A pollen diffusion forecasting and early warning method based on a meteorological numerical model

Abstract

The application relates to the technical fields of meteorological environment prediction and computer information processing, and discloses a pollen diffusion prediction and early warning method based on a meteorological numerical model, which combines meteorological grid data and pollen source emission intensity, calculates physical transmission flux between nodes in upwind and downwind directions and meteorological physical transmission estimated time consumption, and calculates a predictive load proportion coefficient in combination with system load tolerance; when the coefficient reaches a threshold value, a time margin parameter is generated by comparing the above meteorological time consumption and data task migration time consumption, and a full-amount synchronization or a minimal metadata shadow degradation injection process is controlled according to the parameter; when the observation concentration of the downwind node exceeds the standard, a dynamic geographic fence is generated and an early warning instruction is pushed to a terminal; and finally, a terminal state confirmation frame is received to statistically calculate an effective blocking rate of the equipment. The application adapts data migration progress to physical diffusion speed, guarantees the timeliness of early warning, and dynamically optimizes cloud computing power scheduling based on physical feedback.

Description

Technical Field

[0001] This invention relates to the field of meteorological and environmental forecasting and computer information processing technology, specifically to a pollen dispersal forecasting and early warning method based on meteorological numerical models. Background Technology

[0002] With climate change, pollen allergies are increasingly impacting public health, making pollen concentration forecasting and early warning a crucial aspect of environmental monitoring. In practical applications, accurate pollen warnings rely not only on precise modeling of natural physical and meteorological conditions but also on the efficient and timely processing and distribution of massive amounts of warning signals by the underlying computer network system. However, when extreme weather conditions cause pollen to spread rapidly and over a large area, the monitoring network generates enormous data processing and transmission demands within a short period, often placing immense pressure on the computing power and network bandwidth of the IT system. Existing environmental forecasting systems, when dealing with such data flows driven by sudden changes in the physical environment, typically lack time-adaptability to the physical reality in their underlying scheduling mechanisms.

[0003] To address the aforementioned issues, existing technologies have explored relevant approaches. For example, Chinese patent document CN120125053A discloses a pollen forecasting method based on ensemble learning, which extracts meteorological factors and static surface data and uses a machine learning model to numerically predict pollen concentration. Chinese patent document CN120177298A discloses a pollen dispersal monitoring method, device, electronic equipment, and storage medium, which achieves dynamic synchronization and load balancing of pollen source data by evaluating changes in the number of monitoring points in a cloud platform distributed system. While these existing technologies have made progress in concentration calculation and data manageability, they generally suffer from a disconnect between information transmission flow and actual physical diffusion flow. The former focuses on pure numerical prediction, failing to consider the computational bottlenecks and transmission congestion caused by sudden surges in prediction data; the latter relies solely on conventional computer load logic, neglecting to incorporate natural factors such as meteorological wind speed and spatial distance—which determine the actual arrival time of pollen—into the IT scheduling evaluation. This results in the system continuing to perform routine full data cross-node synchronization when the physical transmission driven by weather is extremely short. The network transmission and persistent read / write time often lags behind the actual arrival time of pollutants, causing the early warning to lose its timeliness.

[0004] In addition, existing early warnings are mostly delivered in a one-way manner. The cloud platform downwind cannot obtain the actual execution status of the physical protection actions of the terminal devices, and cannot dynamically adjust the system's load tolerance based on the blocking feedback of the real environment. When faced with high-concurrency early warnings, it is easy to cause blind accumulation and ineffective consumption of cloud computing resources. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a pollen dispersal forecasting and early warning method based on meteorological numerical models. This method solves the problems of conventional environmental monitoring systems, which struggle to match the actual dispersal speed of pollutants caused by sudden meteorological changes, leading to delays in issuing early warning commands. Furthermore, the system scheduling mechanism lacks data support for terminal protection action response status, and cloud resource allocation relies on fixed strategies, making it impossible to perform closed-loop adjustments based on the actual barrier status, which can easily cause system communication link blockage or excessive resource allocation.

[0006] To address the aforementioned problems, in a first aspect, the present invention provides a pollen dispersal forecasting and early warning method based on a meteorological numerical model, comprising: Based on meteorological forecast grid data and the initial emission intensity of pollen sources, the physical transport flux between upwind and downwind nodes and the estimated time of meteorological physical transport are calculated. Based on the physical transmission throughput and the system load tolerance attribute value of the downwind node, calculate the predictive load ratio coefficient; When the predictive load ratio coefficient reaches the preset load ratio threshold, the task migration time is determined, the estimated time of meteorological physical transmission is compared with the task migration time to generate a time margin parameter, and the upwind node is controlled to transmit the full pollen source data to the downwind node according to the time margin parameter to complete the full data attribute synchronization, or the upwind node is controlled to extract the core features of the pollen source to generate simplified metadata and inject it into the downwind node to achieve simplified metadata shadow degradation injection. When the observed concentration at the downwind node exceeds a preset concentration threshold, a dynamic geofence is generated, and an early warning instruction packet is pushed to the protective terminal within the dynamic geofence.

[0007] Furthermore, the core features of the pollen source include: pollen classification code, current highest concentration extreme value, and affected coordinate boundary field.

[0008] Furthermore, the calculation of the physical transport flux between the upwind and downwind nodes includes: Extract the zonal component of horizontal wind speed, the meridional component of horizontal wind speed, and the atmospheric boundary layer height of the target grid at time t from the meteorological forecast grid data; Calculate the latitudinal distance difference and the longitudinal distance difference between the upwind node and the downwind node; A wind speed composite vector is obtained by synthesizing the zonal component and the meridional component of the horizontal wind speed, and the spatial connection direction between the upwind node and the downwind node is determined by the difference between the zonal distance and the difference between the meridional distance; if the atmospheric boundary layer height is lower than the set height safety threshold, the height safety threshold is used to replace the atmospheric boundary layer height to obtain the verified atmospheric boundary layer height. The physical transport flux across the spatial cross-section of the upwind and downwind nodes is calculated by multiplying the initial pollen emission intensity at time t, the verified atmospheric boundary layer height, and the effective projection component of the wind speed composite vector along the spatial line.

[0009] Furthermore, the calculation of the estimated time for meteorological physical transmission includes: calculating the spatial straight-line physical distance between the upwind node and the downwind node; Extract the horizontal wind field components from the meteorological forecast grid data, and calculate the absolute wind speed scalar value after synthesizing the horizontal wind field components. If the absolute wind speed scalar value is lower than the set system wind speed lower limit, the time calculation for the upwind node and the downwind node will be stopped, and the estimated time for meteorological physical transmission will be marked as the maximum value. If the absolute wind speed scalar value is greater than or equal to the lower limit of the system wind speed, the estimated time for meteorological physical transmission is obtained by dividing the spatial straight-line physical distance by the absolute wind speed scalar value.

[0010] Further, the calculation of the predictive load proportion factor includes: Cluster the upwind nodes that point to the same downwind node into an associated upstream set; extract the number of pollen source data record entries or weighted volume maintained in the memory of each upwind node in the associated upstream set as the task data storage weight; When the read system load tolerance attribute value is lower than the set hard drop protection threshold, the system load tolerance attribute value is replaced by the hard drop protection threshold to obtain the verified system load tolerance attribute value. The product of the physical transmission throughput and the task data storage weight is summed within the associated upstream set and divided by the verified system load tolerance attribute value to obtain the predictive load ratio coefficient.

[0011] Furthermore, based on the time margin parameter, the upwind node transmits full pollen source data to the downwind node to complete full data attribute synchronization, including: Based on the detected available transmission bandwidth of the network layer and the volume of the full data of the pollen source to be migrated, the migration time of the task is estimated. The difference between the estimated time of meteorological physical transmission and the time of task migration is calculated to generate the time margin parameter. If the time margin parameter is greater than or equal to a preset safe time threshold, the upwind node is controlled to apply a read-only concurrent lock to the business table data to be migrated, and all data entities are migrated to the persistent storage layer of the downwind node. After verification, the read-only concurrent lock is released and the read and write control is transferred to complete the synchronization of all data attributes.

