Meteorological environment intelligent perception and real-time monitoring method and system

Abstract

The application provides a meteorological environment intelligent sensing and real-time monitoring method and system, relates to the technical field of gas phase monitoring, and obtains meteorological environment original data by arranging multiple meteorological environment sensing nodes in a target monitoring area, and generates a multidimensional meteorological state vector according to a preset time window; constructs a meteorological state change vector according to the multidimensional meteorological state vectors of adjacent time windows, compares the meteorological state change vectors of different meteorological environment sensing nodes on a regional scale, forms a regional change benchmark; through consistency determination of the meteorological state change vectors of each meteorological environment sensing node and the regional change benchmark, a local meteorological abnormal evolution process is identified, and a corresponding identifier is generated; for the identified local abnormal area, the collection time window of the meteorological environment original data is dynamically adjusted, and the meteorological state change vector is updated according to the adjusted time window, so that the continuous sensing and real-time monitoring of the local meteorological environment change process are realized.

Description

A method and system for intelligent sensing and real-time monitoring of meteorological environment Technical Field

[0001] This invention relates to the field of gas phase monitoring technology, and in particular to a method and system for intelligent sensing and real-time monitoring of meteorological environment. Background Technology

[0002] Existing meteorological and environmental monitoring technologies typically involve deploying fixed meteorological monitoring stations in the target area to collect meteorological elements such as temperature, humidity, air pressure, wind speed, and precipitation. Various sensors sample data at preset time intervals and upload the collected meteorological data to a central server via wired or wireless communication. The server then performs data storage, statistical analysis, and visualization, providing fundamental data support for weather forecasting and environmental assessments.

[0003] However, in application scenarios such as monitoring localized severe convective weather in mountainous areas, the aforementioned existing technologies have significant shortcomings. Due to the limited spatial distribution density of fixed meteorological stations, when heavy rainfall or gusts occur in localized areas such as valleys and slopes within a short period of time, the meteorological data collected by nearby monitoring stations may still appear normal or show delayed changes. This results in the server's real-time monitoring results based on this data failing to reflect actual meteorological changes, thereby affecting the accurate judgment of the timing of early warnings for sudden geological disasters. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for intelligent sensing and real-time monitoring of meteorological environment, which aims to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, a method for intelligent sensing and real-time monitoring of meteorological environment, the method comprising:

[0007] Acquire raw meteorological and environmental data collected by multiple meteorological and environmental sensing nodes within the target monitoring area at multiple consecutive collection times. The raw meteorological and environmental data includes temperature, humidity, wind speed, and precipitation intensity.

[0008] Based on the raw meteorological and environmental data, according to the preset time window, the raw meteorological and environmental data corresponding to the same meteorological and environmental sensing node at multiple consecutive collection times are combined and processed to generate a multi-dimensional meteorological state vector.

[0009] Based on the multidimensional meteorological state vector, the multidimensional meteorological state vectors within adjacent time windows are differentially processed to generate a meteorological state change vector that characterizes the change of meteorological state over time.

[0010] Based on the meteorological state change vector, the meteorological state change vectors corresponding to different meteorological environment sensing nodes within the same time window are compared and processed to calculate the degree of change difference and generate a regional change benchmark.

[0011] Based on the regional change benchmark, the consistency of the meteorological state change vectors corresponding to each meteorological environment sensing node is determined. When the meteorological state change vector corresponding to a certain meteorological environment sensing node deviates from the regional change benchmark, a local meteorological anomaly evolution identifier corresponding to that meteorological environment sensing node is generated.

[0012] Based on the local meteorological anomaly evolution indicators, the time window for collecting the original meteorological environment data in the corresponding area is adjusted, and the meteorological state change vector is regenerated based on the adjusted time window.

[0013] Based on the regenerated meteorological state change vector, the local meteorological anomaly evolution markers are updated, and the real-time meteorological environment monitoring results of the local meteorological anomaly evolution area are output.

[0014] Preferably, based on the meteorological state change vector, the meteorological state change vectors corresponding to different meteorological environment sensing nodes within the same time window are compared and processed to calculate the degree of change difference and generate a regional change benchmark, including:

[0015] Based on the meteorological state change vector, the change information corresponding to temperature, humidity, wind speed and precipitation intensity contained therein is broken down and processed to generate element change components corresponding to each meteorological element.

[0016] Based on the element change components, the element change components from different meteorological environment sensing nodes under the same meteorological element type are aligned to generate an aligned set of element change components.

[0017] Based on the aligned set of element change components, the stability of the element change components corresponding to each meteorological environment sensing node is evaluated, and abnormal element change components whose change characteristics are inconsistent with those of most nodes are identified.

[0018] The element change components identified as abnormal are removed, and the remaining element change components are assigned corresponding weights according to the historical stability of the nodes and then summarized to generate a summary result of element change that represents the overall change status of the meteorological element within the time window.

[0019] The regional change benchmark is generated by combining the summary results of element changes corresponding to different meteorological elements. The regional change benchmark is then updated in subsequent time windows based on the newly acquired summary results of element changes, so that the regional change benchmark can evolve dynamically over time.

[0020] Preferably, based on the regional change benchmark, the consistency of the meteorological state change vectors corresponding to each meteorological environment sensing node is determined. When the meteorological state change vector corresponding to a certain meteorological environment sensing node deviates from the regional change benchmark, a local meteorological anomaly evolution indicator corresponding to that meteorological environment sensing node is generated, including:

[0021] Based on the meteorological state change vectors corresponding to each meteorological environment sensing node, the corresponding meteorological state change trajectories are generated by performing correlation processing in chronological order.

[0022] Based on the regional change benchmark, the change status of meteorological state change trajectory in each time window is matched one by one to generate the matching result corresponding to each time window.

[0023] Based on the matching results, a comprehensive judgment is made on the correlation between the magnitude, direction, and trend of the meteorological state change trajectory within multiple consecutive time windows and the regional change benchmark, generating a trajectory consistency judgment result.

[0024] When the trajectory consistency determination result shows that the meteorological state change trajectory continuously deviates from the regional change benchmark within multiple consecutive time windows, and the direction of deviation remains consistent, the meteorological state change process of the meteorological environment sensing node is determined to be an abnormal evolution process.

[0025] Based on the abnormal evolution process, a local meteorological abnormal evolution identifier associated with the corresponding meteorological environment sensing node is generated to characterize the systematic separation between the meteorological state evolution path of the node's location and the overall evolution path of the target monitoring area.

[0026] Preferably, based on the local meteorological anomaly evolution indicator, the time window for collecting raw meteorological environmental data in the corresponding area is adjusted, and a meteorological state change vector is regenerated based on the adjusted time window, including:

[0027] Based on the local meteorological anomaly evolution markers, the corresponding meteorological environment sensing nodes are obtained, and the duration and intensity of the anomaly evolution corresponding to the meteorological environment sensing node are determined based on the local meteorological anomaly evolution markers.

[0028] Based on the duration and intensity of the abnormal evolution, the time window for collecting raw meteorological and environmental data at the meteorological and environmental sensing nodes is adjusted in stages to generate an adjusted collection time window corresponding to the degree of abnormal evolution.

