Meteorological anomaly detection and early warning system and method based on big data

Abstract

The invention relates to the field of meteorological detection, in particular to a meteorological anomaly detection and early warning system and method based on big data. The method comprises the following steps: collecting meteorological station data and an unmanned aerial vehicle detection area, setting an unmanned aerial vehicle monitoring group through the meteorological station data and the unmanned aerial vehicle detection area, constructing an unmanned aerial vehicle monitoring network, and flowing condition data; setting a flight record height, analyzing the flow condition data, obtaining a meteorological monitoring value, carrying out monitoring planning on the unmanned aerial vehicle through the meteorological monitoring value, and collecting meteorological index data and meteorological monitoring data; obtaining a meteorological effect value of the meteorological monitoring area according to the meteorological index data and the meteorological monitoring data, obtaining an analysis result through meteorological effect value analysis, and predicting the meteorological monitoring data according to the analysis result to obtain meteorological prediction data; and according to the meteorological prediction data and the analysis result, generating an early warning signal and a regional early warning scheme. The meteorological disaster loss can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of meteorological detection, and particularly to a meteorological anomaly detection and early warning system and method based on big data. Background Art

[0002] With the intensification of global climate change, the occurrence frequency and intensity of extreme meteorological events (such as typhoons, heavy rains, sandstorms, extreme high temperatures, etc.) are continuously increasing, seriously threatening the ecological environment, agricultural production, traffic safety and residents' lives in the Xinjiang region; in recent years, with the development of big data, artificial intelligence (AI), cloud computing, Internet of Things and unmanned aerial vehicle technologies, it has brought technological breakthroughs to the meteorological anomaly detection and early warning system, making it possible to predict meteorological anomalies with higher accuracy and more real-time.

[0003] In the traditional meteorological early warning system, it mainly relies on equipment such as ground meteorological stations, meteorological satellites, and radars for monitoring and prediction. It is difficult to meet the actual needs of the Xinjiang region in terms of the accuracy and timeliness of early warning; the station density in the Xinjiang region is limited, and how to obtain sudden meteorological changes in real time; for the Xinjiang region, meteorological events such as typhoons, heavy rains, sandstorms, and extreme high temperatures often occur, seriously affecting various fields such as agriculture, transportation, energy, and ecology in the Xinjiang region. How to detect the abnormal occurrence of various meteorological conditions is a problem that we need to solve. Summary of the Invention

[0004] The purpose of the present invention is to propose a meteorological anomaly detection and early warning method based on big data for the problems existing in the background art.

[0005] The technical solution of the present invention: A meteorological anomaly detection and early warning method based on big data, including the following steps:

[0006] S1. Collect meteorological station data and the detection area of the unmanned aerial vehicle. Through the meteorological station data and the detection area of the unmanned aerial vehicle, set up an unmanned aerial vehicle monitoring group, construct an unmanned aerial vehicle monitoring network, and collect data on the flow situation;

[0007] S2. Set the flight record height, analyze the data on the flow situation to obtain meteorological monitoring values, conduct monitoring planning for the unmanned aerial vehicle through the meteorological monitoring values, and collect meteorological index data and meteorological monitoring data;

[0008] S3. According to the meteorological index data and the meteorological monitoring data, obtain the meteorological effect value of the meteorological monitoring area. Through the analysis of the meteorological effect value, obtain the analysis result, and predict the meteorological monitoring data according to the analysis result to obtain meteorological prediction data;

[0009] S4. Generate a warning signal and a regional warning plan according to the meteorological prediction data and the analysis result.

[0010] Preferably, the process of collecting meteorological station data and the UAV detection area, setting up the UAV monitoring group through the meteorological station data and the UAV detection area, constructing the UAV monitoring network, and the process of the flow situation data includes:

[0011] The meteorological station information includes the meteorological station positioning information and the meteorological station monitoring area; the meteorological station corresponding to the meteorological station positioning information is recorded as a meteorological point;

[0012] According to the UAV detection area and the meteorological station monitoring area, obtain the meteorological monitoring area, respectively construct monitoring links and transmission links between adjacent UAVs and between the UAV and the meteorological point within the meteorological monitoring area, and construct the UAV monitoring network according to the UAV, the meteorological point, the monitoring link and the transmission link; the flow situation data includes the biological flow time and the biological flow quantity; the biological flow quantity includes the animal flow quantity and the human flow quantity.

