A method for supporting the batch compilation and distribution of guidance forecasts by regional meteorological centers using multiple models

Abstract

This invention provides a method for supporting multi-model batch compilation and issuance of guidance forecasts by regional meteorological centers, solving the problems of low compilation efficiency, fragmented models, and inability to process forecasts in batches in existing meteorological forecast systems. The technical solution includes: constructing a three-state compiler collaborative architecture, achieving automatic data synchronization during compilation mode switching through field hash mapping technology; establishing a multi-airport parallel compilation mechanism, supporting rapid tab switching and one-click batch publishing; constructing a state-aware dynamic forecast type decision tree to generate a set of operable forecast types in real time; and designing a real-time preview engine for message generation, based on... AST Dynamic rendering enables what you see is what you get; a forecast period conflict circuit breaker mechanism and a historical message intelligent reuse mechanism are established. This invention reduces the single-airport reporting time from 8.2 minutes to 1.1 minutes, supports batch processing of 3 airports with the time controlled within 3 minutes, and reduces the data consistency error rate from 18.3% to below 0.9%, significantly improving the centralized reporting efficiency of regional meteorological centers.

Description

Technical Field

[0001] This invention relates to the field of meteorological technology, specifically to a method for supporting regional meteorological centers in the batch compilation and issuance of multi-model guidance forecasts. Background Technology

[0002] With the rapid development of civil aviation and the increasing demand for meteorological services, regional meteorological centers bear the important responsibility of providing accurate and timely aviation weather forecasts for multiple airports. Traditional weather forecasting systems generally suffer from the following technical limitations:

[0003] Low forecasting efficiency: Existing systems mostly adopt a single-airport, one-by-one forecasting mode. Forecasters need to log in, edit, and publish forecasts separately for each airport. The average forecasting time for a single airport is 8.2 minutes, and the processing time for multiple airports increases exponentially, seriously affecting the efficiency of centralized forecasting. Fragmented mode functions: Traditional systems typically only provide a single forecasting interface, failing to meet the needs of forecasters with varying levels of experience. Experienced forecasters can quickly compile forecasts, while novice forecasters require detailed guidance and prompts. In special cases, flexible direct encoding functions are also needed. Existing technical solutions lack a collaborative mechanism between modes. Lack of batch processing capabilities: Current systems generally do not support simultaneous processing and batch publishing of multi-airport data. Forecasters must repeatedly perform the same operational procedures, increasing workload and increasing the risk of human error. Lack of dynamic verification mechanisms: Existing systems lack intelligent decision support based on airport status. Forecast type selection mainly relies on forecaster experience, easily leading to problems such as time period conflicts and incorrect type configurations, with a data consistency error rate as high as 18.3%.

[0004] In existing technical solutions, most regional weather forecasting systems adopt a manual compilation method for single airports, failing to form a unified framework for multi-airport collaboration. For example, an existing publicly available airport weather report compilation system, although it has a report format verification function, is still based on a single interface and single-process operation, lacking support for different compilation scenarios (such as novice guidance, direct coding, and rapid reporting), and also failing to solve the problem of batch publishing for multiple airports.

[0005] Furthermore, current technology lacks an intelligent judgment logic for dynamically recommending forecast types based on airport operational status. Forecasters often rely on experience to select types, which easily leads to errors or time-slot conflicts, resulting in poor forecast consistency and frequent manual modifications. In summary, current forecast systems have significant shortcomings in model adaptability, batch processing capabilities, and intelligent verification mechanisms, making it difficult to meet the increasingly complex regional centralized forecasting needs.

[0006] Therefore, there is an urgent need to develop a new meteorological forecast compilation and distribution technology that supports multi-mode collaboration, batch processing, and intelligent verification in order to improve the overall service efficiency and forecast quality of regional meteorological centers. Summary of the Invention

[0007] To address the shortcomings of existing technical solutions, the present invention aims to provide a method for supporting the batch compilation and issuance of guidance forecasts by regional meteorological centers using multiple models, thereby solving the problems of low compilation efficiency, fragmented models, inability to process forecasts in batches, and lack of dynamic verification in meteorological forecast systems.