[0029] Based on the adjusted collection time window, the original meteorological and environmental data collected by the meteorological and environmental sensing node at multiple consecutive collection times are reacquired.

[0030] Based on the newly acquired meteorological environment data, corresponding multidimensional meteorological state vectors and meteorological state change vectors are regenerated to reflect the evolution of meteorological state after changes in the degree of anomalous evolution.

[0031] Preferably, the anomalous element change components are removed, and the remaining element change components are assigned corresponding weights according to the historical stability of the nodes before being aggregated to generate a summary result of element change representing the overall change state of the meteorological element within the time window, including:

[0032] Acquire the element change components corresponding to each meteorological environment sensing node within a predetermined historical time range and multiple historical time windows to form historical element change data;

[0033] Based on historical data on element changes, the element change components of each meteorological environment sensing node within each historical time window are compared with the regional change benchmark within the corresponding time window to generate a historical consistency record that represents the consistency results within each historical time window.

[0034] Based on historical consistency records, the duration of time during which each meteorological environment sensing node maintains a consistent state of change within a historical time range is accumulated to generate historical stability evaluation results for each meteorological environment sensing node.

[0035] Based on the historical stability evaluation results, different historical stability levels are divided into multiple stability levels, and each stability level is mapped to a preset weight range to generate the weight value corresponding to each meteorological environment sensing node.

[0036] After removing the element change components identified as abnormal, the remaining element change components are weighted and summarized according to their weight values ​​to generate a summary result of element change that represents the overall change status of the meteorological element within the current time window.

[0037] In subsequent time windows, the summary results of element changes are used as input data to update historical consistency records, so that the weight values ​​participate in the continuous updating process of regional change benchmarks.

[0038] Preferably, based on the matching results, a comprehensive judgment is made on the correlation between the magnitude, direction, and trend of the meteorological state change trajectory within multiple consecutive time windows and the regional change benchmark, generating a trajectory consistency judgment result, including:

[0039] Obtain the matching results of meteorological state change trajectories within multiple consecutive time windows, and use them as trajectory matching data;

[0040] Based on trajectory matching data, the direction of change of meteorological state change trajectory within each time window is compared with the direction of change of regional change benchmark to generate a direction consistency judgment result.

[0041] If the direction consistency determination results show that the direction of change is consistent, the amplitude of the change of the meteorological state trajectory in multiple consecutive time windows is accumulated based on the trajectory matching data to generate the amplitude accumulation result.

[0042] When the amplitude accumulation result meets the preset accumulation conditions, the trend of meteorological state change trajectory in multiple consecutive time windows is compared with the trend of regional change benchmark based on trajectory matching data to generate trend consistency judgment result.

[0043] The trajectory consistency determination results are jointly processed based on the directional consistency determination results, the amplitude accumulation results, and the trend consistency determination results to generate trajectory consistency determination results that characterize whether the trajectory of meteorological state change constitutes an abnormal evolution process.

[0044] Preferably, based on the duration and intensity of the anomalous evolution, the time window for collecting raw meteorological and environmental data at the meteorological and environmental sensing nodes is adjusted in stages to generate an adjusted collection time window corresponding to the degree of anomalous evolution, including:

[0045] Obtain the meteorological environment sensing node corresponding to the local meteorological anomaly evolution marker, and obtain the anomaly evolution duration and anomaly evolution intensity information associated with the local meteorological anomaly evolution marker;

[0046] Based on the abnormal evolution duration information, determine whether the abnormal evolution duration has reached the preset duration trigger condition. When the duration trigger condition is reached, generate a duration-driven window adjustment command.

[0047] Based on the abnormal evolution intensity information, determine whether the abnormal evolution intensity has reached the preset intensity triggering condition. When the intensity triggering condition is reached, generate an intensity-driven window adjustment command.

[0048] When generating both duration-driven and intensity-driven window adjustment commands simultaneously, the intensity-driven window adjustment command shall be selected as the effective command.

[0049] Based on the effective window adjustment instruction, the corresponding adjusted acquisition time window is determined from multiple preset acquisition time window levels, and the process switches between different acquisition time window levels when the abnormal evolution continues to change.

[0050] Secondly, a meteorological environment intelligent sensing and real-time monitoring system, the system comprising:

[0051] The data acquisition module is used to acquire raw meteorological and environmental data collected by multiple meteorological and environmental sensing nodes within the target monitoring area at multiple consecutive acquisition times. The raw meteorological and environmental data includes temperature, humidity, wind speed, and precipitation intensity.

[0052] The state vector generation module is used to combine and process the raw meteorological and environmental data corresponding to multiple consecutive collection times of the same meteorological and environmental sensing node according to a preset time window, based on the raw meteorological and environmental data, to generate a multi-dimensional meteorological state vector.

[0053] The change vector generation module is used to perform differential processing on the multidimensional meteorological state vector within adjacent time windows based on the multidimensional meteorological state vector, and generate a meteorological state change vector that represents the change of meteorological state over time.

[0054] The regional change benchmark generation module is used to compare the meteorological state change vectors corresponding to different meteorological environment sensing nodes within the same time window based on the meteorological state change vector, calculate the degree of change difference, and generate a regional change benchmark.

[0055] The anomaly evolution determination module is used to determine the consistency of the meteorological state change vectors corresponding to each meteorological environment sensing node based on the regional change benchmark. When the meteorological state change vector corresponding to a certain meteorological environment sensing node deviates from the regional change benchmark, a local meteorological anomaly evolution identifier corresponding to that meteorological environment sensing node is generated.

[0056] The data acquisition time window adjustment module is used to adjust the data acquisition time window of the corresponding area's meteorological environment raw data according to the local meteorological anomaly evolution indicator, and regenerate the meteorological state change vector based on the adjusted data acquisition time window;

[0057] The monitoring results output module is used to update the local meteorological anomaly evolution markers based on the regenerated meteorological state change vector, and output the real-time meteorological environment monitoring results of the local meteorological anomaly evolution area.

[0058] The above-described solution of the present invention has at least the following beneficial effects:

[0059] By setting up multiple meteorological environment sensing nodes within the target monitoring area and acquiring raw meteorological environment data at multiple consecutive collection times, and then combining and processing the data from the same node according to a preset time window, discrete single-point sampling results can be transformed into a meteorological state description with temporal continuity. This allows meteorological environment monitoring to no longer be limited to instantaneous values ​​at a single moment, but to reflect the overall change state of meteorological elements within a certain time range, thus providing a more stable and complete data foundation for subsequent analysis.

[0060] Based on this, by performing differential processing on the meteorological state within adjacent time windows, a meteorological state change vector is generated. The changes of different meteorological environment sensing nodes within the same time window are compared to form a regional change benchmark. This can characterize the overall change structure of the meteorological environment at the regional scale, avoid the judgment bias caused by relying solely on data from individual monitoring points, and provide a unified reference standard for meteorological changes within the region.

[0061] Furthermore, the consistency between the meteorological state change vectors corresponding to each meteorological environment sensing node and the regional change benchmark is determined. When the change process of a certain node deviates from the overall regional change structure, a corresponding local meteorological anomaly evolution indicator is generated. This improves the basis for anomaly identification from single-value anomalies to deviations over time, which is beneficial for identifying the abnormal development trend of the local meteorological environment before the meteorological values ​​reach a significant level of anomaly.

