Artificial influence weather operation effect evaluation method and system

Abstract

The invention relates to the technical field of meteorological engineering and disaster prevention and reduction, in particular to an artificial influence weather operation effect evaluation method and system, and the method comprises the steps: obtaining artificial influence weather operation information, meteorological environment data and geographic information data of a target region in a preset operation period; dynamically determining at least one key evaluation parameter of the operation based on the operation information, the meteorological environment data and the geographic information data, the key evaluation parameter including at least one of an operation influence area and an operation effective time; and obtaining a comprehensive water increase rate suitable for the target area, inputting the comprehensive water increase rate and the dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculating to obtain an artificial water increase amount and an operation effect evaluation index based on the artificial water increase amount. According to the method, quantitative evaluation of the artificial influence weather operation effect is realized, the long-standing difficulties of high qualitative and subjectivity and the like in weather modification operation evaluation are overcome, and reliable technical support is provided for disaster prevention and reduction.

Description

Technical Field

[0001] This invention relates to the technical fields of meteorological engineering and disaster prevention and mitigation, and in particular to a method and system for evaluating the effectiveness of artificial weather modification operations. Background Technology

[0002] Weather modification (such as artificial rainmaking and snowmaking) is an important means of developing and utilizing atmospheric cloud water resources and alleviating water shortages, and it is widely used in drought relief, disaster prevention, and ecological restoration. However, how to scientifically and accurately assess the actual effects of weather modification operations has always been a technical challenge in this field. Existing technologies mainly rely on traditional assessment methods, which have revealed a series of limitations in practice.

[0003] First, existing assessment methods are mostly qualitative, lacking quantitative support. For example, a common approach is to compare precipitation data between the experimental and control areas through simple statistical comparisons. This method is highly susceptible to the spatiotemporal variability of natural precipitation, leading to subjective results. It cannot accurately isolate the true increase in water volume caused by artificial catalysis, resulting in often vague assessment conclusions that are difficult to use for decision support. Furthermore, qualitative assessments rely on expert experience, are prone to human bias, and cannot provide quantifiable economic benefit indicators, such as input-output ratios, limiting their application in resource optimization.

[0004] Secondly, existing technologies often use static, empirically assigned parameters, resulting in poor adaptability. During the evaluation process, key parameters such as the area of ​​impact and the effective duration of the operation are frequently based on fixed empirical values ​​(e.g., a uniform radius of influence or duration), failing to consider the specific conditions of each operation, such as weather changes and differences in catalyst diffusion. For example, the influence of terrain in mountainous and plain areas, and wind field variations in different seasons, can significantly alter the catalyst's effective range and duration. However, existing methods cannot dynamically adjust parameters, leading to evaluation results that are out of sync with reality and have low accuracy.

[0005] Furthermore, existing evaluation methods are mostly "post-event summary" models, lacking guidance. Typically, evaluations are conducted after the operation is completed, serving only as a summary of historical effects. They cannot predict potential benefits before the operation to aid decision-making, nor can they dynamically optimize the plan based on real-time monitoring data during the operation. This lag makes weather modification operations lack foresight, leads to blind resource allocation, and easily results in wasted investment or missed optimal operating opportunities. For example, during sudden weather events, existing technologies struggle to quickly adjust operational plans to cope with changes.

[0006] In summary, existing artificial weather modification operation effectiveness evaluation technologies have shortcomings such as being primarily qualitative, having fixed parameters, and lacking sufficient post-event summarization and integration. Therefore, there is room for improvement. Summary of the Invention

[0007] In order to achieve a quantitative evaluation of the effectiveness of weather modification operations and overcome the long-standing difficulties in the evaluation of weather modification operations, such as the strong qualitative and subjective nature of such evaluations, and to provide reliable technical support for disaster prevention and mitigation, this application provides a method and system for evaluating the effectiveness of weather modification operations.

[0008] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution:

[0009] A method for evaluating the effectiveness of weather modification operations, the method comprising the following steps:

[0010] Acquire information on weather modification operations, meteorological and environmental data, and geographic information data for the target area during the predetermined operation period;

[0011] Based on the aforementioned operational information, meteorological environmental data, and geographic information data, at least one key evaluation parameter for this operation is dynamically determined. The key evaluation parameter includes at least one of the following: operational impact area and operational effective time.

[0012] Obtain the comprehensive water increase rate applicable to the target area, input the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculate the artificial water increase volume and the operation effect evaluation index based on the artificial water increase volume.

[0013] By adopting the above technical solution, multi-source data is acquired and integrated in real time, avoiding errors caused by data gaps or static assumptions in traditional methods, ensuring the reliability of the assessment input. Key parameters are dynamically calculated based on actual operating conditions and environment, enabling the assessment results to be adjusted in real time with weather changes, improving the adaptability and practicality of the method. Inputting parameters into a preset quantitative assessment model directly outputs artificial water enhancement and economic benefit indicators (such as input-output ratio), transforming traditional qualitative assessment into a quantifiable decision support tool, which is conducive to resource optimization and scientific management. By integrating multi-source data (including operation information, meteorological and environmental data, and geographic information data) and dynamically determining key assessment parameters (such as the area affected by the operation and the effective time of the operation), quantitative assessment of the effect of artificial weather modification operations is realized, overcoming the long-standing difficulties of qualitative and subjective assessment in artificial weather modification operations, and providing reliable technical support for disaster prevention and mitigation.

[0014] In a preferred embodiment, this application can be further configured such that: the dynamic determination of the operation's impact area specifically includes:

[0015] Based on the operational information, the latitude and longitude of the operational point and the operational method are determined. Based on the meteorological environment data, wind field data is obtained. Based on the latitude and longitude of the operational point, the operational method, and the wind field data, the diffusion trajectory and range of the catalyst in the air are simulated to generate the initial influence zone.

[0016] Grid precipitation product data is determined based on the meteorological environment data, and areas where precipitation actually occurred in the target area during the effective operation time are identified based on the grid precipitation product data as effective precipitation areas;

[0017] The initial impact area and the effective precipitation are overlaid in geospatial analysis, and the intersection area is taken as the actual effective impact area of ​​this operation.

[0018] Calculate the area of ​​the actual effective impact area and determine it as the operation impact area.

