Multi-source data fusion and tornado intelligent identification method

Abstract

The invention discloses a multi-source data fusion and intelligent tornado identification method, and relates to the technical field of electric digital data processing. The multi-source data fusion and tornado intelligent identification method comprises the following steps: obtaining a quality control evaluation index; obtaining a gridding accuracy index; obtaining an intelligent identification evaluation score; and obtaining tornado early warning duration. According to the invention, quality control is carried out on the acquired dual-polarization phased array radar data to acquire the quality control evaluation index, and then the acquired multi-source real-time data is inputted into the constructed severe convection identification model to acquire the severe convection weather susceptible area. And finally, whether radar lattice point data is acquired or not is judged based on the acquired lattice point accuracy index, and whether intelligent identification is completed or not is judged according to the acquired intelligent identification evaluation score, so that the effect of improving the fusion relevance between the intelligent identification result of the tornado and the multi-source data is achieved. The problem that in the prior art, the relevance between a tornado intelligent recognition result and multi-source data fusion is not high is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a method for multi-source data fusion and tornado intelligent identification. Background Art

[0002] With the rapid development of information technology and the advent of the big data era, all walks of life are facing the challenges and opportunities of explosive data growth. In the fields of meteorological monitoring, disaster warning, environmental protection, etc., the acquisition, integration and analysis of multi-source data have become the key to improving prediction accuracy and response speed. Especially in the prediction and identification of extreme weather events such as tornadoes, the traditional single data source method can no longer meet the requirements of high precision and real-time performance. Therefore, multi-source data fusion technology came into being. It integrates and comprehensively analyzes data from different channels and formats, providing new ideas and methods for intelligent identification of tornadoes.

[0003] The existing technology preferentially selects data objects and preprocesses them, then weights and combines the preprocessed data and inputs them into numerical weather forecast models and statistical forecast models to obtain forecast results. Finally, feature extraction is performed on the forecast results to obtain feature information before the tornado is generated, and dynamic monitoring and identification of tornadoes are achieved based on the feature information.

[0004] For example, the invention patent with announcement number: CN116842127B announces an adaptive auxiliary decision-making intelligent method and system based on multi-source dynamic data, including: processing the acquired dynamic data and static data separately and storing the data separately; encoding the static data and the standardized dynamic data respectively based on the text encoder and visual encoder of the fine-tuned visual-language model to obtain text and image features; obtaining the category with the greatest similarity through target recognition, and obtaining the category of the target by comparing the index.

[0005] For example, the patent application with publication number: CN118885974A discloses an intelligent target fusion recognition method and system based on radar multimodal data, which includes: obtaining radar multimodal data of a target to be identified; inputting the acquired radar multimodal data into a trained multimodal radar data target fusion recognition model, extracting single-mode features in sequence, and obtaining multiple single-mode features respectively; based on the multiple single-mode features, performing inter-modal similarity feature and intrinsic feature learning based on multimodal feature learning subspace constraints, compressing the learned multiple inter-modal similarity features, and obtaining multimodal similarity features; performing attention fusion on the learned multiple intrinsic features and the multimodal similarity features, and outputting the recognition probability, to obtain the classification recognition result of the target in the radar multimodal data of the target to be identified.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the existing technology, statistical forecasting models usually rely on historical data and statistical laws, and may not be able to fully capture the complexity and nonlinear characteristics of tornadoes. Secondly, feature extraction algorithms may not be able to accurately capture all key feature information before a tornado is generated, which in turn leads to a decrease in the accuracy of the recognition results. There is a problem that the correlation between the tornado intelligent recognition results and the fusion of multi-source data is not high. Summary of the invention

[0007] The embodiments of the present application solve the problem in the prior art that the tornado intelligent identification results are not highly correlated with the multi-source data fusion by providing a multi-source data fusion and tornado intelligent identification method, thereby improving the correlation between the tornado intelligent identification results and the multi-source data fusion.

[0008] The embodiment of the present application provides a multi-source data fusion and tornado intelligent identification method, comprising the following steps: step 1, obtaining dual-polarization phased array radar data of a preset band and performing quality control to obtain a quality control evaluation index, and judging whether to obtain multi-source real-time data based on the obtained quality control evaluation index, wherein the quality control evaluation index is used to evaluate the error correction effect of the dual-polarization phased array radar data within a preset time period; step 2, inputting the obtained multi-source real-time data into a constructed severe convection identification model to obtain a severe convective weather prone area, and nesting and gridding the dual-polarization phased array radar data in the severe convective weather prone area to obtain a gridded accurate The gridding accuracy index is used to evaluate the accuracy and reliability of nested gridding of dual-polarization phased array radar data; step three, based on the obtained gridding accuracy index, determine whether to obtain radar grid data; if radar grid data is obtained, then perform intelligent recognition on the obtained radar grid data to obtain an intelligent recognition evaluation score, and at the same time, determine whether intelligent recognition is completed according to the obtained intelligent recognition evaluation score, and the intelligent recognition evaluation score is used to evaluate the degree of matching between radar grid data and intelligent recognition conditions; step four, if intelligent recognition is completed, then design a membership function according to the obtained radar grid data and combine the fuzzy logic method to obtain the tornado warning duration.

[0009] Furthermore, the quality control evaluation index is obtained by obtaining the signal-to-noise ratio, ground object echo removal efficiency, isolated echo removal efficiency and electromagnetic interference suppression efficiency of the dual-polarization phased array radar data during the quality control process within a preset time period; the ground object echo removal efficiency represents the ratio of the difference between the initial ground object echo intensity of the dual-polarization phased array radar data before quality control within the preset time period and the actual ground object echo intensity after quality control within the preset time period to the initial ground object echo intensity; the isolated echo removal efficiency represents the ratio of the difference between the initial number of isolated echoes of the dual-polarization phased array radar data before quality control within the preset time period and the actual number of isolated echoes after quality control within the preset time period to the initial number of isolated echoes; the electromagnetic interference suppression efficiency represents the ratio of the difference between the initial electromagnetic interference intensity of the dual-polarization phased array radar data before quality control within the preset time period and the actual electromagnetic interference intensity after quality control within the preset time period to the initial electromagnetic interference intensity.

