffer layer is formed, the scaling response rules of the breathing-type spectral buffer layer are determined based on the dynamic energy state of the rhythmic energy release and absorption mechanism. Specifically, the periodic changes in energy release and absorption in the rhythmic energy release and absorption mechanism are used as the basic reference, and the balance and diffusion rate of energy release and absorption between frames are used as the main control indicators to define the scaling threshold of the buffer layer. When the energy release phase of the rhythmic energy release and absorption mechanism is detected to be continuously enhanced, it indicates that external disturbance factors (such as dense clouds or rising surface heat flow) are intensifying. At this time, the buffer layer needs to automatically compress its range to concentrate energy within a smaller space to prevent energy overflow from causing local spectral collapse. Conversely, when the rhythmic energy release and absorption mechanism is in the energy absorption phase and the disturbance intensity is weakening, the buffer layer automatically expands outward to allow energy to gradually diffuse in a more balanced manner over a larger spatial range, thereby improving overall radiation stability. Through this dynamic response method, the breathing-type spectral buffer layer forms a periodic scaling in the time dimension and a flexible envelope structure in the spatial dimension, enabling the transfer of multi-band energy to adaptively adjust according to the strength of environmental disturbances.

[0087] After determining the buffer layer's expansion and contraction response rules, dynamic adjustments to the breathing-style spectral buffer layer are implemented in a time-continuous frame sequence, based on the operational state of the rhythmic energy release mechanism. Specifically, within each frame, the energy diffusion range is divided into three layers—a core control zone, a transition buffer zone, and a peripheral stable zone—centered on the energy balance point of the rhythmic energy release mechanism. The core control zone corresponds to the most energy-active region in the rhythmic energy release mechanism, primarily used to absorb drastic energy fluctuations. The transition buffer zone transmits energy changes within the core control zone, ensuring a smooth energy diffusion process. The peripheral stable zone serves as the boundary for energy expansion, preventing energy from overflowing to the spectral edges. When cloud density suddenly increases or surface thermal disturbance intensifies, the transition and peripheral zones of the buffer layer automatically compress towards the core, reducing the area of ​​energy expansion and confining energy fluctuations to a smaller range. Conversely, when the disturbance intensity decreases, the buffer layer gradually expands outward, restoring the original spatial distribution of the transition and peripheral zones, allowing energy to diffuse uniformly over a larger area. Through this continuous dynamic adjustment between frames, the breathing-type spectral buffer layer forms an energy breathing effect that varies with the strength of disturbance in space, and in time it manifests as a periodic expansion and contraction of energy balance process, thereby effectively mitigating the concentrated fluctuations of multi-band energy under environmental changes.

[0088] After the breathing-type spectral buffer layer achieves dynamic expansion and contraction adjustment, the energy distribution adjusted by compression and expansion is recoupled with the rhythmic energy recovery and release mechanism, ensuring that the overall energy transfer process remains coordinated in time and space. Specifically, when cloud cover is dense or thermal disturbances are enhanced, the energy fluctuations restricted by the compression of the buffer layer are absorbed by the rhythmic energy recovery and release mechanism, causing excess energy to be released with a delay in the temporal dimension, thereby preventing the formation of sudden radiation concentration areas in space. Conversely, when disturbances weaken, the energy released after the buffer layer expands is smoothly received by the rhythmic energy recovery and release mechanism, allowing the energy to gradually return to a balanced state in the spatial dimension. Through this two-way energy regulation, the rhythmic energy recovery and release mechanism and the breathing-type spectral buffer layer form a mutually supportive relationship in function: the former controls the temporal rhythm of energy, while the latter regulates the spatial distribution of energy, and together they maintain the continuous stability of radiation changes in the spectral intersection region. As the time series progresses, the breathing effect of the buffer layer and the rhythmic energy recovery and release form a cyclical balance process between multiple frames of images, ensuring that the radiation response of the entire multi-band remote sensing data in the spectral intersection region remains spatiotemporally continuous and consistent.

[0089] The above steps realize the entire process of setting up a breathing-type spectral buffer layer around a rhythmic energy release and absorption mechanism. This process, through adaptive feedback of the energy balance state, enables the buffer layer to automatically compress or expand spatially according to the strength of environmental disturbances, thereby maintaining stable energy transfer and continuous spectral response under dynamic environmental conditions. By combining temporal rhythmic control with spatial breathing regulation, multi-band remote sensing images can maintain continuous stability of radiation variations even under complex conditions such as cloud drift, surface thermal disturbances, and atmospheric changes, achieving spatiotemporal continuity and consistency of multi-band remote sensing fusion data.