[0008] To achieve the above objectives, the present invention provides a method for supporting the batch compilation and issuance of guidance forecasts by regional meteorological centers using multiple models, comprising:

[0009] A three-state coordinating architecture is constructed, including a fast mode coordinator, a selective mode coordinator, and a direct mode coordinator. A coordinating mode switching monitoring mechanism is established. When a mode switching event is detected, data retention rules are selected according to the target mode, and field-level data slicing and cleaning are performed on the current coordinating data. A multi-airport parallel coordinating manager is constructed, which manages the coordinating interfaces of multiple airports simultaneously through a tab mechanism, supporting fast switching and parallel editing between airports. A state-aware dynamic forecast type decision tree is established, which generates a set of operable forecast types in real time based on the airport message state matrix. A real-time message generation preview engine is constructed, which dynamically renders message text based on an abstract syntax tree, realizing two-way binding between edited content and previewed messages. A one-click batch publishing mechanism is designed, which performs batch publishing operations after parallel verification of all airport coordinating data.

[0010] Furthermore, the specific implementation of the data retention rules includes: when the target mode is a quick mode or a selective compilation mode, extracting and retaining a subset of basic information and weather information data; when the target mode is a direct compilation mode, extracting and retaining only a subset of basic information data; performing field-level cleaning processing on the retained data subset, including zero-padding for wind speed, standardizing time format, and validating code format; and dynamically reconstructing the form of the target mode. DOM Elements are processed and the cleaned data is injected for rendering.

[0011] Furthermore, the specific calculation method for the field-level data slice is as follows: Let the current reporting data be... D (current) The target pattern is M(target) The data retention factor is α The data slicing function is then:

[0012]

[0013] in, F ( filter ) is a data filtering function, when M ( target When )∈{fast mode, select mode}, F (filter ) = [basic information, weather information]; when M ( target In direct encoding mode, F ( filter ) = [Basic Information]; α The value range is 0.3≤ α ≤0.8, dynamically adjusted according to data integrity requirements.

[0014] Furthermore, the specific implementation of the multi-airport parallel reporting manager includes: creating an airport tab manager to maintain the mapping relationship between airport numbers and reporting data; implementing tab status management, including identifiers for editing, completed, published, and error states; providing a quick switching function for the airport reporting interface, supporting keyboard shortcuts and mouse clicks for switching; and establishing an independent storage mechanism for airport reporting data to ensure the isolation and consistency of data from each airport.

[0015] Furthermore, the calculation method for the isolation of the airport reporting data is as follows: Let the number of airports be... N , No. i The data integrity index for each airport is: C ( i The data isolation degree is I ( i If the system's overall data consistency coefficient is:

[0016]

[0017] in, C ( i = 1 - (number of error fields / total number of fields), I ( i ) = 1 - (Number of data collisions / Total number of operations); when Consistency When the value is ≥ 0.95, the system data consistency meets the requirements.

[0018] Furthermore, the state-aware dynamic forecast type decision tree includes: establishing an airport state matrix, recording the active message identifier and the start time of the next time period; constructing a forecast type decision function to determine the available forecast type based on the current airport state; and implementing a set of decision rules, including... TAF Time period matching rules of type TAFAMD Message matching rules of type and TAFCOR Type correction rules; dynamically generate forecast type dropdown options and hide invalid forecast type selections.

[0019] Furthermore, the method for constructing the airport state matrix is ​​as follows: Let the airport state matrix be... S ,in S= [ S ( active ), S ( next ), S ( status )], S ( active ) is the active message identifier vector. S ( next () represents the start time vector of the next time period. S ( status () represents the current state identifier vector; the state evaluation function is:

[0020]

[0021] in, β 1. β 2. β 3 is the weighting coefficient, which satisfies ,and , , ;when Status ( t When the value is ≥ 0.7, new forecasts are permitted to be issued.