[0062] After identifying the evolution of local meteorological anomalies, the time window for collecting raw meteorological environmental data in the relevant areas is adjusted according to the corresponding anomaly evolution markers. The meteorological state change vector is then regenerated based on the adjusted time window. This can improve the time resolution capability for anomaly areas while maintaining the original collection rhythm for non-anomaly areas. Thus, targeted enhanced monitoring of key areas can be achieved without increasing the overall monitoring burden.

[0063] By combining the above-mentioned technical means, this invention can reflect the abnormal evolution process of local meteorological environment earlier and more accurately in application scenarios with complex spatial distribution and significant local meteorological changes, such as mountainous areas. This provides more timely and reliable monitoring basis for subsequent disaster early warning and risk assessment, and makes up for the shortcomings of existing fixed meteorological monitoring technologies in monitoring sudden local meteorological changes. Attached Figure Description

[0064] Figure 1 is a flowchart of a meteorological environment intelligent sensing and real-time monitoring method provided by an embodiment of the present invention. Detailed Implementation

[0065] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0066] As shown in Figure 1, an embodiment of the present invention proposes a method for intelligent sensing and real-time monitoring of meteorological environment, the method comprising:

[0067] Acquire raw meteorological and environmental data collected by multiple meteorological and environmental sensing nodes within the target monitoring area at multiple consecutive collection times. The raw meteorological and environmental data includes temperature, humidity, wind speed, and precipitation intensity.

[0068] Based on the raw meteorological and environmental data, according to the preset time window, the raw meteorological and environmental data corresponding to the same meteorological and environmental sensing node at multiple consecutive collection times are combined and processed to generate a multi-dimensional meteorological state vector.

[0069] Based on the multidimensional meteorological state vector, the multidimensional meteorological state vectors within adjacent time windows are differentially processed to generate a meteorological state change vector that characterizes the change of meteorological state over time.

[0070] Based on the meteorological state change vector, the meteorological state change vectors corresponding to different meteorological environment sensing nodes within the same time window are compared and processed to calculate the degree of change difference and generate a regional change benchmark.

[0071] Based on the regional change benchmark, the consistency of the meteorological state change vectors corresponding to each meteorological environment sensing node is determined. When the meteorological state change vector corresponding to a certain meteorological environment sensing node deviates from the regional change benchmark, a local meteorological anomaly evolution identifier corresponding to that meteorological environment sensing node is generated.

[0072] Based on the local meteorological anomaly evolution indicators, the time window for collecting the original meteorological environment data in the corresponding area is adjusted, and the meteorological state change vector is regenerated based on the adjusted time window.

[0073] Based on the regenerated meteorological state change vector, the local meteorological anomaly evolution markers are updated, and the real-time meteorological environment monitoring results of the local meteorological anomaly evolution area are output.

[0074] In this embodiment of the invention, by setting up multiple meteorological environment sensing nodes within the target monitoring area and acquiring raw meteorological environment data such as temperature, humidity, wind speed, and precipitation intensity at multiple consecutive acquisition times, and then combining the raw meteorological environment data corresponding to the same meteorological environment sensing node according to a preset time window, the discretely acquired data can be transformed into a multi-dimensional meteorological state vector with time correlation, so that the meteorological environment state is transformed from a single-point numerical description to an overall state description within a time window, which is beneficial to reflecting the continuous change characteristics of the meteorological environment in the time dimension.

[0075] By performing differential processing on the multidimensional meteorological state vectors within adjacent time windows, a meteorological state change vector is generated. Then, by comparing the meteorological state change vectors corresponding to different meteorological environment sensing nodes within the same time window, a regional change benchmark is formed. This can characterize the overall change structure of meteorological state at the regional scale, avoid local biases caused by relying solely on data from a single node, and provide a unified reference basis for regional meteorological environment changes.

[0076] After obtaining the regional change benchmark, the consistency between the meteorological state change vectors corresponding to each meteorological environment sensing node and the regional change benchmark is determined. When the meteorological state change vector of a certain meteorological environment sensing node deviates from the regional change benchmark, a corresponding local meteorological anomaly evolution indicator is generated. This shifts the focus of anomaly judgment from single-value fluctuations to the deviation of meteorological state over time, which helps to identify situations where the local meteorological environment change path in the region is inconsistent with the overall change path.

[0077] After generating a local meteorological anomaly evolution identifier, the data acquisition time window for the corresponding region's meteorological environment is adjusted based on this identifier. The meteorological state change vector is then regenerated based on the adjusted acquisition time window. This allows for a more granular temporal characterization of the meteorological environment changes in the anomaly region, while maintaining the original acquisition rhythm in the non-anomaly region. This enables differentiated monitoring of meteorological environment changes in different regions. Furthermore, the updated meteorological state change vector is used to continuously correct the local meteorological anomaly evolution identifier, ensuring that the monitoring results remain consistent with the actual meteorological environment changes.

[0078] For example, in mountainous meteorological monitoring scenarios, multiple meteorological environment sensing nodes are distributed in different locations such as valleys, slopes, and ridges. When a valley area experiences inconsistent trends in precipitation intensity and humidity with the surrounding areas within a short period of time, the evolution process of local meteorological anomalies in the area can be identified by comparing multidimensional meteorological state vectors and regional change benchmarks. The collection time window for the area can then be adjusted accordingly to obtain more intensive meteorological environment data, thereby continuously tracking the development of local meteorological changes and facilitating real-time monitoring and understanding of the meteorological environment in the area.

[0079] In a preferred embodiment of the present invention, acquiring raw meteorological and environmental data collected by multiple meteorological and environmental sensing nodes within a target monitoring area at multiple consecutive acquisition times includes:

[0080] Multiple meteorological environment sensing nodes are set up in the target monitoring area according to a predetermined spatial distribution. Each meteorological environment sensing node is equipped with a sensing unit for collecting temperature, humidity, wind speed and precipitation intensity.

[0081] Following a unified data acquisition timing control strategy, each meteorological and environmental sensing node is driven to synchronously perform data acquisition operations at multiple consecutive acquisition times, so as to form a meteorological and environmental raw data sequence with temporal continuity.

[0082] The temperature, humidity, wind speed and precipitation intensity data collected by each meteorological environment sensing node at each collection time are associated and stored with the corresponding node identifier and collection time information to form the raw meteorological environment data set required for subsequent processing.

[0083] In a preferred embodiment of the present invention, based on raw meteorological environmental data and according to a preset time window, the raw meteorological environmental data corresponding to multiple consecutive collection times of the same meteorological environmental sensing node are combined and processed to generate a multi-dimensional meteorological state vector, including:

[0084] The length of the time window used to describe the weather conditions is preset so that each time window covers multiple consecutive data collection moments.

[0085] For the same meteorological environment sensing node, the raw data of temperature, humidity, wind speed and precipitation intensity corresponding to that node are selected within each time window and organized in the order of collection time.

[0086] The processed data is combined and processed to describe the temperature, humidity, wind speed and precipitation intensity within the same time window as a whole, thereby generating a multi-dimensional meteorological state vector to characterize the meteorological state of the meteorological environment sensing node within the time window.