[0013] Preferably, the process of setting the flight record height and analyzing the flow situation data to obtain the meteorological monitoring value includes:

[0014] Set the record flow density, and obtain the meteorological monitoring value Qx according to the biological flow time, the biological flow quantity, the flight record height and the record flow density t ;

[0015]

[0016] Among them, α1 and α2 are the weights of the animal flow quantity and the human flow quantity; d t is the animal flow quantity; r t is the human flow quantity; H is the recorded flight height; S is the area of the meteorological monitoring area; M is the record flow density; t is the biological flow time; Qx t refers to the meteorological monitoring value corresponding to the biological flow time.

[0017] Preferably, the process of adjusting the UAV through the meteorological monitoring value and collecting the meteorological monitoring data and the distribution information is:

[0018] Through the meteorological monitoring value, adjust the flight height of each UAV in the meteorological monitoring area to the meteorological monitoring value, collect the meteorological monitoring data in the meteorological monitoring area, obtain the meteorological historical data of the UAV in the meteorological monitoring area, the meteorological historical data includes the temperature historical value, the precipitation historical value, the wind speed historical value and the historical monitoring time, and obtain the meteorological index data of the meteorological historical data; the meteorological index data includes the temperature record value, the precipitation record value and the wind speed record value; the meteorological monitoring data includes the temperature monitoring value, the precipitation monitoring value, the wind speed monitoring value and the monitoring time.

[0019] Preferably, the process of obtaining the meteorological effect value of the meteorological monitoring area according to the meteorological index data and the meteorological monitoring data includes:

[0020] Obtaining the temperature variation amplitude value, the precipitation fluctuation value, and the wind speed mutation value according to the temperature record value, the precipitation record value, and the wind speed record value of the meteorological index data and the temperature monitoring value, the precipitation monitoring value, and the wind speed monitoring value of the meteorological monitoring data;

[0021] Setting a variation threshold group, where the variation threshold group includes a temperature amplitude threshold, a precipitation wave threshold, and a wind mutation threshold;

[0022] Obtaining the meteorological effect value YQ according to the temperature variation amplitude value, the precipitation fluctuation value, and the wind speed mutation value u ;

[0023]

[0024] where u refers to the meteorological monitoring area, respectively refer to the influence coefficients of the historical meteorological conditions on ; σ1, σ2, and σ3 respectively refer to the temperature influence coefficient, the precipitation influence coefficient, and the wind speed influence coefficient, d refers to the temperature amplitude threshold, D refers to the temperature amplitude threshold, j refers to the precipitation wave threshold, J refers to the precipitation wave threshold, f refers to the wind mutation threshold, and F refers to the wind mutation threshold;

[0025]

[0026] Preferably, the process of obtaining the analysis result through the analysis of the meteorological effect value and predicting the meteorological monitoring data according to the analysis result to obtain the meteorological prediction data includes:

[0027] Analyzing the meteorological effect value to generate analysis result one, analysis result two, and analysis result three;

[0028] When generating analysis result one or analysis result two or analysis result three, obtaining the meteorological historical data corresponding to analysis result one or analysis result two or analysis result three, and obtaining the historical temperature variation amplitude value, the historical precipitation fluctuation value, and the historical wind speed mutation value according to the temperature historical value, the precipitation historical value, and the wind speed historical value of the meteorological historical data, and recording the historical temperature variation amplitude value, the historical precipitation fluctuation value, and the historical wind speed mutation value as the meteorological historical change value;

[0029] Taking several groups of meteorological historical change values and the historical monitoring time as the training set and the test set, and inputting the training set and the test set into the meteorological prediction model to train the meteorological prediction model, obtaining the trained meteorological prediction model, and outputting the corresponding meteorological prediction data, where the meteorological prediction data includes the predicted temperature variation amplitude value, the predicted precipitation fluctuation value, the predicted wind speed mutation value, and the predicted change time.