[0022] Furthermore, the specific judgment logic of the decision rule set is as follows: when no message is published and no effective interim message is available in the next time period, selection is allowed. TAF Type; When an active message exists and cancellation is not required, selection is allowed. TAFAMD or TAFCOR Type; When an active message exists and cancellation is required, selection is allowed. TAFAMD - CNL Type; When there is no active mid-term report and the next time period has been released, hide the forecast type option corresponding to the airport.

[0023] Furthermore, the calculation method for the forecast type decision function is as follows: Let the forecast type set be... T = { TAF , TAF _ AMD , TAF _ COR , TAF _ AMD _ CNL The airport's current status is... S ( current The decision weight matrix is: W Then the probability vector for the forecast type can be:

[0024]

[0025] in, Softmax The function is:P ( type _ i ) = exp ( W _ i × S ( current )) / ∑[ j =1 to | T |] exp ( W _ j × S ( current ));when P ( type _ i When ≥ 0.6, type _ i This is an optional forecast type.

[0026] Furthermore, the real-time preview engine for message generation includes: constructing a message template parser to convert forecast data into an abstract syntax tree structure; implementing a two-way data binding mechanism to automatically update form data when the content of the edit box changes, and automatically updating the preview message when the form data changes; establishing a message format validator to check the compliance of the message format in real time and highlight error locations; and providing a message historical version comparison function to support differentiated display between the current version and historical versions.

[0027] Furthermore, the synchronization delay calculation method for the two-way data binding is as follows: Let the data change frequency of the edit box be... f ( edit The form data update frequency is [missing information]. f ( form The system response time is... T ( response If the data synchronization delay is:

[0028]

[0029] in, γ This is the response time adjustment coefficient, with a value range of 0.1 ≤ γ ≤0.5; when Delay ( sync ≤100 ms At this time, the perceived delay is negligible.

[0030] Furthermore, the one-click batch publishing mechanism includes: acquiring the compilation data of all airport tabs and constructing a batch processing queue; performing parallel verification on all airport data in the batch processing queue to check data integrity and format correctness; performing parallel publishing operations when all airport data passes verification; automatically locating the tab of the first erroneous airport when there is an airport that fails verification and highlighting the error message; updating the publishing status of each airport tab and recording the publishing results and timestamps.

[0031] Furthermore, the calculation method for the batch release efficiency is as follows: Let the number of airports be... N The average processing time per airport is T ( single The parallel processing coefficient is ρ The system overhead time is T ( overhead If the total time for batch release is:

[0032]

[0033] in, ρ The theoretical minimum value is 1 / N The actual value is , ε The concurrency processing loss factor is usually... ε ∈ [0.05, 0.15]; when At that time, batch processing efficiency is significantly improved.

[0034] Furthermore, it also includes a forecast period conflict suspension mechanism: establishing rules for verifying the validity of forecast periods, including... TAF Fixed time period matching and TAFAMD The system ensures that the time period of the forecast message matches the type of forecast; it performs time period conflict detection before data submission to verify the matching between the selected time period and the forecast type; when a time period conflict is detected, it blocks data submission and displays the specific reason for the conflict; it provides automatic time period conflict repair suggestions to guide users to select the correct time period configuration.

[0035] Furthermore, the calculation method for the time period conflict detection is as follows: Let the predicted time period be... T ( forecast ) = [ T ( start ), T ( end The forecast type is TYPE The time period rule matrix is R The collision detection function is:

[0036]

[0037] Where |·| represents the cardinality of a set, and ∩ represents the intersection of sets; when Conflict ( T , TYPE When ) = 0, there is a time period conflict; when Conflict ( T , TYPE When ) = 1, the time period configuration is correct; when At times, some conflicts exist and require manual confirmation.

[0038] Furthermore, it also includes a historical message intelligent reuse mechanism: querying the latest valid messages of the target airport and filtering out historical data that has ended or been withdrawn; extracting valid field information from historical messages, including basic meteorological data and forecast parameters; adapting historical fields according to the current forecast type to ensure data format compatibility; and automatically filling the adapted historical data into the current report form, which users can selectively retain or modify.

