Method for comprehensive environmental risk assessment of cross-media regions by fusing atmospheric and water risk fields

Abstract

The application discloses a cross-medium area comprehensive environmental risk assessment method fusing an atmospheric and water risk field, relates to the technical field of environmental assessment, and comprises the following steps: based on environmental risk assessment requirements, constructing a cross-medium time sequence anchoring zone, forming a unified time axis by fusing wind field data and water flow data, extracting a phase difference of a pollutant concentration peak section, and generating a phase index list. The application realizes time, space and energy coupling analysis of air and water body risk processes by constructing a cross-medium time sequence anchoring zone, a spatial superposition kernel list and a gas-liquid exchange channel fingerprint, dynamically tracks a risk transmission path and identifies an energy aggregation area; meanwhile, the application realizes active dispersion and dynamic balance of risk energy by coupling abnormal spectrum energy budget and a breathing type phase inversion traction, and effectively improves the accuracy of risk assessment and the stability of an environmental system.

Description

Technical Field

[0001] This invention relates to the field of environmental assessment technology, and more specifically to a cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields. Background Technology

[0002] A cross-media regional integrated environmental risk assessment, integrating atmospheric and water risk fields, is a comprehensive assessment method established at the regional scale to address the complex risk processes resulting from the migration, transformation, and interaction of pollutants between air and water bodies. This method simultaneously analyzes atmospheric diffusion, water flow, and their coupling effects. It introduces big data processing technology into the existing framework, enhancing the model's data support and real-time response capabilities through high-frequency acquisition, cleaning, fusion, and correlation mining of multi-source heterogeneous environmental data. Specifically, by integrating multi-source big data such as meteorological parameters (e.g., wind speed, wind direction, temperature, humidity), hydrodynamic parameters (e.g., flow velocity, water level, diffusion coefficient), and pollutant concentration fields, a coupled model of atmospheric and water risk fields is constructed to identify the transmission channels and risk overlap zones of pollutants between air and water bodies. Subsequently, using the spatial distribution, temporal transitions, and energy release pathways of pollutants as core indicators, combined with dynamic big data analysis results, a cross-media comprehensive risk index is formed. This index quantitatively reflects the risk intensity, diffusion trend, and impact range of the regional environment under multi-media interactions, thus providing a scientific basis for pollution prevention and control, emergency response, and ecological restoration.

[0003] Existing technologies have the following shortcomings: Current technologies for assessing regional environmental risks often employ a single-medium independent analysis model, modeling and monitoring air and water pollution separately, lacking a dynamic characterization of the temporal correlation and energy superposition patterns between the two. When multiple pollution sources are triggered simultaneously in different media but with release phase differences, existing technologies struggle to identify the coupled risk evolution process within the spatial superposition zone, easily leading to misjudgments of risk peaks or delayed spatiotemporal responses. Particularly when meteorological and hydrodynamic fields are simultaneously disturbed, atmospheric diffusion waves and water migration waves may superimpose and resonate in specific areas, causing pollution energy to accumulate across media at the interface, resulting in an energy surge far exceeding the expectations of linear superposition, thus triggering a nonlinear risk multiplication effect. The occurrence of such sudden risks often exceeds the predictive capabilities of existing technologies, leading to distorted comprehensive environmental risk assessment results and a significant decrease in the timeliness of regional emergency response.

[0004] 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

[0005] The purpose of this invention is to provide a cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields, comprising the following steps: Based on the needs of environmental risk assessment, a cross-media time-series anchoring band is constructed. By fusing wind field data and water flow data to form a unified time axis, the phase difference of pollutant concentration peak segments is extracted to generate a phase index list, which is used to establish the time-series correspondence between atmospheric and water risk processes. Based on the phase index list, analyze the pollutant peak misalignment segments, calculate the spatial overlap intensity, locate the overlapping area of ​​air and water risks, extract the coupling core point set, and generate an overlay core list to characterize the spatial aggregation characteristics of cross-media risks. Using the superimposed kernel list as input, the temperature gradient, concentration gradient and pressure gradient information of the air-water interface are collected to establish the fingerprint of the gas-liquid exchange channel, calculate the energy transfer path of the interface, generate the cross-layer energy pathway spectrum, and extract the energy anomaly peak to form the coupling anomaly spectrum. Based on the coupled anomaly spectrum, cross-medium risk energy budgeting is carried out to calculate the risk energy accumulation rate and release direction, determine the priority of disposal, compress the risk response time, and generate a continuous traction command stream for dynamic control of the cross-medium risk evolution process. Based on the continuous traction command stream, breathing-type phase reversal traction is executed. The frequency misrelease threshold and negative time delay preset window are set to drive the interface pulse bypass closure, thereby controlling the real-time dispersion and dynamic stability of risk energy between air and water.

[0007] Preferably, the steps for generating the phase index list are as follows: Based on the requirements of environmental risk assessment, the boundary coupling range between the atmosphere and water body is determined, and continuous time series data of aerodynamic and hydrodynamic parameters are obtained under a unified geographic reference coordinate system to establish an initial time frame as the basis for cross-media time series anchoring. The wind field data and water flow data are synchronously segmented and processed according to a unified time axis. The fluctuation trend of the pollutant concentration change curve is calculated. The change curves of the two media are superimposed on the same time reference line to identify the key moment of the pollution concentration peak and determine its location. By comparing the occurrence times of the two types of peak segments using a unified time axis, the phase difference between air and water is extracted and time-series offset data is generated. The corresponding relationships of concentration peaks in different time periods are systematically organized to generate a phase index list, which is used to establish the temporal correspondence between atmospheric and water risk processes.

