An intelligent interactive experience prediction scheme formulation method and system

Abstract

The application discloses an intelligent interaction experience prediction scheme compiling method and system. The method comprises the following steps: constructing an experience prediction scheme compiling target section prediction system; selecting a historical flood sample in an intelligent interaction mode according to the target section prediction system and a basic hydrology database; selecting a rainfall start and end time period and a runoff start and end time period to carry out base flow segmentation in the intelligent interaction mode according to the selected historical flood sample, so as to obtain a rainfall runoff segmentation sample; generating a runoff scheme relationship line in the intelligent interaction mode according to the rainfall runoff segmentation sample and a unit runoff scheme of the target section prediction system; and completing each rainfall center scene compilation in the intelligent interaction mode according to the rainfall runoff segmentation sample and the runoff scheme relationship line. The intelligent interaction mode is used to replace a traditional manual operation mode, and the experience prediction scheme compiling convenience, objectivity and timeliness can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of hydrological forecasting scheme development technology, and in particular to an intelligent interactive empirical forecasting scheme development method and system. Background Technology

[0002] The occurrence and development of flood events are characterized by suddenness and urgency. Flood forecasting and rehearsals require high accuracy and timeliness. Hydrological forecasting schemes are the foundation of flood forecasting operations. Efficient hydrological forecasting scheme preparation techniques are the basic guarantee for ensuring the accuracy and timeliness of flood forecasting.

[0003] Empirical forecasting schemes are a reliable method widely used in the field of current hydrological forecasting technology. All hydrological sections with flood forecasting needs should prioritize the development of an empirical forecasting scheme.

[0004] In the process of realizing this invention, the inventors discovered at least the following problems in the prior art:

[0005] Existing empirical forecasting scheme development techniques are constrained by factors such as the complexity of the scheme development process, the high degree of flexibility in drawing empirical relationship lines, and the difficulty in automation. They mostly rely on manual methods for analysis and statistics, with various relationship lines often being manually drawn on specific grid paper using large rulers. This results in high technical difficulty, numerous subjective factors, and a long development time. Summary of the Invention

[0006] This invention provides a method and system for compiling intelligent interactive experience forecasting schemes. By using intelligent interactive methods to replace traditional manual methods, it can effectively improve the convenience, objectivity and timeliness of experience forecasting scheme compilation.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A method for developing an intelligent interactive experience-based forecasting scheme includes:

[0009] Construct an experience-based forecasting scheme to develop a target section forecasting system;

[0010] Based on the target section forecasting system and basic hydrological database, historical flood samples are selected using an intelligent interactive method.

[0011] Based on the selected historical flood samples, a baseflow segmentation is performed by selecting the rainfall start and end times and the runoff start and end times using an intelligent interactive method, resulting in rainfall-runoff segmentation samples.

[0012] Based on the rainfall-runoff segmentation samples and the unit runoff generation schemes of the target section forecasting system, a runoff generation scheme relationship line is generated using an intelligent interactive method.

[0013] Based on the rainfall-runoff segmentation samples and the runoff generation scheme relationship line, the scenario compilation for each rainfall center is completed using an intelligent interactive method.

[0014] Optionally, the target cross-section forecasting system includes: basic cross-section information, cross-section outlet station composition, and runoff generation and catchment area units contained in the cross-section. The basic cross-section information includes cross-section code, cross-section name, cross-section location, river system where the cross-section is located, and cross-section forecasting calculation period. The cross-section outlet station composition includes cross-section water level representative station code, water level representative station name, cross-section flow station code, flow station name, flow station category, and flow station weight. The runoff generation and catchment area unit composition includes runoff generation and catchment area unit code, unit name, unit contained station code, station name, station category, unit station weight, and runoff generation and catchment scheme type set for the unit.

[0015] Optionally, the step of selecting historical flood samples based on the target section forecasting system and the basic hydrological database using an intelligent interactive method specifically includes:

[0016] The daily and extracted hydrological data of rainfall, water level and evaporation are interpolated into the corresponding time period data for the cross-section forecast calculation period of the target cross-section forecast system.

[0017] Based on the selected year, the rainfall and outlet flow of the cross-section unit are plotted in the form of a process line graph;

[0018] Using intelligent interaction, the start and end times of the flood are obtained by capturing the number of mouse click events. Once the end time is determined, the rainfall, rainfall center, and peak flow value within the selected flood period are automatically calculated.