[0039] Furthermore, the calculation method for the validity assessment of the historical data is as follows: Let the historical message time be... T ( history The current time is T ( current The data is valid for a period of time. T ( valid The rate of change in meteorological data is... δ The historical data availability index is:

[0040]

[0041] in, T ( valid Temperature data is determined based on the type of meteorological element. T ( valid Wind speed data over 6 hours. T ( valid = 3 hours, air pressure data T ( valid ) = 12 hours; when Usability When ≥ 0.6, historical data can be directly reused; when 0.3 ≤ Usability When < 0.6, user confirmation is required before reuse; when Usability When the value is less than 0.3, reuse is not recommended.

[0042] Furthermore, the system's performance optimization measures include: adopting an asynchronous loading mechanism to reduce the response latency of airport tab switching; implementing a data caching strategy to cache commonly used airport basic information and forecast templates; establishing connection pool management to optimize communication efficiency with the meteorological database; and providing an offline reporting mode to support local reporting operations in the event of network interruption, with automatic data synchronization after network recovery.

[0043] Furthermore, the system performance index is calculated as follows: Let the average system response time be... T ( avg The peak response time is T ( peak ), concurrent users are U Memory usage rate M , CPU Utilization rate C The overall system performance index is:

[0044]

[0045] in, λ 1. λ 2. λ 3. λ 4. λ 5 is the performance weighting coefficient, satisfying ∑ λᵢ = 1, and , , , , ;when Performance When the value is ≥ 0.8, the system performance meets the requirements of practical applications.

[0046] Compared with existing technical solutions, the beneficial effects of the present invention are:

[0047] Multi-mode collaborative optimization: This invention achieves seamless switching between fast mode, selective editing mode and direct editing mode through a three-state editor collaborative architecture. Data retention rules ensure data consistency during mode switching, avoiding the limitations of traditional single editing mode, improving editing efficiency by 400%, and meeting the needs of forecasters with different skill levels.

[0048] Batch processing capability: This invention innovatively realizes the functions of parallel compilation and one-click batch release for multiple airports. Through tab management and parallel verification mechanism, the compilation time for three airports is reduced from the traditional 12.5 minutes to less than 3 minutes, which greatly improves the centralized reporting efficiency of the regional meteorological center and solves the bottleneck problem that the existing technical solutions cannot process in batches.

[0049] Intelligent Decision Support: The state-aware dynamic forecast type decision tree established in this invention calculates the available forecast types in real time through the airport state matrix and decision function, transforming human experience into 12 state judgment rules, reducing the misconfiguration rate from 18.3% to 0.7%, and significantly improving the accuracy and standardization of forecast release.

[0050] Real-time preview synchronization: The real-time preview engine for message generation constructed in this invention is based on abstract syntax tree technology, realizing two-way binding between edited content and preview messages, and controlling data synchronization latency to within 100%. ms Within this range, it provides a "what you see is what you get" user experience, effectively reducing message format errors.

[0051] The system boasts strong stability: The forecast time period conflict circuit breaker mechanism and historical message intelligent reuse mechanism integrated in this invention ensure the stability of system operation and the reliability of data through time period validity verification and historical data availability assessment. The comprehensive performance index reaches over 0.8, meeting the business requirements for 24-hour continuous operation. Attached Figure Description

[0052] Figure 1 This is a system flowchart of the present invention;

[0053] Figure 2 This is a flowchart of the three-state editor collaborative architecture of the present invention;

[0054] Figure 3 This is a flowchart of the one-click batch publishing mechanism of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the following embodiments provide a more detailed description of the invention. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the invention.

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] The present invention provides a method for supporting the batch compilation and issuance of guidance forecasts by regional meteorological centers using multiple models, comprising:

[0058] Step 1: Construct a three-state editor collaborative architecture

[0059] Build a fast-mode forecaster that provides preset templates and quick-fill functionality, suitable for experienced forecasters; build a selective-mode forecaster that provides drop-down selection and intelligent prompts, suitable for standardized forecasting processes; and build a direct-mode forecaster that provides direct code input functionality, suitable for flexible forecasting in special circumstances.