[0008] Preferably, when generating the phase index list, the corresponding relationship of pollutant concentration peaks is used as the identification unit with time as the main axis, and wind speed, wind direction, flow velocity, water level and temperature parameters are recorded in each unit. The parameters are arranged in time sequence to form a continuous time series, and a dynamic coupling relationship between air and water risk processes is established on a unified time dimension.

[0009] Preferably, the steps for parsing pollutant peak misalignment segments based on the phase index list, calculating spatial overlap intensity, locating the overlapping area of ​​air and water risks, and generating an overlay kernel list are as follows: Based on the phase index list, the spatial mapping of the correspondence between the peak concentration segments of pollutants in the atmosphere and water is carried out to form a pollution concentration distribution field of air and water media in a unified geographic coordinate system. Based on the phase difference information in the phase index list, the peak positions of the two media are compared, the spatial offset is extracted, and a misaligned segment of the pollutant peak is formed. Spatial aggregation of pollutant peak misalignment segments is performed to calculate the spatial overlap intensity between air and water media and identify risk superposition areas. The spatial overlap intensity distribution is screened and refined to generate a list of superposition kernels, which is used to characterize the spatial clustering characteristics of cross-medium risks.

[0010] Preferably, in the process of generating the overlay nuclear inventory, the spatial coordinates, phase difference characteristics, peak pollution concentration, wind speed, water flow velocity and temperature difference of air and water media are comprehensively summarized and arranged in an orderly manner based on spatial proximity and environmental characteristic similarity, so that the overlay nuclear inventory can accurately characterize the spatial aggregation characteristics of cross-media risks at the regional scale.

[0011] Preferably, the steps for collecting temperature gradient, concentration gradient, and pressure gradient information at the air-water interface and generating a coupled anomaly spectrum using a superimposed kernel list as input are as follows: Using the set of coupled core points in the superimposed core list as the basis for spatial positioning, temperature, concentration and pressure data of air and water media are collected in the interface region to form cross-interface temperature field, concentration field and pressure field. Using the vertical direction as a reference, the parameters of the air-water interface are stratified, and the distribution of the interface temperature gradient, concentration gradient and pressure gradient are calculated to form an energy exchange profile. A fingerprint of the gas-liquid exchange channel is established based on the location and gradient characteristics of the coupling core point to depict the directionality and continuity of energy flow between air and water. Based on the energy transfer path of the gas-liquid exchange channel fingerprint calculation interface, a cross-layer energy pathway spectrum is generated and an abnormal energy peak is extracted to form a coupled anomaly spectrum.

[0012] Preferably, the fingerprint of the gas-liquid exchange channel is established by continuously collecting the variation characteristics of temperature gradient, concentration gradient and pressure gradient within the spatial range of the coupling core point to form a fingerprint spectrum. The fingerprint spectrum is used to characterize the directionality and intensity distribution of energy flow between air and water, and serves as the basis for calculating the cross-layer energy pathway spectrum and identifying coupling anomaly spectrum.

[0013] Preferably, the steps for performing cross-medium risk energy budgeting and generating a continuous traction command stream based on the coupled anomaly spectrum are as follows: Based on the coupled anomaly spectrum, the energy transfer characteristics between air and water are sorted out and analyzed in layers to divide the energy activity region and determine the energy interaction state between the air side and the water side. Calculate the energy accumulation rate and release direction within the energy activity area, and determine the dominant energy flow direction based on the temperature gradient, pressure difference, and pollution concentration change trends. Based on the energy budget results, the priority of cross-medium risk energy disposal is determined, response time windows are set, and a dynamic mapping relationship between energy transfer and intervention behavior is established; A continuous traction command stream is generated based on the priority order and response time window for dynamic control of the risk evolution process across media.

[0014] Preferably, the generation of continuous traction command stream includes matching and integrating the energy flow direction information of the air side and the water side on a unified time axis, so that the time rhythm and control sequence of the energy transfer process are kept synchronized, and the coordinated operation of energy release on the air side and energy absorption on the water side is guided by the command stream, thereby maintaining the dynamic balance and stable transfer of cross-medium risk energy.

[0015] Preferably, the steps for performing breathing-type phase-reflection traction and controlling the dynamic stability of risk energy between air and water based on the continuous traction command flow are as follows: A control path for breathing-type phase reversal traction is established based on the continuous traction command flow. A breathing cycle is formed through the periodic repetition of energy release and absorption, so that the energy flow is in a periodic reversal state. Set a false frequency release threshold to adjust the time difference and frequency difference of energy release between the air side and the water side to prevent energy wave superposition from causing energy accumulation; Set a negative delay preset window to trigger the interface response in advance before energy fluctuations reach the interface, so that energy transfer has feedforward characteristics and forms dynamic pre-control. Under the action of the negative time delay preset window, the interface pulsation bypass is closed, and the risk energy between the air and water is controlled in real time and dynamically stabilized through periodic pulsation.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention constructs a cross-medium temporal anchoring band, a spatially superimposed kernel inventory, and a gas-liquid exchange channel fingerprint, enabling a continuous coupling relationship between risk processes in air and water across three dimensions: time, space, and energy. Through joint analysis of pollutant peak phase differences, spatially misaligned segments, and interfacial energy gradients, it achieves dynamic tracking of cross-medium risk transmission paths, making the risk evolution process observable and controllable. This scheme can effectively identify regions of risk energy accumulation between air and water, accurately capture anomalous behaviors in cross-boundary energy transfer, thereby improving the timeliness and accuracy of regional risk assessment.