[0030] Preferably, the process of generating early warning signals and regional early warning plans based on meteorological prediction data and analysis results includes:

[0031] Obtain the meteorological prediction values of the meteorological monitoring area according to the meteorological prediction data of the meteorological monitoring area; set the meteorological area coefficient, and obtain the meteorological area value according to the meteorological area coefficient and meteorological prediction values of the meteorological monitoring area;

[0032] Generate early warning signal one, early warning signal two, and early warning signal three according to the meteorological standard area value, meteorological area value, and analysis results; send the early warning signals to the Internet of Things center, and the Internet of Things center sends the early warning signals to relevant meteorological early warning personnel to generate a regional early warning plan.

[0033] The present invention also discloses a meteorological anomaly detection and early warning system based on big data, including a management center, which is communicatively connected with a network construction module, a data analysis module, a data processing module, and a data early warning module:

[0034] The network construction module is used to collect meteorological station data and the drone detection area, set up a drone monitoring group through the meteorological station data and the drone detection area, construct a drone monitoring network, and collect flow condition data;

[0035] The data analysis module is used to set the flight record height, analyze the flow condition data to obtain meteorological monitoring values, monitor and plan the drones through the meteorological monitoring values, and collect meteorological index data and meteorological monitoring data;

[0036] The data processing module is used to obtain the meteorological effect value of the meteorological monitoring area according to the meteorological index data and meteorological monitoring data, obtain the analysis result through meteorological effect value analysis, and predict the meteorological monitoring data according to the analysis result to obtain meteorological prediction data;

[0037] The data early warning module is used to generate early warning signals and regional early warning plans according to the meteorological prediction data and analysis results.

[0038] In traditional meteorological early warning systems, monitoring and prediction mainly rely on devices such as ground meteorological stations, meteorological satellites, and radars. It is difficult to meet the actual needs of the Xinjiang region in terms of the accuracy and timeliness of early warnings; the station density in the Xinjiang region is limited, and how to obtain real-time sudden meteorological changes; in the Xinjiang region, meteorological phenomena such as typhoons, heavy rains, sandstorms, and extreme high temperatures often occur, seriously affecting agriculture, transportation, energy, ecology, and other fields in the Xinjiang region. How to detect the abnormal occurrence of various meteorological conditions

[0039] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects: By constructing a drone network, the timeliness of obtaining sudden meteorological change information is improved; uploading data to the Internet of Things center improves the timeliness of obtaining meteorological data in Xinjiang; adjusting the flight altitude of drones through meteorological detection values improves resource utilization in Xinjiang; analyzing meteorological factors in the meteorological monitoring area through meteorological effect values improves the timeliness of discovering meteorological anomalies and the response ability; combining the analysis results to predict meteorological monitoring data to obtain meteorological prediction data helps to improve the early warning ability and reduce the early warning error; through early warning signals and regional early warning plans, meteorological disaster losses are reduced, meteorological services are optimized, which helps agricultural production, transportation safety and ecological environment protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flowchart of an embodiment proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] Embodiment 1, as Figure 1 shown, the method for detecting and warning meteorological anomalies based on big data proposed by the present invention includes the following steps:

[0042] S1. Collect meteorological station data and the drone detection area. Through the meteorological station data and the drone detection area, set up a drone monitoring group, construct a drone monitoring network, and collect flow condition data;

[0043] S2. Set the flight record height, analyze the flow condition data to obtain meteorological monitoring values, plan the monitoring of the drone through the meteorological monitoring values, and collect meteorological index data and meteorological monitoring data;

[0044] S3. Obtain the meteorological effect value of the meteorological monitoring area according to the meteorological index data and the meteorological monitoring data. Through the analysis of the meteorological effect value, obtain the analysis result, and predict the meteorological monitoring data according to the analysis result to obtain meteorological prediction data;

[0045] S4. Generate a warning signal and a regional warning plan according to the meteorological prediction data and the analysis result.

[0046] It should be further noted that in the specific implementation process, the process of collecting meteorological station data and the drone detection area, setting up a drone monitoring group, constructing a drone monitoring network, and collecting flow condition data through the meteorological station data and the drone detection area is as follows:

[0047] The meteorological station information includes the positioning information of the meteorological station and the monitoring area of the meteorological station; the meteorological station corresponding to the meteorological station positioning information is denoted as a meteorological point, and it is stipulated that the meteorological point is at the center of the meteorological monitoring area; the meteorological station monitoring area refers to the monitoring areas corresponding to each meteorological point; the UAV detection area refers to the fixed monitoring range of the UAV.

