A big data-based visualized intelligent weather consultation system

Abstract

This invention relates to the field of big data analytics, and more particularly to a big data-based visualized intelligent weather consultation system. The system includes: an initial loading module for determining the consultation information feature status of the consultation text information based on feature information category parameters and feature information association parameters; a loading analysis module for determining whether to perform effective information association analysis or network environment adaptation analysis based on the consultation information feature status; an effective analysis module for dividing the spatiotemporally associated dataset and determining how to determine the preloading priority coefficient based on association dispersion parameters; and an adaptation analysis module for setting the initial static loading index and determining whether to adjust the initial static loading index based on the bandwidth fluctuation index and bandwidth change evaluation index. This invention improves the smoothness of presenting weather consultation information based on visualization technology.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, and more particularly to a big data-based visualized intelligent weather consultation system. Background Technology

[0002] 3D visualization technology can effectively improve the efficiency and clarity of weather consultations, thereby ensuring the efficiency of weather consultation and judgment and the quality of decision-making. However, the actual effect of presenting weather consultation content in a visualized form is highly dependent on the network environment conditions of the actual scenario. The presentation effect is easily affected by network bandwidth fluctuations. Using a fixed scenario data loading strategy cannot effectively guarantee the presentation quality of weather consultation content while responding to and overcoming the impact of network environment fluctuations in a timely manner. Therefore, how to combine the actual network environment conditions and the analysis results of weather consultation content information based on big data technology to make real-time optimization settings for the loading and transmission process of scenario data, so as to ensure the presentation effect of weather consultation content in a visualized form under different network environment conditions, is a problem that urgently needs to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN118278897A discloses a weather consultation platform based on big data intelligent analysis. It includes a data integration subsystem for continuously classifying and storing collected multi-source meteorological and hydrological data; an operation control subsystem that interacts with the data integration subsystem to develop and manage weather consultation process guides; a comprehensive analysis subsystem that interacts with the data integration and operation control subsystems to generate predictive analysis results; a visualization display subsystem that interacts with the data integration, operation control, and comprehensive analysis subsystems to display multi-source meteorological and hydrological data and the predictive analysis results; and a human-computer interaction method via multi-touch; and a system management subsystem for managing user permissions and configuring parameters on the weather consultation platform. Chinese patent application publication number CN112990681A discloses a power grid emergency command system. Based on underlying infrastructure technologies, it features meticulously designed upper-layer applications and utilizes relevant basic services to support multi-scenario integration in the emergency command center. This enables the construction of a four-dimensional integrated emergency platform encompassing monitoring, analysis, consultation, and command. The underlying infrastructure includes: an emergency business cloud, screen display equipment, a conference system, a satellite communication system, smart mobile terminals, a 3D power map, and a smart voice assistant. Upper-layer applications include: basic services and emergency command center usage scenarios. However, the above solution has the following drawbacks: it fails to specifically optimize the loading strategy for 3D weather consultation scenarios based on the actual consultation and sharing information content, resulting in poor smoothness of the 3D weather scenes presented during actual consultations. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides a big data-based visualized intelligent weather consultation system to overcome the problem in the prior art that the visualization loading strategy for weather consultation scenarios has not been optimized in a targeted manner based on the actual consultation and sharing information content, resulting in poor fluency of the weather scenarios presented in the actual consultation process.

[0005] Therefore, the present invention provides a big data-based visualized intelligent weather consultation system, comprising:

[0006] The initial loading module is used to determine the consultation information feature status of the consultation text information based on the feature information category parameter and the feature information association parameter;

[0007] The loading analysis module is connected to the initial loading module and is used to perform real-time data preloading analysis based on the meteorological characteristic information involved in the weather consultation process, and to determine whether to perform effective information correlation analysis or network environment adaptation analysis for the consultation information loading process according to the characteristic status of the consultation information.

[0008] An effective analysis module, connected to the loading analysis module, is used to divide the spatiotemporal correlation dataset and determine the preloading priority coefficient based on the correlation dispersion parameter, the reference presentation correlation index and the reference information correlation index, or the reference presentation correlation index and the effective coverage index, so as to determine the dataset to be executed first.

[0009] The associated execution module, which is connected to the effective analysis module, includes a first associated execution unit and a second associated execution unit, used to determine the preloading priority coefficient of each dataset to be executed, and to determine whether to perform preloading simplification processing on the priority dataset based on the distribution independence index.

[0010] The adaptation analysis module, which is connected to the loading analysis module, is used to perform network environment adaptation analysis. It sets an initial static loading index based on regional correlation reference values ​​and network quality parameters, and periodically determines whether to adjust the initial static loading index based on the bandwidth fluctuation index and the bandwidth change evaluation index.

[0011] Furthermore, the consultation information feature states include a first type of consultation information feature states and a second type of consultation information feature states;

[0012] The feature information category parameter of the consultation text information in the aforementioned type of consultation information feature state is greater than the preset feature information category parameter or the feature information association parameter is greater than the preset feature information association parameter;

[0013] The feature information category parameter of the consultation text information in the second type of consultation information feature state is less than or equal to the preset feature information category parameter and the feature information association parameter is less than or equal to the preset feature information association parameter.

[0014] Furthermore, if the consultation text information is in a state of a certain type of consultation information characteristic, then the effective analysis module is determined to perform effective information correlation analysis on the consultation information loading process.

[0015] Furthermore, the method for setting up the spatiotemporal correlation dataset is determined based on the feature distribution index of the consultation text information, wherein,

[0016] If the feature distribution index of the consultation text information is greater than the preset feature distribution index, then the spatiotemporal associated dataset is determined based on the timestamp difference index and the feature presentation correlation index.