[0012] Further, the step of controlling the extraction of pollen source core features from the upwind node based on the time margin parameter to generate simplified metadata and injecting it into the downwind node to achieve simplified metadata shadow degradation injection includes: Based on the detected available transmission bandwidth of the network layer and the volume of the full data of the pollen source to be migrated, the migration time of the task is estimated. Calculate the difference between the estimated time of the meteorological physical transmission and the time of the task migration, and generate the time margin parameter; If the time margin parameter is less than the preset safe time threshold, the upwind node is triggered to extract the core features of the pollen source, generate simplified metadata, and inject it into the downwind node to achieve the shadow downgrade injection of the simplified metadata.

[0013] Furthermore, the assessment of the task migration time based on the detected available transmission bandwidth of the network layer and the total data volume of the pollen source to be migrated includes: If the detected available transmission bandwidth of the network layer is lower than the preset lower limit of bandwidth, the calculation of the full migration time will be stopped, and the task migration time will be marked as the maximum value. If the detected available transmission bandwidth of the network layer is greater than or equal to the preset lower limit of bandwidth, the total data volume of the pollen source to be migrated is divided by the available transmission bandwidth of the network layer, and the constant time for the preheating of the underlying system services is added to obtain the migration time of the task.

[0014] Furthermore, the step of controlling the upwind node to extract core features of the pollen source, generate simplified metadata, and inject it into the downwind node to achieve simplified metadata shadow degradation injection specifically includes: The upwind node extracts the core features of the pollen source from the full data of the pollen source to be migrated, forming a minimal metadata set. The upwind node is controlled to directly inject the simplified metadata set across the network into the memory mapping area of ​​the downwind node, generating a memory-level pollen source shadow record that is not verified by a persistent database. The memory-level pollen source shadow record is associated with an applicable prediction period identifier and an expiration time limit. The upwind node is controlled to start an asynchronous message queue, and the remaining non-core business data is backfilled into the persistent storage layer of the downwind node using idle network bandwidth.

[0015] Furthermore, the step of generating a dynamic geofence when the observed concentration at the downwind node exceeds a preset concentration threshold includes: Obtain the sliding time window of the downwind node within a set time period. When the arithmetic mean of the observed concentration sequence within the sliding time window reaches the preset concentration threshold, or the number of consecutive peaks exceeding the preset concentration threshold reaches a set count value, it is determined that the observed concentration of the downwind node exceeds the preset concentration threshold. Obtain the rippled coordinate boundary field transmitted in the full data attribute synchronization or the minimal metadata shadow degradation injection, convert the rippled coordinate boundary field into geographic coordinate points and close it to generate an initial polygon. Establish a linear correspondence between the buffer distance and the real-time wind speed scalar value, and calculate the target buffer distance based on the linear correspondence and the current real-time wind speed scalar value; Using a spatial buffer analysis algorithm, the target buffer distance is extended outward from the boundary of the initial polygon to construct a dynamic geofence with spatial redundancy.

[0016] Furthermore, the step of pushing the early warning instruction package to the protected terminal within the dynamic geofence includes: performing a spatial index search on the device registration list within the dynamic geofence; if the same protected terminal falls within the coverage area of ​​multiple geofences, deduplication is performed according to a preset rule; configuring a timeout retransmission flag and an instruction lifecycle timestamp in the header of the early warning instruction package, and concurrently pushing the early warning instruction package through the IoT signaling channel.

[0017] Furthermore, after pushing the early warning instruction packet to the protective terminal located within the dynamic geofence, the method further includes: receiving a status confirmation frame reported by the protective terminal; The status confirmation frame encapsulates the device machine feature code and the timeout intervention status code. The timeout intervention status code is generated after the protection terminal generates an alarm prompt message on the interactive screen and no screen touch confirmation signal is detected within the set countdown window, and then calls the background process to execute the action of shutting down the airflow.

[0018] Furthermore, after receiving the status confirmation frame reported by the protection terminal, the method further includes calculating the effective blocking rate of the device, specifically including: Open a monitoring time window, and perform deduplication and filtering on the received status confirmation frames based on the device machine feature code to obtain the target confirmation frame set; From the set of target confirmation frames, count the number of terminal devices that successfully transmitted the windproof action ready status; If the total number of registered terminals within the coverage area of ​​the dynamic geofence that are triggered to push the warning instruction packet is greater than zero, then the number of terminal devices is divided by the total number of registered terminals to obtain the effective blocking rate of the device; If the total number of registered terminals is detected to be zero, the calculation of the effective blocking rate of the device is skipped, and the area corresponding to the dynamic geofence is marked as an area with no terminals to be evaluated.

[0019] Furthermore, after calculating the effective blocking rate of the statistical equipment, the process also includes updating the system load tolerance attribute value to adjust the subsequent data scheduling process. Specifically, this includes: using a linear adjustment formula with a positive adjustment gain coefficient, amplifying the system load tolerance attribute value of the downwind node based on the effective blocking rate of the equipment and setting an upper limit threshold to obtain the updated system load tolerance attribute value; using the updated system load tolerance attribute value, recalculating the predictive load ratio coefficient; if the recalculated predictive load ratio coefficient falls below the preset load ratio threshold, then issuing recovery and acceleration signaling to the asynchronous message queue running in the background to improve the network transmission priority of the historical data backfilling task.

[0020] In one preferred embodiment, the memory-level pollen source shadow record is associated with an applicable prediction period identifier and an expiration time limit.

[0021] As a preferred embodiment, before extracting the zonal component of horizontal wind speed, the meridional component of horizontal wind speed, and the atmospheric boundary layer height of the target grid at time t from the meteorological forecast grid data, the method further includes: acquiring the original meteorological observation sequence of regional stations, performing gridded downscaling processing on the original meteorological observation sequence using bilinear interpolation, and generating the meteorological forecast grid data with a spatial resolution of a set accuracy to eliminate wind field distortion errors caused by topographic undulations.

[0022] As a preferred implementation, establishing a linear correspondence between the buffer distance and the real-time wind speed scalar value, and calculating the target buffer distance based on the linear correspondence and the current real-time wind speed scalar value, specifically includes: introducing a geographic information system to extract terrain slope data in the outward extension direction of the initial polygon; determining whether there is a terrain protrusion in the extension direction whose elevation change gradient exceeds a set threshold; if the terrain protrusion exists, then calling the terrain blocking attenuation factor to proportionally reduce the basic buffer distance calculated using the linear correspondence to obtain the target buffer distance.

[0023] As a preferred implementation, the concurrent push of the warning instruction package through the IoT signaling channel specifically includes: constructing a publish-subscribe topic tree based on the MQTT protocol, dividing the dynamic geofence into multiple sub-region subscription nodes; mapping the deduplicated protected terminal to the corresponding sub-region subscription node; setting the QoS level of the warning instruction package to the highest level of service quality label, and sending the warning instruction package to the protected terminal through a heartbeat keep-alive connection to ensure accurate delivery of action instructions and anti-duplication processing.

[0024] On the other hand, the present invention also provides a computing device, the computing device including a memory and a processor, the memory storing program code, and when the processor executes the program code, the processor is used to execute any of the above-described pollen dispersal forecasting and early warning methods based on meteorological numerical models.

[0025] In another aspect, the present invention provides a computer-readable storage medium comprising computer instructions; when the computer instructions are executed in a computing device, the computing device performs any of the above-described pollen dispersion forecasting and early warning methods based on meteorological numerical models.

[0026] The present invention provides a pollen dispersal forecasting and early warning method based on meteorological numerical models, which has the following beneficial effects: This invention compares the estimated time of meteorological physical transmission with the time of task migration. When the estimated time margin parameter is insufficient or the available transmission bandwidth of the network layer is below the lower limit, the system stops the conventional full data synchronization and instead executes a simplified metadata shadow degradation injection process. This process extracts only the core features of pollen and injects them directly into the memory mapping area of ​​downwind nodes, avoiding system blocking caused by persistent database read and write operations. This design ensures that the system can still guarantee that the issuance of early warning signals precedes the actual physical diffusion of pollutants under extreme weather changes or network congestion, thus ensuring the timeliness of early warning tasks.