[0019] After a flood selection is completed, the number of mouse click events automatically resets to 0, and the cycle begins for the second selection.

[0020] After the flood event for the current year is selected, the system will intelligently switch to select the next year until all flood events for all years have been divided.

[0021] Optionally, based on the selected historical flood sample, the baseflow is segmented using an intelligent interactive method by selecting the start and end times of rainfall and the start and end times of runoff to obtain a rainfall-runoff segmentation sample, specifically including:

[0022] At the selected runoff initiation point, referring to the historical typical receding process samples in the selected historical flood samples, the receding process line is extended and supplemented in an intelligent interactive manner to complete the previous flood receding process and generate the pre-peak receding process line.

[0023] At the inflection point of the post-peak process, referring to historical typical receding process samples, the receding process line is extended and supplemented in an intelligent interactive manner to complete the receding process of this flood and generate the post-peak receding process line.

[0024] The measured flood events within the runoff start and end periods are subtracted from the periods of the pre-peak receding water events, and the periods of the post-peak receding water events are replaced with the original measured flood events to obtain the rainfall-runoff segmentation samples.

[0025] Optionally, the step of generating a runoff generation scheme based on the rainfall-runoff segmentation samples and the target section forecasting system using an intelligent interactive method to generate a runoff generation scheme relationship line specifically includes:

[0026] Based on the rainfall-runoff segmentation samples, the flood number, flood start and end time, antecedent rainfall, initial rise flow, rainfall, rainfall center, runoff depth, calculated runoff depth, and relative error characteristics of each segmented flood are obtained.

[0027] Based on the flood number, flood start and end time, previous impact rainfall, rising flow, rainfall, rainfall center, runoff depth, calculated runoff depth, relative error characteristics of each segmented flood, and the unit runoff generation scheme of the target section forecasting system, a preliminary runoff generation scheme relationship line is determined in an intelligent interactive manner.

[0028] Based on the preliminary runoff generation scheme relationship line, the predicted runoff depth is calculated and the relative error of the runoff depth is statistically analyzed;

[0029] Based on the predicted runoff depth and the relative error of the runoff depth, the final runoff generation scheme relationship line is obtained.

[0030] Optionally, the step of using an intelligent interactive method to complete the scenario compilation for each rainfall center based on the rainfall-runoff segmentation samples and the runoff generation scheme relationship line specifically includes:

[0031] Based on the rainfall-runoff segmentation samples, the following characteristic information is obtained for each segmented flood: flood number, flood start and end time, rainfall, rainfall center, measured runoff depth, measured net peak flow, measured peak time, calculated runoff depth, calculated net peak flow, net peak relative error, calculated peak time, peak time difference, and deterministic coefficient.

[0032] Based on the flood number, flood start and end time, rainfall, rainfall center, measured runoff depth, measured net peak flow, measured peak time, calculated runoff depth, calculated net peak flow, net peak relative error, calculated peak time, peak time difference, and determination coefficient characteristics of each segmented flood, a preliminary confluence unit line scheme is determined;

[0033] Based on the preliminary runoff unit line scheme and the runoff generation scheme relationship line, the net rainfall process and the unit runoff process are calculated;

[0034] Based on the net rainfall process and the unit confluence process, the confluence unit line scheme under the current rainfall center scenario is obtained;

[0035] Update the selected rainfall center and repeat the above steps to obtain the new rainfall center corresponding to the unit line scheme, until the scenarios for each rainfall center are completed.

[0036] A method system for developing intelligent interactive experience-based forecasting schemes includes:

[0037] The target section forecasting system construction module is used to construct an empirical forecasting scheme and compile a target section forecasting system.

[0038] The historical flood sample selection module is used to select historical flood samples in an intelligent interactive manner based on the target section forecasting system and the basic hydrological database.

[0039] The rainfall-runoff segmentation sample determination module is used to select the start and end times of rainfall and runoff based on the selected historical flood sample, and to perform baseflow segmentation by selecting the start and end times of rainfall and runoff in an intelligent interactive manner to obtain rainfall-runoff segmentation samples.