[0017] If the feature distribution index of the consultation text information is less than or equal to the preset feature distribution index, then the spatiotemporal related dataset is determined based on the spatial correlation index and the timestamp difference index.

[0018] Furthermore, when the correlation dispersion parameter of the real-time consultation information is greater than the preset correlation dispersion parameter, the first correlation execution unit determines the preloading priority coefficient of the dataset to be executed of the real-time consultation information based on the reference presentation correlation index and the reference information correlation index, so as to determine the priority execution dataset, and determines whether to perform preloading simplification processing on the priority execution dataset according to the distribution independence index of the determined priority execution dataset.

[0019] The priority execution dataset is the dataset to be executed that has a preload priority coefficient greater than the first preset loading priority coefficient.

[0020] Furthermore, if the distribution independence index is less than or equal to the preset distribution independence index, then it is determined that preloading and simplification processing should be performed on the priority execution dataset.

[0021] The packaging and aggregation combination is determined based on the region overlap parameter, and data is simplified and packaged for each packaging and aggregation combination.

[0022] The region overlap parameters of any determined packaged aggregation combination are all greater than the preset region overlap parameters.

[0023] Furthermore, when the correlation dispersion parameter of the real-time consultation information is less than or equal to the preset correlation dispersion parameter, the second correlation execution unit determines the preloading priority coefficient of the dataset to be executed for the real-time consultation information based on the reference presentation correlation index and the effective coverage index, so as to determine the dataset to be executed first.

[0024] The preloading priority coefficient is positively correlated with the reference presentation correlation index and the effective coverage index, respectively. The priority execution dataset is the dataset to be executed whose preloading priority coefficient is greater than the second preset loading priority coefficient.

[0025] Furthermore, if the consultation text information is in the state of type II consultation information characteristics, then it is determined that the adaptation analysis module performs network environment adaptation analysis for the consultation information loading process.

[0026] Furthermore, the initial static loading index is determined based on the regional correlation reference value and network quality parameters, and the priority loading coefficient is determined based on the correlation index of the characteristics of each meteorological feature information with the real-time consultation information.

[0027] The priority loading coefficient and the feature exhibit a positive correlation.

[0028] Furthermore, if the bandwidth fluctuation index obtained at any time is greater than the preset bandwidth fluctuation index or the bandwidth change evaluation index is greater than the preset bandwidth change evaluation index, then it is determined that the initial static loading index should be increased and adjusted according to the bandwidth fluctuation index and the bandwidth change evaluation index.

[0029] The increase in the initial static loading index is positively correlated with both the bandwidth fluctuation index and the bandwidth change assessment index.

[0030] Compared with the prior art, the beneficial effects of the present invention are that, based on the feature information contained in the consultation text information to be presented in accordance with actual needs, the present invention makes targeted optimizations to the data loading decision in the process of presenting weather consultation information through three-dimensional visualization technology, ensuring that the data loading process in the actual consultation presentation is more in line with actual needs, and effectively overcoming the interference caused by network environment fluctuations. The present invention improves the smoothness of the presentation results of weather consultation information based on visualization technology.

[0031] Furthermore, in this invention, the consultation information feature state of the consultation text information is determined based on the feature information category parameters and feature information association parameters. Based on the consultation information feature state, effective information association analysis or network environment adaptation analysis is performed on the consultation information loading process. The feature information category parameters and feature information association parameters characterize the category of meteorological feature information contained in the consultation text information and the association between the corresponding associated regions. This characterizes the pressure of data loading and rendering calculations during the actual presentation process, making the optimization method of data loading decision in the process of presenting weather consultation information more in line with the actual situation of consultation text information, thereby ensuring the effectiveness of the optimization results of data loading decision.

[0032] Furthermore, in this invention, when the consultation information is in a certain state, effective correlation analysis is performed on the consultation information loading process. Due to the superposition and rendering of a large amount of meteorological feature information and the dynamic interaction between regions, the computational complexity of the actual 3D image rendering process is high. It is necessary to continuously predict the correlation between meteorological feature information in real time to assist the actual 3D image rendering process in completing effective data preloading. Based on the feature distribution index, a targeted division method is made for the data corresponding to the rich gas feature information, so that the determined spatiotemporal correlation dataset is more in line with the actual working situation. Based on the correlation dispersion parameter, a targeted setting method is determined for the preloading priority coefficient of the construction correlation dataset, further determining the adaptability of the data preloading process to the actual working process. This invention improves the effectiveness of the data preloading decision.

[0033] Furthermore, in this invention, when the correlation dispersion parameter is greater than the preset correlation dispersion parameter, it is additionally determined whether to perform preloading and simplification processing on the priority execution dataset based on the distribution independence index. In such cases, the data preloading process still has a large data transmission burden. When the distribution independence index is small, since there is a certain correlation between the priority execution datasets, effective packaging and simplification processing can reduce the network bandwidth requirements of the data transmission process while ensuring the effectiveness of data loading. This invention improves the smoothness of the presentation results of weather consultation information based on visualization technology. Attached Figure Description

[0034] Figure 1 This is a module connection diagram of the big data-based visualized intelligent weather consultation system of the present invention;

[0035] Figure 2 This is a flowchart illustrating how the present invention determines the feature status of consultation information based on feature information category parameters and feature information association parameters.

[0036] Figure 3 This is a flowchart illustrating the present invention for determining effective information correlation analysis or network environment adaptation analysis during the consultation information loading process based on the consultation information feature state of the consultation text information.