[0087] In a preferred embodiment of the present invention, based on a multidimensional meteorological state vector, the multidimensional meteorological state vectors within adjacent time windows are differentially processed to generate a meteorological state change vector characterizing the change of meteorological state over time, including:

[0088] For the same meteorological environment sensing node, select the multidimensional meteorological state vectors corresponding to two adjacent time windows in chronological order;

[0089] The status information of temperature, humidity, wind speed and precipitation intensity in the two time windows were compared one by one to determine the changes of each meteorological element between adjacent time windows.

[0090] The changes of various meteorological elements within adjacent time windows are summarized to form a meteorological state change vector that can simultaneously reflect the magnitude, direction, and trend of change. This vector is used to describe the changes in the meteorological state of the meteorological environment sensing node over time.

[0091] In a preferred embodiment of the present invention, the local meteorological anomaly evolution marker is updated based on the regenerated meteorological state change vector, and the real-time meteorological environment monitoring results of the local meteorological anomaly evolution area are output, including:

[0092] After the data collection time window is adjusted, the corresponding meteorological state change vector is regenerated based on the new time window, and this meteorological state change vector is used as the state change input at the current moment.

[0093] The currently generated meteorological state change vector is compared with the meteorological state change vector previously used to generate local meteorological anomaly evolution indicators to determine whether the evolution direction and intensity of meteorological state changes have changed.

[0094] Based on the comparison results, the existing local meteorological anomaly evolution markers are updated so that the markers can continuously reflect the latest status of the local meteorological environment change process, and the updated results are used as the real-time meteorological environment monitoring output of the local meteorological anomaly evolution area.

[0095] In a preferred embodiment of the present invention, based on the meteorological state change vector, the meteorological state change vectors corresponding to different meteorological environment sensing nodes within the same time window are compared and processed to calculate the degree of change difference and generate a regional change benchmark, including:

[0096] Based on the meteorological state change vector, the change information corresponding to temperature, humidity, wind speed and precipitation intensity contained therein is broken down and processed to generate element change components corresponding to each meteorological element.

[0097] Based on the element change components, the element change components from different meteorological environment sensing nodes under the same meteorological element type are aligned to generate an aligned set of element change components.

[0098] Based on the aligned set of element change components, the stability of the element change components corresponding to each meteorological environment sensing node is evaluated, and abnormal element change components whose change characteristics are inconsistent with those of most nodes are identified.

[0099] The element change components identified as abnormal are removed, and the remaining element change components are assigned corresponding weights according to the historical stability of the nodes and then summarized to generate a summary result of element change that represents the overall change status of the meteorological element within the time window.

[0100] The regional change benchmark is generated by combining the summary results of element changes corresponding to different meteorological elements. The regional change benchmark is then updated in subsequent time windows based on the newly acquired summary results of element changes, so that the regional change benchmark can evolve dynamically over time.

[0101] In this embodiment of the invention, by decomposing the meteorological state change vector into element change components corresponding to different meteorological elements, and aligning and evaluating the stability of change components from different meteorological environment sensing nodes within the same meteorological element dimension, abnormal change sources inconsistent with the overall change structure can be filtered out at the regional scale. Simultaneously, the historical stability of nodes is introduced to weight and summarize the element change components, ensuring that meteorological environment sensing nodes with stable long-term change behavior occupy a higher proportion of influence in the regional change benchmark. This allows the formed regional change benchmark to continuously reflect the dominant change characteristics of the meteorological state within the target monitoring area and to dynamically update over time, reducing the interference of individual abnormal nodes on the regional judgment results.

[0102] In a preferred embodiment of the present invention, based on the aligned set of element change components, a stability assessment is performed on the element change components corresponding to each meteorological environment sensing node to identify anomalous element change components whose change characteristics are inconsistent with those of most nodes, including:

[0103] After completing the alignment processing of the element change components corresponding to each meteorological environment sensing node under the same meteorological element type, the element change components are used as the evaluation objects.

[0104] Using the direction and magnitude of change of the element change components of most meteorological environment sensing nodes under the same meteorological element type as reference change characteristics, the element change components corresponding to each meteorological environment sensing node are compared one by one.

[0105] When the change component of an element corresponding to a certain meteorological environment sensing node is inconsistent with the reference change characteristics in terms of change direction or change magnitude, the change component of that element is marked as an abnormal candidate component.

[0106] The changes of the element change components marked as anomalous candidate components within the current time window are confirmed, and the anomalous element change components whose change characteristics are inconsistent with those of most nodes are finally identified.

[0107] In a preferred embodiment of the present invention, the regional change benchmark is generated by combining the summary results of element changes corresponding to different meteorological elements, and then updated in subsequent time windows based on newly acquired summary results of element changes, so that the regional change benchmark dynamically evolves over time, including:

[0108] Within the current time window, the summary results of the changes in elements corresponding to temperature, humidity, wind speed and precipitation intensity are obtained respectively, and the summary results of the changes in each element are used as input data at the regional level.

[0109] According to the preset element combination rules, the summaries of the element changes corresponding to each meteorological element are uniformly integrated to form a regional change benchmark that can characterize the overall meteorological state change structure of the target monitoring area.

[0110] After entering the subsequent time window, obtain the summary results of the element changes corresponding to each meteorological element generated in the new time window;

[0111] The newly acquired summaries of element changes are correlated with the previously established regional change benchmarks, and the regional change benchmarks are updated accordingly, so that the regional change benchmarks can continuously reflect the changes in the meteorological status of the target monitoring area over time.

[0112] In a preferred embodiment of the present invention, based on a regional change benchmark, a consistency determination is made on the meteorological state change vectors corresponding to each meteorological environment sensing node. When the meteorological state change vector corresponding to a certain meteorological environment sensing node deviates from the regional change benchmark, a local meteorological anomaly evolution identifier corresponding to that meteorological environment sensing node is generated, including:

[0113] Based on the meteorological state change vectors corresponding to each meteorological environment sensing node, the corresponding meteorological state change trajectories are generated by performing correlation processing in chronological order.

[0114] Based on the regional change benchmark, the change status of meteorological state change trajectory in each time window is matched one by one to generate the matching result corresponding to each time window.

[0115] Based on the matching results, a comprehensive judgment is made on the correlation between the magnitude, direction, and trend of the meteorological state change trajectory within multiple consecutive time windows and the regional change benchmark, generating a trajectory consistency judgment result.

[0116] When the trajectory consistency determination result shows that the meteorological state change trajectory continuously deviates from the regional change benchmark within multiple consecutive time windows, and the direction of deviation remains consistent, the meteorological state change process of the meteorological environment sensing node is determined to be an abnormal evolution process.

[0117] Based on the abnormal evolution process, a local meteorological abnormal evolution identifier associated with the corresponding meteorological environment sensing node is generated to characterize the systematic separation between the meteorological state evolution path of the node's location and the overall evolution path of the target monitoring area.