[0040] The runoff generation scheme relationship line generation module is used to generate a runoff generation scheme relationship line based on the rainfall-runoff segmentation sample and the unit runoff generation scheme of the target section forecasting system in an intelligent interactive manner.

[0041] The rainfall center scenario compilation module is used to complete the compilation of each rainfall center scenario in an intelligent interactive manner based on the rainfall-runoff segmentation sample and the runoff generation scheme relationship line.

[0042] Optionally, the target cross-section forecasting system includes: basic cross-section information, cross-section outlet station composition, and runoff generation and catchment area units contained in the cross-section. The basic cross-section information includes cross-section code, cross-section name, cross-section location, river system where the cross-section is located, and cross-section forecasting calculation period. The cross-section outlet station composition includes cross-section water level representative station code, water level representative station name, cross-section flow station code, flow station name, flow station category, and flow station weight. The runoff generation and catchment area unit composition includes runoff generation and catchment area unit code, unit name, unit contained station code, station name, station category, unit station weight, and runoff generation and catchment scheme type set for the unit.

[0043] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0044] This invention provides a method for compiling intelligent interactive empirical forecasting schemes. The method includes: constructing a target section forecasting system for empirical forecasting scheme compilation; selecting historical flood samples using an intelligent interactive approach based on the target section forecasting system and a basic hydrological database; segmenting baseflow based on the selected historical flood samples, selecting rainfall start and end times and runoff start and end times using an intelligent interactive approach to obtain rainfall-runoff segmentation samples; generating runoff generation scheme relationship lines using an intelligent interactive approach based on the rainfall-runoff segmentation samples and the unit runoff generation schemes of the target section forecasting system; and completing the compilation of scenarios for each rainfall center using an intelligent interactive approach based on the rainfall-runoff segmentation samples and the runoff generation scheme relationship lines. This invention uses an intelligent interactive approach to replace traditional manual methods, effectively improving the convenience, objectivity, and timeliness of empirical forecasting scheme compilation. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 Flowchart of the method for developing an intelligent interactive experience forecasting scheme according to the present invention;

[0047] Figure 2 A system structure diagram was prepared for the intelligent interactive experience-based forecasting scheme of this invention; Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] This invention provides a method and system for compiling intelligent interactive experience forecasting schemes. By using intelligent interactive methods to replace traditional manual methods, it can effectively improve the convenience, objectivity and timeliness of experience forecasting scheme compilation.

[0050] 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.

[0051] Example 1:

[0052] Based on existing research, expert consultation, and years of participation in the assessment and evaluation of the river chief system, this invention establishes an indicator system for the compilation of intelligent interactive experience-based forecasting schemes. It proposes two categories of indicator weights for mountainous and plain areas using the analytic hierarchy process, and explores the evaluation process of the fuzzy comprehensive evaluation method using a typical area in Jiangxi Province as an example. The aim is to provide a basis for evaluating the effectiveness of the implementation of the river chief system at the grassroots level and to provide a reference for scientific decision-making.

[0053] Figure 1 The flowchart illustrates the method for developing an intelligent interactive experience-based forecasting scheme according to the present invention. Figure 1 As shown, a method for developing an intelligent interactive experience-based forecasting scheme includes:

[0054] Step 101: Construct an empirical forecasting scheme and compile a target section forecasting system.

[0055] The target cross-section forecasting system includes: basic cross-section information, cross-section outlet station composition, and runoff generation and catchment area units included in the cross-section. The basic cross-section information includes the cross-section code, cross-section name, cross-section location, river system in which the cross-section is located, and cross-section forecast calculation period. The cross-section outlet station composition includes the cross-section water level representative station code, water level representative station name, cross-section flow station code, flow station name, flow station category, and flow station weight. The runoff generation and catchment area unit composition includes the runoff generation and catchment area unit code, unit name, unit included station code, station name, station category, unit station weight, and runoff generation and catchment scheme type set for the unit.

[0056] Step 102: Select historical flood samples using an intelligent interactive method based on the target section forecasting system and the basic hydrological database.

[0057] The Basic Hydrological Database is a professional database established in accordance with the "Standard for Table Structure and Identifiers of Basic Hydrological Database (SL324-2005)".