[0090] This invention constructs a spectral response baseline and forms a radiation fingerprint sequence based on continuous temporal sampling of multi-band remote sensing images, enabling the integrated correlation of multi-band energy change processes in time and space. Combined with the establishment of energy coupling channels and local spectral control windows, the spectral intersection regions affected by cloud drift and surface thermal disturbances are dynamically constrained, thereby effectively eliminating spectral line breaks caused by instantaneous energy imbalances. This ensures that radiation information remains continuous and stable during the fusion process, improving the radiation reconstruction accuracy of remote sensing images in complex environments.

[0091] This invention introduces a rhythmic energy release and take-off mechanism on the basis of a smooth transition zone and sets up a breathing-type spectrum buffer layer, so that energy forms an adaptive rhythmic transfer relationship in multi-frame time sequence. It can automatically adjust the energy diffusion range according to the strength of environmental disturbances, thereby keeping the multi-band energy transfer process coordinated and stable, avoiding abnormal radiation fluctuations caused by local energy mutations, ensuring the continuity and consistency of remote sensing data in spatiotemporal fusion, and providing higher reliability and accuracy for environmental monitoring and surface feature inversion.

[0092] This invention provides, for example Figure 2 The physical environment remote sensing monitoring data processing system shown includes a radiation fingerprint construction module, an energy coupling regulation module, a spectral line rearrangement and connection module, a rhythmic energy regulation module, and a breathing-type spectral buffer module.

[0093] Radiation fingerprint construction module: Based on the temporal continuous sampling results of multi-band remote sensing images, a spectral response baseline is constructed, and the energy transition trajectory of each band under the conditions of cloud movement and surface thermal disturbance is extracted to form the corresponding radiation fingerprint sequence.

[0094] Energy Coupling Control Module: Based on the radiation fingerprint sequence, an energy coupling channel is established in the spectrum intersection region, instantaneous energy imbalance points are identified along the energy transition trajectory, and a local spectrum control window is generated starting from the radiation difference between the two ends of the imbalance point.

[0095] Spectral line rearrangement and connection module: Based on the local spectrum control window, the energy distribution of multi-band remote sensing images is rearranged in frames, and the broken spectral lines are reconnected according to the temporal continuity, so that the processed radiation fingerprint sequence forms a smooth transition band in the spatial dimension.

[0096] Rhythmic energy regulation module: Based on the smooth transition band, a rhythmic energy release mechanism is introduced to dynamically adjust the energy diffusion amplitude of the next frame according to the energy balance state of the previous frame.

[0097] Breathing-type spectrum buffer module: A breathing-type spectrum buffer layer is set up around the rhythmic energy release mechanism. When the cloud layer is dense or the surface thermal disturbance is enhanced, the buffer layer range is automatically compressed and the buffer layer range is expanded when the disturbance is weakened.

[0098] The physical environment remote sensing monitoring data processing method integrating big data provided in this embodiment of the invention is implemented through the aforementioned physical environment remote sensing monitoring data processing system integrating big data. For details of the specific methods and processes of the physical environment remote sensing monitoring data processing system integrating big data, please refer to the embodiments of the aforementioned physical environment remote sensing monitoring data processing method integrating big data, which will not be repeated here.

[0099] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for processing physical environment remote sensing monitoring data integrating big data, characterized in that, Includes the following steps: Step 1: Based on the temporal continuous sampling results of multi-band remote sensing images, construct a spectral response baseline, extract the energy transition trajectory of each band under the conditions of cloud movement and surface thermal disturbance, and form the corresponding radiation fingerprint sequence; Step 2: Based on the radiation fingerprint sequence, establish an energy coupling channel in the spectral intersection region, identify instantaneous energy imbalance points along the energy transition trajectory, and generate a local spectral control window starting from the radiation difference between the two ends of the imbalance point. Step 3: Based on the local spectrum adjustment window, the energy distribution of multi-band remote sensing images is rearranged in frames, and the broken spectral lines are reconnected according to the temporal continuity, so that the processed radiation fingerprint sequence forms a smooth transition band in the spatial dimension. Step 4: Based on the smooth transition band, a rhythmic energy release and contraction mechanism is introduced to dynamically adjust the energy diffusion amplitude of the next frame according to the energy balance state of the previous frame. Step 5: Set up a breathing-type spectrum buffer layer around the rhythmic energy release and release mechanism. When the cloud cover is dense or the surface thermal disturbance is enhanced, the buffer layer range will be automatically compressed, and when the disturbance is weakened, the buffer layer range will be expanded.