[0017] This invention transforms the energy transfer process between air and water from a unidirectional release to a periodic equilibrium process by using energy budgeting based on coupled anomaly spectra and breathing-like phase reflection traction. By setting mis-frequency release thresholds and negative time delay preset windows, the time difference and response rhythm of air and water during energy transfer are coordinated, achieving proactive dispersion and dynamic stability control of risk energy. This scheme can intervene early in the initial stage of energy accumulation, preventing the formation of risk peaks and resonance amplification, ensuring the controllability of cross-medium risks during evolution and the continuous stability of the environmental system. Attached Figure Description

[0018] 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.

[0019] Figure 1 This is a flowchart of the cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields according to the present invention. Detailed Implementation

[0020] 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.

[0021] This invention provides, for example Figure 1 The cross-media regional integrated environmental risk assessment method shown includes the following steps: Based on the needs of environmental risk assessment, a cross-media time-series anchoring band is constructed. By fusing wind field data and water flow data to form a unified time axis, the phase difference of pollutant concentration peak segments is extracted to generate a phase index list, which is used to establish the time-series correspondence between atmospheric and water risk processes. By fusing wind field data and water flow data to form a unified timeline, the phase difference of pollutant concentration peaks is extracted to generate a phase index list, thereby establishing a temporal correspondence between atmospheric and water body risk processes. The specific implementation steps are as follows:

[0022] In the initial stage of cross-media risk assessment, based on the regional environmental risk assessment requirements, the boundary coupling range between the atmosphere and water bodies is determined, and continuous time-series data of aerodynamic and hydrodynamic parameters are acquired under a unified geographic reference coordinate system. Aerodynamic parameters include elements closely related to meteorological field changes, such as wind speed, wind direction, temperature, humidity, and air pressure; hydrodynamic parameters include variables characterizing the motion state of water bodies, such as flow velocity, water level, flow direction, temperature difference, and diffusion coefficient. By synchronously extending the monitoring periods for air and water bodies, an initial time frame is established on the time axis, creating a one-to-one correspondence between atmospheric and water body observation points in the time dimension. This initial time frame serves as the basis for the cross-media time-series anchoring zone, providing a time reference for subsequent data fusion and phase relationship extraction.

[0023] After establishing the initial timeframe, the wind field and water flow data were synchronously segmented and organized according to a unified time axis, taking into account the environmental change characteristics of the atmosphere and water bodies within the same time period. Within each time period, the fluctuation trends of pollutant concentration change curves in the atmospheric and water media were calculated, and the change curves of both media were superimposed on the same time reference line. Through continuous temporal alignment, the temporal relationship between atmospheric diffusion and water transport processes can be visually displayed. Based on this, according to the peak distribution pattern of pollutant concentration changes, the critical moments when pollutant concentrations in the air and water reach their peak values ​​were identified, and the relative positions of these moments on the unified time axis were determined. At this point, the atmospheric risk process and the water risk process are precisely anchored in the time dimension, forming a temporal baseline that can be used for dynamic comparison.

[0024] After the peak concentration segments in the atmosphere and water were clearly anchored, the occurrence times of the two types of peak segments were compared using a unified time axis to extract the phase difference information between the two media. The phase difference reflects the difference in propagation speed and energy transfer delay characteristics of pollution events in air and water. During the extraction process, the leading or lagging order of pollution diffusion waves in air and water was determined by continuously observing the changing trends of wind speed and airflow velocity, and this was identified as a time offset. Each time offset corresponds to the cross-media phase difference value of a pollution peak segment. By summarizing and organizing these phase difference values, a set of time-series offset data characterizing the asynchronous transmission of pollution in air and water was obtained. This offset data constitutes the core information of the cross-media time-series anchoring band, making air risk waves and water risk waves comparable in time.

[0025] After obtaining all phase difference data, the correspondence between atmospheric and water concentration peaks across different time periods is systematically organized, generating a phase index list in chronological order. The phase index list uses time as its main axis and the correspondence between pollutant concentration peaks as its identifying unit. Each unit records key physical parameters such as wind speed, wind direction, flow velocity, water level, and temperature. The phase index list not only reflects the temporal coupling patterns of air and water risk processes but also demonstrates the dynamic correlation of cross-media risk responses under different environmental conditions. This list allows for the direct establishment of correspondence between atmospheric and water risk processes, enabling dynamic coupling between air and water on a unified temporal dimension. At this point, the cross-media temporal anchoring zone is constructed. The establishment of a unified time axis provides continuous temporal reference for subsequent spatial overlay area positioning, coupling core point extraction, and energy pathway spectrum generation, thereby ensuring the temporal coordination and consistency of the atmospheric and water risk assessment process.