[0048] Process the meteorological station monitoring area through the UAV detection area, reduce the UAV detection area proportionally until n equally scaled-down UAV detection areas exactly cover the meteorological station monitoring area completely. Then divide the meteorological station monitoring area into n areas, denoted as meteorological monitoring areas, and n is a positive integer. Then the UAV monitoring group consists of n UAVs. The UAVs are placed in each meteorological monitoring area, and monitoring links and transmission links are respectively constructed between adjacent UAVs within the meteorological monitoring area and between the UAVs and the meteorological points. Through the UAVs, meteorological points, monitoring links, and transmission links, a UAV monitoring network is constructed, and an Internet of Things center is set up within the meteorological points. The Internet of Things center is used to collect and collate the monitoring data of the meteorological monitoring area; the flow situation data includes the biological flow time and the biological flow quantity; the biological flow quantity refers to the quantity of organisms with a living state and generating life activities, including the animal flow quantity and the human flow quantity; the biological flow time refers to the time corresponding to the organisms with a living state when generating life activities.

[0049] It should be further noted that in the specific implementation process, the process of setting the flight record height, analyzing the flow situation data to obtain the meteorological monitoring value, conducting monitoring planning for the UAV through the meteorological monitoring value, and collecting meteorological index data and meteorological monitoring data is as follows:

[0050] The flight record height refers to the recorded flight height of the UAV during monitoring within the meteorological monitoring area.

[0051] Set the recorded flow density. The recorded flow density is the biological flow density of the meteorological monitoring area corresponding to the flight record height. According to the biological flow time, biological flow quantity, flight record height, and recorded flow density, obtain the meteorological monitoring value Qx t ;

[0052]

[0053] Among them, α1 and α2 are the weights of the animal flow quantity and the human flow quantity; d t is the animal flow quantity; r t is the human flow quantity; H is the recorded flight height; S is the area of the meteorological monitoring area; M is the recorded flow density; t is the biological flow time; Qx t refers to the meteorological monitoring value corresponding to the biological flow time.

[0054] Based on the meteorological monitoring values, the flight altitudes of the unmanned aerial vehicles (UAVs) within the meteorological monitoring area are adjusted to the meteorological monitoring values. After the adjustment of the UAVs is completed, the meteorological monitoring data of the meteorological monitoring area is collected, and the meteorological historical data of the UAVs within the meteorological monitoring area is obtained. The meteorological historical data includes temperature historical values, precipitation historical values, wind speed historical values, and historical monitoring times, and the meteorological index data of the meteorological historical data is obtained. The meteorological historical data is the historical meteorological monitoring data of the UAVs, and the meteorological index data refers to the meteorological monitoring data at the previous collection time for the meteorological monitoring data, including temperature recorded values, precipitation recorded values, and wind speed recorded values. The meteorological monitoring data includes temperature monitoring values, precipitation monitoring values, wind speed monitoring values, and monitoring times.

[0055] It should be further noted that during the specific implementation process, the monitoring time and the historical monitoring time are much less than the biological flow time.

[0056] It should be further noted that during the specific implementation process, based on the meteorological index data and the meteorological monitoring data, the meteorological effect value of the meteorological monitoring area is obtained. Through the analysis of the meteorological effect value, the analysis result is obtained, and the process of predicting the meteorological monitoring data based on the analysis result to obtain the meteorological prediction data is as follows:

[0057] Based on the temperature recorded value, precipitation recorded value, and wind speed recorded value of the meteorological index data and the temperature monitoring value, precipitation monitoring value, and wind speed monitoring value of the meteorological monitoring data, the temperature variation amplitude, precipitation fluctuation value, and wind speed mutation value are obtained. The temperature variation amplitude refers to the absolute value of the difference between the temperature monitoring value and the temperature recorded value. The precipitation fluctuation value refers to the absolute value of the difference between the precipitation monitoring value and the precipitation recorded value. The wind speed mutation value refers to the absolute value of the difference between the wind speed monitoring value and the wind speed record.

[0058] A set of variation threshold values is set, and the set of variation threshold values includes a temperature amplitude threshold, a precipitation wave threshold, and a wind speed mutation threshold.