2. The method for processing physical environment remote sensing monitoring data integrating big data according to claim 1, characterized in that, The steps for constructing a spectral response baseline and forming a radiation fingerprint sequence include: After acquiring the temporally continuous sampling results of multi-band remote sensing images, the image data from satellites, drones, or ground observation platforms are organized in the order of shooting time. The radiance values ​​of the observation images at each moment are extracted according to the spectral response range and spatially gridded, so that each grid point corresponds to a set of multi-band radiance responses at a fixed surface location. After obtaining complete time-series radiation variation data, the energy variation amplitude and direction of each band are used as references to extract energy variation feature points as reference nodes, and a continuous spectral response baseline is formed on the time axis through smoothing, so that the radiation variation patterns of different bands are unified in the same time reference system. Along the time extension direction of the spectral response baseline, the changes in energy response of each band are refined, the energy surge point, energy drop point and energy equilibrium point are calibrated, and the transition interval is determined according to the energy change amplitude, forming an energy transition trajectory that reflects the energy evolution path of the band. After obtaining the energy transition trajectory, the energy change information of different bands is reorganized based on the time series characteristics of each trajectory, and a radiation fingerprint sequence is formed with the spectral response baseline as a reference.

3. The method for processing physical environment remote sensing monitoring data integrating big data according to claim 2, characterized in that, The process of calibrating the energy surge point, energy drop point, and energy equilibrium point includes continuously tracking the energy change trend of adjacent time nodes along the time extension direction of the spectral response baseline, and taking the nodes where the energy change amplitude exceeds the preset threshold as the start and end points of the energy transition interval.

4. The method for processing physical environment remote sensing monitoring data integrating big data according to claim 2, characterized in that, The steps for establishing an energy coupling channel and generating a local spectral modulation window in the spectral cross region based on the radiation fingerprint sequence include: After the radiation fingerprint sequence is formed, the energy distribution of adjacent bands in the spectral intersection region is compared point by point to identify the energy response overlap region and the intersection part is regarded as the boundary region of mutual energy influence, based on the response differences between different bands in the same surface area. After identifying the spectral crossover region, an energy coupling channel is established within the crossover region along the energy transition trajectory of each band, corresponding the energy change paths of different bands in the time and space dimensions, so that energy can be continuously transferred within the crossover region to achieve a balanced connection. After the energy coupling channel is established, the energy transition trajectory is analyzed segment by segment along the extension direction of the channel to identify the instantaneous energy imbalance point. The radiation difference between the two ends of the imbalance point is tracked along the energy transition trajectory to obtain the energy transition amplitude before and after the energy imbalance. After identifying the instantaneous energy imbalance point, a local spectrum control window is generated starting from the radiation difference between the two ends of the imbalance point. The energy fluctuation range is limited around the instantaneous energy imbalance point, and a continuous energy transition connection is established between adjacent control windows through the energy coupling channel, forming an energy control band in the spectrum intersection region.

5. The method for processing physical environment remote sensing monitoring data integrating big data according to claim 4, characterized in that, When the local spectrum control window is generated, the radiation difference between the two ends of the instantaneous energy imbalance point in the energy coupling channel is used as the constraint benchmark. In the time dimension, the energy boundaries of adjacent control windows are kept smoothly connected through continuous energy transition, so that the energy fluctuations in the spectrum intersection area form an energy control band in the spatial range.

6. The method for processing physical environment remote sensing monitoring data by integrating big data according to claim 4, characterized in that, The steps for rearranging the energy distribution of multi-band remote sensing images by frame and restoring the radiative continuity of spectral intersection regions based on local spectral modulation windows include: After generating the local spectrum adjustment window, the multi-band remote sensing images are sorted into frames according to the time sampling order for the energy distribution range within each window, and the pixel energy values ​​are filtered and reassigned to the energy distribution range of adjacent frames based on the energy fluctuation range of the local spectrum adjustment window. After completing the energy framing, the energy weights of different bands are rearranged one by one based on the constraints of the local spectrum control window, so that the energy difference between adjacent time frames is controlled within the control range and a continuous energy correlation is established. After the frame rearrangement is completed, the energy change trend between adjacent frames is compared and analyzed along the time dimension to identify the temporal position and spatial range of the broken spectral lines. The broken spectral lines are then reconnected according to the principle of temporal continuity to form a continuous transition band for energy changes. After the broken spectral lines are reconnected, a smooth transition band is generated in the spatial dimension based on the reconnected frame sequence. By spatially superimposing and registering the rearranged energy distribution, the radiation fingerprint sequence forms a continuous radiation distribution structure in the spectral intersection region.

7. The method for processing physical environment remote sensing monitoring data integrating big data according to claim 6, characterized in that, During the process of reconnecting broken spectral lines after frame rearrangement, the energy fluctuation range provided by the local spectrum control window is used as a constraint. The energy difference between adjacent frames is smoothly extended in the time axis direction, and the energy of adjacent pixels is continuously connected in the spatial dimension, so that the energy change forms a consistent transition structure in time and space.