[0118] In this embodiment of the invention, by correlating meteorological state change vectors in the time dimension to form a meteorological state change trajectory, and combining this trajectory with a regional change benchmark to match its changes over multiple consecutive time windows, anomaly identification can be transformed from numerical deviation at a single point in time to a holistic judgment of the change path. Based on this, by comprehensively judging the correspondence between the direction, magnitude, and trend of change, anomaly identification is given continuity and directional consistency constraints, which helps distinguish between short-term fluctuations and continuous evolution processes, thereby improving the accuracy of identifying local meteorological environmental anomalies.

[0119] In a preferred embodiment of the present invention, based on the meteorological state change vectors corresponding to each meteorological environment sensing node, correlation processing is performed in chronological order to generate corresponding meteorological state change trajectories, including:

[0120] For each meteorological environment sensing node, the meteorological state change vector generated within multiple consecutive time windows is obtained in chronological order.

[0121] The meteorological state change vectors of the same meteorological environment sensing node in different time windows are arranged and associated according to the generation time order to form a data sequence reflecting the continuous process of meteorological state change of the node;

[0122] Using data sequences as meteorological state change trajectories, this method describes the evolution path of meteorological state changes at a meteorological environment sensing node within multiple time windows.

[0123] In a preferred embodiment of the present invention, based on a regional change benchmark, the change status of meteorological state change trajectories within each time window is matched one by one to generate matching results corresponding to each time window, including:

[0124] After obtaining the meteorological state change trajectory, the change status of the meteorological state change trajectory within each time window is used as the matching object.

[0125] Using the regional change benchmark within the corresponding time window as a matching reference, the change status of the meteorological state change trajectory within the time window is compared with the regional change benchmark.

[0126] Based on the comparison results, determine whether the direction, magnitude and trend of the meteorological state change trajectory within the time window are consistent with the regional change benchmark.

[0127] The judgment results within each time window are recorded separately, and matching results corresponding to each time window are generated for subsequent consistency judgment of meteorological state change trajectory.

[0128] In a preferred embodiment of the present invention, the meteorological environment raw data acquisition time window for the corresponding area is adjusted according to the local meteorological anomaly evolution indicator, and a meteorological state change vector is regenerated based on the adjusted acquisition time window, including:

[0129] Based on the local meteorological anomaly evolution markers, the corresponding meteorological environment sensing nodes are obtained, and the duration and intensity of the anomaly evolution corresponding to the meteorological environment sensing node are determined based on the local meteorological anomaly evolution markers.

[0130] Based on the duration and intensity of the abnormal evolution, the time window for collecting raw meteorological and environmental data at the meteorological and environmental sensing nodes is adjusted in stages to generate an adjusted collection time window corresponding to the degree of abnormal evolution.

[0131] Based on the adjusted collection time window, the original meteorological and environmental data collected by the meteorological and environmental sensing node at multiple consecutive collection times are reacquired.

[0132] Based on the newly acquired meteorological environment data, corresponding multidimensional meteorological state vectors and meteorological state change vectors are regenerated to reflect the evolution of meteorological state after changes in the degree of anomalous evolution.

[0133] In this embodiment of the invention, by obtaining the duration and intensity of anomaly evolution based on local meteorological anomaly evolution markers, and adjusting the collection time window accordingly, a clear correspondence can be established between the collection frequency and the degree of change in the meteorological environment. When the anomaly evolution is dominated by duration, the collection time window is adjusted step by step to enhance time resolution; when the anomaly evolution is dominated by intensity change, the collection density is rapidly increased through cross-level adjustment. This allows for targeted enhanced monitoring of local anomaly areas without changing the overall monitoring architecture. Furthermore, when the anomaly evolution state changes, the system switches between different collection time window levels to maintain synchronization between the monitoring process and the evolution of the meteorological environment.

[0134] In a preferred embodiment of the present invention, the corresponding meteorological environment sensing node is obtained based on the local meteorological anomaly evolution identifier, and the duration and intensity of the anomaly evolution corresponding to the meteorological environment sensing node are determined based on the local meteorological anomaly evolution identifier, including:

[0135] After generating a local meteorological anomaly evolution identifier, the meteorological environment sensing node identifier information associated with the identifier is read to determine the specific meteorological environment sensing node where the anomaly evolution occurred.

[0136] For the identified meteorological environment sensing node, the meteorological state change vector of the node in multiple consecutive time windows is traced back to determine the time range within which the meteorological state change vector of the node is continuously judged as an abnormal evolution.

[0137] The number of consecutive time windows within this time range that are determined to be abnormal evolution is used as the description result of the duration of abnormal evolution;

[0138] Meanwhile, based on the degree of deviation of the meteorological state change vector of the meteorological environment sensing node from the regional change benchmark during the abnormal evolution, the change amplitude of the abnormal evolution process is summarized and described to form a description result of the abnormal evolution intensity, which is used to characterize the degree of abnormal evolution of the meteorological state of the node.

[0139] In a preferred embodiment of the present invention, the element change components identified as abnormal are removed, and the remaining element change components are assigned corresponding weights according to the historical stability of the nodes before being summarized to generate a summary result of element change representing the overall change state of the meteorological element within the time window, including:

[0140] Acquire the element change components corresponding to each meteorological environment sensing node within a predetermined historical time range and multiple historical time windows to form historical element change data;

[0141] Based on historical data on element changes, the element change components of each meteorological environment sensing node within each historical time window are compared with the regional change benchmark within the corresponding time window to generate a historical consistency record that represents the consistency results within each historical time window.

[0142] Based on historical consistency records, the duration of time during which each meteorological environment sensing node maintains a consistent state of change within a historical time range is accumulated to generate historical stability evaluation results for each meteorological environment sensing node.

[0143] Based on the historical stability evaluation results, different historical stability levels are divided into multiple stability levels, and each stability level is mapped to a preset weight range to generate the weight value corresponding to each meteorological environment sensing node.

[0144] After removing the element change components identified as abnormal, the remaining element change components are weighted and summarized according to their weight values ​​to generate a summary result of element change that represents the overall change status of the meteorological element within the current time window.

[0145] In subsequent time windows, the summary results of element changes are used as input data to update historical consistency records, so that the weight values ​​participate in the continuous updating process of regional change benchmarks.

[0146] In this embodiment of the invention, by continuously recording the element change components of each meteorological environment sensing node within a predetermined historical time range, and combining this with the conformity with the regional change benchmark within the historical time window to form a historical consistency record, the stability of node changes during long-term operation can be explicitly characterized. Based on this, the historical stability level is divided into different levels and mapped to corresponding weights, so that element change components no longer participate with equal weight during the aggregation process, but are distinguished according to the node's historical performance. This results in the generated element change aggregation result reflecting the change characteristics of stable nodes more comprehensively. Simultaneously, this aggregation result is used to update the historical consistency record, allowing the weights to be dynamically adjusted over time, which helps the regional change benchmark gradually conform to the actual regional meteorological change structure during long-term operation.

[0147] In a preferred embodiment of the present invention, based on historical consistency records, the duration of time during which each meteorological environment sensing node maintains a consistent state of change within a historical time range is accumulated to generate a historical stability evaluation result for each meteorological environment sensing node, including:

[0148] For each meteorological environment sensing node, the number of time windows in which it was determined to be consistent with the regional change benchmark within each historical time window range is counted.

[0149] The number of time windows in which the weather environment sensing node maintains a consistent or cumulative state of change is used as the basis for describing the stable operating time of the weather environment sensing node.