[0026] Based on the phase index list, analyze the pollutant peak misalignment segments, calculate the spatial overlap intensity, locate the overlapping area of ​​air and water risks, extract the coupling core point set, and generate an overlay core list to characterize the spatial aggregation characteristics of cross-media risks. After constructing the cross-media temporal anchoring band and generating the phase index list, in order to analyze the spatial interaction characteristics of pollutant risk processes between the atmosphere and water bodies, the temporal and spatial correspondence information recorded in the phase index list is analyzed in depth. By identifying the spatial misalignment distribution of pollutant concentration peaks, the spatial overlap intensity between risk fields of different media is calculated, the overlapping area of ​​air and water risks is located, and then the coupling core point set is extracted and an overlay kernel list is generated to characterize the spatial aggregation pattern of cross-media risks. The specific implementation steps are as follows:

[0027] Based on the generated phase index list, the spatial mapping of the correspondence between the peak concentration segments of atmospheric and water pollutants recorded therein is performed. By unfolding the environmental parameters such as wind speed, wind direction, flow velocity, water level, and temperature contained in each phase index unit according to a unified geographic coordinate system, pollution concentration distribution fields for air and water media are formed in the spatial distribution map. The pollution concentration distribution field of air media mainly reflects the spatial distribution trend of pollutants with changes in wind direction and altitude, while the pollution concentration distribution field of water media reflects the migration characteristics of pollutants in the horizontal and vertical directions. By accurately superimposing the two distribution fields on the spatial coordinates, a spatial projection relationship between the atmospheric pollution source and the water pollution response area is established, so that the cross-media pollution process has an observable overlap basis in space, providing a prerequisite for the subsequent identification of misaligned segments.

[0028] After spatial mapping, the corresponding regions of air and water pollutant concentration peaks are compared and analyzed spatially based on the phase difference information of each time period in the phase index list. Due to differences in the propagation speed, media resistance, and energy dissipation paths of pollutants in the two media, there is often a spatial offset between the air pollution peak area and the water pollution peak area. To characterize this offset, the geographic coordinates of the concentration peak locations in the air and water pollution concentration distribution fields are continuously extracted, and the offset is recorded in the form of spatial distance. The spatial offset between each pair of peak locations corresponds to a pollutant peak misalignment segment, which is represented in three-dimensional space as a set of spatial distance vectors between the air pollutant peak area and the water pollutant peak area. By continuously analyzing the distribution patterns of these spatial vectors in geographic space, the spatial trends of pollutant migration and superposition in different regions can be determined, providing basic data for subsequent calculations of spatial overlap intensity.

[0029] After obtaining the spatial vector information of the misaligned segments of pollutant peaks, these segments are further aggregated and calculated to form the spatial overlap intensity distribution between risk fields of different media. Spatial overlap intensity represents the degree of spatial mutual influence between the peak concentration regions of pollutants in air and water media; its physical meaning is the degree of overlap of the peak concentrations of pollutants in two media within the same geographic unit. By spatially aggregating the misaligned segments in a unified coordinate system, regions where high concentrations of air pollutants and high concentrations of water pollutants coexist in three-dimensional space are identified, and the overlap ratio and spatial energy coupling degree of these regions are calculated. When the spatial overlap intensity exceeds a certain threshold, it indicates that air and water risk processes are coupled and superimposed within this region, forming a risk energy accumulation area. Through this quantitative description of spatial overlap intensity, the intersection zone of atmospheric diffusion waves and water migration waves can be intuitively identified, providing a basis for determining the spatial boundary of the risk superposition zone.

[0030] After determining the spatial overlap intensity distribution, areas with high overlap intensity are further spatially screened and refined. Spatial coordinates, environmental parameters, and risk characteristics within these areas are summarized and integrated to form a set of coupled core points. Each coupled core point represents a key location where air and water media exhibit spatial risk superposition and energy transfer. To further express the spatial aggregation characteristics of cross-media risks, all coupled core points are arranged in an orderly manner according to spatial proximity and environmental characteristic similarity, generating a superposition kernel list. The superposition kernel list records multi-dimensional parameters such as the spatial location, phase difference characteristics, peak pollution concentration, wind speed, water flow velocity, and temperature difference of each core point, constituting a core description of the spatial distribution of cross-media risks. Through the superposition kernel list, the spatial superposition relationship between the atmospheric risk field and the water risk field can be clearly displayed at the regional scale, providing a basic positioning basis for subsequent interface gradient information collection and the establishment of gas-liquid exchange channel fingerprints.

[0031] Using the superimposed kernel list as input, the temperature gradient, concentration gradient and pressure gradient information of the air-water interface are collected to establish the fingerprint of the gas-liquid exchange channel, calculate the energy transfer path of the interface, generate the cross-layer energy pathway spectrum, and extract the energy anomaly peak to form the coupling anomaly spectrum. After analyzing the misaligned peak segments of pollutants, calculating the spatial overlap intensity, and generating the superposition kernel inventory, to reveal the energy transfer mechanism at the air-water interface and construct an energy expression system for cross-media risk, it is necessary to use the superposition kernel inventory as input, collect temperature gradient, concentration gradient, and pressure gradient information at the air-water interface, establish a fingerprint of the gas-liquid exchange channel, calculate the interface energy transfer path, generate a cross-layer energy pathway spectrum, and extract energy anomaly peaks to form a coupled anomaly spectrum. This step achieves a quantitative expression of cross-media energy transfer characteristics through continuous analysis of the physical state of the interface layer and construction of gradient structures. The specific implementation steps are as follows:

[0032] Using the set of coupled core points recorded in the overlay core list as the basis for spatial positioning, an interface region between the air and water media is defined within the vertical range above and below each coupled core point, and the distribution of environmental field parameters within this region is obtained. The interface region includes the air-side boundary layer, the water surface layer, and the gas-liquid transition zone between the two. Based on this, temperature data, pollutant concentration data, and pressure data at the interface are collected to ensure that a continuous vertical parameter distribution from air to water can be obtained at the spatial location of each coupled core point. The temperature gradient reflects the heat transfer state between air and water, the concentration gradient reveals the migration trend of pollutants on both sides of the interface, and the pressure gradient reflects the driving force of the energy balance at the gas-liquid interface. By continuously collecting multiple sets of parameters at each core point location, the original data sequence of cross-interface temperature field, concentration field, and pressure field can be constructed, laying the physical foundation for the extraction of interface gradient information.