[0037] Figure 4 This is a flowchart illustrating the method for setting up a spatiotemporal correlation dataset based on the feature distribution index of consultation text information according to the present invention. Detailed Implementation

[0038] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0039] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0040] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0041] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0042] Please see Figures 1 to 4 As shown, the present invention provides a big data-based visualized intelligent weather consultation system, comprising:

[0043] The initial loading module is used to determine the consultation information feature status of the consultation text information based on the feature information category parameter and the feature information association parameter;

[0044] The loading analysis module is connected to the initial loading module and is used to perform real-time data preloading analysis based on the meteorological characteristic information involved in the weather consultation process, and to determine whether to perform effective information correlation analysis or network environment adaptation analysis for the consultation information loading process according to the characteristic status of the consultation information.

[0045] An effective analysis module, connected to the loading analysis module, is used to divide the spatiotemporal correlation dataset and determine the preloading priority coefficient based on the correlation dispersion parameter, the reference presentation correlation index and the reference information correlation index, or the reference presentation correlation index and the effective coverage index, so as to determine the dataset to be executed first.

[0046] The associated execution module, which is connected to the effective analysis module, includes a first associated execution unit and a second associated execution unit, used to determine the preloading priority coefficient of each dataset to be executed, and to determine whether to perform preloading simplification processing on the priority dataset based on the distribution independence index.

[0047] The adaptation analysis module, which is connected to the loading analysis module, is used to perform network environment adaptation analysis. It sets an initial static loading index based on regional correlation reference values ​​and network quality parameters, and periodically determines whether to adjust the initial static loading index based on the bandwidth fluctuation index and the bandwidth change evaluation index.

[0048] This invention is used to optimize the data loading and transmission process in real time during the presentation of weather consultation information using 3D visualization technology, avoiding the impact of network environment limitations on the presentation effect. In this invention, the text content information to be displayed during the current consultation process is recorded as consultation text information. Each meteorological feature information in the consultation text information is determined based on various consultation-related data obtained by the consultation participants. The categories of consultation-related data in this invention include, but are not limited to: ground observation data (real-time temperature, air pressure, humidity, wind speed, precipitation, visibility, etc.), upper-air observation data, radar detection data (convective cell or cloud precipitation characteristics), and satellite remote sensing data: using satellite cloud images (visible light, infrared, water vapor channels) to monitor the overall distribution and evolution trend of weather systems and historical comparison data.

[0049] The consultation text information contains several meteorological feature information. This invention can identify these features based on preset meteorological feature information content. Each meteorological feature information has a corresponding associated region and a timestamp. The associated region is the geographical location where the content of each meteorological feature information occurs, and the timestamp is the specific time at which the content of each meteorological feature information occurs. The timestamp in this invention is specific to the second. The categories of meteorological feature information that may be included in the consultation text information in this invention include, but are not limited to: meteorological data, disaster types, meteorological elements, geographical regions, physical processes, time information, and meteorological magnitudes. The meteorological feature information corresponding to meteorological data includes, but is not limited to: real-time temperature, air pressure, humidity, wind speed, and precipitation values. The meteorological feature information corresponding to disaster types includes, but is not limited to: typhoons and rainstorms. The meteorological characteristics information corresponding to meteorological elements such as blizzards, strong winds, sandstorms, hail, high temperatures, and drought includes, but is not limited to: cold fronts, warm fronts, shear lines, low vortices, subtropical highs, jet streams, convective clouds, echoes, temperature fields, humidity fields, and wind fields. The meteorological characteristics information corresponding to geographical regions includes, but is not limited to: provinces, cities, counties, river basins, mountain ranges, and coastlines. The meteorological characteristics information corresponding to physical processes includes, but is not limited to: lifting, convergence, water vapor transport, energy accumulation, and triggering mechanisms. The meteorological characteristics information corresponding to meteorological magnitudes includes, but is not limited to: torrential rain, level 12 gusts, and red alerts. How to identify the meteorological characteristics information contained in the consultation text information and how to set the categories of meteorological characteristics information corresponding to different contents are contents that are easily understood by those skilled in the art and will not be elaborated here.

[0050] The system monitors the meteorological features reported during real-time weather consultations. If these meteorological features change, it performs data preloading analysis based on the real-time consultation information. The real-time consultation information refers to the meteorological features reported at the moment when the meteorological features changed most recently. The system determines priority datasets for execution based on the real-time consultation information and preloads data from closest to furthest timestamps corresponding to each priority dataset. Priority datasets with timestamps closer to the current moment are preloaded. These priority datasets are datasets with a preloading priority coefficient greater than a second preset loading priority coefficient. For a single spatiotemporal correlated dataset, the timestamp is the average of the timestamps corresponding to each meteorological feature in that spatiotemporal correlated dataset.

[0051] This invention utilizes several consultation optimization records. Each consultation optimization record records at least one instance of real-time optimization of the data loading and transmission process when presenting weather consultation information based on visualization technology. These optimizations include feature information category parameters, feature information association parameters, feature distribution index, timestamp difference index, feature presentation association index, spatial association index, preloading priority coefficient, distribution independence index, regional overlap parameters, bandwidth fluctuation index, and bandwidth change evaluation index. Each consultation optimization record also has a corresponding qualification mark, which records whether the smoothness of presenting weather consultation information using 3D visualization technology meets user needs.