[0027] This invention generates a dynamic geofence based on observed concentration exceeding a threshold, pushes early warning commands to the affected area, and introduces a default degradation response mechanism at the protection terminal. When the terminal does not detect a user's touch confirmation signal within a set countdown window, it automatically invokes a background process to shut down the airflow and reports a status confirmation frame. This mechanism ensures that the device can automatically complete physical blocking without human intervention, and provides the system with real physical protection data support within a specific geographical area through accurate execution feedback from the device.

[0028] This invention uses the effective blocking rate of devices within a dynamic geofence as a reverse intervention factor to update the system load tolerance attribute value of downwind nodes, and recalculates the predictive load ratio coefficient accordingly. This reverse closed-loop adjustment mechanism directly uses the actual protection effect in the physical space as the basis for cloud resource allocation. When it is confirmed that the proportion of terminals that have completed the blocking action in a local area meets the standard, the system actively relaxes the threshold limit on the computing power accumulation of target nodes, releasing network bandwidth and computing resources to accelerate the historical data backfilling task left over from the compensation and degradation process, effectively reducing the resource redundancy consumption maintained by the system due to blind defense. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the system architecture of an embodiment of the present invention; Figure 2 This is a flowchart illustrating a method according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the principle of meteorological feature extraction and physical transport parameter calculation in an embodiment of the present invention. Figure 4 This is a schematic diagram illustrating the predictive load ratio calculation and triggering principle of an embodiment of the present invention. Figure 5 This is a schematic diagram illustrating the hierarchical asynchronous degradation scheduling principle of an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the principle of dynamic geofence generation and concurrent distribution of early warning signaling in an embodiment of the present invention. Figure 7 This is a schematic diagram of the terminal state machine feedback and load reverse closed-loop regulation principle according to an embodiment of the present invention; Figure 8 This is a verification diagram of the pollen diffusion physical field and dynamic geofencing generation in an embodiment of the present invention; Figure 9 This is a test graph showing system response delay and device blocking rate under a sudden network congestion scenario, as described in an embodiment of the present invention. Figure 10 This is a smooth curve diagram of computing node resource scheduling in an embodiment of the present invention.

[0030] Among them, 100 is the early warning system; 200 is the meteorological interface; 300 is the central control node; 400 is the computing node; 410 is the upwind node; 420 is the downwind node; 500 is the sensing and execution component; 510 is the sensor; and 520 is the protection terminal. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] like Figure 1 As shown, Figure 1 This is a schematic diagram of a system architecture according to an embodiment of the present invention. The present invention provides an early warning system 100, which may include: a meteorological interface 200, a central control node 300, a computing node 400, and a sensing and execution component 500.

[0033] The central control node 300 establishes a data communication connection with the external meteorological interface 200 via a network protocol to acquire gridded meteorological forecast data. The computing node 400 is deployed on a cloud service platform and includes multiple independent node units managing different geographical sub-regions. Based on the wind field direction, the node's associated grid position relationship, and the physical transmission flux direction within the current forecast period, the central control node 300 dynamically determines the logical role of each pair of topologically related nodes. Under a specific node pair relationship, the node unit on the physical transmission flux output side is identified as the upwind node 410, and the node unit on the physical transmission flux input side is identified as the downwind node 420. The same node unit can be identified as either the upwind node 410 or the downwind node 420 in different node pair relationships or different forecast periods. The central control node 300 establishes control links with both the upwind node 410 and the downwind node 420, and is responsible for issuing underlying data distribution strategies and monitoring the task migration status between nodes. The sensing and execution component 500 is deployed in the physical area to be monitored, including sensors 510 and protection terminals 520. Sensors 510 are connected to the computing nodes in the corresponding area to report environmental concentration data. The protection terminal 520 includes a device terminal with an action execution mechanism or a user interface, used to receive early warning command packets issued by the computing nodes and to feed back action status confirmation frames to the upper-layer network.

[0034] See Figure 2 , Figure 2 This is a flowchart of a method according to an embodiment of the present invention. The present invention provides an early warning method, comprising the following steps: S10, the central control node 300 reads the weather forecast grid data through the meteorological interface 200, and analyzes the weather forecast grid data through the meteorological data parsing component to extract the two-dimensional wind speed component and boundary layer height parameter. Combined with the preset initial emission intensity of pollen source, the physical transport flux from the upwind node 410 associated grid to the downwind node 420 associated grid and the estimated time of meteorological physical transport are calculated to construct the pollen diffusion vector matrix. S20, the central control node 300 calculates the predictive load ratio coefficient of the downwind node 420 at the future prediction time by integrating the task data storage weight of the upwind node 410 and the system load tolerance attribute value of the downwind node 420 based on the physical transmission throughput. S30, when the predictive load ratio reaches the set scheduling threshold condition, the central control node 300 evaluates the task migration time within the system based on the available bandwidth of the network layer and the volume of the full data of the pollen source to be migrated, compares the estimated time of meteorological and physical transmission with the task migration time to generate a time margin parameter, and controls the upwind node 410 and downwind node 420 to execute the full data attribute synchronization process or the simplified metadata shadow degradation injection process according to the time margin parameter. S40, when the central control node 300 detects that the observed concentration collected by the sensor 510 received by the downwind node 420 exceeds the set concentration threshold, it controls the downwind node 420 to parse the data recording boundary to generate a dynamic geofence, and instructs the downwind node 420 to push a warning instruction package containing a status confirmation callback request field to the protection terminal 520 within the coverage area of ​​the dynamic geofence through the Internet of Things signaling channel. S50, the central control node 300 receives the effective blocking rate of the equipment as statistics and feedback from the downwind node 420, uses the effective blocking rate of the equipment as a reverse intervention factor to update the system load tolerance attribute value of the downwind node 420, and uses the updated system load tolerance attribute value to adjust the subsequent data migration and backfilling process. The effective blocking rate of the equipment is obtained by the downwind node 420 based on the status confirmation frames reported by the protection terminal 520. The status confirmation frames are generated by the protection terminal 520 after executing the hardware windproof logic to cut off the air intake channel or the user interaction confirmation logic to receive the confirmation signal on the interactive screen.

[0035] In actual system deployment, the parameters involved in the above steps, such as scheduling threshold, polling cycle, buffer distance, bandwidth lower limit, wind speed lower limit, monitoring time window length, and load adjustment coefficient, are initialized and configured by the central control node 300 based on the deployment area scale, network quality, number of terminals, and historical operation statistics, and are dynamically corrected during system operation based on real monitoring and scheduling feedback.

[0036] See Figure 3 , Figure 3 This is a schematic diagram illustrating the principle of meteorological feature extraction and physical transmission parameter calculation according to an embodiment of the present invention. To achieve the conversion of gridded meteorological parameters to a bottom-level digital matrix, step S10 includes the following sub-steps: S101, the central control node 300 calls the data communication interface of the external meteorological service system through the meteorological interface 200 to periodically retrieve gridded meteorological numerical forecast files for the target monitoring area. For the network transmission of meteorological numerical forecast files and the extraction of basic data, those skilled in the art can use standard data exchange protocols and general meteorological data parsing components for processing. File reading and grid data structure mapping are well-known technologies in the field and will not be elaborated upon here. In this embodiment, to ensure scheduling timeliness, the data retrieval cycle can be set to be consistent with the model output frequency of the meteorological forecasting agency, for example, once per hour. After completing the underlying data parsing, the central control node 300 extracts the current time... The zonal component of horizontal wind speed within the target grid in the geospatial area to be monitored Meridional component of horizontal wind speed and atmospheric boundary layer height .

[0037] S102, combined with the initial emission intensity of pollen sources preset in the target grid, the central control node 300 calculates the physical transport flux from the target grid associated with the upwind node 410 to the adjacent grid associated with the downwind node 420.