[0010] Furthermore, the specific steps for obtaining the gridding accuracy index include: E1, real-time monitoring of the nested gridding of the dual-polarization phased array radar data in the Cartesian coordinate system, judging whether the number of peak position consistency within a preset time period is greater than the preset number of peak position consistency, if so, executing E2, otherwise re-nesting the gridding; E2, obtaining the peak position consistency number score, and judging whether the time variation coefficient is equal to the reference time variation coefficient, if so, obtaining the gridding success rate and the gridding timestamp and combining the obtained quality control evaluation index and the peak position consistency number score to obtain the gridding accuracy index. The gridding accuracy index is obtained, otherwise the gridding timestamp is obtained and the gridding accuracy index is obtained by combining the obtained quality control evaluation index and the peak position consistency number score; the peak position consistency number score represents the ratio of the difference between the peak position consistency number and the preset peak position consistency number to the preset peak position consistency number; the time variation coefficient represents the variation amplitude of the dual-polarization phased array radar data over time; the gridding success rate represents the ratio of the amount of dual-polarization phased array radar data successfully nested and gridded within a preset time period to the total amount of dual-polarization phased array radar data to be nested and gridded.

[0011] Furthermore, the specific limiting expression of the grid accuracy index is: ; Where t is the number of the preset time period, , T is the total number of preset time periods, e is a natural constant, It represents the gridding accuracy index of the dual-polarization phased array radar data in the nested gridding process within the t-th preset time period. represents the quality control evaluation index of the dual-polarization phased array radar data in the quality control process within the t-th preset time period, represents the preset quality control evaluation index, It represents the number of consistent peak positions of dual-polarization phased array radar data in the nested gridding process in the t-th preset time period, It represents the number of peak position coincidences of the dual-polarization phased array radar data in the nested gridding process in the t-th preset time period, Indicates the preset peak position consistency number, represents the success rate of gridding during the nested gridding process of dual-polarization phased array radar data in the tth preset time period, Represents the gridded timestamp of the dual-polarization phased array radar data during the nested gridding process in the tth preset time period, It represents the time variation coefficient of the dual-polarization phased array radar data in the process of nested gridding in the t-th preset time period, Represents the reference time variation coefficient.

[0012] Furthermore, the intelligent recognition evaluation score is obtained by the following method: real-time monitoring of the real-time response speed of the radar grid data to obtain an average response speed score in the intelligent recognition process within a preset time period, wherein the average response speed score is the ratio of the difference between the average response speed and the reference average response speed to the average response speed reference deviation; at the end of the preset time period, the amount of missed recognition data is obtained and it is determined whether the amount of missed recognition data is less than the maximum allowable amount of missed recognition data, if so, the missed recognition rate score is obtained and intelligent recognition for the next time period is performed, otherwise intelligent recognition is performed again, wherein the missed recognition rate score is the ratio of the amount of missed recognition data to the total amount of radar grid data to be intelligently recognized; the signal-to-noise ratio of the radar grid data in the intelligent recognition process within the preset time period is obtained, and the intelligent recognition evaluation score is obtained by combining the obtained average response speed score, the missed recognition rate score and the gridding accuracy index.

[0013] Furthermore, the specific process of judging whether the intelligent recognition is completed according to the obtained intelligent recognition evaluation score is as follows: judging whether the obtained intelligent recognition evaluation score is greater than the preset intelligent recognition evaluation score: if the obtained intelligent recognition evaluation score is greater than the preset intelligent recognition evaluation score, the intelligent recognition is completed and the radar grid point data that has completed the intelligent recognition is marked as an identified tornado; if the obtained intelligent recognition evaluation score is not greater than the preset intelligent recognition evaluation score, the corresponding radar grid point data is marked as an unidentified tornado.

[0014] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By acquiring dual-polarization phased array radar data in a preset band and performing quality control to obtain a quality control evaluation index, the acquired multi-source real-time data is input into the constructed severe convective identification model to obtain areas prone to severe convective weather. Finally, based on the acquired grid accuracy index, it is determined whether to obtain radar grid data. At the same time, it is determined whether the intelligent identification is completed according to the acquired intelligent identification evaluation score, thereby achieving more accurate acquisition of the intelligent identification evaluation score, and then achieving an improvement in the correlation between the tornado intelligent identification results and the fusion of multi-source data, effectively solving the problem of low correlation between the tornado intelligent identification results and the fusion of multi-source data in the prior art.

[0015] 2. By obtaining the peak position consistency score, it is determined whether the time variation coefficient is equal to the reference time variation coefficient. If so, the gridding success rate and gridding timestamp are obtained and combined with the obtained quality control evaluation index and the peak position consistency score to obtain the gridding accuracy index. Otherwise, the gridding timestamp is obtained and combined with the obtained quality control evaluation index and the peak position consistency score to obtain the gridding accuracy index, thereby achieving more accurate acquisition of the gridding accuracy index, and then achieving improved accuracy and reliability of nested gridding.

[0016] 3. When the amount of missed identification data at the end of the preset time period is less than the maximum allowed amount of missed identification data, the signal-to-noise ratio of the radar grid data in the intelligent identification process within the preset time period is obtained, and the intelligent identification evaluation score is obtained by combining the obtained average response speed score, missed identification rate score and grid accuracy index, thereby achieving improved accuracy and reliability in obtaining the intelligent identification evaluation score, and further improving the accuracy and real-time performance of tornado intelligent identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a multi-source data fusion and tornado intelligent identification method provided in an embodiment of the present application; Figure 2 A flowchart of multi-source real-time data fusion provided in an embodiment of the present application; Figure 3 A block diagram of a severe convection identification model provided in an embodiment of the present application; Figure 4 A schematic diagram of nested gridding of dual-polarization phased array radar data provided in an embodiment of the present application; Figure 5 A comparison chart of the two-kilometer altitude effect before and after nested gridding of SA radar data provided in an embodiment of the present application; Figure 6 A roadmap for intelligent identification of tornadoes provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The embodiment of the present application solves the problem of low correlation between tornado intelligent identification results and multi-source data fusion in the prior art by providing a multi-source data fusion and tornado intelligent identification method. The dual-polarization phased array radar data of a preset band is obtained and quality control is performed to obtain a quality control evaluation index. At the same time, it is determined whether to obtain multi-source real-time data based on the obtained quality control evaluation index. Then, the obtained multi-source real-time data is input into the constructed severe convection identification model to obtain the severe convective weather prone area. At the same time, the dual-polarization phased array radar data is nested and gridded in the severe convective weather prone area to obtain a gridding accuracy index. Finally, based on the obtained gridding accuracy index, it is determined whether to obtain radar grid data. If radar grid data is obtained, intelligent identification is performed on the obtained radar grid data to obtain an intelligent identification evaluation score. At the same time, it is determined whether intelligent identification is completed based on the obtained intelligent identification evaluation score. If intelligent identification is completed, a membership function is designed based on the obtained radar grid data and the tornado warning duration is obtained in combination with the fuzzy logic method, thereby improving the correlation between tornado intelligent identification results and multi-source data fusion.