[0058] The method for selecting flood samples is as follows:

[0059] First, the daily and extracted hydrological data, including rainfall and evaporation data, are interpolated to the corresponding time-period data for the forecast calculation period set in step 101. Second, the rainfall and outlet flow of the cross-section are plotted as a process line graph according to the selected year. Third, the flood start time (the first click corresponds to the cave) and end time (the second click corresponds to the time) are obtained by capturing the number of mouse click events in an intelligent interactive manner. After the end time is determined, the rainfall, rainfall center, and peak flow value within the selected flood period are automatically calculated. Fourth, after a flood event is selected, the number of mouse click events is automatically reset to 0, and the selection of the second event begins. Fifth, after the flood event selection for the current year is completed, the intelligent interactive method switches to select the next year, repeating steps three and four until all events for all years are divided.

[0060] Step 103: Based on the selected historical flood samples, the start and end times of rainfall and runoff are selected using an intelligent interactive method to perform baseflow segmentation, thereby obtaining rainfall-runoff segmentation samples.

[0061] The method for obtaining rainfall-runoff segmentation samples is as follows:

[0062] First, at the selected runoff start point, referring to historical typical receding process samples, the receding process line is extended and supplemented in an intelligent interactive manner to complete the previous flood receding process and generate the pre-peak receding process line. Second, at the post-peak inflection point, referring to historical typical receding process samples, the receding process line is extended and supplemented in an intelligent interactive manner to complete the current flood receding process and generate the post-peak receding process line. Third, the measured flood process within the runoff start and end period is subtracted from the time period of the pre-peak receding process, and the time period of the post-peak receding process is replaced with the original measured flood process to obtain the rainfall-runoff segmentation sample, that is, the net flood process.

[0063] Step 104: Based on the rainfall-runoff segmentation samples and the unit runoff generation schemes of the target section forecasting system, generate a runoff generation scheme relationship line using an intelligent interactive method.

[0064] The runoff generation schemes include the "P+Pa-R relationship method" and the "P-Pa-R relationship method", where P is the rainfall, Pa is the anterior impact rainfall, and R is the runoff depth.

[0065] The generation and implementation of the production flow scheme relationship line are as follows:

[0066] First, based on the segmented rainfall-runoff data, the flood number, flood start and end times, antecedent rainfall, initial flood discharge, rainfall, rainfall center, runoff depth, calculated runoff depth, and relative error characteristics of each segmented flood are obtained. Second, the generated flood number, flood start and end times, antecedent rainfall, initial flood discharge, rainfall, rainfall center, runoff depth, calculated runoff depth, and relative error characteristics of each segmented flood are statistically displayed in a tabular format, with calculated runoff depth and relative error initially left blank. Third, the rainfall P, runoff depth R, and antecedent rainfall Pa on the day the rainfall started are plotted as scatter plots on a graph, with the horizontal axis representing runoff depth R and the vertical axis representing rainfall P. Or, the rainfall P plus the preceding rainfall Pa; Fourth, using an intelligent interactive method, add control points for the runoff-proliferation relationship line by plotting on the graph, generally 5 to 8 points. After the control points are plotted, a smooth curve algorithm is used to automatically generate a smooth curve passing through the plotted control points, which is the preliminary runoff-proliferation relationship line; Fifth, based on the preliminary runoff-proliferation relationship line, calculate the predicted runoff depth and statistically analyze the relative error of the runoff depth, and fill it into the table list; Sixth, using the graphical control control point movement event tracking method, dynamically update the runoff-proliferation relationship line scheme in real time, while calculating the predicted runoff depth, the relative error value of the runoff depth, and statistically analyzing the pass rate information, until a specific accuracy level or the highest pass rate is reached, and the final runoff-proliferation relationship line is determined.

[0067] Step 105: Based on the rainfall-runoff segmentation samples and the runoff generation scheme relationship line, use an intelligent interactive method to complete the compilation of scenarios for each rainfall center, i.e., generate unit line schemes.