[0038] As a preferred approach, the initial pollen emission intensity within the target grid is a pre-stored and periodically updated baseline parameter of the central control node 300. This parameter is obtained by extracting historical vegetation cover data for the grid area and weighting it with the average observed concentration from the same period in previous years. This parameter can be updated offline daily, weekly, or by pollen season stage and participates in physical transport flux calculations as a fixed input within the current scheduling cycle. When the latest measured calibration data becomes available, the central control node 300 can correct this parameter before writing it into subsequent prediction cycles. In the specific spatial mapping and calculation process, a specific upwind node 410 is designated as the node... The corresponding downwind node 420 is a node. The central control node 300 calculates node coordinates based on the latitude and longitude coordinate conversion model of the underlying map service. With nodes latitudinal distance difference between Difference between meridional and longitudinal distances To avoid excessive dilution of pollen grains in the vertical diffusion space, the central control node 300 calculates the target prediction time according to the following formula. Physical transmission throughput : ; In this formula, Represents the target prediction time. Downwind node Pointing to downwind node Physical transmission throughput; Representative node The grid belongs to at time Recorded initial emission intensity of pollen sources; Represents the height of the atmospheric boundary layer, used to constrain the volume of matter mixing in the vertical direction; The prediction time step set for the system is determined based on the temporal resolution of the meteorological data. Representative node The grid in The zonal component of horizontal wind speed at any given moment; Representative node With nodes The difference in latitudinal distance between them; Representative node With nodes The meridional distance difference between them is preferably 1 to 3 hours; the product term in the latter part of the formula calculates the wind speed composite vector at the node. To node The effective projected component along the spatial connection. This physical transport flux characterizes the amount of material transported across the spatial cross-section between two nodes per unit time. To prevent the atmospheric boundary layer height from approaching zero due to meteorological data anomalies, which could lead to system computational overflow, the central control node 300 will perform a check before executing this calculation. Perform a safety lower limit check. If the value is lower than the set height safety threshold, automatically take the safety threshold and substitute it into the formula.

[0039] S103, the central control node 300 calculates the meteorological material flow from the nodes based on the geometric composite vector of wind speed and the absolute geographical distance between nodes. Transmit to node Meteorological physical transmission estimated time The parameters are aggregated and the pollen diffusion vector matrix under the updated state is constructed. This matrix is ​​then written into the memory structure of the central control node as the underlying baseline data for routing scheduling.

[0040] Specifically, the central control node 300 executes the following formula to calculate the time consumption: ; In this formula, the numerator represents the nodes. With nodes The linear physical distance between them is represented by the denominator, which is the absolute wind speed scalar value synthesized from the components of the horizontal wind field. In actual physical scenarios, this absolute wind speed scalar value will be close to zero when the target area is in a calm wind state. To address this, the central control node 300 introduces calm wind judgment logic in its calculations. When the calculated denominator value is lower than the system wind speed lower limit (e.g., 0.1 m / s), it directly stops the time calculation for the current node pair and marks its estimated physical transmission time as infinity, thereby avoiding division by zero errors and interrupting subsequent meaningless resource migration scheduling. Under normal wind speed conditions, the central control node 300 traverses all grid node combinations with topological spatial connections within the control range of the early warning system 100, and performs system-level aggregation of the physical transmission flux and meteorological physical transmission estimated time calculated independently between each node pair to generate the pollen diffusion vector matrix in the updated state.

[0041] Specifically, assuming the early warning system's control range includes There are 1 computational node unit, and the pollen diffusion vector matrix is ​​of size . The characteristic square matrix, where row indices correspond to upwind nodes and column indices correspond to downwind nodes; the elements in this pollen diffusion vector matrix It is composed of an eigenvector containing two parameters, and its expression is: ;in, To calculate the nodes Pointing to node Physical transmission throughput For the node Pointing to node The estimated time of meteorological physical transmission; when the node With nodes When there is no physical transport flux between them (e.g., in calm winds or not downwind locations), the corresponding matrix elements are... Assign a value of zero. The value is assigned to infinity.

[0042] The pollen diffusion vector matrix is ​​written into the memory structure of the central control node 300 as the underlying baseline data for subsequent computing nodes 400 to perform task load routing and scheduling, thereby providing a reference for evaluating the time margin of computing power allocation.

[0043] See Figure 4 , Figure 4 This is a schematic diagram illustrating the predictive load scaling factor calculation and triggering principle according to an embodiment of the present invention. To achieve cross-domain mapping of physical environment changes to digital computing power scheduling, step S20 above includes the following sub-steps: S201, the central control node 300 reads the pollen diffusion vector matrix generated above, and establishes a logical mapping relationship between the meteorological topology and the computing node 400 based on the direction vector of physical transport flux in the matrix.

[0044] For any downwind node 420, the central control node 300 filters out all upwind nodes 410 whose physical transmission throughput points to that downwind node 420, and clusters these upwind nodes 410 into an associated upstream set. After establishing the logical mapping relationship, the central control node 300 pulls the task data storage weight of each upwind node 410 in the associated upstream set through the internal monitoring interface. In this embodiment, the task data storage weight is specifically reflected as the total number of entries of pollen source data records to be processed maintained in the current memory of the upwind node 410, or it is obtained by weighted conversion of the number of record entries and the corresponding data volume. This value is used to reflect the scale of the basic data that may need to be migrated.

[0045] S202, after obtaining the basic parameters, the central control node 300 integrates the physical transmission throughput with the task data storage weight.

[0046] The intensity of pollen diffusion at the meteorological level is used as an external amplification factor, directly applied to the existing data scale of upstream nodes. This characterizes the potential for a synchronous increase in processing requests related to downstream observation data access, concentration determination calculation, dynamic geofencing generation, early warning instruction distribution, and terminal status confirmation feedback due to pollen diffusion at future forecast times. This quantifies the environmental changes in the physical world as the concurrent access pressure the system will need to withstand in the future. Combined with the system load tolerance attribute of downwind node 420, the central control node 300 calculates the predictive load ratio coefficient of downwind node 420 at future forecast times. Specifically, the following formula is used for calculation: ; In this formula, Representing the downwind node 420, Represents the upstream set of related products The upwind node is 410. For a moment The following nodes Pointing to node Physical transmission throughput; For nodes The acquired task data storage weight; This represents the system load tolerance attribute value for downwind node 420. As a preferred approach, the system load tolerance attribute value... The initial value can be calculated using a linear weighted model based on the number of CPU cores and memory capacity allocated to downwind node 420. For example, normalized weight coefficients corresponding to the number of CPU cores and memory capacity can be pre-calibrated, and the products of the two can be summed to quantify the heterogeneous underlying hardware resources into a unified carrying capacity assessment index. This attribute value is dynamically adjusted during system operation based on feedback from the physical actions of subsequent end devices. The numerator of the formula accumulates the values ​​of all nodes under the influence of the wind field through summation. The surge in business pressure. To ensure the stability of the system's backend operations and avoid the denominator approaching zero due to abnormal initialization or excessive dynamic reduction of the system load tolerance attribute value, the central control node 300 in the operation program... A hard fall protection threshold is set (e.g., set to the minimum available system resource baseline constant 1.0). When the actual system load tolerance attribute value read is lower than this fall protection threshold, the program automatically replaces the variable with the fall protection threshold, thereby avoiding the risk of computational overflow.

[0047] S203, the central control node 300 continuously polls and monitors the predictive load ratio coefficients of all downwind nodes 420 in the system in order to capture potential resource overload risks.

[0048] The polling monitoring cycle can be shorter than or equal to the data retrieval and update cycle of the meteorological forecast grid. When the meteorological forecast grid data has not yet been updated, the central control node 300 continues to perform polling calculations based on the most recently generated pollen diffusion vector matrix. When new meteorological forecast grid data is detected, the corresponding pollen diffusion vector matrix is ​​reconstructed and the subsequent scheduling results are updated. By establishing a digital comparison mechanism between the predictive load ratio coefficient and the set scheduling threshold conditions, the central control node 300 determines whether to trigger the cross-node data and task migration process. For the specific assignment logic of the scheduling threshold conditions, those skilled in the art can conduct stress tests and empirical calibration based on the upper limit of the concurrent carrying capacity of the actual cloud server cluster. In a typical implementation scenario, the scheduling threshold condition can be set as the critical ratio of the node's computing power safety margin, preferably between 0.85 and 0.95. The threshold optimization configuration process is a well-known technology in this field and will not be described in detail here. When the predictive load ratio of a specific downwind node 420 is detected to reach or exceed the scheduling threshold, the central control node 300 determines that the weather change will bring instantaneous concurrent requests that exceed the node's normal processing capacity. It then generates a scheduling trigger signal for the downwind node 420 and transfers the system control flow to the data scheduling and communication bandwidth allocation verification and execution phase.