[0052] Specifically, the consultation information feature states include a first type of consultation information feature states and a second type of consultation information feature states;

[0053] The feature information category parameter of the consultation text information in the aforementioned type of consultation information feature state is greater than the preset feature information category parameter or the feature information association parameter is greater than the preset feature information association parameter;

[0054] The feature information category parameter of the consultation text information in the second type of consultation information feature state is less than or equal to the preset feature information category parameter and the feature information association parameter is less than or equal to the preset feature information association parameter.

[0055] The process involves acquiring the consultation text information required for the current weather consultation process and identifying the meteorological feature information contained in the consultation text information. The feature information category parameter is the number of categories corresponding to different meteorological feature information contained in the consultation text information. The feature information association parameter is the average number of meteorological feature information with regional association relationships contained in the consultation text information / the number of different meteorological feature information contained in the consultation text information. For any two meteorological feature information, if the associated regions corresponding to the two meteorological feature information are the same or there is meteorological interference between the associated regions corresponding to the two meteorological feature information, then it is determined that there is a regional association relationship between the two meteorological feature information. How to determine whether there is meteorological interference between the associated regions corresponding to two meteorological feature information is a subject already mastered by those skilled in the art. For example, the existence of direct exchange or indirect propagation of water vapor, energy, and momentum between the associated regions can determine that there is meteorological interference between the associated regions.

[0056] The values ​​of the preset feature information category parameter and the preset feature information association parameter can be determined by the user according to the actual working scenario. For example, the user can set them according to the consultation optimization record. The higher the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology, the smaller the value of the preset feature information category parameter and the value of the preset feature information association parameter. A method for determining the value of the preset feature information category parameter is provided, which is the maximum value of the feature information category parameter of the consultation text information in the second type of consultation information feature state in the consultation optimization record that meets the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology. A method for determining the value of the preset feature information association parameter is provided, which is the maximum value of the feature information association parameter of the consultation text information in the second type of consultation information feature state in the consultation optimization record that meets the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology.

[0057] Specifically, if the consultation text information is in a state of a certain type of consultation information characteristic, then the effective analysis module is determined to perform effective information correlation analysis on the consultation information loading process.

[0058] If the consultation text information is in a certain consultation information characteristic state, it indicates that the consultation text information needs to load a large number of data types during the actual consultation display process, or that there are correlations between the related areas corresponding to many meteorological feature information, resulting in a large amount of data that needs to be loaded and a high difficulty in analyzing the priority of data loading. Furthermore, due to the superimposed rendering of many meteorological feature information and the dynamic interaction between areas, the computational complexity of the actual 3D image rendering process is high, which further affects the smoothness of presenting weather consultation information through 3D visualization technology. In this case, it is necessary to make real-time predictions based on the correlation between various meteorological feature information during the presentation of weather consultation information through 3D visualization technology to ensure that the meteorological feature information can be effectively preloaded during the actual presentation process.

[0059] Specifically, the method for setting up the spatiotemporal correlation dataset is determined based on the feature distribution index of the consultation text information, wherein,

[0060] If the feature distribution index of the consultation text information is greater than the preset feature distribution index, then the spatiotemporal associated dataset is determined based on the timestamp difference index and the feature presentation correlation index.

[0061] If the feature distribution index of the consultation text information is less than or equal to the preset feature distribution index, then the spatiotemporal related dataset is determined based on the spatial correlation index and the timestamp difference index.

[0062] Wherein, the feature distribution index = the maximum value of the number of different meteorological feature information corresponding to each associated region in the consultation text information / the average value of the number of different meteorological feature information corresponding to each associated region in the consultation text information. The value of the preset feature distribution index can be determined by the user according to the actual work scenario. For example, the user can set it according to the consultation optimization record. A method for determining the value of the preset feature distribution index is provided. The consultation optimization record of the spatiotemporal association dataset determined based on the spatial association index and the timestamp difference index is recorded as the second set reference record. The maximum value of the feature distribution index in the second set reference record that meets the user's requirements for the smoothness of the presentation of weather consultation information through three-dimensional visualization technology is recorded as the preset feature distribution index. The spatiotemporal association dataset is a set of consultation association data used to determine several meteorological feature information.

[0063] If the feature distribution index of the consultation text information is greater than the preset feature distribution index, it indicates that the meteorological feature information in the consultation text information mainly corresponds to certain related regions. In this case, determining the spatiotemporal correlation dataset should focus more on the correlation between the categories of meteorological feature information and the degree of correlation between temporal relationships. For any spatiotemporal correlation dataset determined at this time, the timestamp difference index between any two meteorological feature information corresponding to the spatiotemporal correlation dataset is less than or equal to the preset timestamp difference index, and the feature presentation correlation index is greater than the preset feature presentation correlation index. For any two meteorological feature information, the timestamp difference index = the interval between the timestamps corresponding to the two meteorological feature information / 60s, and the feature presentation correlation index = the number of times the categories corresponding to the two meteorological feature information in the consultation optimization record are simultaneously used in the 3D image rendering process / the maximum number of times the categories corresponding to the two meteorological feature information in the consultation optimization record are used in the 3D image rendering process. The preset timestamp difference index and the preset feature distribution index are also considered. The value of the feature presentation correlation index can be determined by the user based on the actual work scenario. For example, the user can set it based on the consultation optimization records. The higher the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology, the smaller the value of the preset timestamp difference index and the larger the value of the preset feature presentation correlation index. A method for determining the value of the preset timestamp difference index is provided. The consultation optimization records of the spatiotemporal correlation dataset determined based on the timestamp difference index and the feature presentation correlation index are denoted as the first set of reference records. The minimum value of the timestamp difference index in the first set of reference records that meets the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology is denoted as the preset timestamp difference index. A method for determining the value of the preset feature presentation correlation index is provided. The average value of the feature presentation correlation index in the first set of reference records that meets the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology is denoted as the preset feature presentation correlation index.