[0068] The unit line scheme is generated as follows:

[0069] First, based on the rainfall-runoff segmentation samples, obtain the flood number, flood start and end time, rainfall, rainfall center, measured runoff depth, measured net peak flow, measured peak time, calculated runoff depth, calculated net peak flow, net peak relative error, calculated peak time, peak time difference, and deterministic coefficient characteristic information for each segmented flood; Second, combine the flood number, flood start and end time, rainfall, rainfall center, measured runoff depth, measured net peak flow, measured peak time, calculated runoff depth, calculated net peak flow, net peak relative error, calculated peak time difference, and peak time difference characteristic information generated in step 103 with the characteristic information of each segmented flood. The current time difference and deterministic coefficient characteristic information are statistically displayed in a tabular format, where the calculated net peak flow, net peak relative error, calculated peak time, peak time difference, and deterministic coefficient are initially empty; third, the graphical area is divided into left and right sections, with the left side used for intelligent interactive drawing of the unit line process, and the right side used to display the measured net flood process and analyze and draw the calculated net flood process in real time; fourth, flood events are classified and filtered according to the location of the rainfall center of each flood event, and the list displays flood events that match the selected rainfall center, with the selected flood event (defaulting to the first) in the list. The precipitation process and the measured net flood process are plotted as process lines on the right side of the graphic area; fifth, on the left side of the graphic area, control points for the runoff unit line are added to the graphic using an intelligent interactive method, generally 5 to 8. After the control points are drawn, a smooth curve algorithm is used and a unit line total control constraint is superimposed to automatically generate a smooth curve approaching the drawn control points, which is the preliminary determined runoff unit line scheme; sixth, based on the preliminary generated runoff unit line scheme and the runoff generation relationship line scheme determined in step 104, the net rainfall process and the unit runoff process are calculated and plotted. The following steps are performed: First, on the right side of the graphical area, simultaneously calculate the net peak flow, net peak relative error, peak occurrence time, peak occurrence time difference, and coefficient of determination, and fill these values ​​into a table. Second, using the graphical control point movement event tracking method, dynamically update the unit flow scheme in real time, simultaneously calculating the net peak flow, net peak relative error, peak occurrence time, peak occurrence time difference, and coefficient of determination, and statistically analyzing the net peak flow qualification rate, peak occurrence time difference qualification rate, and average coefficient of determination information until a specific accuracy level is reached or each qualification rate is at its highest, thus determining the unit flow scheme for that rainfall center scenario. Third, select a new rainfall center and repeat steps five through seven to obtain a new unit flow scheme corresponding to the new rainfall center, until all rainfall center scenarios are completed.

[0070] Steps 101 to 105 all include persistent storage functionality, which is used to store the intermediate state data and result data generated in steps 104 to 105, and supports retrieving the most recently compiled information data results in steps 101 to 105.

[0071] The production flow relationship line results and confluence unit line results obtained in steps 104 and 105 can be queried and displayed in the production flow confluence scheme results set in step 101.

[0072] This invention provides an intelligent interactive method for developing empirical forecasting schemes. It employs an intelligent interactive approach to achieve online operation of the entire process, including flood selection, flood event segmentation, unit runoff generation scheme development, and unitline confluence scheme development. This method effectively improves the convenience, objectivity, and timeliness of empirical forecasting scheme development.

[0073] Example 2:

[0074] Figure 2 A system structure diagram is prepared for the intelligent interactive experience forecasting scheme of this invention, such as... Figure 2 As shown, a system for developing intelligent interactive experience-based forecasting schemes includes:

[0075] Target section forecasting system construction module 201 is used to construct an empirical forecasting scheme and compile a target section forecasting system.

[0076] The historical flood sample selection module 202 is used to select historical flood samples in an intelligent interactive manner based on the target section forecasting system and the basic hydrological database.

[0077] The rainfall-runoff segmentation sample determination module 203 is used to select the start and end times of rainfall and the start and end times of runoff based on the selected historical flood sample, and to perform baseflow segmentation to obtain rainfall-runoff segmentation samples.

[0078] The runoff generation scheme relationship line generation module 204 is used to generate a runoff generation scheme relationship line based on the rainfall-runoff segmentation sample and the unit runoff generation scheme of the target section forecast system in an intelligent interactive manner.

[0079] The rainfall center scenario compilation module 205 is used to complete the compilation of each rainfall center scenario in an intelligent interactive manner based on the rainfall-runoff segmentation sample and the runoff generation scheme relationship line.

[0080] The target cross-section forecasting system includes: basic cross-section information, cross-section outlet station composition, and runoff generation and catchment area units contained in the cross-section. The basic cross-section information includes cross-section code, cross-section name, cross-section location, river system in which the cross-section is located, and cross-section forecast calculation period. The cross-section outlet station composition includes cross-section water level representative station code, water level representative station name, cross-section flow station code, flow station name, flow station category, and flow station weight. The runoff generation and catchment area unit composition includes runoff generation and catchment area unit code, unit name, unit contained station code, station name, station category, unit station weight, and runoff generation and catchment scheme type set by the unit.