[0150] Based on the proportion of stable operating time within the historical time range, the long-term stability of each meteorological environment sensing node is classified and described, thereby generating an evaluation result reflecting the historical stability of each meteorological environment sensing node.

[0151] In a preferred embodiment of the present invention, based on the historical stability evaluation results, different historical stability levels are divided into multiple stability levels, and each stability level is mapped to a preset weight range to generate a weight value corresponding to each meteorological environment sensing node, including:

[0152] Based on the historical stability evaluation results, the meteorological environment sensing nodes are divided into multiple stability levels according to the length of stable operation time, so that there are clear distinguishing boundaries between different stability levels.

[0153] For each stability level, a corresponding weight range is pre-defined so that the weight range corresponding to meteorological and environmental sensing nodes with higher stability levels is generally higher than that of meteorological and environmental sensing nodes with lower stability levels.

[0154] The stability level of each meteorological environment sensing node is matched with its corresponding weight range, and a specific weight value is determined within that weight range, thereby generating a weight value that corresponds one-to-one with each meteorological environment sensing node.

[0155] In a preferred embodiment of the present invention, the method for setting the preset weight interval includes:

[0156] During the system initialization phase, the number of weight intervals is determined based on the number of meteorological and environmental sensing nodes and their historical operating characteristics within the target monitoring area, ensuring that the number of weight intervals is consistent with the number of stability levels.

[0157] Multiple consecutive weight intervals are set in ascending order to ensure that there is no overlap between adjacent weight intervals and that each weight interval corresponds to a unique stable level.

[0158] When setting weight intervals, the weight intervals with higher stability levels are made to have a higher overall numerical range than the weight intervals with lower stability levels, thereby ensuring that the historical stability level can play a differentiating role in the weighted aggregation process.

[0159] During system operation, when the historical stability evaluation results change, the corresponding weight interval is rematched based on the updated stability level to ensure that the weight interval setting is consistent with the long-term change characteristics of the meteorological environment sensing nodes.

[0160] In a preferred embodiment of the present invention, based on the matching results, a comprehensive judgment is made on the correspondence between the magnitude, direction, and trend of the meteorological state change trajectory within multiple consecutive time windows and the regional change benchmark, generating a trajectory consistency judgment result, including:

[0161] Obtain the matching results of meteorological state change trajectories within multiple consecutive time windows, and use them as trajectory matching data;

[0162] Based on trajectory matching data, the direction of change of meteorological state change trajectory within each time window is compared with the direction of change of regional change benchmark to generate a direction consistency judgment result.

[0163] If the direction consistency determination results show that the direction of change is consistent, the amplitude of the change of the meteorological state trajectory in multiple consecutive time windows is accumulated based on the trajectory matching data to generate the amplitude accumulation result.

[0164] When the amplitude accumulation result meets the preset accumulation conditions, the trend of meteorological state change trajectory in multiple consecutive time windows is compared with the trend of regional change benchmark based on trajectory matching data to generate trend consistency judgment result.

[0165] The trajectory consistency determination results are jointly processed based on the directional consistency determination results, the amplitude accumulation results, and the trend consistency determination results to generate trajectory consistency determination results that characterize whether the trajectory of meteorological state change constitutes an abnormal evolution process.

[0166] In this embodiment of the invention, by first determining the direction of change, then accumulating the magnitude of change under the premise of consistent direction, and further introducing a comparison of the trend of change after the accumulation condition is met, the determination of abnormal evolution has a clear determination order and triggering conditions. This processing method can identify the evolution process in which the magnitude of a single change does not reach an abnormal level but continues to accumulate over time, and can also distinguish the transitional state where the direction is consistent but the trend has not yet formed, thereby avoiding misjudging short-term disturbances as abnormal evolution, and making the generated trajectory consistency determination results more consistent with the actual law of meteorological conditions evolving over time.

[0167] In a preferred embodiment of the present invention, the method for setting the preset accumulation condition includes:

[0168] During the system initialization phase, based on the conventional time scale of meteorological environmental changes within the target monitoring area, the number of time windows used to describe the cumulative process of meteorological state changes is pre-set, so that the cumulative process covers multiple consecutive time windows.

[0169] For the change amplitude information corresponding to each time window in the meteorological state change trajectory, the change amplitude is recorded window by window in chronological order to form a continuous change record sequence;

[0170] When the change amplitude records within multiple consecutive time windows show a continuous increase or decrease while maintaining a consistent direction of change, it is determined that the change amplitude meets the accumulation requirement, thus serving as the basis for judging whether the preset accumulation condition has been met.

[0171] The above method enables the preset accumulation conditions to reflect the continuous accumulation characteristics of meteorological state changes over time, rather than the instantaneous changes within a single time window.

[0172] In a preferred embodiment of the present invention, the meteorological environment raw data acquisition time window of the meteorological environment sensing node is adjusted in stages according to the duration and intensity of the abnormal evolution, generating an adjusted acquisition time window corresponding to the degree of abnormal evolution, including:

[0173] Obtain the meteorological environment sensing node corresponding to the local meteorological anomaly evolution marker, and obtain the anomaly evolution duration and anomaly evolution intensity information associated with the local meteorological anomaly evolution marker;

[0174] Based on the abnormal evolution duration information, determine whether the abnormal evolution duration has reached the preset duration trigger condition. When the duration trigger condition is reached, generate a duration-driven window adjustment command.

[0175] Based on the abnormal evolution intensity information, determine whether the abnormal evolution intensity has reached the preset intensity triggering condition. When the intensity triggering condition is reached, generate an intensity-driven window adjustment command.

[0176] When generating both duration-driven and intensity-driven window adjustment commands simultaneously, the intensity-driven window adjustment command shall be selected as the effective command.

[0177] Based on the effective window adjustment instruction, the corresponding adjusted acquisition time window is determined from multiple preset acquisition time window levels, and the process switches between different acquisition time window levels when the abnormal evolution continues to change.

[0178] In this embodiment of the invention, by acquiring the duration and intensity of anomalous evolution separately, and triggering different adjustment paths based on different information, the changes in the acquisition time window have a clear decision-making basis. When the anomalous evolution is mainly characterized by an extended duration, the acquisition time window is adjusted step by step according to the level, which is conducive to stably tracking the change process; when the anomalous evolution is mainly characterized by a sudden change in intensity, the acquisition rhythm is quickly changed through cross-level adjustment, which helps to capture key change nodes. When the anomalous evolution state changes, switching between different acquisition time window levels can ensure that the acquisition frequency always matches the degree of anomalous evolution, thereby improving the ability to characterize the meteorological change process in the anomalous area while ensuring data continuity.

[0179] In a preferred embodiment of the present invention, the method for setting the duration trigger condition includes:

[0180] In the initial stage of system operation, the common duration range of meteorological anomaly evolution processes within the target monitoring area is statistically analyzed based on historical meteorological monitoring data.

[0181] Within the common duration range, the time length used to distinguish between short-term fluctuations and continuous evolution processes is selected as the duration trigger condition, so that this time length covers multiple continuous time windows;

[0182] During system operation, when the abnormal evolution state corresponding to a certain meteorological environment sensing node persists for multiple consecutive time windows and the duration reaches the duration triggering condition, the duration-driven acquisition time window adjustment logic is triggered.