[0064] If the feature distribution index of the consultation text information is less than or equal to the preset feature distribution index, it indicates that the meteorological feature information in the consultation text information corresponds relatively evenly to different associated regions. At this time, the determination of the spatiotemporal association dataset needs to focus on ensuring the degree of spatial and temporal association. For any spatiotemporal association dataset determined at this time, the spatial association index between any two meteorological feature information corresponding to the spatiotemporal association dataset is greater than the preset spatial association index and the timestamp difference index is less than or equal to the preset timestamp difference index. For any two meteorological feature information, the spatial association index is the shortest distance between the associated regions corresponding to the two meteorological feature information. The value of the preset spatial association index can be determined by the user according to the actual work scenario. For example, the user can set it according to the consultation optimization record. The higher the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology, the larger the value of the preset spatial association index. A method for determining the value of the preset spatial association index is provided, which is the average value of the spatial association index in the second set of reference records that meets the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology.

[0065] Specifically, when the correlation dispersion parameter of real-time consultation information is greater than the preset correlation dispersion parameter, the first correlation execution unit determines the preloading priority coefficient of the dataset to be executed of real-time consultation information based on the reference presentation correlation index and the reference information correlation index, so as to determine the priority execution dataset, and determines whether to perform preloading simplification processing on the priority execution dataset according to the distribution independence index of the determined priority execution dataset.

[0066] The priority execution dataset is the dataset to be executed that has a preload priority coefficient greater than the first preset loading priority coefficient.

[0067] Wherein, the correlation dispersion parameter = the number of different meteorological feature information corresponding to the correlation area corresponding to the real-time consultation information and the number of different meteorological feature information corresponding to each correlation area that has meteorological interference with the correlation area / the number of different meteorological feature information in the consultation text information. The value of the preset correlation dispersion parameter can be determined by the user according to the actual work scenario. For example, the user can set it according to the consultation optimization record. The higher the user's requirements for the smoothness of the presentation of weather consultation information through 3D visualization technology, the consultation optimization record that determines the preloading priority coefficient of the dataset to be executed for real-time consultation information based on the reference presentation correlation index and the reference information correlation index is recorded as the first loading reference record. The minimum value of the correlation dispersion parameter of the real-time consultation information in the first loading reference record that meets the user's requirements for the smoothness of the presentation of weather consultation information through 3D visualization technology is recorded as the preset correlation dispersion parameter.

[0068] If the correlation dispersion parameter of the real-time consultation information is greater than the preset correlation dispersion parameter, it indicates that the process of rendering the weather consultation information using 3D visualization technology at the current moment may involve relatively rich meteorological feature information. At this time, the analysis scope of data preloading that may be involved in the subsequent rendering process of the weather consultation information is large. It is necessary to further analyze the correlation degree of each spatiotemporal correlation dataset with the subsequent weather consultation information reporting process. At this time, the determination of the preloading priority coefficient of the dataset to be executed should not only consider the synergistic effect between different meteorological feature information in the rendering process of 3D visualization, but also consider the logical relationship between real-time consultation information and other meteorological feature information in the consultation text information, so as to further ensure the effectiveness of the preloading decision. The dataset to be executed is a spatiotemporal correlation dataset containing real-time consultation information or any related consultation information. The related consultation information is the meteorological feature information corresponding to the correlation area corresponding to the real-time consultation information and the correlation areas that have meteorological interference with the correlation area. The priority execution dataset is the dataset to be executed with a preloading priority coefficient greater than the first preset loading priority coefficient.

[0069] For a single dataset to be executed, the preloading priority coefficient is the sum of the reference presentation correlation index and the reference information correlation index. The reference presentation correlation index is the average of the effective presentation correlation indices of each meteorological feature information corresponding to the dataset to be executed, and the reference information correlation index is the average of the text information correlation indices of each meteorological feature information corresponding to the dataset to be executed. For a single meteorological feature information, the effective presentation correlation index is the average of the feature presentation correlation indices between the meteorological feature information and the real-time consultation information and its various related consultation information, and the text information correlation index is the text correlation index between the meteorological feature information and the real-time consultation information. For any two meteorological feature information, the text correlation index is equal to the sum of the text correlation indices of the two meteorological feature information. The number of validly related fields between the information and the number of text fields in the consultation text information are considered. If the two meteorological feature information pieces exist in the same text field, then that text field is recorded as a validly related field between the two meteorological feature information pieces. If the two meteorological feature information pieces do not exist in the same text field, but there is a logical transition word between the two existing text fields, then the two text fields are recorded as validly related fields between the two meteorological feature information pieces. The text fields are determined based on the division of consultation text information using comma symbols. The comma symbols include, but are not limited to, periods and semicolons. How to divide consultation text information based on comma symbols is a content that is easy for those skilled in the art to understand, and will not be elaborated here.

[0070] The value of the first preset loading priority coefficient can be determined by the user according to the actual work scenario. For example, the user can set it according to the consultation optimization record. The higher the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology, the smaller the value of the first preset loading priority coefficient. A method for determining the value of the first preset loading priority coefficient is provided, in which the consultation optimization record that determines the preloading priority coefficient of the dataset to be executed based on the reference presentation correlation index and the effective coverage index is recorded as the first loading reference record, and the minimum value of the preloading priority coefficient of the dataset to be executed in the first loading reference record that meets the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology is recorded as the first preset loading priority coefficient.