[0049] See Figure 5 , Figure 5This is a schematic diagram of a hierarchical asynchronous degradation scheduling principle according to an embodiment of the present invention. To prevent sudden weather changes from causing the diffusion of matter in physical space to precede the exchange of computing power and data in the computer system, and to ensure the timeliness of the early warning issuance channel, step S30 above includes the following sub-steps: S301, after the scheduling trigger signal is generated, the central control node 300 uses the underlying network management interface to read the available network layer transmission bandwidth between the current upwind node 410 and downwind node 420. .

[0050] As a preferred method, the available transmission bandwidth can be calculated by comprehensively measuring the round-trip latency and packet loss rate of periodically sending network probe packets between nodes. Simultaneously, the central control node 300 acquires the total volume of all data entities of the pollen sources to be migrated in the upwind node 410. Based on the obtained network environment and data scale parameters, and by introducing constant time for system underlying service warm-up and resource allocation. The central control node 300 calculates the task migration time required to complete this data cross-node transfer. In this embodiment, The specific value can be pre-defined based on the average time taken to start the service process in the historical scheduling logs, for example, a range of 1 to 3 seconds. The specific calculation formula is as follows: ; In this formula, the system superimposes the dynamic latency of data transmission with the fixed latency of underlying resource allocation to form a quantitative assessment of the cross-domain scheduling capability of the network system. This is to avoid limiting the available transmission bandwidth during link congestion, node disconnection, or severe network jitter. The value approaching zero caused computational anomalies. The central control node 300 checked the value before executing the calculation. Perform link validity verification; when When the bandwidth is below the preset lower limit, stop calculating the full migration time of the current node pair, mark the task migration time as unavailable or extremely high, and directly enter the simplified metadata shadow degradation injection process.

[0051] S302, Central control node 300 retrieves the estimated time consumption of meteorological and physical transmission calculated in the previous steps. And combined with the task migration time just calculated Generation time margin parameter Its calculation logic is the difference between the two: ; By comparing the actual material dispersion process in nature with the underlying network transmission process of the system within a unified time dimension, the system assesses whether there is a sufficient window of opportunity between nodes to perform a complete data handover. After calculating the specific values, the central control node 300 sets the time margin parameter... With preset safety time threshold A comparison is performed. In this embodiment, the safety time threshold... This is used to absorb latency deviations caused by underlying network transmission jitter and node state switching; its value can be set from 10 to 30 seconds. Based on the comparison results, the central control node 300 drives the system to enter different scheduling execution branches.

[0052] S303, regarding time margin parameters Greater than or equal to the safe time threshold In this situation, the central control node 300 determined that the current meteorological conditions were relatively stable and that the system had sufficient time to complete the routine data handover.

[0053] At this point, the central control node 300 controls the upwind node 410 to execute a full data attribute synchronization process with the downwind node 420. During this process, the upwind node 410 applies a read-only concurrent lock to the data objects to be migrated within its internally associated business table data, restricting write operations on new data. The upwind node 410 migrates all data entities, including historical monitoring records and complete attribute dimensions, to the persistent storage layer of the downwind node 420 via a network synchronization communication protocol. After confirming that the data entity migration is complete and the underlying verification is consistent, the upwind node 410 releases the concurrent lock, and the downwind node 420 takes over complete read and write control of this portion of the data.

[0054] S304, when the time margin parameter Less than the safe time threshold (Including extreme cases where the time margin parameter is negative) When the central control node 300 determines that it has encountered a sudden weather change or severe network congestion, continuing to perform full data migration will cause underlying read and write blockage and slow down the establishment of the early warning channel.

[0055] To ensure low-latency delivery of alarm commands, the central control node 300 triggers a simplified metadata shadow degradation injection process. The upwind node 410, through internal field decomposition logic, extracts the core features necessary to support warning triggering from the full dataset to be migrated, forming a simplified metadata set. This simplified metadata set includes pollen classification codes, the current highest concentration extreme value, and affected coordinate boundary fields. These fields are sufficient to support the downwind node 420 in determining the warning type, generating dynamic geofences, and encapsulating and distributing warning command packages. The affected coordinate boundary field is defined based on the coordinates of the vertices of a geometric polygon predicted by the upwind node 410 and the wind speed vector extending towards the downwind node 420, and is used to define the potentially affected physical area. The upwind node 410 allocates high-priority microservice signaling channels within the system to directly inject a minimal metadata set across the network into the memory mapping area of ​​the downwind node 420. This generates a memory-level pollen source shadow record on the downwind node 420. This memory-level pollen source shadow record is a lightweight, temporary data substitute that skips persistent I / O operations on the underlying disk database, resides solely in the node's volatile memory, and contains only the core dimension fields required to trigger a single warning. For example, in regular scheduling, a full data entity might be hundreds of megabytes in size (containing historical concentration curves from the past few months, sensor maintenance logs, etc.), while the shadow record is only a few kilobytes of memory key-value pairs (e.g., containing only: pollen type A, extreme concentration 200, and polygon boundary coordinates). This allows the downwind node to directly read features from memory within milliseconds and trigger warnings "with disease / downgrade," without waiting for massive amounts of basic data to be slowly written to disk. After receiving the record, downwind node 420 does not perform storage verification through a persistent database, but directly enters the standby mode for issuing warning instructions based on the state in memory. This memory-level pollen source shadow record is associated with at least a generation timestamp, source node identifier, applicable forecast period identifier, and expiration time limit. The applicable forecast period identifier is uniformly allocated and issued by the central control node 300 based on the time resolution window of the currently read meteorological forecast grid data (e.g., "20260420-14:00 to 15:00"). The expiration time limit is an absolute expiration timestamp or lifecycle countdown parameter calculated based on the end time of the applicable forecast period plus the maximum backfill tolerance delay time for non-core data backfill set by the system. When the corresponding full data entity has completed backfilling and passed the consistency check, or when the current forecast period ends and no further update is triggered, downwind node 420 deletes or overwrites the corresponding shadow record to prevent expired shadow states from continuing to participate in subsequent warning determinations.After the basic early warning channel is established, the upwind node 410 starts an independent asynchronous message queue in the background and gradually fills the remaining non-core business data back into the persistent storage medium of the downwind node 420 using the idle bandwidth of network transmission, so as to balance the timeliness of core early warning and the final consistency of subsequent data.

[0056] like Figure 6 As shown, Figure 6 This is a schematic diagram illustrating the principle of dynamic geofence generation and concurrent distribution of early warning signaling according to an embodiment of the present invention. To achieve precise delivery of virtual data to physical space control, step S40 includes the following sub-steps: S401, when the actual time in the physical environment advances to the aforementioned future prediction time, the sensor 510 deployed in the physical area to be monitored continuously collects the pollen concentration in the ambient air and reports the concentration to the downwind node 420.

[0057] Downwind node 420 receives the observed concentration and compares it with a set concentration threshold in real time. In this embodiment, for the photoelectric scattering acquisition and underlying data analysis of pollen concentration, those skilled in the art can use a standard optical dust sensor module combined with a particulate matter identification algorithm for processing. The acquisition principle and data reporting mechanism are well-known technologies in the field and will not be elaborated here. The set concentration threshold is graded and calibrated according to the medical induction standards of different allergens. As a preferred method, the trigger concentration threshold of easily allergenic pollen can be set as the threshold of the number of pollen particles per unit volume of air, for example, 50 to 100 particles per cubic meter of air. To avoid false triggering of the system due to instantaneous dust or accidental electrical noise from the sensor, downwind node 420 introduces a sliding time window confirmation mechanism in the comparison logic. Specifically, the downwind node 420 continuously records the observed concentration sequence within a specific time period (e.g., 3 to 5 minutes). This specific time period can cover multiple consecutive sampling moments. Only when the arithmetic mean of the sequence reaches the concentration threshold, or when the number of consecutive peaks exceeding the concentration threshold reaches a set count value, does the downwind node 420 determine that the target physical area has been substantially affected by the pollen air mass, thereby triggering the subsequent geospatial delineation procedure. To reduce the interference of abnormal sampling values ​​on the determination results, the downwind node 420 can also perform outlier removal, sensor offline status filtering, or consistency verification with observations from adjacent sensors on the continuously collected sequences.