[0019] By adopting the above technical solutions, physical diffusion simulation is used to simulate the catalyst diffusion trajectory using wind field data and operational methods to generate the initial impact zone. This takes into account atmospheric turbulence and topographic effects, avoiding the limitations of simple geometric assumptions. Spatial filtering of effective precipitation is achieved by identifying effective precipitation areas through gridded precipitation products, ensuring that the assessment only targets areas where precipitation actually occurred and reducing the risk of false reporting. Precise intersection of GIS overlay is used to perform spatial overlay analysis of the initial impact zone and the effective precipitation zone, taking the intersection as the actual effective impact area, thus achieving dynamic matching between the catalyst's effective range and the precipitation area. Standardization of area calculation is achieved by using a spherical area algorithm to avoid map projection distortion and ensure that the area value is scientifically reliable. The steps for dynamically determining the operational impact area, through physical simulation and spatial analysis, significantly improve the scientificity and accuracy of area calculation.

[0020] In a preferred embodiment, this application can be further configured such that: the dynamic determination of the job validity time specifically includes:

[0021] Based on the job information, the job start time and job duration are obtained, and the job duration is determined based on the job start time and job end time.

[0022] Based on the operation method and equipment type, query the predefined catalyst diffusion effect duration rule library to determine the corresponding subsequent effective effect duration of the catalyst;

[0023] By combining the geographical and climatic characteristics of the target area, the duration of the catalyst's subsequent effective impact is localized and corrected to obtain the corrected duration of the effective impact.

[0024] The effective time of the operation is obtained by adding the duration of the operation to the corrected effective impact duration.

[0025] By adopting the above technical solutions, the accurate extraction of operation duration is achieved by directly calculating the operation duration from the operation information, avoiding errors from manual recording; rule-based duration prediction is based on querying a predefined rule base according to the operation method and equipment type, and using historical experimental data to determine the subsequent impact duration of the catalyst, improving the scientific nature of the prediction; the adaptability of localized correction is achieved by combining geographical and climatic characteristics (such as terrain complexity and climate background) to correct the impact duration, making the parameters more consistent with the actual situation in the region; the completeness of time integration is achieved by adding the operation duration with the corrected impact duration to obtain the total effective time, ensuring that the evaluation covers the entire action cycle of the catalyst. The step of dynamically determining the effective operation time achieves adaptive optimization of time parameters through the duration rule base and localized correction.

[0026] In a preferred embodiment, this application can be further configured such that: obtaining the comprehensive water increase rate applicable to the target area specifically includes:

[0027] Empirical research data on multiple historical weather modification operations in the target area were collected, and the empirical research data included multiple localized water increase rate reference values;

[0028] Statistical analysis was performed on the multiple localized water increase rate reference values, and their arithmetic mean was calculated as the benchmark water increase rate.

[0029] A robust statistical method is used to process the baseline water increase rate, and its predetermined proportion is taken as the authoritative comprehensive water increase rate of the target area.

[0030] The comprehensive water increase rate is stored as a key parameter in the model and can be selectively invoked according to the type of operation or season.

[0031] By adopting the above technical solution, empirical research data from multiple operations in the target area were collected, covering different seasons and weather types, avoiding the bias of single data. The arithmetic mean of the localized water increase rate reference value was calculated to eliminate the influence of extreme values ​​and obtain the benchmark water increase rate. Robust statistical methods such as Trimmed mean were used to process the benchmark water increase rate, and a predetermined proportion was taken as the comprehensive water increase rate to resist the interference of outliers. The comprehensive water increase rate was stored in the model database and could be selectively called according to the operation type or season, realizing the flexible application of parameters. The steps to obtain the comprehensive water increase rate are based on historical empirical data and statistical processing, ensuring the authority and operability of the water increase efficiency parameters.

[0032] In a preferred embodiment, this application can be further configured as follows: the step of inputting the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model to calculate the artificial water increase and the operational effectiveness evaluation index based on the artificial water increase specifically includes:

[0033] Based on the gridded precipitation product data and the area affected by the operation, the total precipitation in the operation area during the effective time of the operation and the total duration of the entire precipitation process is calculated.

[0034] The increased precipitation is calculated based on the total precipitation in the work area during the entire precipitation process, and the artificial water replenishment is calculated based on the increased precipitation and the area affected by the work.

[0035] The output benefits are calculated based on the artificial water increase, and the input-output ratio is calculated using the output benefits. The output benefits and the input-output ratio constitute the operation effect evaluation index.

[0036] By adopting the above technical solution, based on gridded precipitation products and the area affected by the operation, the total precipitation is calculated using time integration and spatial integration methods, taking into account the spatiotemporal variability of precipitation. The increased precipitation is calculated through the comprehensive water increase rate and converted into artificial water increase based on the area. The increased water increase is transformed into output benefits and input-output ratio, and economic indicators (such as water price and cost) are introduced, so that the evaluation results not only focus on physical water increase but also reflect economic feasibility and support resource allocation decisions. The calculation steps of the quantitative evaluation model integrate physical formulas and economic indicators, realizing a comprehensive quantification of the operation effect.

[0037] In a preferred embodiment, this application can be further configured as follows: before obtaining the comprehensive water increase rate applicable to the target area, inputting the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculating the artificial water increase volume and the operational effectiveness evaluation index based on the artificial water increase volume, the artificial weather modification operation effectiveness evaluation method further includes:

[0038] Acquire weather forecast data and operational plan information for the target area, wherein the weather forecast data includes predicted wind field data and predicted precipitation field area;

[0039] Based on the work plan information and weather forecast data, the predicted impact area and effective time of the planned operation are dynamically predicted.

[0040] By substituting the predicted impact area, the predicted effective time of the operation, and the comprehensive water increase rate into the quantitative assessment model, the potential artificial water increase and potential benefits are predicted.

[0041] Based on the comparison between the potential artificial water increase and potential benefits and the operational input costs, a decision recommendation on whether to carry out this operation is generated.

[0042] By adopting the above technical solutions, weather forecast data and operational plan information are obtained. Numerical models are used to predict wind and precipitation fields, providing scientific input for forecasting. The impact area and effective time of operations are dynamically predicted based on forecast data. Through diffusion simulation and spatial analysis, the forecast results can reflect future weather conditions. The forecast parameters are substituted into a quantitative model to calculate the potential artificial water enhancement volume and benefits, output economic expectations, support cost-benefit analysis, and generate decision suggestions through threshold rules. This achieves data-driven scientific decision-making, reduces subjective bias, and realizes the transformation from passive evaluation to proactive decision-making by simulating the effects of operations in advance using forecast data.