[0183] This setting allows the duration trigger condition to be used to identify situations where meteorological conditions deviate from the overall evolution path over a long period, thus avoiding frequent adjustments to the data collection rhythm for short-term disturbances.

[0184] In a preferred embodiment of the present invention, the method for setting the intensity triggering condition includes:

[0185] During the system initialization phase, a reference change level is determined to describe the intensity of anomaly evolution based on the historical variation range of different meteorological elements within the target monitoring area.

[0186] During system operation, the deviation of the meteorological state change vector of the meteorological environment sensing node from the regional change benchmark during the abnormal evolution period is compared with the reference change level.

[0187] When the degree of deviation reaches or exceeds the preset intensity triggering condition within a single time window, it is determined that the intensity of abnormal evolution has reached the triggering requirement, thereby triggering the intensity-driven acquisition time window adjustment logic.

[0188] By setting the above methods, the intensity triggering conditions can respond quickly to sudden changes in meteorological conditions.

[0189] In a preferred embodiment of the present invention, the method for setting multiple acquisition time window levels includes:

[0190] During the system configuration phase, multiple acquisition time window levels of different lengths are pre-set so that the acquisition time window levels are distributed in a stepped manner over the time span.

[0191] The acquisition time window levels are sorted from largest to smallest according to the time span, so that acquisition time windows with larger time spans correspond to lower acquisition frequencies, and acquisition time windows with smaller time spans correspond to higher acquisition frequencies.

[0192] Configure corresponding switching conditions for each acquisition time window level, so that the system can switch between different acquisition time window levels according to the duration and intensity of abnormal evolution;

[0193] During system operation, the corresponding acquisition time window level is selected according to the effective window adjustment command, and the acquisition time window level is re-evaluated when the abnormal evolution state changes, thereby realizing the dynamic adjustment of the acquisition time window between multiple levels.

[0194] Embodiments of the present invention also provide a meteorological environment intelligent sensing and real-time monitoring system, the system comprising:

[0195] The data acquisition module is used to acquire raw meteorological and environmental data collected by multiple meteorological and environmental sensing nodes within the target monitoring area at multiple consecutive acquisition times. The raw meteorological and environmental data includes temperature, humidity, wind speed, and precipitation intensity.

[0196] The state vector generation module is used to combine and process the raw meteorological and environmental data corresponding to multiple consecutive collection times of the same meteorological and environmental sensing node according to a preset time window, based on the raw meteorological and environmental data, to generate a multi-dimensional meteorological state vector.

[0197] The change vector generation module is used to perform differential processing on the multidimensional meteorological state vector within adjacent time windows based on the multidimensional meteorological state vector, and generate a meteorological state change vector that represents the change of meteorological state over time.

[0198] The regional change benchmark generation module is used to compare the meteorological state change vectors corresponding to different meteorological environment sensing nodes within the same time window based on the meteorological state change vector, calculate the degree of change difference, and generate a regional change benchmark.

[0199] The anomaly evolution determination module is used to determine the consistency of the meteorological state change vectors corresponding to each meteorological environment sensing node based on the regional change benchmark. When the meteorological state change vector corresponding to a certain meteorological environment sensing node deviates from the regional change benchmark, a local meteorological anomaly evolution identifier corresponding to that meteorological environment sensing node is generated.

[0200] The data acquisition time window adjustment module is used to adjust the data acquisition time window of the corresponding area's meteorological environment raw data according to the local meteorological anomaly evolution indicator, and regenerate the meteorological state change vector based on the adjusted data acquisition time window;

[0201] The monitoring results output module is used to update the local meteorological anomaly evolution markers based on the regenerated meteorological state change vector, and output the real-time meteorological environment monitoring results of the local meteorological anomaly evolution area.

[0202] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0203] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0204] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0205] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent sensing and real-time monitoring of meteorological environment, characterized in that, The method includes: acquiring raw meteorological environmental data collected by multiple meteorological environmental sensing nodes within a target monitoring area at multiple consecutive acquisition times, the raw meteorological environmental data including temperature, humidity, wind speed, and precipitation intensity; based on the raw meteorological environmental data, according to a preset time window, combining the raw meteorological environmental data corresponding to the same meteorological environmental sensing node at multiple consecutive acquisition times to generate a multidimensional meteorological state vector; based on the multidimensional meteorological state vector, performing differential processing on the multidimensional meteorological state vectors within adjacent time windows to generate a meteorological state change vector representing the change of meteorological state over time; and based on the meteorological state change vector, mapping the meteorological state changes corresponding to different meteorological environmental sensing nodes within the same time window to... The data are compared and processed to calculate the degree of difference in change, generating a regional change benchmark. Based on the regional change benchmark, the consistency of the meteorological state change vectors corresponding to each meteorological environment sensing node is determined. When the meteorological state change vector corresponding to a certain meteorological environment sensing node deviates from the regional change benchmark, a local meteorological anomaly evolution indicator is generated for that meteorological environment sensing node. Based on the local meteorological anomaly evolution indicator, the time window for collecting the original meteorological environment data in the corresponding region is adjusted, and the meteorological state change vector is regenerated based on the adjusted time window. Based on the regenerated meteorological state change vector, the local meteorological anomaly evolution indicator is updated, and the real-time meteorological environment monitoring results of the local meteorological anomaly evolution region are output.

2. The method for intelligent sensing and real-time monitoring of meteorological environment according to claim 1, characterized in that, Based on the meteorological state change vector, the meteorological state change vectors corresponding to different meteorological environment sensing nodes within the same time window are compared and processed to calculate the degree of change difference and generate a regional change benchmark. This includes: splitting the change information corresponding to temperature, humidity, wind speed, and precipitation intensity contained in the meteorological state change vector to generate element change components corresponding to each meteorological element; aligning the element change components from different meteorological environment sensing nodes under the same meteorological element type to generate an aligned set of element change components; assessing the stability of the element change components corresponding to each meteorological environment sensing node based on the aligned set of element change components, identifying anomalous element change components whose change characteristics are inconsistent with most nodes; removing the identified anomalous element change components, and summarizing the remaining element change components according to the historical stability of the nodes to generate a summary result of element change representing the overall change state of the meteorological element within the time window; combining the summary results of element change corresponding to different meteorological elements to generate a regional change benchmark, and updating the regional change benchmark in subsequent time windows based on newly acquired summary results of element change to ensure that the regional change benchmark evolves dynamically over time.