[0019] The technical solution in the embodiment of the present application is to solve the problem that the above-mentioned tornado intelligent identification results and multi-source data fusion are not highly correlated. The overall idea is as follows: By acquiring dual-polarization phased array radar data in a preset band and performing quality control to obtain a quality control evaluation index, the acquired multi-source real-time data is input into the constructed severe convection identification model to obtain areas prone to severe convective weather. Finally, based on the acquired grid accuracy index, it is determined whether to obtain radar grid data. At the same time, according to the acquired intelligent recognition evaluation score, it is determined whether the intelligent recognition is completed, thereby achieving the effect of improving the correlation between the tornado intelligent recognition results and the fusion of multi-source data.

[0020] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0021] like Figure 1As shown, it is a flow chart of the multi-source data fusion and tornado intelligent identification method provided by an embodiment of the present application, and the method includes the following steps: step 1, obtaining dual-polarization phased array radar data of a preset band and performing quality control (differential phase correction is performed on the dual-polarization phased array radar data through the differential phase quality control algorithm provided in the radar system, that is, the backward differential phase error existing in the measured differential phase value in the dual-polarization phased array radar data is removed) to obtain a quality control evaluation index, and at the same time, based on the obtained quality control evaluation index, it is determined whether to obtain multi-source real-time data, the preset band includes the X band and the C band, the quality control is used to correct the error in the dual-polarization phased array radar data, and the quality control evaluation index is used to evaluate the error correction effect of the dual-polarization phased array radar data within a preset time period; step 2, inputting the obtained multi-source real-time data into the constructed severe convection identification model to obtain the severe convective weather prone area, and at the same time, performing quality control on the dual-polarization phased array radar data in the severe convective weather prone area The gridding is nested to obtain a gridding accuracy index, which is used to evaluate the accuracy and reliability of nested gridding of dual-polarization phased array radar data. Step three: determine whether to obtain radar grid data based on the obtained gridding accuracy index. If radar grid data is obtained, then perform intelligent recognition on the obtained radar grid data to obtain an intelligent recognition evaluation score, and at the same time determine whether intelligent recognition is completed based on the obtained intelligent recognition evaluation score. The intelligent recognition evaluation score is used to evaluate the degree of match between radar grid data and intelligent recognition conditions. Step four: if intelligent recognition is completed, design a membership function based on the obtained radar grid data and combine it with the fuzzy logic method to obtain the tornado warning duration. The membership function includes a TVS membership function and a TDS membership function. The TVS membership function is used for tornado graded warning to obtain the tornado warning release time, and for tornado touchdown warning to obtain the tornado formation time. The tornado warning duration is the duration between the tornado warning release time and the tornado formation time.

[0022] In this embodiment, the dual-polarization phased array radar data includes reflectivity factors of horizontal polarization and vertical polarization (used to reflect the intensity and distribution of radar echoes, which is helpful to identify the echo characteristics of tornadoes), radial velocity (indicates the movement direction and speed of radar echoes relative to the radar station), differential reflectivity factor (used to reflect the size and shape differences of precipitation particles, which is helpful to distinguish different types of precipitation, such as raindrops and hail, so as to more accurately identify tornadoes), differential propagation phase shift (indicates the phase change caused by the scattering and absorption of precipitation particles during the propagation of electromagnetic waves); multi-source real-time data is used to verify the accuracy of real-time observation data; real-time observation data It includes radar echo data, meteorological element observation data (such as temperature, humidity, air pressure, wind direction and wind speed) and geographic information data (such as terrain, landform undulation amplitude and vegetation coverage); the severe convection identification model is used to identify the probability of occurrence of severe convective weather (such as tornado) in the coverage area of ​​the dual-polarization phased array radar; the severe convective weather prone area indicates the area where the probability of occurrence of severe convective weather is greater than the preset probability of occurrence; the nested gridding is used to convert the dual-polarization phased array radar data into a regular grid form, and the radar grid data (representing the dual-polarization phased array radar data after the nested gridding is completed) is used to reflect the distribution information of meteorological elements in geographic space.

[0023] It should be added that Figure 2 As shown, it is a flow chart of multi-source real-time data fusion provided in an embodiment of the present application. Multi-source real-time data includes but is not limited to sounding data, ground automatic station data, satellite data and numerical forecast data. Multi-source real-time data fusion is the fusion between sounding data, ground automatic station data, satellite data and numerical forecast data. Among them, the sounding data has the characteristics of high vertical resolution, usually including temperature, humidity, air pressure, wind direction and wind speed at different levels from the ground to high altitude, and is mainly measured in real time by high-altitude observation equipment (such as meteorological sounding balloons with built-in meteorological sensors); the ground automatic station data has the characteristics of high density and high frequency. The satellite data are characterized by wide coverage and high observation frequency. They are provided by meteorological satellites (usually equipped with visible light, infrared and microwave remote sensing instruments) and are used to monitor cloud cover, cloud type, precipitation, ocean and surface radiation and reflection characteristics in real time around the world. The numerical forecast data are characterized by high precision and long time. They are used to predict changes in meteorological elements in the future and are mainly provided by the meteorological numerical forecast system.