[0059] Based on the temperature variation amplitude, precipitation fluctuation value, and wind speed mutation value, the meteorological effect value YQ is obtained u ;

[0060]

[0061] where u refers to the meteorological monitoring area, respectively refer to the influence coefficients of the historical meteorological conditions on ; σ1, σ2, and σ3 respectively refer to the temperature influence coefficient, precipitation influence coefficient, and wind speed influence coefficient. d refers to the temperature amplitude threshold, D refers to the temperature amplitude threshold, j refers to the precipitation wave threshold, J refers to the precipitation wave threshold, f refers to the wind speed mutation threshold, and F refers to the wind speed mutation threshold.

[0062]

[0063] It should be further noted that in the specific implementation process, the historical meteorological situation refers to the meteorological situation corresponding to the meteorological index data. For meteorological monitoring data, the meteorological situation corresponding to the meteorological index data is known information; in the calculation process of the meteorological effect value, normalization processing has been carried out;

[0064] Analyze the meteorological effect value. When the meteorological effect value is equal to 0, the meteorological situation in the meteorological monitoring area is normal, and analysis result one is generated;

[0065] When the meteorological effect value is not equal to 0, set the meteorological effect standard value. When the meteorological effect value is greater than or equal to the meteorological effect standard value, the meteorological situation in the meteorological monitoring area is abnormal, and analysis result two is generated; when the meteorological effect value is less than the meteorological effect standard value, the meteorological situation in the meteorological monitoring area is normal, and analysis result three is generated;

[0066] When analysis result one or analysis result two or analysis result three is generated, obtain the meteorological historical data corresponding to analysis result one or analysis result two or analysis result three, perform corresponding difference calculations on the temperature historical value, precipitation historical value, and wind speed historical value of the meteorological historical data in adjacent historical orders, obtain the historical temperature change amplitude, historical precipitation fluctuation value, and historical wind speed mutation value, and record the historical temperature change amplitude, historical precipitation fluctuation value, and historical wind speed mutation value as the meteorological historical change value;

[0067] Use several groups of meteorological historical change values and historical monitoring times as the training set and test set, input the training set and test set into the meteorological prediction model, train the meteorological prediction model, obtain the trained meteorological prediction model, and output the corresponding meteorological prediction data. The meteorological prediction data includes the predicted temperature change amplitude, predicted precipitation fluctuation value, predicted wind speed mutation value, and predicted change time.

[0068] It should be further noted that in the specific implementation process, the process of generating warning signals and regional warning plans based on the meteorological prediction data and analysis results is as follows:

[0069] Obtain the meteorological prediction value of the meteorological monitoring area according to the meteorological prediction data of the meteorological monitoring area;

[0070] The predicted temperature variation amplitude, predicted precipitation fluctuation value, and predicted wind speed mutation value of meteorological prediction data are weighted with the temperature influence coefficient, precipitation influence coefficient, and wind speed influence coefficient to obtain a meteorological prediction value; a meteorological region coefficient is set, where the meteorological region coefficient refers to the influence coefficient of the meteorological prediction degree of a meteorological monitoring region on other meteorological monitoring regions, and the meteorological region coefficient of the meteorological monitoring region is weighted with the meteorological prediction value to obtain a meteorological region value;

[0071] It should be further noted that in the specific implementation process, during the calculation of the meteorological region value, the meteorological region coefficient and the meteorological prediction value have been normalized;

[0072] A meteorological standard region value is set; when only analysis result one and analysis result three are generated in the meteorological station monitoring region, if the meteorological region value is greater than or equal to the meteorological standard region value, warning signal one is generated; when analysis result two is generated in the meteorological station monitoring region, if the meteorological region value is less than the meteorological standard region value, warning signal two is generated; when analysis result two is generated in the meteorological station monitoring region, if the meteorological region value is greater than or equal to the meteorological standard region value, warning signal three is generated; when only analysis result one and analysis result three are generated in the meteorological station monitoring region, if the meteorological region value is less than the meteorological standard region value, a monitoring signal is generated; warning signal one or warning signal two or warning signal three is sent to the Internet of Things center, and the Internet of Things center sends warning signal one or warning signal two or warning signal three to the relevant meteorological warning personnel to generate a regional warning plan.