[0033] After collecting the raw interface data, the temperature, concentration, and pressure parameters of the air-water interface region were stratified with the vertical direction as a reference. Stratification analysis distinguishes the air layer, the water surface layer, and the gas-liquid transition layer near the interface, making the spatial distribution of parameters in each layer clearer. Based on the spatial coordinates of each coupling core point, the temperature, concentration, and pressure distributions on the air side were sequentially projected downwards to the water surface layer, and the parameter differences between each layer were continuously calculated in the vertical direction, thus obtaining the numerical distributions of the interface temperature gradient, interface concentration gradient, and interface pressure gradient. At this point, the three gradient fields jointly describe the cross-interface energy and matter transfer characteristics between air and water. Changes in the temperature gradient reveal the flow path of heat between the gas and liquid phases, changes in the concentration gradient reflect the diffusion tendency of pollutants between the two phases, and changes in the pressure gradient embody the dynamic mechanism of energy transfer at the interface. By establishing these gradient distributions within the spatial range of different coupling core points, an energy exchange profile across the medium interface is formed, providing data support for the establishment of gas-liquid exchange channels.

[0034] After clarifying the interface gradient information, based on the location and gradient characteristics of each coupling core point in the overlay kernel list, a gas-liquid exchange channel fingerprint between air and water is constructed. The gas-liquid exchange channel fingerprint is a structured description characterizing the energy and mass transfer features at the interface. Its essence lies in the directionality, intensity, and interaction patterns of energy flow between the air and water sides at different coupling core points. By comprehensively summarizing the continuous spatial distribution of temperature, concentration, and pressure gradients, the morphology of energy and mass transfer channels at the interface can be depicted. Each transfer channel corresponds to a specific energy flow direction, extending from the high-energy region on the air side to the energy-receiving region on the water side. By systematically integrating these channels, a complete gas-liquid exchange channel fingerprint can be formed. This map not only reflects the continuity of energy transfer between different core points but also reveals the spatial patterns of interfacial energy flow, providing a clear physical basis for subsequent calculations of interfacial energy transfer paths.

[0035] Based on the fingerprint of gas-liquid exchange channels, energy transfer paths between the air and water interfaces are calculated, and cross-layer energy pathway spectra are generated based on the energy channel structure. The construction of energy transfer paths is centered on interfacial gradient changes, tracking continuous vertical changes in temperature, concentration, and pressure along the channel direction to obtain the spatial trajectory of energy flow. The cross-layer energy pathway spectra are a concentrated expression of these energy paths, describing the order of energy conduction, intensity distribution, and coupling state between different media. Through multi-level analysis of the energy pathway spectra, anomalous regions in the energy transfer process can be identified. When a sudden change in the gradient change of an energy path or a significant increase in energy flux density occurs, it indicates the presence of energy accumulation or nonlinear interaction behavior. By extracting and organizing these anomalous points, coupled anomaly spectra are generated. The coupled anomaly spectra record the spatiotemporal location, energy intensity, and characteristic parameters of anomalous energy exchange at the air-water interface, serving as a crucial basis for subsequent cross-medium risk energy budgeting. Through the identification and organization of coupled anomaly spectra, anomalous patterns of cross-boundary energy transfer can be clearly identified, providing a traceable physical reference for risk energy accumulation analysis and dynamic control.

[0036] Based on the coupled anomaly spectrum, cross-medium risk energy budgeting is carried out to calculate the risk energy accumulation rate and release direction, determine the priority of disposal, compress the risk response time, and generate a continuous traction command stream for dynamic control of the cross-medium risk evolution process. After establishing the fingerprint of the gas-liquid exchange channel, generating the cross-layer energy pathway spectrum, and extracting the coupled anomaly spectrum, in order to further quantify the dynamic changes of cross-medium energy, a cross-medium risk energy budget is conducted. This allows for the calculation of the risk energy accumulation rate and release direction, determination of priority handling order, compression of risk response time, and generation of a continuous traction command stream for dynamic control of the cross-medium risk evolution process. The specific steps are as follows:

[0037] Based on the formed coupled anomaly spectrum, the energy transfer characteristics between air and water are comprehensively organized and analyzed in layers. In this stage, the coupled anomaly spectrum records the distribution of anomalous peaks in energy transfer at the gas-liquid interface, including energy accumulation locations, anomaly intensities, duration intervals, and relationships between adjacent anomalous peaks. Using this information as input, energy anomaly points are divided into different energy activity regions according to temporal order and spatial distribution, and the energy interaction state between the air and water sides within each region is determined. On the air side, energy activity regions typically exhibit a combined response of increased wind speed, temperature, and enhanced pollution concentration; while on the water side, this manifests as changes in flow velocity, enhanced interface disturbances, and pollutant redistribution. By corresponding to these states, a spatial distribution structure of energy inflow, energy retention, and energy release zones is constructed, providing a zoning basis for cross-medium risk energy budgeting and enabling subsequent calculations of energy accumulation rates to have a traceable spatial framework.