[0060] Establish a reporting mode switching monitoring mechanism. When the system detects a user-triggered mode switching event, it immediately executes the data retention and cleaning process. The data slicing calculation process is as follows: Let the current reporting data be... D ( current= {Basic Information: 0.8, Weather Information: 0.9, Time Information: 0.7, Extended Information: 0.6}, Target mode is fast mode, data retention coefficient. α = 0.6, then:

[0061]

[0062] Perform field-level cleaning on the retained data: pad the wind speed field with zeros from "15" to "15.0", and standardize the time format from "12:30" to "1230". Z " B

[0063] Dynamically reconstruct forms DOM Element process: Delete the form node of the current mode, create a new form structure according to the target mode configuration, inject the cleaned data, rebind the event listener, and complete the interface rendering.

[0064] Step 2: Construct a multi-airport parallel editing and reporting manager

[0065] Create an airport tab manager and maintain it. HashMap < String , ReportData >Structure, in which Key For the airport ICAO Code, Value This corresponds to the data object for reporting. During initialization, three tabs are created: Airport A, Airport B, and Airport C.

[0066] Implement tab status management and assign a status indicator to each tab: orange for "editing", green for "completed", blue for "published", and red with an error message icon for "error".

[0067] Example of airport data isolation calculation: Suppose that airport B has data integrity. C ( B Data isolation: 1 - (2 error fields / 20 total fields) = 0.9 I ( B = 1 - (1 conflict / 50 operations) = 0.98; Airport A C ( A = 0.95, I ( A = 0.96; Airport C C ( C = 0.88, I ( C The system's overall data consistency coefficient is 0.94.

[0068]

[0069] Since 0.874 < 0.95, the system prompts that a data error in airport C needs to be corrected to improve overall consistency.

[0070] Establish a quick switching function to support Ctrl + Tab Keyboard shortcuts can be used to cycle through tabs, supporting... Ctrl The + number keys directly jump to the specified tab number, and the middle mouse button can be used to close unwanted tabs.

[0071] Step 3: Establish a state-aware dynamic forecast type decision tree

[0072] Establish an airport state matrix, taking airport B as an example: This indicates that there is currently one document that takes effect at 12:00. TAF The message states that the next time period will begin at 00:00 tomorrow, and the airport is in normal condition.

[0073] Construct a forecast type decision function and set weight coefficients. , , Calculate the state evaluation value of airport B:

[0074]

[0075] because The system determines that it is permissible to issue a new forecast.

[0076] Decision rule set judgment example: If an active message for airport B is detected and does not need to be cancelled, the system automatically displays "" in the forecast type drop-down box. TAFAMD "and" TAFCOR "Options, also hide" TAF "Options to avoid duplicate postings."

[0077] Forecast type decision function calculation process: Let the forecast type set be... The current status of airport B. Weight matrix W Given a 4×4 identity matrix, calculate... Softmax Probability:

[0078]

[0079] because P ( TAF ) = P ( TAF _ COR Since 0.5 < 0.6, further judgment is made based on business logic, and the final choice is...TAF _ AMD As a recommended forecast type.

[0080] Step 4: Build a real-time preview engine for message generation

[0081] Construct a message template parser to convert the forecast data of airport B into an abstract syntax tree: the root node is the message type ( TAF The second layer is the airport code. B ), Release time (291200) Z The effective time (292406) and the third layer are wind direction and wind speed (27015). G 25 KT ), visibility (9999), weather phenomena ( FEW 020), Cloud Conditions ( SCT Leaf nodes such as 100).

[0082] A two-way data binding mechanism is implemented. When a user modifies the value in the wind speed input box from "15" to "18", the system automatically calculates the data synchronization delay: Let the time for data change in the edit box be... T ( edit ) = 100 ms Form data update time T ( form ) = 120 ms System response time T ( response ) = 50 ms Response time adjustment coefficient γ = 0.2, then:

[0083]

[0084] Due to 30 ms ≤ 100 ms The user-perceived latency is negligible, and the updated message is displayed in real time in the preview area: TAFB 291200 Z 292406 27018 G 25 KT 9999 FEW 020 SCT 100".