[0073] Embodiment 2, the meteorological anomaly detection and warning system based on big data proposed in the present invention is applied to the meteorological anomaly detection and warning method based on big data described in Embodiment 1, and specifically includes a management center, which is communicatively connected with a network construction module, a data analysis module, a data processing module, and a data warning module:

[0074] The network construction module is used to collect meteorological station data and the drone detection area, set up a drone monitoring group through the meteorological station data and the drone detection area, construct a drone monitoring network, and collect flow condition data;

[0075] The data analysis module is used to set the flight record height, analyze the flow condition data to obtain meteorological monitoring values, plan the monitoring of the drone through the meteorological monitoring values, and collect meteorological index data and meteorological monitoring data;

[0076] The data processing module is used to obtain the meteorological effect value of the meteorological monitoring region according to the meteorological index data and the meteorological monitoring data, analyze through the meteorological effect value to obtain an analysis result, and predict the meteorological monitoring data according to the analysis result to obtain meteorological prediction data;

[0077] The data warning module is used to generate warning signals and regional warning plans according to meteorological prediction data and analysis results.

[0078] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto, and various changes can be made without departing from the spirit of the present invention within the scope of knowledge possessed by those skilled in the art.

Claims

1. A method for detecting and warning meteorological anomalies based on big data, characterized in that It includes the following steps: S1. Collect meteorological station data and the UAV detection area. Based on the meteorological station data and the UAV detection area, set up UAV monitoring groups, construct a UAV monitoring network, and obtain flow condition data; S2. Set the flight record height, analyze the flow condition data to obtain meteorological monitoring values, conduct monitoring planning for the UAVs based on the meteorological monitoring values, and collect meteorological index data and meteorological monitoring data; S3. Based on the meteorological index data and the meteorological monitoring data, obtain the meteorological effect value of the meteorological monitoring area. Through the analysis of the meteorological effect value, obtain the analysis result, and predict the meteorological monitoring data based on the analysis result to obtain meteorological prediction data; S4. Generate warning signals and regional warning plans based on the meteorological prediction data and the analysis result.

2. The method for meteorological anomaly detection and early warning based on big data according to claim 1, characterized in that, The process of collecting meteorological station data and the UAV detection area, setting up UAV monitoring groups, constructing a UAV monitoring network, and obtaining flow condition data includes: The meteorological station information includes the meteorological station positioning information and the meteorological station monitoring area; the meteorological station corresponding to the meteorological station positioning information is denoted as a meteorological point; Based on the UAV detection area and the meteorological station monitoring area, obtain the meteorological monitoring area. Build monitoring links and transmission links respectively between adjacent UAVs and between UAVs and meteorological points within the meteorological monitoring area. Based on the UAVs, meteorological points, monitoring links, and transmission links, construct a UAV monitoring network; the flow condition data includes biological flow time and biological flow quantity; the biological flow quantity includes animal flow quantity and human flow quantity.

3. The method for detecting and warning meteorological anomalies based on big data according to claim 2, wherein The process of setting the flight record height and analyzing the flow condition data to obtain meteorological monitoring values includes: Set the recording flow density, and obtain the meteorological monitoring value Qx according to the biological flow time, the biological flow quantity, the flight recording altitude, and the recording flow density t ; Among them, α1 and α2 are the weights of the animal flow quantity and the human flow quantity; d t is the animal flow quantity; r t is the human flow quantity; H is the recorded flight altitude; S is the area of the meteorological monitoring area; M is the recorded flow density; t is the biological flow time; Qx t refers to the meteorological monitoring value corresponding to the biological flow time.

4. The method for detecting and warning of meteorological anomalies based on big data according to claim 3, characterized in that, The process of adjusting the UAVs based on the meteorological monitoring values and collecting meteorological monitoring data and distribution information is: Based on the meteorological monitoring values, adjust the flight heights of the UAVs within the meteorological monitoring area to the meteorological monitoring values, collect the meteorological monitoring data of the meteorological monitoring area, obtain the meteorological historical data of the UAVs within the meteorological monitoring area. The meteorological historical data includes temperature historical values, precipitation historical values, wind speed historical values, and historical monitoring time, and obtain the meteorological index data of the meteorological historical data; the meteorological index data includes temperature record values, precipitation record values, and wind speed record values; the meteorological monitoring data includes temperature monitoring values, precipitation monitoring values, wind speed monitoring values, and monitoring time.