[0058] S402, after entering the geospatial delineation procedure, the downwind node 420 parses the pollen source data records stored inside it. The pollen source data records include persistent records written by the aforementioned full data attribute synchronization process, or memory-level shadow records generated by the minimal metadata shadow degradation injection process, and extract the affected coordinate boundary fields obtained in the previous pre-synchronization process.

[0059] When a valid swept coordinate boundary field is not obtained in the current cycle, the downwind node 420 reverts to the default boundary of its managed geographic sub-region, the most recently valid boundary field, or a temporary boundary formed by expanding outward based on the current observed concentration spatial distribution, to ensure that the dynamic geofence generation process is not interrupted. Combining the actual terrain mapping parameters of the target area, the downwind node 420 uses a spatial geometry processing algorithm to convert the swept coordinate boundary field into a series of geographic coordinate points with latitude and longitude attributes, and sequentially connects adjacent geographic coordinate points to form an initial dynamic geofence. The specific implementation process of the spatial geometry processing algorithm is as follows: extracting the grid intersection points or relative azimuth and distance vectors relative to a specific reference point recorded in the swept coordinate boundary field, and calling the underlying coordinate system transformation model to map them into an absolute two-dimensional latitude and longitude coordinate sequence; subsequently, using a convex hull algorithm or following a clockwise edge topology order, the latitude and longitude coordinate sequence is sequentially connected to construct a closed two-dimensional polygon outline. Considering the edge divergence effect of natural airflow diffusion, after generating a polygonal closed region, the downwind node 420 utilizes the spatial buffer analysis algorithm of the geographic information system to construct a final dynamic geofence with spatial redundancy by extending the initial polygonal boundary outwards by a set buffer distance. The specific implementation process of the spatial buffer analysis algorithm is as follows: using each boundary segment of the initial polygonal closed region as a reference, it is equidistantly translated outwards along the normal direction by a set buffer distance to generate a sequence of parallel line segments; for the vertices where the original adjacent line segments intersect, a circular arc interpolation algorithm with a set radius (equal to the buffer distance) is used to generate a smooth connection curve; finally, all translated parallel line segments and the circular arc connection curve are geometrically spliced ​​together to construct a closed envelope that expands uniformly outwards. When calculating the buffer distance, the system establishes a linear correspondence between the buffer distance and the real-time wind speed scalar value. The real-time wind speed scalar value can be obtained by the downwind node 420 calling the most recently issued meteorological grid data of the target area from the central control node 300, or by real-time reporting from the field meteorological acquisition units deployed within the target area. As a preferred approach, the base buffer distance can be set at 500 meters, and the buffer redundancy can be increased by 100 meters for every 1 meter / second increase in the local wind speed obtained in real time. The final buffer distance value is controlled and constrained between 500 meters and 1500 meters, so as to ensure that communication equipment in the diffusion edge area can be effectively covered.

[0060] S403, after establishing the specific coverage boundary of the dynamic geofence, the downwind node 420 performs a spatial index retrieval of the device registration list within the system network that is within the geographical range, and determines the group of all protected terminal 520 nodes within the target range.

[0061] When the same protective terminal 520 falls within the coverage area of ​​multiple dynamic geofences simultaneously, the downwind node 420 performs deduplication and single-instruction effectiveness control according to preset rules prioritizing warning level, latest timestamp, or the node to which the fence belongs, to avoid duplicate issuance leading to terminal state machine conflicts. The device registration list pre-maintains the fixed installation coordinates of each protective terminal 520, or the mobile terminal periodically reports its current location coordinates to support spatial matching and device filtering after dynamic geofence generation. The downwind node 420 generates a warning instruction packet for this pollution diffusion event. The message payload of this warning instruction packet not only encapsulates the action code for controlling the device hardware to cut off the wind path, but also forcibly writes a status confirmation callback request field. The specific physical function of the status confirmation callback request field is to instruct each underlying receiver to return a clear execution status confirmation to the upper-layer network along the original communication signaling link after completing the windproof action response. To address the risk of network packet loss during high-concurrency distribution, the header of the warning instruction packet includes both a timeout retransmission flag and a command lifecycle timestamp. This allows the downwind node 420 to execute targeted retransmission logic if it does not receive an acknowledgment frame within a set waiting window. Based on this encapsulation configuration, the downwind node 420 invokes a standardized IoT signaling channel to concurrently push the warning instruction packet to multiple protected terminals 520 within the dynamic geofence coverage area. In specific implementation scenarios, this IoT signaling channel can be built using lightweight message publish-subscribe protocols such as MQTT or CoAP. This concurrent delivery mechanism ensures that the system can drive a massive number of IoT devices in the area to switch from monitoring standby mode to physical windbreak blocking mode within a very short response period.

[0062] See Figure 7 , Figure 7 This is a schematic diagram of terminal state machine feedback and load reverse closed-loop regulation according to an embodiment of the present invention. To achieve dynamic intervention of device actions in the physical environment on the scheduling of computing power in the digital space, step S50 above includes the following sub-steps: After receiving the warning instruction packet, the S501 and the protection terminal 520 execute the corresponding physical protection actions according to the internal firmware configuration.

[0063] For the hardware driver control and user interface response logic of the protective terminal 520, those skilled in the art can use conventional microcontroller bus instructions and interface component development frameworks for processing. Its underlying driver and touch recognition mechanisms are well-known technologies in the field and will not be elaborated upon here. In specific implementation scenarios, the protective terminal 520 can cut off the air intake channel of the fresh air system by driving a relay to close, or generate an alarm message on the interactive screen for user confirmation. Since users may temporarily leave the site in real-world applications, preventing timely interaction, a default degradation response mechanism is built into the interaction logic to avoid the device state machine from being indefinitely suspended. If no screen touch confirmation signal is detected within a set countdown window (e.g., 15 seconds), the protective terminal 520 automatically calls a background process to close the airflow baffle and generates a timeout intervention status code. After completing the above hardware windproof logic or user interaction confirmation logic, the protective terminal 520 internally generates a status confirmation frame. This status confirmation frame encapsulates the device machine feature code and an action execution result identifier field. Once the message data is encapsulated, the protection terminal 520 reports the status confirmation frame to the downwind node 420 along the existing network communication link.

[0064] S502, downwind node 420, while issuing the early warning command package, activates its internal clock to record the monitoring time window to assess the actual defense effect in the physical space.

[0065] In this embodiment, the monitoring time window can be set to 30 to 60 seconds to cover the normal network latency of the IoT link and the mechanical execution time of device hardware actions. During the monitoring time window, the downwind node 420 continuously collects status confirmation frames returned by each protection terminal 520. To prevent data statistical overlap caused by the network communication timeout retransmission mechanism, the downwind node 420 performs memory deduplication and filtering on the collected frame data based on the device machine signature. When the monitoring time window expires, the downwind node 420 calculates the effective blocking rate of devices within the dynamic geofence space based on the aggregated valid frame records. The system uses the following formula to calculate this value: ; In this formula, Indicates the effective blocking rate of the device; This represents the total number of registered protective terminals 520 that are currently within the coverage area of ​​the dynamic geofence and for which the system has attempted to issue early warning command packets; This represents the number of terminal devices that successfully transmitted a message indicating that their windproof action is ready within the specified monitoring time window. This also applies to special blank areas within the dynamic geofence where no registered protective devices exist. In the boundary case where the denominator is zero, the system has a built-in bypass truncation mechanism in its underlying logic. When a zero denominator is detected, the algorithm skips the division operation module and marks the region as a region with no terminals to evaluate, so that the effective blocking rate of this device does not participate in the positive feedback update of the system load tolerance attribute value; or it assigns a neutral default value (e.g., 0) to avoid the program triggering a division-by-zero exception and causing the statistics process to crash.