[0024] Specifically, the specific construction steps of the severe convection identification model are as follows: according to the small and medium-scale convection characteristics, no less than 100 representative parameters are selected from the historical severe convection database as the input of the severe convection identification model, including the atmospheric structure state parameters at different heights of each grid point, gridded radar parameters, and the spatiotemporal variables of these parameters and the cloud top temperature, as shown in Table 1, which is a statistical table of some input parameters of the severe convection identification model provided in the embodiment of the present application: Table 1 Statistics of some input parameters of severe convection identification model Abbreviation name Abbreviation name K K Index TQC Ventilation tube parameters JI Jefferson Index LS Dry Warm Cover Index SI Sachs Index CCL Convective condensation height Faust Faust Index CCL_T Temperature at Convective Condensation Height ICC Barber Convective Instability Index CCL_mod Corrected Convective Condensation Height LIMax Maximum lift index Tg Convection temperature BI Convective stability index SWEAT Severe Weather Threat Index MDPI Potential Downwash Index Wd_S Static stability Teffer Teffer Index SSI Storm Severity Index TvT Charba Total Index SWISS00 Swiss thunderstorm 1 Hd_020 Mixed phase layer SWISS12 Swiss Thunderstorm 2 Hd_204 BB growth layer Shr Rough Richardson Shear ZH 0 degree altitude SRH Storm relative helicity ZH20 -20 Height Z Echo strength ZH30 -30 Height ET Echo top height ZH40 -40 Height VIL Vertically accumulated liquid water content Integra1Q The whole layer specific humidity integral The parameters in Table 1 form a severe convection training data set (70% for training and 30% for testing). The deep learning algorithm in the machine learning algorithm is used for cross-testing and training to establish a severe convection recognition model for the development of severe convective weather such as thunderstorms, gales, tornadoes, etc. with self-learning function. Figure 3 As shown, it is a block diagram of the severe convection identification model provided in the embodiment of the present application. Through the establishment and application of this model, the intelligent identification of severe convective weather such as tornadoes is successfully realized. More importantly, the model can also be integrated and correlated with multi-source data, thereby achieving an improvement in the correlation between the tornado intelligent identification results and the fusion of multi-source data.

[0025] Furthermore, the quality control evaluation index is obtained by obtaining the signal-to-noise ratio (the ratio of signal power to noise power, i.e., the ratio of the signal power to the noise power) of the dual-polarization phased array radar data during the quality control process within a preset time period, i.e., ), the ground object echo removal efficiency (i.e., the limiting expression of the quality control evaluation index ), isolated echo removal efficiency (i.e., the limiting expression of the quality control evaluation index ) and the electromagnetic interference suppression efficiency (i.e., the limiting expression of the quality control evaluation index ) processing; the ground object echo removal efficiency represents the ratio of the difference between the initial ground object echo intensity of the dual-polarization phased array radar data before quality control within the preset time period and the actual ground object echo intensity after the quality control within the preset time period to the initial ground object echo intensity; the isolated echo removal efficiency represents the ratio of the difference between the initial isolated echo number of the dual-polarization phased array radar data before quality control within the preset time period and the actual isolated echo number after the quality control within the preset time period to the initial isolated echo number; the electromagnetic interference suppression efficiency represents the ratio of the difference between the initial electromagnetic interference intensity of the dual-polarization phased array radar data before quality control within the preset time period and the actual electromagnetic interference intensity after the quality control within the preset time period to the initial electromagnetic interference intensity.

[0026] Among them, the signal-to-noise ratio is measured in real time by a signal-to-noise ratio tester, the ground object echo intensity and electromagnetic interference intensity are measured in real time by an optical fiber microcrack detector, and the ground object echo, isolated echo and electromagnetic wave are acquired by the radar receiver in the radar system.

[0027] In this embodiment, the specific limiting expression of the quality control evaluation index is: ; ; ; ; Where t is the number of the preset time period, , T is the total number of preset time periods, e is a natural constant, represents the quality control evaluation index of the dual-polarization phased array radar data in the quality control process within the t-th preset time period, 1 represents the signal-to-noise ratio of the dual-polarization phased array radar data during the quality control process within a preset time period. It represents the ground object echo removal efficiency of dual-polarization phased array radar data in the tth preset time period, It represents the initial ground object echo intensity before quality control of the dual-polarization phased array radar data in the t-th preset time period, It represents the actual ground object echo intensity after quality control of the dual-polarization phased array radar data in the t-th preset time period, represents the isolated echo removal efficiency of the dual-polarization phased array radar data in the tth preset time period, represents the number of initial isolated echoes before quality control of dual-polarization phased array radar data in the tth preset time period, It represents the actual number of isolated echoes after quality control of the dual-polarization phased array radar data in the tth preset time period, represents the electromagnetic interference suppression efficiency of the dual-polarization phased array radar data in the tth preset time period, represents the initial electromagnetic interference intensity of the dual-polarization phased array radar data before quality control in the tth preset time period, It represents the actual electromagnetic interference intensity of the dual-polarization phased array radar data after quality control in the tth preset time period.

[0028] It should be understood that the quality control evaluation index increases with the increase of the signal-to-noise ratio, and decreases with the increase of the ground echo removal efficiency, the isolated echo removal efficiency and the electromagnetic interference suppression efficiency. It should be noted that (1) the signal-to-noise ratio also indirectly affects the value of the electromagnetic interference suppression efficiency. When the signal-to-noise ratio increases, it indicates that the radar system's ability to recognize signals is enhanced, which means that the radar system can more effectively distinguish between radar signals and electromagnetic interference signals, thereby improving the electromagnetic interference suppression efficiency.

[0029] (2) The ground object echo removal efficiency also indirectly affects the value of the isolated echo removal efficiency. When the ground object echo removal efficiency increases, there will be fewer ground object echoes remaining in the dual-polarization phased array radar data, which helps to reduce the occurrence of isolated echoes. Isolated echoes are sometimes caused by improper ground object echo processing, such as ground object echoes not being completely removed or being mistakenly identified as target echoes. Therefore, improving the ground object echo removal efficiency can indirectly improve the isolated echo removal efficiency.

[0030] By considering the above-mentioned indirect influence mechanism, it is helpful to identify tornadoes more accurately and reduce false alarms and missed alarms, thereby improving the reliability of intelligent identification results, and further achieving an improvement in the correlation between tornado intelligent identification results and multi-source data fusion, effectively solving the problem of low correlation between tornado intelligent identification results and multi-source data fusion in the prior art.