[0071] Specifically, if the distribution independence index is less than or equal to the preset distribution independence index, then it is determined that the priority execution dataset will undergo preloading and simplification processing.

[0072] The packaging and aggregation combination is determined based on the region overlap parameter, and data is simplified and packaged for each packaging and aggregation combination.

[0073] The region overlap parameters of any determined packaged aggregation combination are all greater than the preset region overlap parameters.

[0074] Wherein, the distribution independence index = the number of different associated regions of meteorological feature information corresponding to each priority execution dataset / the number of covered associated regions. For a single associated region, if the associated region has meteorological feature information corresponding to each priority execution dataset, then the associated region is recorded as a covered associated region. The value of the preset distribution independence index can be determined by the user according to the actual work scenario. For example, the user can set it according to the consultation optimization record. The higher the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology, the larger the value of the preset distribution independence index. A method for determining the value of the preset distribution independence index is provided, in which the consultation optimization record that loads and aggregates the covered associated data according to the distribution independence index is recorded as the aggregation reference record, and the maximum value of the distribution independence index in the aggregation reference record that meets the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology is recorded as the preset distribution independence index.

[0075] If the distribution independence index is less than or equal to the preset distribution independence index, it indicates that there is a certain correlation between the determined priority execution datasets. Based on the degree of correlation between the priority execution datasets, they are combined and packaged to complete joint transmission, further reducing the network bandwidth requirement during data transmission. The packaged set is a collection of several priority execution datasets with consistent set timestamps. For a single packaged set, the region overlap parameter = the number of overlapping related regions corresponding to the packaged set / the number of different related regions of meteorological feature information corresponding to each priority execution dataset within the packaged set. For a single related region, if the related region is related to the... If each priority execution dataset within the package set contains meteorological feature information, then the associated region is recorded as an overlapping associated region. The value of the preset region overlap parameter can be determined by the user based on the actual work scenario. For example, the user can set it based on the consultation optimization record. The higher the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology, the larger the value of the preset region overlap parameter. A method for determining the value of the preset region overlap parameter is provided, which is the minimum value of the region overlap parameter of the package set in the consultation optimization record that meets the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology.

[0076] The process of simplifying and packaging data for any packaged aggregation includes: simplifying the consultation and correlation data of meteorological feature information corresponding to each overlapping and related area of ​​the packaged aggregation; for a single overlapping and related area, the consultation and correlation data of meteorological feature information corresponding to the overlapping and related area is recorded as a transmission data packet; during the transmission process, the recording method of timestamps and related areas is changed from recording each consultation and correlation data in the transmission data packet separately to recording only the transmission data packet; the transmission data packet only records its corresponding overlapping and related areas, and the timestamp recorded in the transmission data packet is the average of the timestamps corresponding to each meteorological feature information in the transmission data packet.

[0077] Specifically, when the correlation dispersion parameter of real-time consultation information is less than or equal to the preset correlation dispersion parameter, the second correlation execution unit determines the preloading priority coefficient of the dataset to be executed for real-time consultation information based on the reference presentation correlation index and the effective coverage index, so as to determine the dataset to be executed first.

[0078] The preloading priority coefficient is positively correlated with the reference presentation correlation index and the effective coverage index, respectively. The priority execution dataset is the dataset to be executed whose preloading priority coefficient is greater than the second preset loading priority coefficient.

[0079] If the correlation dispersion parameter of the real-time consultation information is less than or equal to the preset correlation dispersion parameter, it indicates that the number of meteorological feature information that may be involved in the 3D image rendering process of the weather consultation information through 3D visualization technology at the current moment is relatively small. At this time, the preloading analysis process of the spatiotemporal correlation dataset only determines the priority of preloading by evaluating the category of the real-time consultation information and the situation of each spatiotemporal correlation dataset that has a certain correlation with it. This can ensure the coverage effect of the preloading decision on the meteorological feature information that may be involved in the subsequent weather consultation information reporting process, so that the executed preloading decision can adapt to the actual consultation and reporting process. At this time, the preloading priority coefficient of each dataset to be executed is determined by analyzing the record of the synergistic effect between each meteorological feature information in the 3D visualization 3D image rendering process. The priority dataset to be executed is the dataset to be executed with a preloading priority coefficient greater than the second preset loading priority coefficient. For a single spatiotemporal correlation dataset, the set timestamp is the average of the timestamps corresponding to each meteorological feature information of the spatiotemporal correlation dataset.

[0080] For a single dataset to be executed, the preloading priority coefficient is the sum of the reference presentation correlation index and the effective coverage index. The reference presentation correlation index is the average of the effective presentation correlation indices of each meteorological feature information corresponding to the dataset to be executed. The effective coverage index is the number of meteorological feature information corresponding to the dataset to be executed that is used simultaneously with the real-time consultation information in the 3D image rendering process / the number of meteorological feature information corresponding to the dataset to be executed.

[0081] The value of the second preset loading priority coefficient can be determined by the user according to the actual work scenario. For example, the user can set it according to the consultation optimization record. The higher the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology, the smaller the value of the second preset loading priority coefficient. A method for determining the value of the second preset loading priority coefficient is provided, in which the consultation optimization record that determines the preloading priority coefficient of the dataset to be executed based on the reference presentation correlation index and the effective coverage index is recorded as the second loading reference record, and the minimum value of the preloading priority coefficient of the dataset to be executed in the second loading reference record that meets the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology is recorded as the second preset loading priority coefficient.