[0038] After the energy activity zone is identified, cross-media risk energy budgeting continues. The core of energy budgeting lies in calculating the accumulation rate and release direction of energy between air and water. The accumulation rate reflects the accumulation trend of energy per unit time, while the release direction characterizes the tendency of energy transfer from one medium to another. Using the energy flow paths recorded in the energy pathway spectrum as a reference, within each energy activity zone, the rate of change of temperature gradient, the magnitude of pressure difference change, and the intensity of pollution concentration change on both the air and water sides are analyzed, and the trends of these physical quantities characterize the energy accumulation process at the interface. When the temperature and pressure gradients increase simultaneously in the same direction, it indicates that energy is continuously transferring in that direction; when the concentration gradient tends to equilibrium, it indicates that an energy release process is underway. Through systematic analysis of these gradient changes, the dominant direction of energy flow can be determined, and the energy accumulation and release rates between the air and water media can be calculated, thereby obtaining the overall trend of energy flow. In this process, the synchronicity of energy changes on the air and water sides reflects the connectivity of risk energy transmission, while the difference in accumulation rate and release direction reveals the nonlinear characteristics of cross-media energy exchange.

[0039] After calculating the energy accumulation rate and release direction, the priority order for cross-media risk energy disposal is determined based on the energy budget results. The priority order is determined by a comprehensive judgment of energy accumulation rate, energy release direction, and spatial superposition intensity. When the energy accumulation rate is high and the release direction is concentrated on the air-to-water transfer, it indicates that risk energy may accumulate on the water surface, and interface intervention should be prioritized. When the release direction is from the water to the air and accompanied by frequent air layer energy fluctuations, atmospheric risk dispersal measures should be prioritized. By ranking the energy accumulation states of different regions, a risk energy disposal sequence is constructed, giving a temporal order to risk management on the air and water sides. While determining the disposal order, a response time window is set based on the temporal duration characteristics of energy accumulation, allowing energy changes in each risk zone to be controlled within the shortest response period, thereby compressing the overall risk response time. Based on this, the energy budget results are transformed into disposal logic, establishing a dynamic mapping relationship between risk energy transfer and intervention actions, so that the energy budget is not only used for assessment but also for guiding the response.

[0040] Based on priority order and response time window, a continuous traction command stream is generated for dynamic control of cross-media risk evolution. The continuous traction command stream is a dynamic command sequence based on energy budget results, including energy transfer paths, time response rhythms, and interface control behaviors. By temporally matching and directionally integrating energy flow information from the air and water sides, a command sequence is formed on a unified time axis, synchronizing atmospheric and water risk control actions. Each command stream corresponds to a specific risk energy control task, such as guiding energy release from the air side, mitigating energy accumulation on the water surface, or adjusting the energy transfer frequency at the gas-liquid interface. The generation of the continuous traction command stream considers not only the direction and intensity of energy flow but also the temporal patterns of energy transfer, enabling risk control to respond in real time to energy changes. When the continuous traction command stream is executed, the energy interaction between air and water will gradually reach equilibrium according to the set rhythm, suppressing the accumulation trend of risk energy and smoothly guiding the energy release process, thus keeping the cross-media risk evolution process under control.

[0041] Based on the continuous traction command stream, a breathing-type phase reversal traction is executed, and a frequency misrelease threshold and a negative time delay preset window are set to drive the interface pulse bypass closure, thereby controlling the real-time dispersion and dynamic stability of risk energy between air and water. After completing the cross-medium risk energy budgeting based on coupled anomaly spectra, calculating the risk energy accumulation rate and release direction, determining the priority disposal order, and generating a continuous traction command stream, in order to achieve real-time dispersion and dynamic stabilization of risk energy between air and water, a breathing-style phase reflection traction is executed based on the continuous traction command stream. By setting an off-frequency release threshold and a negative time delay preset window, the interface pulsating bypass is driven to close, enabling an adaptive dynamic equilibrium to be formed in the energy transfer process between the atmospheric side and the water side. The specific steps are as follows:

[0042] Based on the generated continuous traction command stream, a control path for breathing-style phase-reflection traction is established. The continuous traction command stream contains information such as energy accumulation rate, release direction, time response rhythm, and processing sequence. Using this information as input, an energy response cycle is defined on the time axis, making the energy change process on the air and water sides exhibit a periodic cyclical characteristic. The principle of breathing-style phase-reflection traction lies in utilizing the periodic variation characteristics of energy fluctuations, guiding energy release and absorption alternately between air and water, creating a breathing-like reciprocating rhythm in energy flow. In this stage, based on the energy release direction in the command stream, the high-energy region on the air side is used as the energy output source, and the relatively low-energy region on the water side is used as the energy absorption end, establishing an energy reflection path. As time progresses, when energy accumulates to a set threshold on the water side, the reverse traction process is initiated, and energy on the water side is fed back to the air side, achieving periodic reflection of energy flow. Through this reciprocating traction mechanism, energy transfer between air and water is no longer unidirectional accumulation, but forms a cyclical pattern with breathing characteristics, creating a stable foundation for subsequent frequency and time regulation.

[0043] After establishing a breathing-style phase-reflection traction cycle, a frequency-shifting release threshold is set to regulate the frequency relationship between different energy fluctuation processes. The frequency-shifting release threshold defines the time and frequency difference between energy release on the air and water sides to avoid the superposition of energy waves from both sides within the same time period, forming energy peak resonance. Based on the time rhythm information in the continuous traction command stream, the energy release processes on the air and water sides are divided into different frequency bands, staggering the energy release phases to maintain asynchronous release during energy exchange. When the air-side energy release reaches the set threshold, the water-side energy absorption process is immediately triggered; conversely, when the water-side energy release slows down, the air-side energy release restarts. By precisely controlling the frequency shifting relationship in time, the energy release and absorption between air and water form an alternating state. The setting of the frequency-shifting release threshold makes the time structure of energy transfer more stable, preventing energy superposition fluctuations caused by synchronous release, thereby reducing energy accumulation at the interface and maintaining the balance of cross-medium energy flow. This sub-step combines time shifting with energy release rhythm, enabling the entire system's energy exchange to have adaptive adjustment capabilities.