[0031] Furthermore, the specific process of judging whether to obtain multi-source real-time data based on the obtained quality control evaluation index is as follows: judging whether the obtained quality control evaluation index is greater than the preset quality control evaluation index: if the obtained quality control evaluation index is greater than the preset quality control evaluation index, it indicates that the result of quality control meets the expected requirements and multi-source real-time data is obtained; if the obtained quality control evaluation index is not greater than the preset quality control evaluation index, it indicates that the result of quality control does not meet the expected requirements. At this time, multi-source real-time data is not obtained and the preset band parameters are adjusted. Quality control is re-performed according to the adjusted preset band parameters until the obtained quality control evaluation index is greater than the preset quality control evaluation index; the preset band parameters include the center frequency of the band, the scanning mode of the radar (such as sector scanning, conical scanning) and the pulse repetition frequency.

[0032] In this embodiment, the preset quality control evaluation index is represented by the sum and average of the historical quality control evaluation indices in the historical quality control processes in the preset database; assuming that in a certain quality control process, the radar system adopts the following preset band parameters: center frequency: 10GHz; scanning mode: sector scanning; pulse repetition frequency: 1kHz; at this time, the obtained quality control evaluation index is 1.2 (the preset quality control evaluation index is 1.5).

[0033] In order to improve the quality control effect, the preset band parameters can be adjusted: adjust the center frequency from 10GHz to 12GHz; adjust the scanning mode from sector scanning to conical scanning; adjust the pulse repetition frequency from 1kHz to 1.5kHz or 2kHz; it should be noted that the above adjustment is a complete preset band parameter adjustment. After each complete preset band parameter adjustment, quality control needs to be re-performed to obtain a new quality control evaluation index. If the new quality control evaluation index reaches or exceeds 1.5, the adjustment is successful, otherwise, continue to adjust. Through the above preset band parameter adjustment steps, the detection effect of the radar system can be gradually optimized, the accuracy of quality control can be improved, and a more accurate evaluation of the quality control effect can be achieved.

[0034] Furthermore, the specific steps of nesting and gridding the dual-polarization phased array radar data in areas prone to severe convective weather are as follows: generating radar information based on the dual-polarization phased array radar data after quality control, and using an adaptive algorithm to fuse the generated radar information into a Cartesian coordinate system to form three-dimensional grid point (including longitude, latitude and altitude) information (with uniform spatial resolution); the radar information is used to visualize the weather phenomena in the area covered by the dual-polarization phased array radar; the Cartesian coordinate system is used to visualize the positioning and distribution of the dual-polarization phased array radar data in three-dimensional space, wherein the Cartesian coordinate system consists of three mutually perpendicular axes, representing longitude (X-axis), latitude (Y-axis) and altitude (Z-axis), respectively, and in this coordinate system, the position of each point can be represented by a triple (X, Y, Z).

[0035] like Figure 4 As shown, it is a schematic diagram of the nested gridding of dual-polarization phased array radar data provided in an embodiment of the present application. The resolution of the dual-polarization phased array radar data after the nested gridding is significantly improved, which helps to more clearly observe the internal fine structure of clouds in severe convective weather, improves the accuracy and reliability of obtaining the internal microphysical structure of severe convective cloud systems (such as supercells and mesocyclones that produce tornadoes), and lays the foundation for the intelligent identification of tornadoes.

[0036] Take the SA radar data obtained by Guangzhou SA (CINRAD-SA, China New Generation Weather Radar) as an example. Figure 5 As shown, it is a comparison diagram of the two-kilometer altitude effect before and after the nested gridding of SA radar data provided by the embodiment of the present application, wherein (a) is before the nested gridding, and (b) is after the nested gridding. Figure 5It can be seen that the convective cloud structure after nested gridding is clearer and more accurate than the convective cloud results between nested gridding. This example uses nested gridding to help improve the accuracy and reliability of dual-polarization phased array radar data acquisition, thereby realizing the adaptive nesting of dual-polarization phased array radar data.

[0037] Furthermore, the specific steps of obtaining the gridding accuracy index include: E1, when the obtained quality control evaluation index is greater than the preset quality control evaluation index, real-time monitoring of the nested gridding of the dual-polarization phased array radar data in the Cartesian coordinate system, and judging whether the number of peak position consistency within the preset time period is greater than the preset number of peak position consistency, if so, executing E2, otherwise re-nesting the gridding; E2, obtaining the peak position consistency number score (i.e., the number of peak position consistency in the restriction expression of the gridding accuracy index) ), and determine whether the time variation coefficient is equal to the reference time variation coefficient. If so, obtain the gridding success rate (i.e., the gridding accuracy index in the limiting expression). ) and the gridded timestamp (i.e., the gridded accuracy index in the constraint expression ) and combined with the obtained quality control evaluation index (i.e., the grid accuracy index in the constraint expression ) and the peak position consistency score are used to obtain the gridded accuracy index. Otherwise, the gridded timestamp is obtained and combined with the obtained quality control evaluation index and the peak position consistency score to obtain the gridded accuracy index.

[0038] Among them, the peak position consistency score represents the ratio of the difference between the peak position consistency number (representing the number of successful matches between the peak values ​​of the dual-polarization phased array radar data and the corresponding reference peak positions in the Cartesian coordinate system) and the preset peak position consistency number to the preset peak position consistency number; the time variation coefficient represents the variation amplitude of the dual-polarization phased array radar data over time; the reference time variation coefficient represents the variation amplitude of the reference dual-polarization phased array radar data over time; the gridding success rate represents the ratio of the amount of dual-polarization phased array radar data successfully nested and gridded within a preset time period to the total amount of dual-polarization phased array radar data to be nested and gridded; the gridding timestamp is obtained by the actual nesting gridding duration of the dual-polarization phased array radar data (i.e. the total duration to complete the nesting gridding) and the reference nesting gridding duration.

[0039] The specific limiting expression of the grid accuracy index is: ; ; ; ; Where t is the number of the preset time period, , T is the total number of preset time periods, e is a natural constant, It represents the gridding accuracy index of the dual-polarization phased array radar data in the nested gridding process within the t-th preset time period. represents the quality control evaluation index of the dual-polarization phased array radar data in the quality control process within the t-th preset time period, represents the preset quality control evaluation index, It represents the number of consistent peak positions of dual-polarization phased array radar data in the nested gridding process in the t-th preset time period, It represents the number of peak position coincidences of the dual-polarization phased array radar data in the nested gridding process in the t-th preset time period, Indicates the preset peak position consistency number, represents the success rate of gridding during the nested gridding process of dual-polarization phased array radar data in the tth preset time period, represents the amount of successfully nested gridded data in the nested gridding process of dual-polarization phased array radar data in the t-th preset time period, represents the total amount of dual-polarization phased array radar data to be nested and gridded, Represents the gridded timestamp of the dual-polarization phased array radar data during the nested gridding process in the tth preset time period, represents the actual nested gridding duration of the dual-polarization phased array radar data during the nested gridding process in the tth preset time period, Indicates the reference nested grid duration, It represents the time variation coefficient of the dual-polarization phased array radar data in the process of nested gridding in the t-th preset time period, Represents the reference time variation coefficient.