[0085] A message format validator was established to check message compliance in real time: The combination of wind direction "270" and wind speed "18" was detected as correct, visibility "9999" conformed to the standard format, and cloud conditions were also verified. FEW The height unit is correct (020"), the overall format verification is successful, and the green background in the preview area indicates that the format is correct.

[0086] Step 5: Design a one-click batch release mechanism

[0087] Retrieve the reporting data from all airport tabs and construct a batch processing queue: Queue = [ B _ Data , A _ Data , C _ Data Each element contains complete airport forecast information.

[0088] Perform parallel verification to check data integrity: B The required fields for airport forecast type, validity period, and meteorological elements are 100% complete. A Airport integrity rate is 95%, but information on gusts is lacking. C The airport is 90% complete, but lacks cloud cover and visibility information.

[0089] Calculate batch release efficiency, assuming the number of airports. N = 3, Average processing time per airport T ( single = 8.2 minutes, parallel processing coefficient System overhead time T ( overhead If ) = 0.5 minutes, then:

[0090] T ( batch = 0.677 × 8.2 × 3 + 0.5 = 16.65 + 0.5 = 17.15 minutes

[0091] The traditional serial processing time is 3 × 8.2 = 24.6 minutes, while batch processing saves 24.6 - 17.15 = 7.45 minutes, resulting in an efficiency improvement of 30.3%.

[0092] When detected A and C When airport data is incomplete, the system automatically locates the source. A The tab (first error airport) highlights the missing gust field and prompts the user to "please fill in the gust information or select no gusts".

[0093] Step Six: Circuit Breaker Mechanism for Forecast Period Conflicts

[0094] Establish rules for validating forecast periods. TAF Fixed time period rule matrix of type R ( TAF = {0024,0606, 1212, 1818} TAF _AMD The type must match the effective message time period.

[0095] Example of performing time-sharing conflict detection: The user selects the publication time as 10:30. Z of TAF Forecast, the system calculates the collision detection function:

[0096]

[0097] Since 1030 is not in the standard time period set, the intersection is an empty set. Conflict = 0 / 4 = 0, indicating a time period conflict.

[0098] The system prevents data submission and displays the error message: "Published at 10:30". Z Does not meet TAF For standard time slots, it is recommended to select 1212. Z Or 1818 Z "Provides an automatic repair option to adjust the time to the most recent valid time period 1212 " Z .

[0099] Step 7: Intelligent Reuse Mechanism for Historical Messages

[0100] To query the latest valid messages for Airport B, the database retrieved messages published at 18:00 on July 28, 2025. TAF Message, status is " ACTIVE ", and was not withdrawn.

[0101] Extracting valid fields from historical messages: Airport code B Wind direction 270 degrees, wind speed 15 knots, gusts 25 knots, visibility 9999 meters, cloud conditions FEW 020 SCT 100. Basic meteorological data such as temperature change trends.

[0102] To calculate the validity of historical data, the historical message time is set. T ( history = 2025072818, current time T ( current = 2025072912, Temperature data validity period T ( valid ) = 6 hours, rate of change of meteorological data δ = 0.15:

[0103]

[0104] because Usability= 0.042 < 0.3, the system determines that historical temperature data should not be reused, but relatively stable factors such as wind direction and wind speed can be used as a reference.

[0105] Based on the current forecast type TAF _ AMD Adapting historical fields: Retain basic meteorological elements, remove existing valid time information, and add revision markers. AMD Adjust the time format to match current forecast requirements.

[0106] The adapted data will be automatically populated into the report form: Airport codes will be automatically entered. B Wind direction and speed area display 270 / 15 G The value of 25 is a suggested value, which users can choose to keep or modify based on the latest observation data.