[0043] In a preferred embodiment, this application can be further configured as follows: after obtaining the comprehensive water increase rate applicable to the target area, inputting the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculating the artificial water increase volume and the operational effectiveness evaluation index based on the artificial water increase volume, the artificial weather modification operational effectiveness evaluation method further includes:

[0044] Real-time acquisition of actual monitoring data during the operation, including actual wind field data and actual precipitation data;

[0045] The actual monitoring data is compared with the weather forecast data for the target area to calculate the prediction deviation;

[0046] The work plan information is dynamically adjusted based on the prediction deviation, including adjusting the work point location, work intensity, and work duration;

[0047] Based on the adjusted work plan information and real-time meteorological data, the potential artificial water increase and potential benefits are updated, and an operation plan optimization instruction is generated.

[0048] By adopting the above technical solution, real-time wind field and precipitation data are collected through IoT devices with an update frequency of minutes, ensuring the timeliness of monitoring. The actual data is compared with the forecast data to calculate the deviation index, quantitatively assess the accuracy of the forecast, and dynamically adjust the location, intensity, and duration of the operation point based on the deviation. The optimal operation point is recalculated through optimization algorithms to compensate for forecast errors. The potential benefits are updated based on the adjusted parameters, and optimization instructions are generated to form a closed-loop control, ensuring that the operation is always directed towards maximizing benefits.

[0049] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0050] A weather modification operation effectiveness evaluation system, the weather modification operation effectiveness evaluation system comprising:

[0051] The operation data acquisition module is used to acquire artificial weather modification operation information, meteorological environmental data, and geographic information data for the target area during the predetermined operation period;

[0052] The key assessment parameter determination module is used to dynamically determine at least one key assessment parameter for this operation based on the operation information, meteorological environment data, and geographic information data. The key assessment parameter includes at least one of the operation impact area and the effective operation time.

[0053] The operation effect evaluation module is used to obtain the comprehensive water increase rate applicable to the target area, input the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculate the artificial water increase and the operation effect evaluation index based on the artificial water increase.

[0054] By adopting the above technical solution, multi-source data is acquired and integrated in real time, avoiding errors caused by data gaps or static assumptions in traditional methods, ensuring the reliability of the assessment input. Key parameters are dynamically calculated based on actual operating conditions and environment, enabling the assessment results to be adjusted in real time with weather changes, improving the adaptability and practicality of the method. Inputting parameters into a preset quantitative assessment model directly outputs artificial water enhancement and economic benefit indicators (such as input-output ratio), transforming traditional qualitative assessment into a quantifiable decision support tool, which is conducive to resource optimization and scientific management. By integrating multi-source data (including operation information, meteorological and environmental data, and geographic information data) and dynamically determining key assessment parameters (such as the area affected by the operation and the effective time of the operation), quantitative assessment of the effect of artificial weather modification operations is realized, overcoming the long-standing difficulties of qualitative and subjective assessment in artificial weather modification operations, and providing reliable technical support for disaster prevention and mitigation.

[0055] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0056] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for evaluating the effectiveness of weather modification operations.

[0057] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0058] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for evaluating the effectiveness of weather modification operations.

[0059] In summary, this application includes at least one of the following beneficial technical effects:

[0060] 1. Real-time acquisition and fusion of multi-source data avoids errors caused by data gaps or static assumptions in traditional methods, ensuring the reliability of assessment inputs. Key parameters are dynamically calculated based on actual operating conditions and environment, enabling assessment results to be adjusted in real time according to weather changes, improving the adaptability and practicality of the method. Inputting parameters into a preset quantitative assessment model directly outputs artificial water enhancement volume and economic benefit indicators (such as input-output ratio), transforming traditional qualitative assessment into a quantifiable decision support tool, which is conducive to resource optimization and scientific management. By integrating multi-source data (including operation information, meteorological and environmental data, and geographic information data) and dynamically determining key assessment parameters (such as operation impact area and operation effective time), quantitative assessment of the effects of artificial weather modification operations is achieved, overcoming the long-standing difficulties of qualitative and subjective assessment in artificial weather modification operations, and providing reliable technical support for disaster prevention and mitigation.

[0061] 2. Based on gridded precipitation products and the area affected by the operation, the total precipitation is calculated using time integration and spatial integration methods, taking into account the spatiotemporal variability of precipitation. The increased precipitation is calculated through the comprehensive water increase rate and converted into artificial water increase based on the area. The increased water increase is transformed into output benefits and input-output ratio. Economic indicators (such as water price and cost) are introduced so that the evaluation results not only focus on physical water increase but also reflect economic feasibility and support resource allocation decisions. The calculation steps of the quantitative evaluation model integrate physical formulas and economic indicators to achieve a comprehensive quantification of the operation effect.

[0062] 3. Acquire weather forecast data and operational plan information, utilize numerical models to predict wind and precipitation fields to provide scientific input for forecasting, dynamically predict the impact area and effective time of operations based on forecast data, and enable the forecast results to reflect future weather conditions through diffusion simulation and spatial analysis. Substitute the forecast parameters into the quantitative model to calculate the potential artificial water enhancement volume and benefits, output economic expectations, support cost-benefit analysis, and generate decision suggestions through threshold rules, thereby realizing data-driven scientific decision-making, reducing subjective blindness, and simulating the operation effect in advance through forecast data, realizing the transformation from passive evaluation to proactive decision-making.

[0063] 4. Real-time collection of actual wind field and precipitation data via IoT devices, with updates at the minute level, ensures timely monitoring. The actual data is compared with the forecast data to calculate the deviation index, quantitatively assess the forecast accuracy, and dynamically adjust the location, intensity, and duration of the operation point based on the deviation. The optimal operation point is recalculated through optimization algorithms to compensate for forecast errors. The potential benefits are updated based on the adjusted parameters, and optimization instructions are generated to form a closed-loop control, ensuring that the operation always moves in the direction of maximizing benefits. Attached Figure Description

[0064] Figure 1 This is a flowchart of a method for evaluating the effectiveness of weather modification operations in one embodiment of this application;

[0065] Figure 2 This is a flowchart illustrating the implementation of step S20 in the method for evaluating the effectiveness of weather modification operations in one embodiment of this application.