5. The method for meteorological anomaly detection and early warning based on big data according to claim 1 or 4, characterized in that, The process of obtaining the meteorological effect value of the meteorological monitoring area based on the meteorological index data and the meteorological monitoring data includes: Based on the temperature record value, precipitation record value, and wind speed record value of the meteorological index data and the temperature monitoring value, precipitation monitoring value, and wind speed monitoring value of the meteorological monitoring data, obtain the temperature variation amplitude value, precipitation fluctuation value, and wind speed mutation value; Set a group of amplitude threshold values, and the group of amplitude threshold values includes a temperature amplitude threshold, a precipitation fluctuation threshold, and a wind speed mutation threshold; Obtain the meteorological effect value YQ according to the temperature change amplitude, precipitation fluctuation value, and wind speed mutation value u ; Among them, u refers to the meteorological monitoring area, respectively referring to the influence coefficients of historical meteorological conditions on ; σ1, σ2, and σ3 respectively refer to the temperature influence coefficient, precipitation influence coefficient, and wind speed influence coefficient, d refers to the temperature amplitude threshold, D refers to the temperature amplitude threshold, j refers to the descending wave threshold, J refers to the descending wave threshold, f refers to the wind sudden threshold, and F refers to the wind sudden threshold; 6. The method for detecting and warning meteorological anomalies based on big data according to claim 5, characterized in that The process of obtaining the analysis result through the analysis of the meteorological effect value and predicting the meteorological monitoring data based on the analysis result to obtain meteorological prediction data includes: Analyze the meteorological effect value to generate analysis result one, analysis result two, and analysis result three; When generating Analysis Result 1 or Analysis Result 2 or Analysis Result 3, obtain the corresponding meteorological historical data of Analysis Result 1 or Analysis Result 2 or Analysis Result 3. According to the historical temperature value, historical precipitation value, and historical wind speed value of the meteorological historical data, obtain the historical temperature variation amplitude, historical precipitation fluctuation value, and historical wind speed mutation value, and record the historical temperature variation amplitude, historical precipitation fluctuation value, and historical wind speed mutation value as meteorological historical variation values; Use several groups of meteorological historical variation values and historical monitoring times as the training set and test set, and input the training set and test set into the meteorological prediction model to train the meteorological prediction model. Obtain the meteorological prediction model after completion of training and output the corresponding meteorological prediction data. The meteorological prediction data includes predicted temperature variation amplitude, predicted precipitation fluctuation value, predicted wind speed mutation value, and predicted change time.

7. The method for detecting and warning meteorological anomalies based on big data according to claim 6, characterized in that, The process of generating warning signals and regional warning plans based on meteorological prediction data and analysis results includes: Obtain the meteorological prediction value of the meteorological monitoring area according to the meteorological prediction data of the meteorological monitoring area; set the meteorological area coefficient, and obtain the meteorological area value according to the meteorological area coefficient and meteorological prediction value of the meteorological monitoring area; Generate Warning Signal 1, Warning Signal 2, and Warning Signal 3 according to the meteorological standard area value, meteorological area value, and analysis result; send the warning signal to the Internet of Things center, and the Internet of Things center sends the warning signal to relevant meteorological warning personnel to generate a regional warning plan.

8. A meteorological anomaly detection and warning system based on big data, specifically applied to the meteorological anomaly detection and warning method based on big data according to any one of claims 1 to 7, including a management center, characterized in that, The management center is communicatively connected to a network construction module, a data analysis module, a data processing module, and a data warning module: The network construction module is used to collect meteorological station data and the drone detection area. Through the meteorological station data and the drone detection area, set up a drone monitoring group, construct a drone monitoring network, and collect data on the flow situation; The data analysis module is used to set the flight record height, analyze the data on the flow situation to obtain meteorological monitoring values, plan the monitoring of the drone through the meteorological monitoring values, and collect meteorological index data and meteorological monitoring data; The data processing module is used to obtain the meteorological effect value of the meteorological monitoring area according to the meteorological index data and meteorological monitoring data, obtain the analysis result through the analysis of the meteorological effect value, and predict the meteorological monitoring data according to the analysis result to obtain meteorological prediction data; The data warning module is used to generate warning signals and regional warning plans according to the meteorological prediction data and analysis results.

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