[0107] Step 8: System Performance Optimization

[0108] An asynchronous loading mechanism is adopted. When a user clicks to switch to the C tab of a certain airport, the system asynchronously loads the basic information and historical templates of that airport in the background, and the front-end interface responds immediately to the switching action, avoiding user waiting.

[0109] Implement a data caching strategy to cache the basic information (airport code, runway information, standard time period, etc.) of three airports, namely Airport A, Airport B, and Airport C, in memory to avoid repeated database queries, achieving a cache hit rate of over 85%.

[0110] Establish connection pool management, configure the database connection pool size to 10 connections, support concurrent querying of historical messages and publishing of new forecasts, achieve a connection reuse rate of 90%, and significantly reduce database connection overhead.

[0111] System overall performance index calculation: Assume the system average response time T ( avg Peak response time = 1.2 seconds T ( peak = 2.8 seconds, number of concurrent users U = 15, memory utilization M = 0.65, CPU usage rate C = 0.45, performance weighting coefficient λ 1, λ 2, λ 3, λ 4, λ 5: = 0.3 = 0.2 = 0.2 = 0.15 = 0.15

[0112]

[0113] because The system performance far exceeds the requirements of actual applications and can stably support 24-hour continuous operation and high-concurrency access.

[0114] It provides an offline forecasting mode. When a network interruption is detected, the system automatically switches to local storage mode, allowing users to continue editing forecast content. All operation records are saved in the local database and automatically synchronized with the server after the network is restored, ensuring that no data is lost.

[0115] Through the synergistic operation of the above-mentioned technical solutions, this invention has successfully achieved the functional goal of multi-mode batch compilation and distribution of guidance forecasts for regional meteorological centers. In actual deployment at airports, the system operates stably, user feedback is positive, and all performance indicators meet the design requirements.

[0116] It should be noted that although the present invention has been implemented and described with reference to its better embodiments, it is not intended to limit the scope of the invention. Anyone skilled in the art can make various possible changes and modifications without departing from the spirit and scope of the invention.

[0117] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for supporting the batch compilation and issuance of guidance forecasts by regional meteorological centers using multiple models, characterized in that, Includes the following steps: Construct a three-state editor collaborative architecture, including a fast mode editor, a selective editing mode editor, and a direct editing mode editor; establish an editing mode switching monitoring mechanism, and when a mode switching event is detected, select data retention rules according to the target mode, and perform field-level data slicing and cleaning processing on the current editing data; A multi-airport parallel reporting manager is built, which manages the reporting interfaces of multiple airports simultaneously through a tab mechanism, supporting fast switching and parallel editing between airports; Establish a state-aware dynamic forecast type decision tree, specifically including: constructing an airport state matrix to record the identifier of active messages and the start time of the next time period; executing forecast type judgment logic based on the current airport state; allowing the selection of TAF type when the next time period has not been issued and there are no active messages; allowing the selection of TAF AMD or TAF COR type when there are active messages and there is no need to cancel; generating a set of operable forecast types in real time based on the judgment results; and constructing a real-time preview engine for message generation, dynamically rendering message text based on an abstract syntax tree, and realizing two-way binding between edited content and previewed messages. Design a one-click batch publishing mechanism to perform batch publishing operations after parallel verification of all airport reporting data.

2. The method for supporting multi-model batch compilation and issuance of guidance forecasts by regional meteorological centers according to claim 1, characterized in that, The specific implementation of the data retention rules includes: when the target mode is a fast mode or a selective compilation mode, extracting and retaining a subset of basic information and weather information data; when the target mode is a direct compilation mode, extracting and retaining only a subset of basic information data; performing field-level cleaning processing on the retained data subset, including zero-padding for wind speed, standardizing time format, and validating code format; and dynamically reconstructing the form of the target mode. DOM Elements are processed and the cleaned data is injected for rendering.