3. The method for intelligent sensing and real-time monitoring of meteorological environment according to claim 1, characterized in that, Based on the regional change benchmark, the consistency of meteorological state change vectors corresponding to each meteorological environment sensing node is determined. When the meteorological state change vector corresponding to a certain meteorological environment sensing node deviates from the regional change benchmark, a local meteorological anomaly evolution identifier corresponding to that meteorological environment sensing node is generated. This includes: associating the meteorological state change vectors corresponding to each meteorological environment sensing node in chronological order to generate corresponding meteorological state change trajectories; matching the change states of the meteorological state change trajectories in each time window according to the regional change benchmark to generate matching results for each time window; comprehensively determining the correspondence between the change amplitude, change direction, and change trend of the meteorological state change trajectory in multiple consecutive time windows and the regional change benchmark based on the matching results to generate trajectory consistency determination results; when the trajectory consistency determination results indicate that the meteorological state change trajectory continuously deviates from the regional change benchmark in multiple consecutive time windows, and the direction of deviation remains consistent, the meteorological state change process of that meteorological environment sensing node is determined to be an anomaly evolution process; based on the anomaly evolution process, a local meteorological anomaly evolution identifier associated with the corresponding meteorological environment sensing node is generated to characterize the systematic separation between the meteorological state evolution path of the area where the node is located and the overall evolution path of the target monitoring area.

4. The method for intelligent sensing and real-time monitoring of meteorological environment according to claim 1, characterized in that, Based on the local meteorological anomaly evolution identifier, the time window for collecting raw meteorological environmental data in the corresponding area is adjusted, and a meteorological state change vector is regenerated based on the adjusted collection time window. This includes: obtaining the corresponding meteorological environment sensing node based on the local meteorological anomaly evolution identifier, and determining the duration and intensity of the anomaly evolution corresponding to the meteorological environment sensing node based on the local meteorological anomaly evolution identifier; adjusting the raw meteorological environmental data collection time window of the meteorological environment sensing node in stages based on the duration and intensity of the anomaly evolution, generating an adjusted collection time window corresponding to the degree of anomaly evolution; re-acquiring the raw meteorological environmental data collected by the meteorological environment sensing node at multiple consecutive collection times based on the adjusted collection time window; and regenerating the corresponding multidimensional meteorological state vector and meteorological state change vector based on the re-acquired raw meteorological environmental data to reflect the evolution of meteorological state after the change in the degree of anomaly evolution.

5. The method for intelligent sensing and real-time monitoring of meteorological environment according to claim 2, characterized in that, Anomaly-identified element change components are removed, and the remaining element change components are weighted according to the historical stability of the nodes before being aggregated to generate a summary result representing the overall change status of the meteorological element within a time window. This includes: acquiring the element change components corresponding to each meteorological environment sensing node within a predetermined historical time range and multiple historical time windows to form historical element change data; comparing the element change components of each meteorological environment sensing node within each historical time window with the regional change benchmark within the corresponding time window based on the historical element change data to generate a historical consistency record representing the consistency results within each historical time window; and based on the historical consistency record, analyzing the historical changes of each meteorological environment sensing node within the historical time window. The duration of consistent changes within a given time frame is accumulated to generate historical stability evaluation results for each meteorological environmental sensing node. Based on these results, different historical stability levels are categorized into multiple stability grades, and each grade is mapped to a preset weight range to generate weight values ​​for each meteorological environmental sensing node. After removing anomalous element change components, the remaining element change components are weighted and aggregated according to their weight values ​​to generate a summary result representing the overall change status of the meteorological element within the current time window. In subsequent time windows, this summary result is used as input data to update historical consistency records, ensuring that weight values ​​participate in the continuous updating process of the regional change benchmark.

6. The method for intelligent sensing and real-time monitoring of meteorological environment according to claim 3, characterized in that, Based on the matching results, a comprehensive judgment is made on the correspondence between the magnitude, direction, and trend of meteorological state change trajectories across multiple consecutive time windows and the regional change benchmark, generating a trajectory consistency judgment result. This includes: acquiring the matching results corresponding to the meteorological state change trajectories across multiple consecutive time windows as trajectory matching data; comparing the direction of change of the meteorological state change trajectories across each time window with the direction of change of the regional change benchmark based on the trajectory matching data to generate a direction consistency judgment result; if the direction consistency judgment result indicates that the direction of change remains consistent, accumulating the magnitude of change of the meteorological state change trajectories across multiple consecutive time windows based on the trajectory matching data to generate a magnitude accumulation result; if the magnitude accumulation result meets preset accumulation conditions, comparing the trend of change of the meteorological state change trajectories across multiple consecutive time windows with the trend of change of the regional change benchmark based on the trajectory matching data to generate a trend consistency judgment result; and jointly processing the direction consistency judgment result, the magnitude accumulation result, and the trend consistency judgment result to generate a trajectory consistency judgment result used to characterize whether the meteorological state change trajectory constitutes an abnormal evolution process.

7. The method for intelligent sensing and real-time monitoring of meteorological environment according to claim 4, characterized in that, Based on the duration and intensity of abnormal evolution, the time windows for collecting raw meteorological environmental data from meteorological environmental sensing nodes are adjusted in a tiered manner to generate adjusted collection time windows corresponding to the degree of abnormal evolution. This includes: acquiring the meteorological environmental sensing node corresponding to the local meteorological abnormal evolution identifier, and acquiring the duration and intensity information of the abnormal evolution associated with that identifier; determining whether the duration of the abnormal evolution has reached a preset duration trigger condition based on the duration information, and generating a duration-driven window adjustment command when the duration trigger condition is reached; determining whether the intensity of the abnormal evolution has reached a preset intensity trigger condition based on the intensity information, and generating an intensity-driven window adjustment command when the intensity trigger condition is reached; when both duration-driven and intensity-driven window adjustment commands are generated simultaneously, the intensity-driven window adjustment command is selected as the effective command; and determining the corresponding adjusted collection time window from multiple preset collection time window levels based on the effective window adjustment command, and switching between different collection time window levels when the duration of the abnormal evolution changes.

8. A meteorological environment intelligent sensing and real-time monitoring system, characterized in that, The system, applicable to any one of claims 1 to 7, comprises: a data acquisition module for acquiring raw meteorological environmental data collected by multiple meteorological environmental sensing nodes within a target monitoring area at multiple consecutive acquisition times, the raw meteorological environmental data including temperature, humidity, wind speed, and precipitation intensity; a state vector generation module for combining and processing the raw meteorological environmental data corresponding to the same meteorological environmental sensing node at multiple consecutive acquisition times according to a preset time window, based on the raw meteorological environmental data, to generate a multidimensional meteorological state vector; a change vector generation module for performing differential processing on the multidimensional meteorological state vector within adjacent time windows based on the multidimensional meteorological state vector, to generate a meteorological state change vector characterizing the change of meteorological state over time; and a regional change benchmark generation module for generating different meteorological environmental data within the same time window based on the meteorological state change vector. The system compares and processes the meteorological state change vectors corresponding to the environmental sensing nodes, calculates the degree of difference in change, and generates a regional change benchmark. The anomaly evolution judgment module determines the consistency of the meteorological state change vectors corresponding to each meteorological environmental sensing node based on the regional change benchmark. When the meteorological state change vector of a certain meteorological environmental sensing node deviates from the regional change benchmark, a local meteorological anomaly evolution identifier is generated for that meteorological environmental sensing node. The data acquisition time window adjustment module adjusts the data acquisition time window of the corresponding region's meteorological environment based on the local meteorological anomaly evolution identifier and regenerates the meteorological state change vector based on the adjusted acquisition time window. The monitoring result output module updates the local meteorological anomaly evolution identifier based on the regenerated meteorological state change vector and outputs the real-time meteorological environment monitoring results for the region with the local meteorological anomaly evolution.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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