8. The method for processing physical environment remote sensing monitoring data integrating big data according to claim 7, characterized in that, The steps to introduce a rhythmic energy release and harvesting mechanism while maintaining spectral continuity based on a smooth transition band include: After the smooth transition zone is formed, for the rearranged and connected multi-band remote sensing image frame sequence, the energy distribution range in the spatial dimension and the energy change trend in the temporal dimension of each band are extracted frame by frame. By comparing the energy differences between adjacent frames, the dynamic characteristics of energy accumulation and energy dissipation are determined. After obtaining the energy distribution and diffusion trend of each frame, the energy adjustment benchmark of the rhythmic energy release mechanism is determined based on the smooth transition band. The energy balance state of the previous frame is used as a reference standard, and the initial amplitude range of energy release is determined according to the degree of energy distribution balance and gradient amplitude. After determining the energy expansion and contraction reference range, the energy diffusion amplitude of the next frame is dynamically adjusted along the time sequence according to the energy distribution state of the current frame, so that the energy forms a rhythmic fluctuation with appropriate expansion and contraction between consecutive frames and constitutes a time-continuous energy transfer chain. After the dynamic adjustment of the energy diffusion amplitude is completed, the rhythmically adjusted energy frame sequence is superimposed with the smooth transition zone, so that the energy release area and the energy absorption area are spatially matched and form a continuous energy propagation chain.

9. The method for processing physical environment remote sensing monitoring data integrating big data according to claim 8, characterized in that, The steps for establishing a respiratory-type spectral buffer layer around the rhythmic energy release and absorption mechanism and maintaining stable radiation changes include: Based on the stable operation of the rhythmic energy release mechanism, for the multi-band remote sensing image frame sequence after dynamic energy adjustment, the distribution characteristics of energy in the spatial and temporal dimensions are extracted frame by frame. With the energy diffusion boundary of the rhythmic energy release mechanism as a reference, the target area that needs to be set with a breathing-type spectral buffer layer is determined and the initial buffer layer structure is formed. After the initial buffer layer is formed, the expansion and contraction response rules of the breathing-type spectral buffer layer are determined based on the energy dynamic state of the rhythmic energy release and contraction mechanism. The periodic changes in energy release and absorption are used as a reference to define the expansion and contraction threshold of the buffer layer, so as to achieve adaptive adjustment to the strength of external disturbances. After determining the scaling response rules, the dynamic adjustment of the breathing-type spectral buffer layer is implemented in the continuous time frame sequence around the operation state of the rhythmic energy release mechanism. The energy diffusion range is divided into the core control area, the transition buffer zone and the peripheral stable area, and the energy breathing effect is formed by the periodic expansion and contraction of the buffer layer. After achieving dynamic scaling and adjustment in the breathing-type spectral buffer layer, the energy distribution after compression and expansion is recoupled with the rhythmic energy release and release mechanism, so that the energy remains coordinated in the time and space dimensions.

10. A physical environment remote sensing monitoring data processing system integrating big data, used to implement the physical environment remote sensing monitoring data processing method integrating big data as described in any one of claims 1-9, characterized in that, It includes a radiation fingerprint construction module, an energy coupling regulation module, a spectral line rearrangement and connection module, a rhythmic energy regulation module, and a breathing-type spectral buffer module; Radiation fingerprint construction module: Based on the temporal continuous sampling results of multi-band remote sensing images, a spectral response baseline is constructed, and the energy transition trajectory of each band under the conditions of cloud movement and surface thermal disturbance is extracted to form the corresponding radiation fingerprint sequence. Energy Coupling Control Module: Based on the radiation fingerprint sequence, an energy coupling channel is established in the spectrum intersection region, instantaneous energy imbalance points are identified along the energy transition trajectory, and a local spectrum control window is generated starting from the radiation difference between the two ends of the imbalance point. Spectral line rearrangement and connection module: Based on the local spectrum control window, the energy distribution of multi-band remote sensing images is rearranged in frames, and the broken spectral lines are reconnected according to the temporal continuity, so that the processed radiation fingerprint sequence forms a smooth transition band in the spatial dimension. Rhythmic energy regulation module: Based on the smooth transition band, a rhythmic energy release mechanism is introduced to dynamically adjust the energy diffusion amplitude of the next frame according to the energy balance state of the previous frame. Breathing-type spectrum buffer module: A breathing-type spectrum buffer layer is set up around the rhythmic energy release mechanism. When the cloud layer is dense or the surface thermal disturbance is enhanced, the buffer layer range is automatically compressed and the buffer layer range is expanded when the disturbance is weakened.

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