[0081] Example 3:

[0082] This embodiment provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the intelligent interactive experience prediction scheme compilation method of Embodiment 1.

[0083] Alternatively, the aforementioned electronic device may be a server.

[0084] In addition, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the intelligent interactive experience forecasting scheme compilation method of Embodiment 1.

[0085] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0089] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0090] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for developing an intelligent interactive experience-based forecasting scheme, characterized in that, The method for developing the intelligent interactive experience-based forecasting scheme includes: Construct an experience-based forecasting scheme to develop a target section forecasting system; Based on the target section forecasting system and basic hydrological database, historical flood samples are selected using an intelligent interactive method. Based on the historical flood samples, a smart interactive method was used to select the start and end times of rainfall and runoff to perform baseflow segmentation, resulting in rainfall-runoff segmentation samples. Based on the rainfall-runoff segmentation samples and the unit runoff generation schemes of the target section forecasting system, a runoff generation scheme relationship line is generated using an intelligent interactive method. Based on the rainfall-runoff segmentation samples and the runoff generation scheme relationship line, the confluence scheme of each rainfall center scenario unit is compiled using an intelligent interactive method. The target cross-section forecasting system includes: basic cross-section information, cross-section outlet station composition, and runoff generation and catchment area units contained in the cross-section. The basic cross-section information includes cross-section code, cross-section name, cross-section location, river system where the cross-section is located, and cross-section forecast calculation period. The cross-section outlet station composition includes cross-section water level representative station code, water level representative station name, cross-section flow station code, flow station name, flow station category, and flow station weight. The runoff generation and catchment area unit composition includes runoff generation and catchment area unit code, unit name, unit contained station code, station name, station category, unit station weight, and runoff generation and catchment scheme type set by the unit. The step of selecting historical flood samples based on the target section forecasting system and basic hydrological database using an intelligent interactive method specifically includes: The daily and extracted hydrological data of rainfall, water level and evaporation are interpolated into the corresponding time period data for the cross-section forecast calculation period of the target cross-section forecast system. Based on the selected year, the rainfall and outlet flow of the cross-section unit are plotted in the form of a process line graph; Using intelligent interaction, the start and end times of the flood are obtained by capturing the number of mouse click events. Once the end time is determined, the rainfall, rainfall center, and peak flow value within the selected flood period are automatically calculated. After a flood selection is completed, the number of mouse click events automatically resets to 0, and the cycle begins for the second selection. After the flood event for the current year is selected, the intelligent interactive switch will select the next year until all flood events for all years are divided. Based on the historical flood samples, a smart interactive method is used to select the start and end times of rainfall and runoff to perform baseflow segmentation, obtaining rainfall-runoff segmentation samples, specifically including: At the selected runoff initiation point, referring to the historical typical receding process samples in the historical flood sample, the receding process line is extended and supplemented in an intelligent interactive manner to complete the previous flood receding process and generate the pre-peak receding process line. At the inflection point of the post-peak process, referring to historical typical receding process samples, the receding process line is extended and supplemented in an intelligent interactive manner to complete the receding process of this flood and generate the post-peak receding process line. Subtract the measured flood process within the runoff start and end time period from the time period of the pre-peak receding process, and replace the original measured flood process with the time period of the post-peak receding process to obtain the rainfall-runoff segmentation sample. The unit runoff generation scheme based on the rainfall-runoff segmentation samples and the target section forecasting system adopts an intelligent interactive method to generate a runoff generation scheme relationship