[0044] After establishing the frequency-shifting release mechanism, a negative time delay preset window is set to further refine the temporal structure of the energy response. The negative time delay preset window is used to trigger the interface response before energy fluctuations reach the interface, giving energy transfer a feedforward characteristic. When the energy change trend on the air side indicates that it is about to reach the release threshold, the energy absorption mechanism on the water side is activated in advance; similarly, when the energy accumulation on the water side tends to peak, the energy diffusion process on the air side enters a slow-release state in advance. By introducing a negative time delay in the time dimension, the energy transfer response no longer lags behind the change but intervenes in advance, achieving dynamic pre-control of cross-medium energy. The introduction of the negative time delay preset window makes the energy flow between air and water no longer a passive response state but has forward guiding characteristics. In actual operation, the negative time delay preset window dynamically adjusts its advance time interval according to the changing trend of the energy accumulation rate in the traction command flow to maintain the continuity and coordination of the interface response. When the energy exchange between air and water stabilizes, the negative time delay preset window automatically shortens to maintain the natural rhythm of the energy wave. When energy fluctuations are strong or phase shifts intensify, the preset window lengthens to intervene in the energy release process earlier and prevent energy accumulation from exceeding limits. In this way, the interface energy transfer has a time-adaptive adjustment function, providing a stable time basis for subsequent pulse bypass control.

[0045] Under the influence of the negative time delay preset window, the interfacial pulsating bypass is closed, thereby controlling the real-time dispersion and dynamic stability of risky energy between air and water. The interfacial pulsating bypass refers to an auxiliary pathway for energy exchange at the gas-liquid interface. When energy accumulates or abruptly changes in the main transfer path, the bypass channel temporarily opens to divert some energy, preventing energy peaks in local areas. When the breathing-type phase reflection traction, frequency misalignment release threshold, and negative time delay preset window work together, the bypass channel is in a pulsating open state, periodically opening and closing with changes in the energy flow rhythm. In the initial stage of energy accumulation, the bypass is partially open to mitigate the energy growth rate; when energy transfer tends to balance, the bypass gradually closes, allowing energy to return to the main transfer path. When energy fluctuations occur, the bypass reopens, dispersing small amounts of energy through pulsating rhythm, thus maintaining overall system stability. Through the periodic pulsating closing process, the risky energy between air and water is evenly distributed in space and time, preventing sudden increases or decreases in interfacial energy and maintaining a dynamically stable state of cross-medium energy transfer.

[0046] This invention constructs a cross-medium temporal anchoring band, a spatially superimposed kernel inventory, and a gas-liquid exchange channel fingerprint, enabling a continuous coupling relationship between risk processes in air and water across three dimensions: time, space, and energy. Through joint analysis of pollutant peak phase differences, spatially misaligned segments, and interfacial energy gradients, it achieves dynamic tracking of cross-medium risk transmission paths, making the risk evolution process observable and controllable. This scheme can effectively identify regions of risk energy accumulation between air and water, accurately capture anomalous behaviors in cross-boundary energy transfer, thereby improving the timeliness and accuracy of regional risk assessment.

[0047] This invention transforms the energy transfer process between air and water from a unidirectional release to a periodic equilibrium process by using energy budgeting based on coupled anomaly spectra and breathing-like phase reflection traction. By setting mis-frequency release thresholds and negative time delay preset windows, the time difference and response rhythm of air and water during energy transfer are coordinated, achieving proactive dispersion and dynamic stability control of risk energy. This scheme can intervene early in the initial stage of energy accumulation, preventing the formation of risk peaks and resonance amplification, ensuring the controllability of cross-medium risks during evolution and the continuous stability of the environmental system.

[0048] 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 cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields, characterized in that, Includes the following steps: Based on the needs of environmental risk assessment, a cross-media time-series anchoring band is constructed. By fusing wind field data and water flow data to form a unified time axis, the phase difference of pollutant concentration peak segments is extracted to generate a phase index list. Based on the phase index list, analyze the pollutant peak misalignment segments, calculate the spatial overlap intensity, locate the risk overlap area of ​​air and water bodies, extract the coupling core point set, and generate the superposition core list; Using the superimposed kernel list as input, the temperature gradient, concentration gradient and pressure gradient information of the air-water interface are collected to establish the fingerprint of the gas-liquid exchange channel, calculate the energy transfer path of the interface, generate the cross-layer energy pathway spectrum, and extract the energy anomaly peak to form the coupling anomaly spectrum. Based on the coupled anomaly spectrum, cross-medium risk energy budgeting is carried out to calculate the risk energy accumulation rate and release direction, determine the priority of disposal, compress risk response time, and generate a continuous traction command stream. Based on the continuous traction command stream, breathing-type phase reversal traction is executed. The frequency misrelease threshold and negative time delay preset window are set to drive the interface pulse bypass closure, thereby controlling the real-time dispersion and dynamic stability of risk energy between air and water.

2. The cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields according to claim 1, characterized in that, The steps for generating the phase index list are as follows: Based on the requirements of environmental risk assessment, the boundary coupling range between the atmosphere and water body is determined, and continuous time series data of aerodynamic and hydrodynamic parameters are obtained under a unified geographic reference coordinate system to establish an initial time frame as the basis for cross-media time series anchoring. The wind field data and water flow data are synchronously segmented and processed according to a unified time axis. The fluctuation trend of the pollutant concentration change curve is calculated. The change curves of the two media are superimposed on the same time reference line to identify the key moment of the pollution concentration peak and determine its location. By comparing the occurrence times of the two types of peak segments using a unified time axis, the phase difference between air and water is extracted and time-series offset data is generated. The corresponding relationships of concentration peaks in different time periods are systematically organized to generate a phase index list.