[0082] Specifically, if the consultation text information is in the state of type II consultation information characteristics, then it is determined that the adaptation analysis module performs network environment adaptation analysis on the consultation information loading process.

[0083] If the consultation text information is in the second-class consultation information characteristic state, it indicates that the data types that need to be loaded in the actual consultation display process are relatively small, and there are correlations between the related areas corresponding to the few meteorological feature information. In this case, the actual 3D image rendering process has relatively small requirements for data loading and computational complexity. However, when presenting weather consultation information through 3D visualization technology, it is still necessary to consider the fluctuation of the network environment in a timely manner and make optimization decisions on the data loading process in real time to further ensure the smoothness of the presentation of weather consultation information through 3D visualization technology.

[0084] Specifically, the initial static loading index is determined based on regional correlation reference values ​​and network quality parameters, and the priority loading coefficient is determined based on the correlation index of the characteristics of each meteorological feature information with the real-time consultation information.

[0085] The priority loading coefficient and the feature exhibit a positive correlation.

[0086] In the network environment adaptation analysis, the associated regions corresponding to the real-time consultation information are obtained, and an initial loading range is set to preload the map rendering data of the region corresponding to the initial loading range. The initial loading range is a circular region with the center point of the geometric shape formed by the associated regions corresponding to the real-time consultation information on the horizontal ground as the center and the initial range length as the radius. The initial range length is positively correlated with the initial static loading index. The initial static loading index is the sum of the regional association reference value and the network quality parameter. The regional association reference value = the number of associated regions corresponding to meteorological feature information that has a regional association relationship with the real-time consultation information / the number of different associated regions corresponding to each meteorological feature information contained in the consultation text information. The network quality parameter = the maximum amount of data that the transmission network can transmit at the moment of obtaining the real-time consultation information / the maximum amount of data that the transmission network can transmit in the consultation optimization record. The larger the priority loading coefficient of the meteorological feature information, the higher the priority of preloading the consultation associated data corresponding to the meteorological feature information.

[0087] Specifically, if the bandwidth fluctuation index obtained at any time is greater than the preset bandwidth fluctuation index or the bandwidth change evaluation index is greater than the preset bandwidth change evaluation index, then it is determined that the initial static loading index should be increased and adjusted according to the bandwidth fluctuation index and the bandwidth change evaluation index.

[0088] The increase in the initial static loading index is positively correlated with both the bandwidth fluctuation index and the bandwidth change assessment index.

[0089] In this invention, a cyclic network monitoring cycle is applied. The duration of the network monitoring cycle can be determined by the user. The higher the user's requirements for the smoothness of the presentation of weather consultation information through 3D visualization technology, the shorter the duration of the network monitoring cycle. A network monitoring cycle of 10 seconds is provided. At the end of each network monitoring cycle, the network bandwidth parameters of the current weather consultation information presentation process are detected, and the bandwidth fluctuation index and bandwidth change evaluation index are determined based on the network bandwidth parameters obtained each time, thereby determining whether to perform optimization on the initial loading settings.

[0090] If the current time is the end time of any network monitoring cycle, the bandwidth fluctuation index = standard deviation of the network bandwidth parameters obtained in each monitoring and evaluation phase / average value of the network bandwidth parameters obtained in each monitoring and evaluation phase; the bandwidth change evaluation index = (number of times the network bandwidth parameters obtained in each monitoring and evaluation phase show a downward trend / number of times the network bandwidth parameters are obtained in the monitoring and evaluation phase) + [(average value of network bandwidth parameters obtained in the phase evaluation phase - value of the network bandwidth parameter obtained at the current time) / average value of network bandwidth parameters obtained in the phase evaluation phase]. For any network bandwidth parameter obtained, this... If the value of the network bandwidth parameter obtained this time is less than the value of the network bandwidth parameter obtained at the previous time, it is determined that the network bandwidth parameter obtained this time shows a downward trend. The end time of the monitoring and evaluation phase is the current time. The duration of the monitoring and evaluation phase can be determined by the user. The higher the user's requirements for the smoothness of the presentation of weather consultation information through 3D visualization technology, the longer the duration of the monitoring and evaluation phase. One value for the duration of the monitoring and evaluation phase is provided. The duration of the monitoring and evaluation phase is 20 times the duration of the network monitoring cycle. The network bandwidth parameter is the amount of data that can be transmitted per unit time.

[0091] If the bandwidth fluctuation index determined in the current network monitoring cycle is greater than the preset bandwidth fluctuation index or the bandwidth change assessment index is greater than the preset bandwidth change assessment index, then the initial static loading index will be increased based on the currently obtained bandwidth fluctuation index and bandwidth change assessment index. The increase in the initial static loading index is positively correlated with the bandwidth interference index, which is the sum of the bandwidth fluctuation index and the bandwidth change assessment index. If the determined bandwidth interference index is negative, then the initial static loading index will not be increased.

[0092] The values ​​of the preset bandwidth fluctuation index and the preset bandwidth change evaluation index can be determined by the user based on the actual working scenario. For example, the user can set them based on the consultation optimization records. The higher the user's requirement for the smoothness of the presentation of weather consultation information through 3D visualization technology, the smaller the value of the preset bandwidth fluctuation index and the preset bandwidth change evaluation index. A method for determining the value of the preset bandwidth fluctuation index is provided, in which the consultation optimization records that perform optimization for the initial loading settings are recorded as monitoring reference records, and the average value of the bandwidth fluctuation index in the monitoring reference records that meet the user's requirements for the smoothness of the presentation of weather consultation information through 3D visualization technology is recorded as the preset bandwidth fluctuation index. A method for determining the value of the preset bandwidth change evaluation index is provided, in which the average value of the bandwidth change evaluation index in the monitoring reference records that meet the user's requirements for the smoothness of the presentation of weather consultation information through 3D visualization technology is recorded as the preset bandwidth change evaluation index.