line, specifically including: Based on the rainfall-runoff segmentation samples, the flood number, flood start and end time, antecedent rainfall, initial rise flow, rainfall, rainfall center, runoff depth, calculated runoff depth, and relative error characteristics of each segmented flood are obtained. Based on the flood number, flood start and end time, previous impact rainfall, rising flow, rainfall, rainfall center, runoff depth, calculated runoff depth, relative error characteristics of each segmented flood, and the unit runoff generation scheme of the target section forecasting system, a preliminary runoff generation scheme relationship line is determined in an intelligent interactive manner. The intelligent interaction method is as follows: using a graphical control point movement event tracking method, the control points of the runoff generation relationship line are plotted in the coordinate system, and a smooth curve algorithm is used to intelligently generate the relationship line passing through the control points. During the movement of the control points, the runoff generation relationship line is dynamically updated in real time and the relative error of runoff depth is statistically analyzed. Based on the calculated runoff depth and the relative error of the runoff depth, the pass rate is statistically analyzed in real time to obtain the final runoff production scheme relationship line with the highest pass rate; The process of compiling confluence schemes for each rainfall center scenario unit using an intelligent interactive method, based on the rainfall-runoff segmentation samples and the runoff generation scheme relationship line, includes the following specific implementation steps: First, based on the segmented rainfall-runoff samples, the following characteristic information is obtained for each segmented flood: flood number, flood start and end time, rainfall, rainfall center, measured runoff depth, measured net peak flow, measured peak time, calculated runoff depth, calculated net peak flow, net peak relative error, calculated peak time, peak time difference, and deterministic coefficient. Second, the following characteristic information is used to analyze the characteristics of each segmented flood generated in the rainfall-runoff segmentation sample process: flood number, flood start and end time, rainfall, rainfall center, measured runoff depth, measured net peak flow, measured peak time, calculated runoff depth, calculated net peak flow, net peak relative error, calculated peak time, peak time difference, and deterministic coefficient. The statistical information is displayed in a tabular format, with the calculated net peak flow, net peak relative error, calculated peak time, peak time difference, and determination coefficient initially left blank. Third, the graphical area is divided into left and right sections: the left side is used for drawing the unit curve process in an interactive manner, and the right side is used to display the measured net flood process and analyze and draw the calculated net flood process in real time. Fourth, the flood events are categorized and filtered based on the location of the rainfall center. The list displays flood events with the same rainfall center as the selected events, and the rainfall process and measured net flood process of the selected flood events are plotted as process lines on the right side of the graphical area. Fifth, on the left side of the graphic area, control points for the confluence unit line are added to the graphic using intelligent interactive methods. After the control points are drawn, a smooth curve algorithm is used and a total unit line control constraint is superimposed to automatically generate a smooth curve approaching the drawn control points, which is the preliminary determined confluence unit line scheme. Sixth, based on the preliminary generated confluence unit line scheme and the production-running relationship line scheme determined in the production-running relationship line generation stage, the net rainfall process and unit confluence process are calculated and drawn on the right side of the graphic area. At the same time, the net peak flow, net peak relative error, peak occurrence time, peak occurrence time difference, and determinism coefficient are statistically calculated. Seventh, using the graphical control point movement event tracking method, dynamically update the unit flow scheme in real time, and simultaneously calculate the net peak flow, net peak relative error, peak occurrence time, peak occurrence time difference, and deterministic coefficient, and statistically analyze the net peak flow qualification rate, peak occurrence time difference qualification rate, and average deterministic coefficient information until a specific accuracy level is reached or each qualification rate is at its highest, thus determining the unit flow scheme under the rainfall center scenario; Eighth, select a new rainfall center again, and repeat steps five to seven above to obtain the new unit flow scheme corresponding to the rainfall center, until all rainfall center scenarios are completed.