[0066] S503 After generating local area statistics, downwind node 420 feeds back the effective blocking rate of the equipment to central control node 300 via the control link.

[0067] The central control node 300 receives the effective blocking rate of the device and sets it as a reverse intervention factor to reshape the system load tolerance attribute value of the downwind node 420. As a preferred approach, the effective blocking rate of the device within multiple consecutive monitoring time windows can be averaged or weighted before participating in the update calculation to reduce the impact of instantaneous network jitter or individual terminal anomalies on system scheduling decisions. This intervention mechanism constructs a virtual-physical integrated scheduling closed loop. That is, when a large number of physical devices at the end have completed substantial isolation actions such as wind path disconnection, the target area's sensitivity to the timeliness of subsequent supplementary early warning signals is significantly reduced. At this time, the cloud computing node logically has redundant space to accommodate higher backend service backlog pressure. As a preferred approach, the central control node 300 executes the following linear adjustment formula: ; in, This represents the current system load tolerance attribute value for downwind node 420. This is the updated system load tolerance attribute value; The preset positive adjustment gain coefficient is mainly used to constrain the response span of feedback adjustment, and its value range is set from 0.5 to 2.0 according to the resource redundancy of the cloud platform. This formula maps the physical state of the terminal device to the resource model of the computing node. When the effective blocking rate of devices in a specific geographical area is high, the system assesses that the physical layer of sensitization hazards has been relatively isolated, and the sensitivity of subsequent supplementary early warning and backfill services in the corresponding area to millisecond-level timeliness decreases. Therefore, the central control node 300 is allowed to moderately relax the load tolerance parameter of the downwind node 420 at the scheduling strategy level. This relaxation is only used for the calculation of the subsequent predictive load ratio coefficient and the priority control of background data migration, and does not mean that the upper limit of the underlying hardware resources is actually increased. In order to prevent the system load tolerance attribute value from being accumulated and amplified in multiple rounds of feedback and deviating from the actual hardware carrying capacity, the central control node 300 updates the value of the load tolerance attribute. An upper limit threshold is set, and in subsequent polling cycles, the load gradually decreases back to the basic load tolerance range based on the latest effective blocking rate of the devices or a preset attenuation rule. The central control node 300 uses the updated system load tolerance attribute value to apply adjustment constraints to the data migration and backfilling process within the system. At the execution level, the updated system load tolerance attribute value is substituted into the evaluation formula of the aforementioned predictive load ratio coefficient. When the recalculated predictive load ratio falls back and stabilizes below the set system scheduling threshold and continues to meet the set polling monitoring cycle (e.g., no limit is exceeded in 3 consecutive polling checks to prevent policy oscillations caused by network jitter), it indicates that the end-point physical protection has taken effect and the instantaneous concurrent pressure in the cloud has been relieved. At this time, the central control node 300 issues recovery and acceleration signaling to the asynchronous message queue running in the background, gradually increasing the network transmission priority of the remaining redundant historical data backfilling task, using the idle communication bandwidth and computing resources released by the system to accelerate the execution of the background backfilling operation of business data, and continuing to advance in subsequent polling cycles until the full data entity passes the underlying consistency check, thereby achieving low-latency early warning and final data lifecycle consistency closed loop.

[0068] In a specific implementation scenario, let's take the monitoring of allergenic pollen (such as cypress pollen) in spring in a certain city and the linkage control of terminal fresh air equipment as an example. The system hardware includes a central control node deployed on a cloud server, and computing nodes A and B, which respectively manage the upwind area (including a large botanical garden, denoted as Area A) and the downwind area (high-density residential area, denoted as Area B). Area B has 10,000 fresh air devices (i.e., protection terminals 520) equipped with intelligent networked control valves registered on its intranet, and optical pollen sensors 510 are deployed in its street areas.

[0069] The early warning scheduling and execution process is as follows: In the meteorological sensing and flux calculation step (corresponding to step S10), the central control node periodically acquires meteorological grid forecast data. At a certain scheduling moment, the mean northwest wind speed vector of the target grid environment is extracted to be 8 m / s, and the atmospheric boundary layer height is 800 m. The central control node, combined with the historical vegetation emission intensity of the botanical garden in area A, calculates the physical transport flux from the associated node in area A to the associated node in area B, and uses a formula to estimate the estimated time for meteorological material transport from area A to area B. It is 900 seconds (i.e., 15 minutes).

[0070] In the predictive load assessment step (corresponding to step S20), the central control node detects that compute node A maintains service data to be migrated within the current scheduling period in its memory, with a physical volume of approximately 800MB. Combined with the physical transmission throughput, the predictive load ratio coefficient for node B at the predicted future time is calculated to be 0.92. This value exceeds the set scheduling threshold (0.85), triggering the system's cross-node scheduling process.

[0071] In the time margin verification and degradation injection step (corresponding to step S30), the central control node detects that the available network layer transmission bandwidth between current nodes A and B is 5Mbps. The calculation shows that the task migration time for 800MB of data is... Approximately 1280 seconds. At this point, the time margin parameter... Seconds. Because the time margin parameter is less than the safe time threshold, the central control node determines that regular full data synchronization will trigger an early warning blockage, and then triggers the simplified metadata shadow degradation injection process. Node A extracts pollen type as Cypress and extreme value warning as 2000 grains / m from the data to be migrated. 3 The metadata set, consisting of the polygon boundary coordinates of area A, occupies minimal total bandwidth and is injected into the memory-mapped area of ​​node B within one second via a high-priority signaling channel, putting node B into an alert standby state. Simultaneously, 800MB of full business data is transferred to an independent asynchronous message queue in the background to await low-priority backfilling. In the dynamic geofence generation and signaling distribution step (corresponding to step S40), when the environmental time progresses to the predicted elapsed time node, the average value of the continuous monitoring sequence reported by sensor 510 in area B reaches the trigger threshold of 100 particles / m³. Node B directly retrieves the degradation metadata in memory, extracts the coordinate boundary field, and, combined with the current measured wind speed of 8 m / s, calculates the buffer distance (500-meter base value + 8 m / s × 100 meters = 1300 meters) according to the preset linear expansion benchmark, generating a dynamic geofence covering part of the grid in area B. Node B then sends an early warning command packet with a status confirmation callback request to 10,000 fresh air devices within the coverage area of ​​the geofence via the MQTT protocol.

[0072] In the end-point status feedback and resource adjustment step (corresponding to step S50), the fresh air equipment executes the air intake channel cut-off action after receiving the instruction. Within the 45-second monitoring time window after startup, Node B receives and counts the deduplicated status confirmation frames, finding that a total of 9500 devices have successfully responded, and calculating the effective blocking rate of devices within the dynamic geofence space to be 95%. The central control node receives this effective blocking rate and updates the system load tolerance attribute value of Node B according to the preset intervention factor formula. As the physical space protection takes effect, the equivalent concurrent pressure of Node B is relieved, and the central control node adjusts the network priority accordingly, using the released idle communication bandwidth to accelerate the background backfilling and synchronization of the remaining 800MB of data in Node A, in order to ensure the eventual consistency of data throughout its lifecycle.

[0073] To verify the technical effectiveness of this solution, a server cluster simulation platform was used for comparative testing. Seven consecutive days of historical simulated data with meteorological abrupt changes were input, and the response metrics of the three architectures were compared. Control group 1 (centralized architecture) uses a unified cloud-based command distribution system and lacks pre-emptive meteorological forecasting intervention. Control group 2 (standard edge computing architecture) uses independent computing at edge nodes and lacks degraded scheduling logic based on time margin verification; the system defaults to full data migration. The experimental group (the solution of this invention) executes a complete hierarchical asynchronous degraded scheduling strategy including steps S10 to S50.