[0040] In this embodiment, the preset peak position consistency number is represented by the result of summing and averaging the historical peak position consistency numbers of the dual-polarization phased array radar data in the preset database within the historical time period, the reference time variation coefficient is represented by the result of summing and averaging the historical time variation coefficients of the dual-polarization phased array radar data in the preset database within the historical time period, and the reference nested gridding duration is represented by the result of summing and averaging the historical nested gridding durations of the dual-polarization phased array radar data in the preset database, thereby improving the accuracy and reliability of obtaining the gridding accuracy index.

[0041] It is important to understand that when When the gridding accuracy index increases with the increase of the quality control evaluation index, the peak position consistency score and the gridding success rate, and decreases with the increase of the gridding timestamp. Among them, the peak position consistency score increases with the increase of the peak position consistency number, the gridding success rate increases with the increase of the amount of successfully nested gridding data, and the gridding timestamp decreases with the increase of the actual nested gridding time.

[0042] It should be noted that (1) the quality control evaluation index also indirectly affects the value of the peak position consistency score. Suppose there are two sets of data (i.e., dual-polarization phased array radar data) used for tornado intelligent identification. The quality control evaluation index of the first set of data is 1.6, and the quality control evaluation index of the second set of data is 1.4. At this time, in the peak position identification process of the Cartesian coordinate system, the accuracy of the peak position of the tornado identified by the first set of data is greater than the accuracy of the peak position of the tornado identified by the second set of data. This means that as the quality control evaluation index increases, the peak position consistency score will also increase accordingly.

[0043] (2) The number of consistent peak positions also indirectly affects the gridding success rate and the value of the gridding timestamp. When the number of consistent peak positions increases, it means that the number of peak position alignments is greater, that is, the accuracy of the peak position is improved, providing a more accurate reference point for nested gridding, and the corresponding gridding success rate also increases accordingly.

[0044] At this time, since the accuracy of peak position recognition is improved, nested gridding will be more efficient, thereby reducing the time required for nested gridding (i.e., the gridding timestamp is reduced). Through the above indirect influence mechanism, not only the accuracy of tornado intelligent recognition is improved, but also the relevance of multi-source data fusion is enhanced, thereby achieving an improvement in the relevance between tornado intelligent recognition results and multi-source data fusion, effectively solving the problem of low relevance between tornado intelligent recognition results and multi-source data fusion in the prior art.

[0045] Furthermore, the specific process of determining whether to obtain radar grid data based on the obtained grid accuracy index is as follows: determining whether the obtained grid accuracy index is within the grid accuracy threshold range: If the obtained gridding accuracy index is within the gridding accuracy threshold range (including the case where it is equal to the maximum and minimum values ​​of the historical gridding accuracy index), the nested gridding is completed and the radar grid data is obtained; if the obtained gridding accuracy index is not within the gridding accuracy threshold range, the radar grid data is not obtained and the nested gridding is continued until the obtained gridding accuracy index is within the gridding accuracy threshold range and then the radar grid data is obtained.

[0046] In this embodiment, the grid accuracy threshold range represents the range corresponding to the maximum and minimum values ​​of the historical grid accuracy index of the dual-polarization phased array radar data in the preset database within the historical time period; this example ensures that only radar grid data that meets the expected accuracy (set by the preset personnel) is acquired and stored by setting the grid accuracy threshold range, which helps to avoid unnecessary acquisition and storage of low-quality radar grid data, thereby saving storage space and processing resources.

[0047] Furthermore, the intelligent recognition evaluation score is obtained by the following method: when the obtained grid accuracy index is within the grid accuracy threshold range, the real-time response speed of the radar grid data is monitored in real time to obtain the average response speed score of the intelligent recognition process within a preset time period (i.e., the average response speed score in the restriction expression of the intelligent recognition evaluation score). ), the average response speed score is the ratio of the difference between the average response speed (recorded in real time by the deployed speed sensor to reflect the response rate of the radar system to the radar grid data) and the reference average response speed to the average response speed reference deviation; at the end of the preset time period, the amount of missed recognition data is obtained and it is determined whether the amount of missed recognition data is less than the maximum allowable amount of missed recognition data. If so, the missed recognition rate score (i.e., the value in the restriction expression of the intelligent recognition evaluation score) is obtained. ) and perform intelligent recognition in the next time period, otherwise re-perform intelligent recognition, and the missed recognition rate score is the ratio of the amount of missed recognition data to the total amount of radar grid point data to be intelligently recognized; obtain the signal-to-noise ratio of the radar grid point data in the intelligent recognition process within the preset time period (that is, the signal-to-noise ratio in the restriction expression of the intelligent recognition evaluation score ), and combined with the obtained average response speed score, missed recognition rate score and grid accuracy index (i.e., the restricted expression of the intelligent recognition evaluation score ) to obtain the intelligent recognition evaluation score.

[0048] In this embodiment, the specific limiting expression of the intelligent recognition evaluation score is: ; ; ; Where t is the number of the preset time period, , T is the total number of preset time periods, e is a natural constant, It represents the intelligent recognition evaluation score of the radar grid data in the intelligent recognition process in the t-th preset time period. It represents the gridding accuracy index of the dual-polarization phased array radar data in the nested gridding process within the t-th preset time period. represents the grid accuracy threshold range, It represents the signal-to-noise ratio of radar grid point data in the intelligent recognition process within a preset time period. It represents the average response speed score of radar grid point data in the intelligent recognition process within the t-th preset time period. It represents the average response speed of radar grid data in the intelligent recognition process within the t-th preset time period. represents the reference average response speed, Indicates the average response speed reference deviation, It represents the missed recognition rate score of radar grid point data in the intelligent recognition process in the t-th preset time period. It represents the amount of missed data in the intelligent recognition process of radar grid data in the tth preset time period. Indicates the total amount of radar grid data to be intelligently identified, Indicates the maximum amount of data that can be missed.