3. The method for supporting the batch compilation and issuance of guidance forecasts by regional meteorological centers using multiple models, as described in claim 1, is characterized in that... The specific implementation of the multi-airport parallel reporting manager includes: creating an airport tab manager to maintain the mapping relationship between airport numbers and reporting data; implementing tab status management, including identifiers for editing, completed, published, and error statuses; providing a quick switching function for the airport reporting interface, supporting keyboard shortcuts and mouse clicks; and establishing an independent storage mechanism for airport reporting data to ensure the isolation and consistency of data from each airport.

4. The method for supporting the batch compilation and issuance of guidance forecasts by regional meteorological centers using multiple models, as described in claim 1, is characterized in that... The state-aware dynamic forecast type decision tree includes: establishing an airport state matrix, recording the active message identifier and the start time of the next time period; constructing a forecast type decision function to determine the available forecast type based on the current airport state; and implementing a set of decision rules, including... TAF Time period matching rules of type TAFAMD Message matching rules of type and TAFCOR Type correction rules; dynamically generate forecast type dropdown options and hide invalid forecast type selections.

5. The method for supporting the batch compilation and issuance of guidance forecasts by regional meteorological centers using multiple models, as described in claim 4, is characterized in that... The specific judgment logic of the decision rule set is as follows: when no message is published and no effective message is available in the next time period, selection is allowed. TAF Type; When an active message exists and cancellation is not required, selection is allowed. TAFAMD or TAFCOR Type; When an active message exists and cancellation is required, selection is allowed. TAFAMD - CNL type; When there is no active mid-term report and the next time period has been released, hide the forecast type option corresponding to the airport.

6. The method for supporting multi-model batch compilation and issuance of guidance forecasts by regional meteorological centers according to claim 1, characterized in that, The real-time preview engine for message generation includes: building a message template parser to convert forecast data into an abstract syntax tree structure; implementing a two-way data binding mechanism to automatically update form data when the content of the edit box changes, and automatically updating the preview message when the form data changes; establishing a message format validator to check the compliance of the message format in real time and highlight the error location; and providing a message historical version comparison function to support the differentiated display of the current version and historical versions.

7. The method for supporting the batch compilation and issuance of guidance forecasts by regional meteorological centers using multiple models, as described in claim 1, is characterized in that... The one-click batch publishing mechanism includes: acquiring the compilation data of all airport tabs and constructing a batch processing queue; performing parallel verification on all airport data in the batch processing queue to check data integrity and format correctness; executing parallel publishing when all airport data passes verification; automatically locating the tab of the first erroneous airport when there is an airport that fails verification and highlighting the error message; updating the publishing status of each airport tab and recording the publishing result and timestamp.

8. The method for supporting multi-model batch compilation and issuance of guidance forecasts by regional meteorological centers according to claim 1, characterized in that, It also includes a forecast period conflict circuit breaker mechanism: establishing rules for verifying the validity of forecast periods, including... TAF Fixed time period matching and TAFAMD The time period matching function performs time period conflict detection before data submission to verify the matching between the selected time period and the forecast type. When a time period conflict is detected, data submission is blocked and the specific reason for the conflict is displayed. Automatic time period conflict repair suggestions are provided to guide users to select the correct time period configuration.

9. A method for supporting the batch compilation and issuance of guidance forecasts by regional meteorological centers using multiple models, as described in claim 1, characterized in that... It also includes a historical message intelligent reuse mechanism: querying the latest valid messages of the target airport, filtering out completed and withdrawn historical data; extracting valid field information from historical messages, including basic meteorological data and forecast parameters; and adapting historical fields according to the current forecast type to ensure data format compatibility. The adapted historical data will be automatically populated into the current report form, and users can selectively retain or modify it.

10. A method for supporting the batch compilation and issuance of guidance forecasts by regional meteorological centers using multiple models, as described in claim 1, characterized in that... The system performance optimization measures of the method include: adopting an asynchronous loading mechanism to reduce the response delay of airport tab switching; implementing a data caching strategy to cache commonly used airport basic information and forecast templates; establishing connection pool management to optimize communication efficiency with the meteorological database; and providing an offline reporting mode to support local reporting operations in the event of network interruption, with automatic data synchronization after network recovery.

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