[0074] The statistical table of core evaluation indicator data is as follows: Combination Figure 8 The system uses the quiverm function to map the two-dimensional wind speed component vector field in weather forecasts; and utilizes the polyshape and geoplot functions to plot the generated dynamic geofence closure curve based on the boundary coordinate set extracted by the system and the calculated 1300-meter buffer distance. This figure confirms that under different wind speed vector inputs, the generated fence boundary can accurately adapt to the asymmetric edge characteristics of downwind material diffusion.

[0075] like Figure 9 As shown, a time series comparison chart was plotted using a dual Y-axis function group. When the horizontal time axis enters the set high-concurrency network congestion interval, the warning response delay bar chart of the control group shows an exponential increase, and the corresponding effective blocking rate line on the right drops sharply below the warning line; while the curve data of the experimental group of this invention does not show significant fluctuations in the congestion interval, verifying the robustness of the degradation scheduling strategy in a weak network environment.

[0076] like Figure 10 As shown, the plot and fill functions are used to plot the changes in the load distribution of the node's central processing unit. Figure 10The baseline curve (dashed line) without feedback adjustment exhibits multiple load spikes exceeding 90%, easily triggering system crashes. The smoothed load curve (solid line) of this solution, after introducing the reverse intervention factor formula, shows that the peak values ​​are effectively reduced. Specifically, the gray shading area between the base load below the solid and dashed lines visually illustrates the data backfilling task performed after redundant resources are released. When the end-point physical protection takes effect on a large scale and the system's equivalent concurrent pressure is relieved, the central control node utilizes the released idle computing power and network bandwidth to gradually backfill the entire historical data that was temporarily deferred during the previous downgraded scheduling (i.e., the extended task processing interval in the latter half). This quantitatively demonstrates that this method can transform instantaneous concurrent computing power pressure into a smooth background processing process, successfully achieving peak shaving and valley filling of computing resources.

[0077] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A pollen dispersal forecasting and early warning method based on meteorological numerical models, characterized in that, include: Based on meteorological forecast grid data and the initial emission intensity of pollen sources, the physical transport flux between upwind and downwind nodes and the estimated time of meteorological physical transport are calculated. Based on the physical transmission throughput and the system load tolerance attribute value of the downwind node, calculate the predictive load ratio coefficient; When the predictive load ratio coefficient reaches the preset load ratio threshold, the task migration time is determined, the estimated time of meteorological physical transmission is compared with the task migration time to generate a time margin parameter, and the upwind node is controlled to transmit the full pollen source data to the downwind node to complete the full data attribute synchronization, or the upwind node is controlled to extract the core features of the pollen source to generate simplified metadata and inject it into the downwind node to achieve simplified metadata shadow degradation injection. When the observed concentration at the downwind node exceeds a preset concentration threshold, a dynamic geofence is generated, and an early warning instruction packet is pushed to the protective terminal within the dynamic geofence.

2. The pollen dispersal forecasting and early warning method based on meteorological numerical models according to claim 1, characterized in that, The core features of the pollen source include: pollen classification code, current highest concentration extreme value, and affected coordinate boundary field.

3. The pollen dispersal forecasting and early warning method based on meteorological numerical models according to claim 1, characterized in that, The calculation of the physical transmission flux between the upwind and downwind nodes includes: Extract the zonal component of horizontal wind speed, the meridional component of horizontal wind speed, and the atmospheric boundary layer height of the target grid at time t from the meteorological forecast grid data; Calculate the latitudinal distance difference and the longitudinal distance difference between the upwind node and the downwind node; A wind speed composite vector is obtained by synthesizing the zonal component and the meridional component of the horizontal wind speed, and the spatial connection direction between the upwind node and the downwind node is determined based on the difference between the zonal distance and the difference between the meridional distance. If the atmospheric boundary layer height is lower than the set height safety threshold, then the height safety threshold is used to replace the atmospheric boundary layer height to obtain the verified atmospheric boundary layer height. The physical transport flux across the spatial cross-section of the upwind and downwind nodes is calculated by multiplying the initial pollen emission intensity at time t, the verified atmospheric boundary layer height, and the effective projection component of the wind speed composite vector along the spatial line.

4. The pollen dispersal forecasting and early warning method based on meteorological numerical models according to claim 1, characterized in that, The calculation of the estimated time for meteorological physical transport includes: Calculate the physical distance between the upwind node and the downwind node; Extract the horizontal wind field components from the meteorological forecast grid data, and calculate the absolute wind speed scalar value after synthesizing the horizontal wind field components. If the absolute wind speed scalar value is lower than the set system wind speed lower limit, the time calculation for the upwind node and the downwind node will be stopped, and the estimated time for meteorological physical transmission will be marked as the maximum value. If the absolute wind speed scalar value is greater than or equal to the lower limit of the system wind speed, the estimated time for meteorological physical transmission is obtained by dividing the spatial straight-line physical distance by the absolute wind speed scalar value.

5. The pollen dispersal forecasting and early warning method based on meteorological numerical models according to claim 1, characterized in that, The calculation of the predictive load scaling factor includes: Cluster the upwind nodes that are all pointing to the same downwind node into an associated upstream set; Extract the number or weighted volume of pollen source data records to be processed maintained in the memory of each upwind node in the associated upstream set, and use it as the data storage weight for the task. When the read system load tolerance attribute value is lower than the set hard drop protection threshold, the system load tolerance attribute value is replaced by the hard drop protection threshold to obtain the verified system load tolerance attribute value. The product of the physical transmission throughput and the task data storage weight is summed within the associated upstream set and divided by the verified system load tolerance attribute value to obtain the predictive load ratio coefficient.

6. The pollen dispersal forecasting and early warning method based on meteorological numerical models according to claim 1, characterized in that, Based on the time margin parameter, control the transmission of full pollen source data from the upwind node to the downwind node to complete full data attribute synchronization, including: Based on the detected available transmission bandwidth of the network layer and the volume of the full data of the pollen source to be migrated, the migration time of the task is estimated. Calculate the difference between the estimated time of the meteorological physical transmission and the time of the task migration, and generate the time margin parameter; If the time margin parameter is greater than or equal to the preset safe time threshold, then the upwind node is controlled to apply a read-only concurrent lock to the business table data to be migrated, and all data entities are migrated to the persistent storage layer of the downwind node. After verification, the read-only concurrent lock is released and the read and write control is transferred to complete the synchronization of all data attributes.

7. The pollen dispersal forecasting and early warning method based on meteorological numerical models according to claim 5, characterized in that, The step of controlling the extraction of pollen source core features from the upwind node and generating simplified metadata based on the time margin parameter, and injecting it into the downwind node to achieve simplified metadata shadow degradation injection, includes: Based on the detected available transmission bandwidth of the network layer and the volume of the full data of the pollen source to be migrated, the migration time of the task is estimated. Calculate the difference between the estimated time of the meteorological physical transmission and the time of the task migration, and generate the time margin parameter; If the time margin parameter is less than the preset safe time threshold, the upwind node is triggered to extract the core features of the pollen source, generate simplified metadata, and inject it into the downwind node to achieve the shadow downgrade injection of the simplified metadata.

8. The pollen dispersal forecasting and early warning method based on meteorological numerical models according to claim 6, characterized in that, The assessment of the task migration time based on the detected available transmission bandwidth of the network layer and the volume of the full data of the pollen source to be migrated includes: If the detected available transmission bandwidth of the network layer is lower than the preset lower limit of bandwidth, the calculation of the full migration time will be stopped, and the task migration time will be marked as the maximum value. If the detected available transmission bandwidth of the network layer is greater than or equal to the preset lower limit of bandwidth, the total data volume of the pollen source to be migrated is divided by the available transmission bandwidth of the network layer, and the constant time for the preheating of the underlying system services is added to obtain the migration time of the task.

9. A computing device, characterized in that, The computing device includes a memory and a processor. The memory stores program code. When the processor executes the program code, the processor is used to execute the pollen dispersal forecasting and early warning method based on meteorological numerical models as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes computer instructions; when the computer instructions are executed in a computing device, the computing device performs the pollen dispersal forecasting and early warning method based on a meteorological numerical model as described in any one of claims 1-8.

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