[0066] Figure 3 This is another implementation flowchart of step S20 in the artificial weather modification operation effect evaluation method in one embodiment of this application;

[0067] Figure 4 This is a flowchart illustrating the implementation of step S30 in the method for evaluating the effectiveness of weather modification operations in one embodiment of this application.

[0068] Figure 5 This is another implementation flowchart of step S30 in the artificial weather modification operation effect evaluation method in one embodiment of this application;

[0069] Figure 6 This is a flowchart illustrating the implementation of the predictive assessment step performed before the start of the operation in an embodiment of the weather modification operation effectiveness evaluation method of this application;

[0070] Figure 7 This is a flowchart illustrating the implementation of dynamic optimization steps performed during the operation process in the artificial weather modification operation effect evaluation method in one embodiment of this application;

[0071] Figure 8 This is a principle block diagram of an artificial weather modification operation effect evaluation system according to one embodiment of this application;

[0072] Figure 9 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0073] The present application will be further described in detail below with reference to the accompanying drawings.

[0074] In one embodiment, such as Figure 1 As shown, this application discloses a method for evaluating the effectiveness of weather modification operations, which specifically includes the following steps:

[0075] S10: Obtain information on weather modification operations, meteorological and environmental data, and geographic information data for the target area during the predetermined operation period.

[0076] In this embodiment, weather modification operation information refers to specific parameters recorded during the operation, including the latitude and longitude of the operation point, the operation method (such as rocket launch or aircraft seeding), the start and end times of the operation, and the type and amount of catalyst used. Meteorological environmental data includes wind field data (such as wind speed and direction), gridded precipitation product data (such as precipitation intensity distribution retrieved by radar or satellite), and real-time or historical observation data such as temperature and humidity. Geographic information data involves spatial data such as topographic elevation, land use type, and water system distribution of the target area, used to assist in analyzing the impact range of the operation.

[0077] Specifically, operational information can be extracted in real time from the weather modification operation management system. Meteorological and environmental data are obtained through meteorological satellites, ground observation stations, and numerical weather prediction models, while geographic information data is retrieved from a Geographic Information System (GIS) database. Data preprocessing includes format standardization, missing value imputation, and spatiotemporal alignment to ensure data consistency. For example, wind field data is simulated using a WRF model, and gridded precipitation product data is generated by fusing GPM satellite precipitation data with ground rain gauge data, achieving a spatial resolution of up to 1 km × 1 km. This integration of multi-source data provides reliable input for subsequent dynamic parameter determination and avoids assessment bias caused by incomplete data.

[0078] S20: Based on the operation information, meteorological environment data, and geographic information data, dynamically determine at least one key evaluation parameter for this operation. The key evaluation parameter includes at least one of the operation impact area and the effective operation time.

[0079] In this embodiment, key evaluation parameters are the core variables for assessing the effectiveness of weather modification operations. These parameters need to be dynamically calculated based on real-time data to avoid errors caused by fixed values. The operational impact area refers to the actual area effectively affecting precipitation after catalyst diffusion (unit: square kilometers), and the effective operation time refers to the total duration from the start of the operation until the catalyst's effect completely disappears (unit: hours). Dynamic determination emphasizes that parameters must be adjusted in real-time according to meteorological conditions. For example, changes in the wind field will affect the catalyst diffusion range, and the spatiotemporal distribution of precipitation will affect the effective area.

[0080] Specifically, the operational information provides the location and method of the operation, meteorological environmental data provides wind and precipitation fields, and geographic information data provides the topographic background. Then, based on the physical diffusion model and spatial analysis techniques, key parameters are calculated. The process is divided into two sub-processes: dynamically determining the area affected by the operation and dynamically determining the effective time of the operation. Each sub-process is further broken down into detailed steps to ensure accuracy. The dynamic determination of key assessment parameters improves the objectivity and accuracy of the assessment, laying the foundation for subsequent quantitative assessment.

[0081] S30: Obtain the comprehensive water increase rate applicable to the target area, input the comprehensive water increase rate and dynamically determined key evaluation parameters into the preset quantitative evaluation model, and calculate the artificial water increase volume and the operation effect evaluation index based on the artificial water increase volume.

[0082] In this embodiment, the comprehensive water increase rate is the average water increase efficiency of historical operations in the target area, representing the proportion of increased precipitation per unit area. The quantitative evaluation model is a mathematical model constructed based on physical equations and statistical methods, used to convert input parameters into quantitative results.

[0083] Specifically, the process consists of two sub-processes: obtaining the overall water increase rate and performing model calculations. First, the overall water increase rate is derived from historical data to ensure local representativeness. Then, key parameters (such as the area affected by the operation and the effective operation time) are input into the model along with the overall water increase rate, and the evaluation results are derived through calculation. The model design considers uncertainties and error propagation to improve reliability, achieving quantitative and economic assessment of the operation's effectiveness and providing data support for decision-making.

[0084] In this embodiment, real-time acquisition and fusion of multi-source data avoids errors caused by data gaps or static assumptions in traditional methods, ensuring the reliability of the assessment input. Key parameters are dynamically calculated based on actual operating conditions and the environment, enabling the assessment results to be adjusted in real time according to weather changes, thus improving the adaptability and practicality of the method. Inputting parameters into a preset quantitative assessment model directly outputs artificial water enhancement and economic benefit indicators (such as input-output ratio), transforming traditional qualitative assessment into a quantifiable decision support tool, which is helpful for resource optimization and scientific management. By integrating multi-source data (including operation information, meteorological and environmental data, and geographic information data) and dynamically determining key assessment parameters (such as the area affected by the operation and the effective time of the operation), a quantitative assessment of the effect of artificial weather modification operations is achieved, overcoming the long-standing difficulties of qualitative and subjective assessment in artificial weather modification operations, and providing reliable technical support for disaster prevention and mitigation.

[0085] In one embodiment, such as Figure 2 As shown, in step S20, which involves dynamically determining the area affected by the operation, the specific steps include:

[0086] S21: Based on the operation information, determine the latitude and longitude of the operation point and the operation method; based on the meteorological environment data, obtain wind field data; and based on the latitude and longitude of the operation point, the operation method, and the wind field data, simulate the diffusion trajectory and range of the catalyst in the air to generate the initial influence zone.