[0049] Among them, the reference average response speed is represented by the sum and average of the historical average response speeds of the radar grid point data in the preset database within the historical time period, the average response speed reference deviation is represented by the sum and average of the historical average response speed deviations of the radar grid point data in the preset database within the historical time period, and the maximum allowable missed identification data amount is the maximum value of the missed identification data amount of the radar grid point data in the preset database (i.e., the historical database that completes intelligent identification) within the historical time period.

[0050] Specifically, assuming that the grid accuracy threshold range is between 1 and 2 (assuming that the total amount of radar grid data to be intelligently identified within the preset time period is 100) and the maximum amount of missed identification data allowed is 10, the change statistics of the intelligent identification evaluation score are shown in Table 2: Table 2 Statistics of changes in intelligent recognition evaluation scores

[0051] It should be understood that, from the first and second sets of data in Table 2, it can be seen that the intelligent recognition evaluation score decreases with the increase of the missed recognition rate score, and from the third, fourth and fifth sets of data in Table 2, it can be seen that the score increases with the increase of the grid accuracy index, signal-to-noise ratio and average response speed score, among which the average response speed score increases with the average response speed deviation (i.e. ) increases, and the missed recognition rate score increases with the increase of the amount of missed recognition data.

[0052] It should be noted that (1) the gridding accuracy index also indirectly affects the value of the average response speed score. When the gridding accuracy index decreases, it indicates that the deviation between the dual-polarization phased array radar data after nested gridding and the radar grid data increases. This deviation may cause the radar system to misjudge or delay when identifying tornadoes, and then cause the radar system to respond to the radar grid data. There is a delay in the response rate. (That is, the average response speed deviation increases, and the corresponding average response speed score also increases accordingly.)

[0053] (2) The average response speed score also indirectly affects the value of the missed identification rate score. When the average response speed score increases, it means that the radar system needs a longer time to analyze and identify after receiving the radar grid data, which may cause some tornado events to be missed during the identification process, further exacerbating the risk of missed identification (i.e., increasing the number of missed identifications of radar grid data).

[0054] From the above analysis, it can be seen that there is an interactive relationship between the grid accuracy index, the average response speed score and the missed recognition rate score. By considering the above indirect influence mechanism, the correlation of these indicators can be improved, which is helpful to optimize the performance of the tornado intelligent recognition system, and then achieve the improvement of the correlation between the tornado intelligent recognition results and the multi-source data fusion, which effectively solves the problem of low correlation between the tornado intelligent recognition results and the multi-source data fusion in the prior art.

[0055] Furthermore, the specific process of judging whether the intelligent recognition is completed according to the obtained intelligent recognition evaluation score is as follows: judging whether the obtained intelligent recognition evaluation score is greater than the preset intelligent recognition evaluation score: if the obtained intelligent recognition evaluation score is greater than the preset intelligent recognition evaluation score, the intelligent recognition is completed and the radar grid data that has completed the intelligent recognition is marked as an identified tornado; if the obtained intelligent recognition evaluation score is not greater than the preset intelligent recognition evaluation score, the corresponding radar grid data is marked as an unidentified tornado.

[0056] In this embodiment, the preset intelligent recognition evaluation score is represented by the sum and average of the historical intelligent recognition evaluation scores of the radar system in the preset database within the historical time period, such as Figure 6 As shown, a roadmap for intelligent identification of tornadoes provided in an embodiment of the present application is provided, which visualizes the intelligent identification results and data processing status through the display screen of the radar system, helps decision makers to quickly understand the current intelligent identification status of tornadoes, realizes automated and intelligent data management, and further improves the correlation between the intelligent identification results of tornadoes and the fusion of multi-source data.

[0057] In summary, the embodiment of the present application obtains dual-polarization phased array radar data of a preset band and performs quality control to obtain a quality control evaluation index, and then inputs the obtained multi-source real-time data into the constructed severe convection identification model to obtain areas prone to severe convective weather. Finally, based on the obtained grid accuracy index, it is determined whether to obtain radar grid data, and at the same time, based on the obtained intelligent recognition evaluation score, it is determined whether the intelligent recognition is completed, thereby achieving more accurate acquisition of the intelligent recognition evaluation score, and then achieving an improvement in the correlation between the tornado intelligent recognition results and the fusion of multi-source data, effectively solving the problem of low correlation between the tornado intelligent recognition results and the fusion of multi-source data in the prior art.

[0058] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0062] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. Multi-source data fusion and tornado intelligent identification method, characterized in that: The following steps are involved: Step 1: Acquire dual-polarization phased array radar data of a preset band and perform quality control to obtain a quality control evaluation index, and determine whether to acquire multi-source real-time data based on the obtained quality control evaluation index, wherein the quality control evaluation index is used to evaluate the error correction effect of the dual-polarization phased array radar data within a preset time period; Step 2: Input the acquired multi-source real-time data into the established severe convection identification model to obtain the severe convective weather prone area, and at the same time, nested gridding the dual-polarization phased array radar data in the severe convective weather prone area to obtain a gridding accuracy index, which is used to evaluate the accuracy and reliability of nested gridding of dual-polarization phased array radar data; Step 3: determine whether to obtain radar grid point data based on the obtained grid accuracy index; if radar grid point data is obtained, then perform intelligent recognition on the obtained radar grid point data to obtain an intelligent recognition evaluation score; and determine whether intelligent recognition is completed based on the obtained intelligent recognition evaluation score, wherein the intelligent recognition evaluation score is used to evaluate the degree of match between the radar grid point data and the intelligent recognition condition; Step 4: If intelligent identification is completed, the membership function is designed according to the acquired radar grid data and the tornado warning duration is obtained in combination with the fuzzy logic method.

2. The multi-source data fusion and tornado intelligent identification method according to claim 1, characterized in that: The dual-polarization phased array radar data includes reflectivity factors, radial velocities, differential reflectivity factors, and differential propagation phase shifts of horizontal polarization and vertical polarization; The severe convective weather prone area refers to an area where the probability of occurrence of severe convective weather is greater than a preset probability of occurrence; The nested gridding is used to convert the dual-polarization phased array radar data into a regular grid form.