[0093] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0094] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A big data-based visualized intelligent weather consultation system, characterized in that: include: The initial loading module is used to determine the consultation information feature status of the consultation text information based on the feature information category parameter and the feature information association parameter; The loading analysis module is connected to the initial loading module and is used to perform real-time data preloading analysis based on the meteorological characteristic information involved in the weather consultation process, and to determine whether to perform effective information correlation analysis or network environment adaptation analysis for the consultation information loading process according to the characteristic status of the consultation information. If the consultation text information is in a state of a certain type of consultation information feature, the effective analysis module will perform effective information correlation analysis on the consultation information loading process; If the consultation text information is in the state of type II consultation information characteristics, the determination and adaptation analysis module will perform network environment adaptation analysis on the consultation information loading process; An effective analysis module, connected to the loading analysis module, is used to divide the spatiotemporal correlation dataset and determine the preloading priority coefficient based on the correlation dispersion parameter, the reference presentation correlation index and the reference information correlation index, or the reference presentation correlation index and the effective coverage index, so as to determine the dataset to be executed first. The associated execution module, which is connected to the effective analysis module, includes a first associated execution unit and a second associated execution unit, used to determine the preloading priority coefficient of each dataset to be executed, and to determine whether to perform preloading simplification processing on the priority dataset based on the distribution independence index. An adaptation analysis module, connected to the loading analysis module, is used to perform network environment adaptation analysis. It sets an initial static loading index based on regional correlation reference values ​​and network quality parameters, and periodically determines whether to adjust the initial static loading index based on bandwidth fluctuation index and bandwidth change evaluation index. The consultation information feature status includes a first type of consultation information feature status and a second type of consultation information feature status; The feature information category parameter of the consultation text information in the aforementioned type of consultation information feature state is greater than the preset feature information category parameter or the feature information association parameter is greater than the preset feature information association parameter; The feature information category parameter of the consultation text information in the second type of consultation information feature state is less than or equal to the preset feature information category parameter and the feature information association parameter is less than or equal to the preset feature information association parameter; The feature information category parameter is the number of categories corresponding to different meteorological feature information contained in the consultation text information, and the feature information association parameter is the average number of meteorological feature information with regional association relationships contained in the consultation text information / the number of different meteorological feature information contained in the consultation text information.

2. The big data-based visualized intelligent weather consultation system according to claim 1, characterized in that, The method for setting up the spatiotemporal correlation dataset is determined based on the feature distribution index of the consultation text information, wherein, If the feature distribution index of the consultation text information is greater than the preset feature distribution index, then the spatiotemporal associated dataset is determined based on the timestamp difference index and the feature presentation correlation index. If the feature distribution index of the consultation text information is less than or equal to the preset feature distribution index, then the spatiotemporal related dataset is determined based on the spatial correlation index and the timestamp difference index.

3. The big data-based visualized intelligent weather consultation system according to claim 2, characterized in that, When the correlation dispersion parameter of real-time consultation information is greater than the preset correlation dispersion parameter, the first correlation execution unit determines the preloading priority coefficient of the dataset to be executed of real-time consultation information based on the reference presentation correlation index and the reference information correlation index, so as to determine the priority execution dataset, and determines whether to perform preloading simplification processing on the priority execution dataset according to the distribution independence index of the determined priority execution dataset. The priority execution dataset is the dataset to be executed that has a preload priority coefficient greater than the first preset loading priority coefficient.

4. The big data-based visualized intelligent weather consultation system according to claim 3, characterized in that, If the distribution independence index is less than or equal to the preset distribution independence index, then it is determined that the dataset to be executed first will be preloaded and simplified. The packaging and aggregation combination is determined based on the region overlap parameter, and data is simplified and packaged for each packaging and aggregation combination. The region overlap parameters of any determined packaged aggregation combination are all greater than the preset region overlap parameters.

5. The big data-based visualized intelligent weather consultation system according to claim 4, characterized in that, When the correlation dispersion parameter of real-time consultation information is less than or equal to the preset correlation dispersion parameter, the second correlation execution unit determines the preloading priority coefficient of the dataset to be executed for real-time consultation information based on the reference presentation correlation index and the effective coverage index, so as to determine the dataset to be executed first. The preloading priority coefficient is positively correlated with the reference presentation correlation index and the effective coverage index, respectively. The priority execution dataset is the dataset to be executed whose preloading priority coefficient is greater than the second preset loading priority coefficient.

6. The big data-based visualized intelligent weather consultation system according to claim 5, characterized in that, The initial static loading index is determined based on the regional correlation reference value and network quality parameters, and the priority loading coefficient is determined based on the correlation index of the characteristics of each meteorological feature information with the real-time consultation information. The priority loading coefficient and the feature exhibit a positive correlation.

7. The big data-based visualized intelligent weather consultation system according to claim 6, characterized in that, If the bandwidth fluctuation index obtained at any time is greater than the preset bandwidth fluctuation index or the bandwidth change evaluation index is greater than the preset bandwidth change evaluation index, then it is determined that the initial static loading index should be increased and adjusted according to the bandwidth fluctuation index and the bandwidth change evaluation index. The increase in the initial static loading index is positively correlated with both the bandwidth fluctuation index and the bandwidth change assessment index.

Images