[0087] Specifically, the latitude and longitude of the operation point are directly obtained from the operation information. The operation method (such as the rocket launch angle and altitude) affects the initial diffusion pattern of the catalyst. Wind field data uses three-dimensional wind field interpolation technology, combined with numerical models (such as the CALPUFF diffusion model) to simulate the transport path of the catalyst cloud. The diffusion trajectory simulation considers atmospheric turbulence and gravitational settling, generating a polygonal region as the initial influence zone. This region represents the maximum area that the catalyst may cover. For example, for rocket operations, the initial influence zone is fan-shaped, and the radius of the fan is calculated based on the catalyst diffusion rate and the wind field.

[0088] S22: Determine gridded precipitation product data based on the meteorological environment data, and identify areas in the target area where precipitation actually occurred within the effective operation time based on the gridded precipitation product data, as effective precipitation areas.

[0089] Specifically, gridded precipitation product data is rasterized precipitation intensity data with a time resolution down to the minute level. Using a threshold method, such as precipitation intensity greater than 0.1 mm / h, effective precipitation areas are identified, and precipitation grid points within the effective operation time are extracted. The effective precipitation area represents the spatial range of actual precipitation, ensuring that the assessment only targets areas with precipitation and avoiding false reporting.

[0090] S23: Perform geospatial overlay analysis on the initial impact area and the effective precipitation, and take the intersection area as the actual effective impact area of ​​this operation.

[0091] Specifically, GIS spatial analysis tools are used for overlay analysis. Both the initial influence area and the effective precipitation area are converted into vector polygons. The overlapping area is obtained through intersection operation. This area represents the precipitation area where the catalyst actually takes effect. For example, if the initial influence area is 100 km² and the effective precipitation area is 80 km², the intersection area may be 60 km², which is the actual effective influence area.

[0092] S24: Calculate the area of ​​the actual effective impact area and determine it as the operation impact area.

[0093] Specifically, the area calculation is based on the geographic coordinate system and uses a spherical area algorithm to avoid projection distortion. This is used as a key parameter input into the subsequent model. The determination of the area affected by the operation combines catalyst diffusion and actual precipitation, which improves spatial accuracy and reduces subjective assumptions.

[0094] In one embodiment, such as Figure 3 As shown, in step S20, which involves dynamically determining the effective time of the task, the specific steps include:

[0095] S25: Obtain the job start time and job time based on the job information, and determine the job duration based on the job start time and job end time.

[0096] Specifically, the start and end times of the assignment are extracted from the assignment record, and the duration of the assignment is directly calculated as the time difference. For example, if the assignment starts at 14:00 and ends at 16:00, the duration is 2 hours.

[0097] S26: Based on the operation method and equipment type, query the predefined catalyst diffusion effect duration rule library to determine the corresponding subsequent effective effect duration of the catalyst.

[0098] Specifically, the catalyst diffusion effect duration rule base is a database built based on historical experimental data. It contains empirical effect durations corresponding to different operation methods (such as aircraft seeding or ground generators) and equipment types (such as catalyst particle size). For example, for silver iodide catalysts, the effect duration for aircraft seeding may be 3-4 hours, while that for rocket seeding may be 1-2 hours. The rule base is updated through machine learning to improve its adaptability.

[0099] S27: Based on the geographical and climatic characteristics of the target area, the duration of the catalyst's subsequent effective impact is localized to obtain the corrected duration of the effective impact.

[0100] Specifically, geographical and climatic characteristics include topographic complexity (such as the impact of mountains on airflow) and climatic background (such as rainy or dry seasons). The correction method uses a weighted approach; for example, in areas with complex topography, the duration of influence is multiplied by a correction factor of 0.8; under stable weather conditions, the factor is 1.2. The corrected duration is more in line with local realities.

[0101] S28: Add the duration of the operation to the corrected effective duration of the effect to obtain the effective time of the operation.

[0102] Specifically, the effective time of an operation is the sum of the operation duration and the adjusted impact duration. For example, if the operation duration is 2 hours and the adjusted impact duration is 3 hours, then the effective time of the operation is 5 hours. This parameter ensures that the assessment covers the entire action cycle.

[0103] In one embodiment, such as Figure 4 As shown, in step S30, the comprehensive water increase rate applicable to the target area is obtained, which specifically includes:

[0104] S31: Collect empirical research data on multiple historical weather modification operations in the target area, including multiple localized water increase rate reference values.

[0105] Specifically, the empirical research data comes from academic literature, operation reports, and meteorological department databases, including the measured water increase rate for each operation. For example, the data is calculated by comparing the precipitation difference between the operation area and the control area. The reference values ​​need to cover different seasons and weather types to ensure representativeness.

[0106] S32: Perform statistical analysis on the multiple localized water increase rate reference values ​​and calculate their arithmetic mean as the benchmark water increase rate.

[0107] Specifically, the statistical analysis uses a simple averaging method. For example, if the water increase rates for 10 operations are 15%, 12%, and 18%, then the baseline water increase rate is (15+12+18) / 3=15%. The average value eliminates the influence of extreme values ​​and provides a robust baseline.

[0108] S33: The benchmark water increase rate is processed using a robust statistical method, and its predetermined proportion is taken as the authoritative comprehensive water increase rate of the target area.

[0109] Specifically, robust statistical methods such as M-estimation or Trimmed mean are used to handle outliers. The predetermined percentage value is usually taken as the median of the 25th-75th percentile range. For example, the baseline water increase rate is processed by Trimmed mean (removing the highest and lowest 10%) to obtain 12% as the comprehensive water increase rate, thus improving the reliability of the parameter.

[0110] S34: Store the comprehensive water increase rate as a key parameter in the model, and be able to selectively call it according to the operation type or season.

[0111] Specifically, the overall flood increase rate is stored in a parameter database, and when called, the optimal value is automatically selected based on the operation type (such as rain enhancement or hail prevention) and season (such as summer or winter). For example, the flood increase rate may be higher in summer than in winter.

[0112] In one embodiment, such as Figure 5 As shown, in step S30, the comprehensive water increase rate and dynamically determined key evaluation parameters are input into a preset quantitative evaluation model to calculate the artificial water increase and the operational effectiveness evaluation index based on the artificial water increase, specifically including:

[0113] S35: Based on the gridded precipitation product data and the area affected by the operation, calculate the total precipitation in the operation area during the effective operation time and the total duration of the entire precipitation process.