3. The multi-source data fusion and tornado intelligent identification method according to claim 1, characterized in that: The quality control evaluation index is obtained by obtaining the signal-to-noise ratio, ground object echo removal efficiency, isolated echo removal efficiency and electromagnetic interference suppression efficiency of the dual-polarization phased array radar data during the quality control process within a preset time period; The ground object echo removal efficiency represents the ratio of the difference between the initial ground object echo intensity before quality control of the dual-polarization phased array radar data within a preset time period and the actual ground object echo intensity after quality control within the preset time period to the initial ground object echo intensity; The isolated echo removal efficiency represents the ratio of the difference between the initial isolated echo number before quality control of the dual-polarization phased array radar data within a preset time period and the actual isolated echo number after quality control within the preset time period to the initial isolated echo number; The electromagnetic interference suppression efficiency indicates a ratio of a difference between an initial electromagnetic interference intensity of dual-polarization phased array radar data before quality control within a preset time period and an actual electromagnetic interference intensity after quality control within the preset time period to the initial electromagnetic interference intensity.

4. The multi-source data fusion and tornado intelligent identification method according to claim 1, characterized in that: The specific process of judging whether to acquire multi-source real-time data based on the acquired quality control evaluation index is as follows: Determine whether the obtained quality control evaluation index is greater than the preset quality control evaluation index: If the obtained quality control evaluation index is greater than the preset quality control evaluation index, multi-source real-time data is obtained; If the obtained quality control evaluation index is not greater than the preset quality control evaluation index, the multi-source real-time data is not obtained and the preset band parameters are adjusted; The preset band parameters include the center frequency of the band, the scanning mode of the radar, and the pulse repetition frequency.

5. The multi-source data fusion and tornado intelligent identification method as claimed in claim 1, characterized in that: The specific steps of nesting and gridding the dual-polarization phased array radar data in the area prone to severe convective weather are as follows: Generate radar information based on the dual-polarization phased array radar data after quality control, and form three-dimensional grid point information based on it; The radar information is used to visually describe weather phenomena in the coverage area of ​​the dual-polarization phased array radar.

6. The multi-source data fusion and tornado intelligent identification method according to claim 1, characterized in that: The specific steps of obtaining the grid accuracy index include: E1, real-time monitoring of the nested gridding of the dual-polarization phased array radar data in the Cartesian coordinate system, and judging whether the number of consistent peak positions within a preset time period is greater than the preset number of consistent peak positions. If so, execute E2, otherwise re-nest gridding; E2, obtain the peak position consistent number score, and determine whether the time variation coefficient is equal to the reference time variation coefficient. If so, obtain the gridding success rate and gridding timestamp and combine the obtained quality control evaluation index and the peak position consistent number score to obtain the gridding accuracy index. Otherwise, obtain the gridding timestamp and combine the obtained quality control evaluation index and the peak position consistent number score to obtain the gridding accuracy index. The peak position consistent number score represents the ratio of the difference between the peak position consistent number and the preset peak position consistent number to the preset peak position consistent number; The time variation coefficient represents the variation amplitude of the dual-polarization phased array radar data over time; The gridding success rate indicates the ratio of the amount of dual-polarization phased array radar data successfully gridded within a preset time period to the total amount of dual-polarization phased array radar data to be gridded.

7. The multi-source data fusion and tornado intelligent identification method as claimed in claim 6, characterized in that: The specific limiting expression of the grid accuracy index is: ； Where t is the number of the preset time period, , T is the total number of preset time periods, e is a natural constant, It represents the gridding accuracy index of the dual-polarization phased array radar data in the nested gridding process within the t-th preset time period. represents the quality control evaluation index of the dual-polarization phased array radar data in the quality control process within the t-th preset time period, represents the preset quality control evaluation index, It represents the number of consistent peak positions of dual-polarization phased array radar data in the nested gridding process in the t-th preset time period, It represents the number of peak position coincidences of the dual-polarization phased array radar data in the nested gridding process in the t-th preset time period, Indicates the preset peak position consistency number, represents the success rate of gridding during the nested gridding process of dual-polarization phased array radar data in the tth preset time period, Represents the gridded timestamp of the dual-polarization phased array radar data during the nested gridding process in the tth preset time period, It represents the time variation coefficient of the dual-polarization phased array radar data in the process of nested gridding in the t-th preset time period, Indicates the reference time variation coefficient.

8. The multi-source data fusion and tornado intelligent identification method as claimed in claim 1, characterized in that: The specific process of determining whether to obtain radar grid data based on the obtained grid accuracy index is as follows: Determine whether the obtained grid accuracy index is within the grid accuracy threshold range: If the obtained gridding accuracy index is within the gridding accuracy threshold range, the nested gridding is completed and the radar grid data is obtained; If the obtained gridding accuracy index is not within the gridding accuracy threshold range, the radar grid data will not be obtained and the nested gridding will continue.

9. The multi-source data fusion and tornado intelligent identification method as claimed in claim 1, characterized in that: The intelligent recognition evaluation score is obtained by the following method: Real-time monitoring of the real-time response speed of radar grid point data to obtain an average response speed score in the intelligent recognition process within a preset time period, wherein the average response speed score is a ratio of a difference between the average response speed and a reference average response speed to a reference deviation of the average response speed; At the end of the preset time period, the amount of missed identification data is obtained and it is determined whether the amount of missed identification data is less than the maximum allowable amount of missed identification data. If so, the missed identification rate score is obtained and intelligent identification of the next time period is performed, otherwise, intelligent identification is performed again. The missed identification rate score is the ratio of the amount of missed identification data to the total amount of radar grid point data to be intelligently identified. The signal-to-noise ratio of radar grid data in the intelligent recognition process within a preset time period is obtained, and the intelligent recognition evaluation score is obtained by combining the obtained average response speed score, missed recognition rate score and grid accuracy index.

10. The multi-source data fusion and tornado intelligent identification method according to claim 1, characterized in that: The specific process of judging whether the intelligent recognition is completed according to the obtained intelligent recognition evaluation score is as follows: Determine whether the obtained intelligent recognition evaluation score is greater than the preset intelligent recognition evaluation score: If the obtained intelligent recognition evaluation score is greater than the preset intelligent recognition evaluation score, the intelligent recognition is completed and the radar grid point data that has completed the intelligent recognition is marked as a recognized tornado; If the obtained intelligent recognition evaluation score is not greater than the preset intelligent recognition evaluation score, the corresponding radar grid point data is marked as an unidentified tornado.

Images