2. A system for developing intelligent interactive experience-based forecasting schemes, characterized in that, The intelligent interactive experience-based forecasting scheme development method system includes: The target section forecasting system construction module is used to construct an empirical forecasting scheme and compile a target section forecasting system. The historical flood sample selection module is used to select historical flood samples in an intelligent interactive manner based on the target section forecasting system and the basic hydrological database. The rainfall-runoff segmentation sample determination module is used to select the start and end times of rainfall and the start and end times of runoff based on the historical flood samples, and to perform baseflow segmentation to obtain rainfall-runoff segmentation samples. The runoff generation scheme relationship line generation module is used to generate a runoff generation scheme relationship line based on the rainfall-runoff segmentation sample and the unit runoff generation scheme of the target section forecasting system in an intelligent interactive manner. The confluence scheme compilation module for each rainfall center scenario unit is used to complete the compilation of each rainfall center scenario in an intelligent interactive manner based on the rainfall-runoff segmentation sample and the runoff generation scheme relationship line. The target cross-section forecasting system includes: basic cross-section information, cross-section outlet station composition, and runoff generation and catchment area units contained in the cross-section. The basic cross-section information includes cross-section code, cross-section name, cross-section location, river system where the cross-section is located, and cross-section forecast calculation period. The cross-section outlet station composition includes cross-section water level representative station code, water level representative station name, cross-section flow station code, flow station name, flow station category, and flow station weight. The runoff generation and catchment area unit composition includes runoff generation and catchment area unit code, unit name, unit contained station code, station name, station category, unit station weight, and runoff generation and catchment scheme type set by the unit. The historical flood sample selection module specifically includes: The daily and extracted hydrological data of rainfall, water level and evaporation are interpolated into the corresponding time period data for the cross-section forecast calculation period of the target cross-section forecast system. Based on the selected year, the rainfall and outlet flow of the cross-section unit are plotted in the form of a process line graph; Using intelligent interaction, the start and end times of the flood are obtained by capturing the number of mouse click events. Once the end time is determined, the rainfall, rainfall center, and peak flow value within the selected flood period are automatically calculated. After a flood selection is completed, the number of mouse click events automatically resets to 0, and the cycle begins for the second selection. After the flood event for the current year is selected, the intelligent interactive switch will select the next year until all flood events for all years are divided. The rainfall-runoff segmentation sample determination module specifically includes: At the selected runoff initiation point, referring to the historical typical receding process samples in the historical flood sample, the receding process line is extended and supplemented in an intelligent interactive manner to complete the previous flood receding process and generate the pre-peak receding process line. At the inflection point of the post-peak process, referring to historical typical receding process samples, the receding process line is extended and supplemented in an intelligent interactive manner to complete the receding process of this flood and generate the post-peak receding process line. Subtract the measured flood process within the runoff start and end time period from the time period of the pre-peak receding process, and replace the original measured flood process with the time period of the post-peak receding process to obtain the rainfall-runoff segmentation sample. The production flow scheme relationship line generation module specifically includes: Based on the rainfall-runoff segmentation samples, the flood number, flood start and end time, antecedent rainfall, initial rise flow, rainfall, rainfall center, runoff depth, calculated runoff depth, and relative error characteristics of each segmented flood are obtained. Based on the flood number, flood start and end time, previous impact rainfall, rising flow, rainfall, rainfall center, runoff depth, calculated runoff depth, relative error characteristics of each segmented flood, and the unit runoff generation scheme of the target section forecasting system, a preliminary runoff generation scheme relationship line is determined in an intelligent interactive manner. Based on the preliminary runoff generation scheme relationship line, the predicted runoff depth is calculated and the relative error of the runoff depth is statistically analyzed; Based on the calculated runoff depth and the relative error of the runoff depth, the final runoff generation scheme relationship line is obtained; The confluence scheme compilation module for each rainfall center scenario unit specifically includes: Based on the rainfall-runoff segmentation samples, the following characteristic information is obtained for each segmented flood: flood number, flood start and end time, rainfall, rainfall center, measured runoff depth, measured net peak flow, measured peak time, calculated runoff depth, calculated net peak flow, net peak relative error, calculated peak time, peak time difference, and deterministic coefficient. Based on the flood number, flood start and end time, rainfall, rainfall center, measured runoff depth, measured net peak flow, measured peak time, calculated runoff depth, calculated net peak flow, net peak relative error, calculated peak time, peak time difference, and determination coefficient characteristics of each segmented flood, a preliminary confluence unit line scheme is determined; Based on the preliminary runoff unit line scheme and the runoff generation scheme relationship line, the net rainfall process and the unit runoff process are calculated; Based on the net rainfall process and the unit flow process, the unit flow scheme is dynamically updated in real time using the graphical control point movement event tracking method. At the same time, the net peak flow, net peak relative error, peak occurrence time, peak occurrence time difference, and deterministic coefficient are calculated, and the net peak flow qualification rate, peak occurrence time difference qualification rate, and average deterministic coefficient information are statistically analyzed until a specific accuracy level or the highest qualification rate is reached, thus determining the unit flow scheme under the current rainfall center scenario. Update the selected rainfall center and repeat the above steps to obtain the new rainfall center corresponding to the unit line scheme, until the scenarios for each rainfall center are completed.

Images