[0114] Specifically, gridded precipitation product data provides spatiotemporally continuous precipitation intensity. The total precipitation in the operational area is calculated through spatial integration: First, all gridded precipitation data within the operational impact area are extracted; then, the precipitation at each grid point during the effective operational time is integrated over time (e.g., using the trapezoidal method); finally, the values ​​at each grid point are summed and multiplied by the grid area to obtain the total precipitation. For example, if the operational impact area is 50 km², the average precipitation intensity is 5 mm / h, and the effective operational time is 5 hours, then the total precipitation is 50 × 10⁻⁶. 6 m² × (5 / 1000) m / h × 5 h = 6.25×105 m³.

[0115] S36: Calculate the increased precipitation based on the total precipitation in the work area during the entire precipitation process, and calculate the artificial water replenishment based on the increased precipitation and the area affected by the work.

[0116] Specifically, the increase in precipitation is calculated using the comprehensive water increase rate: Increase in precipitation P = (Pt / S / 1000) × η × (T / Ttotal), where Pt is the total precipitation in the operation area during the entire precipitation process, S is the area affected by the operation, Ttotal is the total duration of the precipitation process, and the artificial water increase is expressed as the volume of increased precipitation. Based on the formula: W = P × S × 1000, the artificial precipitation is calculated. For example, if the total precipitation is 6.25 × 10... 5 If the total water increase rate is 15%, then the artificial water increase is 9.375 × 10⁶ m³. 4 m³, this value quantifies the direct effect of the operation.

[0117] S37: Calculate the output benefits based on the artificial water increase, calculate the input-output ratio using the output benefits, and combine the output benefits and the input-output ratio to form an evaluation index for operational effectiveness.

[0118] Specifically, the output benefit converts the artificial increase in water volume into economic value, and the output benefit is calculated using the formula B = W × m, where m is the unit water price, and the benefit amount is calculated based on the local water price (such as 1 yuan / cubic meter).

[0119] Furthermore, the input-output ratio is calculated using the formula R = (B - K) / K × 100%, where K is the total input cost of the operation, including the cost of catalyst, equipment, and labor. Evaluation indicators also include water-increasing efficiency (the amount of water added per unit of catalyst), used for multi-dimensional evaluation. For example, if the output benefit is 93,750 yuan and the input cost is 50,000 yuan, the input-output ratio is 1.875, indicating that the operation is economically feasible. The quantitative evaluation model combines physical parameters with economic indicators, providing a comprehensive and quantifiable evaluation of the operation's effectiveness.

[0120] In one embodiment, such as Figure 6 As shown, the method for evaluating the effectiveness of weather modification operations also includes a predictive evaluation step performed before the operation begins:

[0121] S101: Obtain weather forecast data and operation plan information for the target area, wherein the weather forecast data includes predicted wind field data and predicted precipitation field area.

[0122] In this embodiment, weather forecast data refers to the predicted values ​​of meteorological elements for future periods generated by numerical weather prediction models, such as wind speed, wind direction, and precipitation intensity; operational plan information includes pre-arranged parameters such as the latitude and longitude of the planned operation point, the operation method (such as rocket or aircraft seeding), the planned operation time, and the amount of catalyst used. These data are the basic inputs for predictive assessment.

[0123] Specifically, weather forecast data is obtained from numerical weather prediction models (such as ECMWF) with a time resolution of up to 6 hours. The predicted wind field data includes three-dimensional wind speed and direction gridded data, and the predicted precipitation field area is a gridded precipitation probability or intensity distribution. The operation plan information includes the planned operation points, time, and method.

[0124] S102: Based on the work plan information and weather forecast data, dynamically predict the predicted impact area and the predicted effective time of the planned operation.

[0125] In this embodiment, the predicted impact area is the potential catalyst-affected area estimated based on forecast data; the predicted effective duration of the operation is the estimated total duration of the operation. Dynamic forecasting emphasizes the use of real-time forecast data rather than historical averages to improve adaptability.

[0126] Specifically, the prediction method borrows the logic of S20 but applies forecast data. For example, predicting the impact area of ​​the operation: First, based on the latitude, longitude, and method of the operation point in the operation plan information, combined with the predicted wind field data, a diffusion model (such as CALPUFF) is used to simulate the catalyst diffusion trajectory to generate the initial predicted impact area. For example, given the predicted wind field, the model outputs a polygonal region. Then, areas with a precipitation probability greater than a threshold (such as 30%) in the predicted precipitation field are extracted as the effective precipitation area. Finally, the predicted operation impact area is obtained through GIS spatial overlay analysis (such as intersection operation); predicting the effective operation time: Based on the operation duration in the operation plan, the catalyst diffusion impact duration rule base is queried (adjusted based on the forecast weather type, such as extended duration under stable weather), and corrected in combination with geographical and climatic characteristics (such as terrain complexity).

[0127] S103: Substitute the predicted impact area, the predicted effective time of the operation, and the comprehensive water increase rate into the quantitative evaluation model to predict the potential artificial water increase and potential benefits.

[0128] In this embodiment, the potential artificial water increase is an estimate of the water increase volume based on prediction parameters; the potential benefit is an economic value estimate. The quantitative assessment model is the same as S30, but the input is the prediction parameters.

[0129] Specifically, the calculation of potential artificial precipitation enhancement is as follows: Based on the predicted precipitation field data and the predicted impact area of ​​the operation, the total predicted precipitation is calculated, similar to S35: time integration and then spatial integration are performed on the precipitation grid points within the effective time of the prediction operation. The formula is: Total predicted precipitation = Σ(grid point precipitation intensity × grid point area × time). For example, if the predicted area is 50 km², the average precipitation intensity is 4 mm / h, and the effective prediction time is 3 hours, then the total precipitation is 50 × 10⁻⁶. 6 m² × (4 / 1000)m / h × 3h = 6×10 5 m³. Then, substituting the comprehensive water increase rate (retrieved from the parameter library, e.g., 15%), calculate the potential artificial water increase: Predicted total precipitation × Comprehensive water increase rate = 6 × 10 5 × 0.15 = 9 × 10 4 m³;

[0130] Further, the potential benefit calculation involves converting the potential artificial increase in water volume into economic value. Based on the local water price, the potential benefit = potential artificial increase in water volume × water price. For example, 9 × 10 4 m³ × 1 yuan / m³ = 90,000 yuan.