3. The cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields according to claim 2, characterized in that, When generating the phase index list, the corresponding relationship of pollutant concentration peaks is used as the identification unit with time as the main axis. In each unit, wind speed, wind direction, flow velocity, water level and temperature parameters are recorded. These parameters are arranged in chronological order to form a continuous time series, establishing a dynamic coupling relationship between air and water risk processes on a unified time dimension.

4. The cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields according to claim 2, characterized in that, The steps for analyzing pollutant peak misalignment segments based on the phase index list, calculating spatial overlap intensity, locating the overlapping area of ​​air and water risks, and generating an overlay kernel list are as follows: Based on the phase index list, the spatial mapping of the correspondence between the peak concentration segments of pollutants in the atmosphere and water is carried out to form a pollution concentration distribution field of air and water media in a unified geographic coordinate system. Based on the phase difference information in the phase index list, the peak positions of the two media are compared, the spatial offset is extracted, and a misaligned segment of the pollutant peak is formed. Spatial aggregation of pollutant peak misalignment segments is performed to calculate the spatial overlap intensity between air and water media and identify risk superposition areas. The spatial overlap intensity distribution is screened and the core is extracted to generate a list of overlay kernels.

5. The cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields according to claim 4, characterized in that, In the process of generating the overlay nuclear inventory, the spatial coordinates, phase difference characteristics, peak pollution concentration, wind speed, water flow velocity and temperature difference of air and water media are comprehensively summarized and arranged in an orderly manner based on spatial proximity and environmental characteristic similarity, so that the overlay nuclear inventory can accurately characterize the spatial aggregation characteristics of cross-media risks at the regional scale.

6. The cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields according to claim 4, characterized in that, The steps for collecting temperature gradient, concentration gradient, and pressure gradient information at the air-water interface and generating a coupled anomaly spectrum using a stacked kernel list as input are as follows: Using the set of coupled core points in the superimposed core list as the basis for spatial positioning, temperature, concentration and pressure data of air and water media are collected in the interface region to form cross-interface temperature field, concentration field and pressure field. Using the vertical direction as a reference, the parameters of the air-water interface are stratified, and the distribution of the interface temperature gradient, concentration gradient and pressure gradient are calculated to form an energy exchange profile. A fingerprint of the gas-liquid exchange channel is established based on the location and gradient characteristics of the coupling core point to depict the directionality and continuity of energy flow between air and water. Based on the energy transfer path of the gas-liquid exchange channel fingerprint calculation interface, a cross-layer energy pathway spectrum is generated and an abnormal energy peak is extracted to form a coupled anomaly spectrum.

7. The cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields according to claim 6, characterized in that, The establishment of the gas-liquid exchange channel fingerprint is achieved by continuously collecting the variation characteristics of temperature gradient, concentration gradient and pressure gradient within the spatial range of the coupling core point to form a fingerprint spectrum. The fingerprint spectrum is used to characterize the directionality and intensity distribution of energy flow between air and water, and serves as the basis for calculating the cross-layer energy pathway spectrum and identifying coupling anomaly spectrum.

8. The cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields according to claim 6, characterized in that, The steps for cross-medium risk energy budgeting and generating a continuous traction command stream based on coupled anomaly spectrum are as follows: Based on the coupled anomaly spectrum, the energy transfer characteristics between air and water are sorted out and analyzed in layers to divide the energy activity region and determine the energy interaction state between the air side and the water side. Calculate the energy accumulation rate and release direction within the energy activity area, and determine the dominant energy flow direction based on the temperature gradient, pressure difference, and pollution concentration change trends. Based on the energy budget results, the priority of cross-medium risk energy disposal is determined, response time windows are set, and a dynamic mapping relationship between energy transfer and intervention behavior is established; A continuous traction command stream is generated based on the priority order and response time window.

9. The cross-media regional integrated environmental risk assessment method that integrates atmospheric and water risk fields according to claim 8, characterized in that, The generation of continuous traction command streams involves matching and integrating energy flow information from the air and water sides on a unified time axis, synchronizing the timing and control sequence of the energy transfer process, and guiding the coordinated operation of energy release from the air side and energy absorption from the water side through the command stream, thereby maintaining the dynamic balance and stable transfer of cross-medium risk energy.

10. The cross-media regional integrated environmental risk assessment method integrating atmospheric and water risk fields according to claim 8, characterized in that, The steps for performing breathing-type phase-reflection traction and controlling the dynamic stabilization of risk energy between air and water based on the continuous traction command flow are as follows: A control path for breathing-type phase reversal traction is established based on the continuous traction command flow. A breathing cycle is formed through the periodic repetition of energy release and absorption, so that the energy flow is in a periodic reversal state. Set a false frequency release threshold to adjust the time difference and frequency difference of energy release between the air side and the water side to prevent energy wave superposition from causing energy accumulation; Set a negative delay preset window to trigger the interface response in advance before energy fluctuations reach the interface, so that energy transfer has feedforward characteristics and forms dynamic pre-control. Under the action of the negative time delay preset window, the interface pulsation bypass is closed, and the risk energy between the air and water is controlled in real time and dynamically stabilized through periodic pulsation.

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