[0131] S104: Based on the comparison between the potential artificial water increase and potential benefits and the cost of operation, generate a decision recommendation on whether to carry out this operation.

[0132] In this embodiment, the operating input costs include direct costs such as catalysts, equipment, and manpower; the decision recommendations are binary (execute / not execute) or multi-level (such as priority, general, postpone) outputs.

[0133] Specifically, the comparison process is achieved through decision rules, including cost-benefit comparison: calculating the predicted input-output ratio = potential benefit / operational input cost. For example, if the potential benefit is 90,000 yuan and the input cost is 50,000 yuan, then the input-output ratio is 1.8. Decision recommendations are generated by setting threshold rules, such as recommending "execution" when the input-output ratio is greater than 1.5, recommending "non-execution" when it is less than 1.0, and considering other factors (such as urgent needs) when the ratio falls between these thresholds, a comprehensive judgment is made, and a recommendation report is automatically generated, including a key parameter table and risk warnings.

[0134] In one embodiment, such as Figure 7 As shown, the method for evaluating the effectiveness of weather modification operations also includes dynamic optimization steps performed during the operation:

[0135] S201: Real-time acquisition of actual monitoring data during the operation process, including actual wind field data and actual precipitation data.

[0136] In this embodiment, the actual monitoring data refers to meteorological elements such as wind speed, wind direction, and precipitation intensity collected in real time by sensors during the operation, which correspond to the forecast data and are used for verification and adjustment.

[0137] Specifically, data acquisition is achieved through IoT devices: actual wind field data is obtained from automatic weather stations, Doppler radar, or drone remote sensing, while actual precipitation data comes from rain gauges or satellite inversion.

[0138] S202: Compare the actual monitoring data with the weather forecast data of the target area and calculate the prediction deviation.

[0139] In this embodiment, the prediction bias is a quantification of the difference between the predicted value and the measured value, such as absolute error or relative error, and is used to evaluate the accuracy of the prediction.

[0140] Specifically, the comparison process is achieved through statistical calculations, aligning actual monitoring data and forecast data at the same time-space points. For example, the actual wind field is interpolated to the forecast grid points, and indicators such as root mean square error (RMSE) or mean absolute error (MAE) are used to calculate the prediction deviation. For example, for wind field, the RMSE is calculated as √[Σ(forecast wind speed - actual wind speed)² / n], in m / s. For precipitation, the relative error is calculated as |forecast precipitation - actual precipitation| / actual precipitation × 100%.

[0141] Furthermore, a deviation threshold is set, and an optimization process is triggered when the deviation exceeds the limit.

[0142] S203: Dynamically adjust the work plan information based on the prediction deviation, including adjusting the work point location, work intensity, and work duration.

[0143] In this embodiment, dynamic adjustment involves modifying the operational parameters in real time based on the deviation analysis results to compensate for forecast errors.

[0144] Specifically, the adjustment strategy is based on optimization algorithms. If the actual wind field deviates from the forecast, causing the catalyst diffusion path to shift, the optimal operating point is recalculated to maximize the predicted area of ​​impact. For example, if the actual wind field shows an increase in easterly winds, the operating point is moved several kilometers westward. The amount of catalyst used is adjusted based on actual precipitation data. If the actual precipitation is weaker than the forecast, the seeding intensity is increased, i.e., the catalyst emission rate is increased. The operation is extended or shortened based on the actual effective impact duration. For example, if the catalyst diffusion is faster than expected, the operation duration is shortened to avoid waste.

[0145] S204: Based on the adjusted work plan information and real-time meteorological data, update the potential artificial water increase and potential benefits, and generate work plan optimization instructions.

[0146] In this embodiment, the optimization instruction is a specific operation command, such as "move the work point to coordinates X,Y" or "increase the catalyst dosage by 10%".

[0147] Specifically, the S103 model calculation is rerun using the adjusted work plan information (such as new work sites) and real-time meteorological data. For example, the total precipitation is recalculated based on the actual precipitation intensity, and the updated water increase is obtained by combining the comprehensive water increase rate. Based on the updated water increase and the recalculated benefits of real-time water price, optimized instructions including action type, parameter values, and execution time are generated.

[0148] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0149] In one embodiment, a system for evaluating the effectiveness of weather modification operations is provided, which corresponds one-to-one with the methods for evaluating the effectiveness of weather modification operations described in the above embodiments. For example... Figure 8 As shown, the artificial weather modification operation effectiveness evaluation system includes an operation data acquisition module, a key evaluation parameter determination module, and an operation effectiveness evaluation module. Detailed descriptions of each functional module are as follows:

[0150] The operation data acquisition module is used to acquire artificial weather modification operation information, meteorological environmental data, and geographic information data for the target area during the predetermined operation period;

[0151] The key assessment parameter determination module is used to dynamically determine at least one key assessment parameter for this operation based on the operation information, meteorological environment data, and geographic information data. The key assessment parameter includes at least one of the operation impact area and the effective operation time.

[0152] The operation effect evaluation module is used to obtain the comprehensive water increase rate applicable to the target area, input the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculate the artificial water increase and the operation effect evaluation index based on the artificial water increase.

[0153] Specific limitations regarding the evaluation system for the effectiveness of weather modification operations can be found in the limitations on the evaluation methods for weather modification operations described above, and will not be repeated here. Each module in the aforementioned evaluation system for the effectiveness of weather modification operations can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in an electronic device, or stored in the memory of an electronic device as software, so that the processor can call and execute the corresponding operations of each module.

[0154] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the database. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for evaluating the effectiveness of weather modification operations.

[0155] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0156] Acquire information on weather modification operations, meteorological and environmental data, and geographic information data for the target area during the predetermined operation period;

[0157] Based on the aforementioned operational information, meteorological environmental data, and geographic information data, at least one key evaluation parameter for this operation is dynamically determined. The key evaluation parameter includes at least one of the following: operational impact area and operational effective time.

[0158] Obtain the comprehensive water increase rate applicable to the target area, input the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculate the artificial water increase volume and the operation effect evaluation index based on the artificial water increase volume.

[0159] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0160] Acquire information on weather modification operations, meteorological and environmental data, and geographic information data for the target area during the predetermined operation period;

[0161] Based on the aforementioned operational information, meteorological environmental data, and geographic information data, at least one key evaluation parameter for this operation is dynamically determined. The key evaluation parameter includes at least one of the following: operational impact area and operational effective time.

[0162] Obtain the comprehensive water increase rate applicable to the target area, input the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculate the artificial water increase volume and the operation effect evaluation index based on the artificial water increase volume.

[0163] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0164] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0165] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for evaluating the effectiveness of weather modification operations, characterized in that, The method for evaluating the effectiveness of weather modification operations includes the following steps: Acquire information on weather modification operations, meteorological and environmental data, and geographic information data for the target area during the predetermined operation period; Based on the aforementioned operational information, meteorological environmental data, and geographic information data, at least one key evaluation parameter for this operation is dynamically determined. The key evaluation parameter includes at least one of the following: operational impact area and operational effective time. Obtain the comprehensive water increase rate applicable to the target area, input the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculate the artificial water increase volume and the operation effect evaluation index based on the artificial water increase volume.

2. The method for evaluating the effectiveness of weather modification operations according to claim 1, characterized in that, The dynamic determination of the area affected by the operation specifically includes: Based on the operational information, the latitude and longitude of the operational point and the operational method are determined. Based on the meteorological environment data, wind field data is obtained. Based on the latitude and longitude of the operational point, the operational method, and the wind field data, the diffusion trajectory and range of the catalyst in the air are simulated to generate the initial influence zone. Grid precipitation product data is determined based on the meteorological environment data, and areas where precipitation actually occurred in the target area during the effective operation time are identified based on the grid precipitation product data as effective precipitation areas; The initial impact area and the effective precipitation are overlaid in geospatial analysis, and the intersection area is taken as the actual effective impact area of ​​this operation. Calculate the area of ​​the actual effective impact area and determine it as the operation impact area.

3. The method for evaluating the effectiveness of weather modification operations according to claim 2, characterized in that, The dynamic determination of the effective time of the operation specifically includes: Based on the job information, the job start time and job duration are obtained, and the job duration is determined based on the job start time and job end time. Based on the operation method and equipment type, query the predefined catalyst diffusion effect duration rule library to determine the corresponding subsequent effective effect duration of the catalyst; By combining the geographical and climatic characteristics of the target area, the duration of the catalyst's subsequent effective impact is localized and corrected to obtain the corrected duration of the effective impact. The effective time of the operation is obtained by adding the duration of the operation to the corrected effective impact duration.

4. The method for evaluating the effectiveness of weather modification operations according to claim 1, characterized in that, The process of obtaining the comprehensive water increase rate applicable to the target area specifically includes: Empirical research data on multiple historical weather modification operations in the target area were collected, and the empirical research data included multiple localized water increase rate reference values; Statistical analysis was performed on the multiple localized water increase rate reference values, and their arithmetic mean was calculated as the benchmark water increase rate. A robust statistical method is used to process the baseline water increase rate, and its predetermined proportion is taken as the authoritative comprehensive water increase rate of the target area. The comprehensive water increase rate is stored as a key parameter in the model and can be selectively invoked according to the type of operation or season.

5. The method for evaluating the effectiveness of weather modification operations according to claim 1, characterized in that, The step of inputting the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model to calculate the artificial water increase and the operational effectiveness evaluation index based on the artificial water increase specifically includes: Based on the gridded precipitation product data and the area affected by the operation, the total precipitation in the operation area during the effective time of the operation and the total duration of the entire precipitation process is calculated. The increased precipitation is calculated based on the total precipitation in the work area during the entire precipitation process, and the artificial water replenishment is calculated based on the increased precipitation and the area affected by the work. The output benefits are calculated based on the artificial water increase, and the input-output ratio is calculated using the output benefits. The output benefits and the input-output ratio constitute the operation effect evaluation index.

6. The method for evaluating the effectiveness of weather modification operations according to claim 1, characterized in that, Before obtaining the comprehensive water increase rate applicable to the target area, inputting the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculating the artificial water increase volume and the operational effectiveness evaluation index based on the artificial water increase volume, the artificial weather modification operation effectiveness evaluation method further includes: Acquire weather forecast data and operational plan information for the target area, wherein the weather forecast data includes predicted wind field data and predicted precipitation field area; Based on the work plan information and weather forecast data, the predicted impact area and effective time of the planned operation are dynamically predicted. By substituting the predicted impact area, the predicted effective time of the operation, and the comprehensive water increase rate into the quantitative assessment model, the potential artificial water increase and potential benefits are predicted. Based on the comparison between the potential artificial water increase and potential benefits and the operational input costs, a decision recommendation on whether to carry out this operation is generated.

7. The method for evaluating the effectiveness of weather modification operations according to claim 6, characterized in that, After obtaining the comprehensive water increase rate applicable to the target area, inputting the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculating the artificial water increase volume and the operational effectiveness evaluation index based on the artificial water increase volume, the artificial weather modification operation effectiveness evaluation method further includes: Real-time acquisition of actual monitoring data during the operation, including actual wind field data and actual precipitation data; The actual monitoring data is compared with the weather forecast data for the target area to calculate the prediction deviation; The work plan information is dynamically adjusted based on the prediction deviation, including adjusting the work point location, work intensity, and work duration; Based on the adjusted work plan information and real-time meteorological data, the potential artificial water increase and potential benefits are updated, and an operation plan optimization instruction is generated.

8. A system for evaluating the effectiveness of weather modification operations, characterized in that, The artificial weather modification operation effectiveness evaluation system includes: The operation data acquisition module is used to acquire artificial weather modification operation information, meteorological environmental data, and geographic information data for the target area during the predetermined operation period; The key assessment parameter determination module is used to dynamically determine at least one key assessment parameter for this operation based on the operation information, meteorological environment data, and geographic information data. The key assessment parameter includes at least one of the operation impact area and the effective operation time. The operation effect evaluation module is used to obtain the comprehensive water increase rate applicable to the target area, input the comprehensive water increase rate and dynamically determined key evaluation parameters into a preset quantitative evaluation model, and calculate the artificial water increase and the operation effect evaluation index based on the artificial water increase.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for evaluating the effectiveness of weather modification operations as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for evaluating the effectiveness of weather modification operations as described in any one of claims 1 to 7.

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